ulmfit_inference_HeriGraph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifying OUV using ULMFiT model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\zzbn1\\\\anaconda3\\\\python.exe'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.executable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from argparse import Namespace\n",
    "from collections import Counter\n",
    "import json\n",
    "import os\n",
    "import re\n",
    "import string\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from scipy.special import softmax\n",
    "\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "import torchtext\n",
    "from torchtext.data import get_tokenizer\n",
    "#tokenizer = get_tokenizer('spacy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fastai\n",
    "from fastai.text.all import * \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from functools import partial\n",
    "import io\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch version 1.10.2\n",
      "GPU-enabled installation? True\n"
     ]
    }
   ],
   "source": [
    "print(\"PyTorch version {}\".format(torch.__version__))\n",
    "print(\"GPU-enabled installation? {}\".format(torch.cuda.is_available()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Vocabulary(object):\n",
    "    \"\"\"Class to process text and extract vocabulary for mapping\"\"\"\n",
    "\n",
    "    def __init__(self, token_to_idx=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            token_to_idx (dict): a pre-existing map of tokens to indices\n",
    "            add_unk (bool): a flag that indicates whether to add the UNK token\n",
    "            unk_token (str): the UNK token to add into the Vocabulary\n",
    "        \"\"\"\n",
    "\n",
    "        if token_to_idx is None:\n",
    "            token_to_idx = {}\n",
    "        self._token_to_idx = token_to_idx\n",
    "\n",
    "        self._idx_to_token = {idx: token \n",
    "                              for token, idx in self._token_to_idx.items()}\n",
    "                \n",
    "    def to_serializable(self):\n",
    "        \"\"\" returns a dictionary that can be serialized \"\"\"\n",
    "        return {'token_to_idx': self._token_to_idx}\n",
    "\n",
    "    @classmethod\n",
    "    def from_serializable(cls, contents):\n",
    "        \"\"\" instantiates the Vocabulary from a serialized dictionary \"\"\"\n",
    "        return cls(**contents)\n",
    "\n",
    "    def add_token(self, token):\n",
    "        \"\"\"Update mapping dicts based on the token.\n",
    "\n",
    "        Args:\n",
    "            token (str): the item to add into the Vocabulary\n",
    "        Returns:\n",
    "            index (int): the integer corresponding to the token\n",
    "        \"\"\"\n",
    "        if token in self._token_to_idx:\n",
    "            index = self._token_to_idx[token]\n",
    "        else:\n",
    "            index = len(self._token_to_idx)\n",
    "            self._token_to_idx[token] = index\n",
    "            self._idx_to_token[index] = token\n",
    "        return index\n",
    "    \n",
    "    def add_many(self, tokens):\n",
    "        \"\"\"Add a list of tokens into the Vocabulary\n",
    "        \n",
    "        Args:\n",
    "            tokens (list): a list of string tokens\n",
    "        Returns:\n",
    "            indices (list): a list of indices corresponding to the tokens\n",
    "        \"\"\"\n",
    "        return [self.add_token(token) for token in tokens]\n",
    "\n",
    "    def lookup_token(self, token):\n",
    "        \"\"\"Retrieve the index associated with the token \n",
    "          or the UNK index if token isn't present.\n",
    "        \n",
    "        Args:\n",
    "            token (str): the token to look up \n",
    "        Returns:\n",
    "            index (int): the index corresponding to the token\n",
    "        Notes:\n",
    "            `unk_index` needs to be >=0 (having been added into the Vocabulary) \n",
    "              for the UNK functionality \n",
    "        \"\"\"\n",
    "        return self._token_to_idx[token]\n",
    "\n",
    "    def lookup_index(self, index):\n",
    "        \"\"\"Return the token associated with the index\n",
    "        \n",
    "        Args: \n",
    "            index (int): the index to look up\n",
    "        Returns:\n",
    "            token (str): the token corresponding to the index\n",
    "        Raises:\n",
    "            KeyError: if the index is not in the Vocabulary\n",
    "        \"\"\"\n",
    "        if index not in self._idx_to_token:\n",
    "            raise KeyError(\"the index (%d) is not in the Vocabulary\" % index)\n",
    "        return self._idx_to_token[index]\n",
    "\n",
    "    def __str__(self):\n",
    "        return \"<Vocabulary(size=%d)>\" % len(self)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self._token_to_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SequenceVocabulary(Vocabulary):\n",
    "    def __init__(self, token_to_idx=None, unk_token='xxunk',\n",
    "                 mask_token='xxpad', begin_seq_token=\"xxbos\",\n",
    "                 end_seq_token=\"xxeos\"):\n",
    "\n",
    "        super(SequenceVocabulary, self).__init__(token_to_idx)\n",
    "\n",
    "        self._mask_token = mask_token\n",
    "        self._unk_token = unk_token\n",
    "        self._begin_seq_token = begin_seq_token\n",
    "        self._end_seq_token = end_seq_token\n",
    "\n",
    "        self.mask_index = 1\n",
    "        self.unk_index = 0\n",
    "        self.begin_seq_index = 2\n",
    "        self.end_seq_index = 3\n",
    "\n",
    "    def to_serializable(self):\n",
    "        contents = super(SequenceVocabulary, self).to_serializable()\n",
    "        contents.update({'unk_token': self._unk_token,\n",
    "                         'mask_token': self._mask_token,\n",
    "                         'begin_seq_token': self._begin_seq_token,\n",
    "                         'end_seq_token': self._end_seq_token})\n",
    "        return contents\n",
    "    \n",
    "    @classmethod\n",
    "    def from_serializable(cls, contents):\n",
    "        \"\"\" instantiates the Vocabulary from a serialized dictionary \"\"\"\n",
    "        return cls(**contents)\n",
    "\n",
    "    \n",
    "    def lookup_token(self, token):\n",
    "        \"\"\"Retrieve the index associated with the token \n",
    "          or the UNK index if token isn't present.\n",
    "        \n",
    "        Args:\n",
    "            token (str): the token to look up \n",
    "        Returns:\n",
    "            index (int): the index corresponding to the token\n",
    "        Notes:\n",
    "            `unk_index` needs to be >=0 (having been added into the Vocabulary) \n",
    "              for the UNK functionality \n",
    "        \"\"\"\n",
    "        if self.unk_index >= 0:\n",
    "            return self._token_to_idx.get(token, self.unk_index)\n",
    "        else:\n",
    "            return self._token_to_idx[token]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OuvVectorizer(object):\n",
    "    \"\"\" The Vectorizer which coordinates the Vocabularies and puts them to use\"\"\"\n",
    "    def __init__(self, ouv_vocab):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            review_vocab (Vocabulary): maps words to integers\n",
    "        \"\"\"\n",
    "        self.ouv_vocab = ouv_vocab\n",
    "        \n",
    "    def vectorize(self, data, vector_length = -1):\n",
    "        \"\"\"Create a collapsed one-hit vector for the ouv data\n",
    "        \n",
    "        Args:\n",
    "            data (str): the ouv description data\n",
    "            vector_length (int): an argument for forcing the length of index vector\n",
    "        Returns:\n",
    "            the vectorized data (np.ndarray)\n",
    "        \"\"\"\n",
    "        indices = []\n",
    "        indices.extend(self.ouv_vocab.lookup_token(token) for token in data.split(' '))\n",
    "        #indices.append(self.ouv_vocab.end_seq_index)\n",
    "        \n",
    "        if vector_length < 0:\n",
    "            vector_length = len(indices)\n",
    "            \n",
    "        out_vector = np.zeros(vector_length, dtype=np.int64)\n",
    "        out_vector[:len(indices)] = indices\n",
    "        out_vector[len(indices):] = self.ouv_vocab.mask_index\n",
    "        \n",
    "        return out_vector, len(indices)\n",
    "\n",
    "    @classmethod\n",
    "    def from_dataframe(cls, vocab, cutoff=5):\n",
    "        \"\"\"Instantiate the vectorizer from the dataset dataframe\n",
    "        \n",
    "        Args:\n",
    "            ouv_df (pandas.DataFrame): the ouv dataset\n",
    "            cutoff (int): the parameter for frequency-based filtering\n",
    "        Returns:\n",
    "            an instance of the OuvVectorizer\n",
    "        \"\"\"\n",
    "        \n",
    "        # Add top words if count > provided count\n",
    "        #word_counts = Counter()\n",
    "        #for data in ouv_df.data:\n",
    "        #    for word in data.split(' '):\n",
    "        #        if word not in string.punctuation:\n",
    "        #            word_counts[word] += 1\n",
    "        \n",
    "        ouv_vocab = SequenceVocabulary()\n",
    "        for token in vocab:\n",
    "            ouv_vocab.add_token(token)\n",
    "        #for word, count in word_counts.items():\n",
    "        #    if count > cutoff:\n",
    "        #        ouv_vocab.add_token(word)\n",
    "\n",
    "        return cls(ouv_vocab)\n",
    "\n",
    "    @classmethod\n",
    "    def from_serializable(cls, contents):\n",
    "        \"\"\"Instantiate a OuvVectorizer from a serializable dictionary\n",
    "        \n",
    "        Args:\n",
    "            contents (dict): the serializable dictionary\n",
    "        Returns:\n",
    "            an instance of the OuvVectorizer class\n",
    "        \"\"\"\n",
    "        ouv_vocab = SequenceVocabulary.from_serializable(contents['ouv_vocab'])\n",
    "        \n",
    "        return cls(ouv_vocab=ouv_vocab)\n",
    "\n",
    "    def to_serializable(self):\n",
    "        \"\"\"Create the serializable dictionary for caching\n",
    "        \n",
    "        Returns:\n",
    "            contents (dict): the serializable dictionary\n",
    "        \"\"\"\n",
    "        return {'ouv_vocab': self.ouv_vocab.to_serializable()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OuvDataset(Dataset):\n",
    "    def __init__(self, ouv_df, vectorizer, vocab):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            ouv_df (pandas.DataFrame): the dataset\n",
    "            vectorizer (ReviewVectorizer): vectorizer instantiated from dataset\n",
    "        \"\"\"\n",
    "        self.ouv_df = ouv_df\n",
    "        self._vectorizer = vectorizer\n",
    "        self.vocab = vocab\n",
    "        \n",
    "        # +0 if not using begin_seq and end seq, +1 if only using begin_seq, +2 if using both begin and end seq tokens\n",
    "        measure_len = lambda context: len(context.split(\" \"))\n",
    "        self._max_seq_length = max(map(measure_len, ouv_df.data)) + 0\n",
    "\n",
    "        self.train_df = self.ouv_df[self.ouv_df.split=='train']\n",
    "        self.train_size = len(self.train_df)\n",
    "\n",
    "        self.val_df = self.ouv_df[self.ouv_df.split=='dev']\n",
    "        self.validation_size = len(self.val_df)\n",
    "\n",
    "        self.test_df = self.ouv_df[self.ouv_df.split=='test']\n",
    "        self.test_size = len(self.test_df)\n",
    "\n",
    "        self._lookup_dict = {'train': (self.train_df, self.train_size),\n",
    "                             'val': (self.val_df, self.validation_size),\n",
    "                             'test': (self.test_df, self.test_size)}\n",
    "\n",
    "        self.set_split('train')\n",
    "\n",
    "    @classmethod\n",
    "    def load_dataset_and_make_vectorizer(cls, ouv_csv, cutoff, vocab):\n",
    "        \"\"\"Load dataset and make a new vectorizer from scratch\n",
    "        \n",
    "        Args:\n",
    "            ouv_csv (str): location of the dataset\n",
    "            cutoff (int): the boundary to set the words into unknown\n",
    "        Returns:\n",
    "            an instance of OuvDataset\n",
    "        \"\"\"\n",
    "        ouv_df = pd.read_csv(ouv_csv)\n",
    "        train_ouv_df = ouv_df[ouv_df.split=='train']\n",
    "        return cls(ouv_df, OuvVectorizer.from_dataframe(cutoff=cutoff, vocab=vocab), vocab=vocab)\n",
    "    \n",
    "    @classmethod\n",
    "    def load_dataset_and_load_vectorizer(cls, ouv_csv, vectorizer_filepath, vocab):\n",
    "        \"\"\"Load dataset and the corresponding vectorizer. \n",
    "        Used in the case in the vectorizer has been cached for re-use\n",
    "        \n",
    "        Args:\n",
    "            ouv_csv (str): location of the dataset\n",
    "            vectorizer_filepath (str): location of the saved vectorizer\n",
    "        Returns:\n",
    "            an instance of OuvDataset\n",
    "        \"\"\"\n",
    "        ouv_df = pd.read_csv(ouv_csv)\n",
    "        vectorizer = cls.load_vectorizer_only(vectorizer_filepath)\n",
    "        return cls(ouv_df, vectorizer, vocab=vocab)\n",
    "\n",
    "    @staticmethod\n",
    "    def load_vectorizer_only(vectorizer_filepath):\n",
    "        \"\"\"a static method for loading the vectorizer from file\n",
    "        \n",
    "        Args:\n",
    "            vectorizer_filepath (str): the location of the serialized vectorizer\n",
    "        Returns:\n",
    "            an instance of ReviewVectorizer\n",
    "        \"\"\"\n",
    "        with open(vectorizer_filepath) as fp:\n",
    "            return OuvVectorizer.from_serializable(json.load(fp))\n",
    "\n",
    "    def save_vectorizer(self, vectorizer_filepath):\n",
    "        \"\"\"saves the vectorizer to disk using json\n",
    "        \n",
    "        Args:\n",
    "            vectorizer_filepath (str): the location to save the vectorizer\n",
    "        \"\"\"\n",
    "        with open(vectorizer_filepath, \"w\") as fp:\n",
    "            json.dump(self._vectorizer.to_serializable(), fp)\n",
    "\n",
    "    def get_vectorizer(self):\n",
    "        \"\"\" returns the vectorizer \"\"\"\n",
    "        return self._vectorizer\n",
    "\n",
    "    def set_split(self, split=\"train\"):\n",
    "        \"\"\" selects the splits in the dataset using a column in the dataframe \n",
    "        \n",
    "        Args:\n",
    "            split (str): one of \"train\", \"val\", or \"test\"\n",
    "        \"\"\"\n",
    "        self._target_split = split\n",
    "        self._target_df, self._target_size = self._lookup_dict[split]\n",
    "\n",
    "    def __len__(self):\n",
    "        return self._target_size\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"the primary entry point method for PyTorch datasets\n",
    "        \n",
    "        Args:\n",
    "            index (int): the index to the data point \n",
    "        Returns:\n",
    "            a dictionary holding the data point's features (x_data) and component for labels (y_target and y_fuzzy)\n",
    "        \"\"\"\n",
    "        row = self._target_df.iloc[index]\n",
    "\n",
    "        ouv_vector, vec_length = \\\n",
    "            self._vectorizer.vectorize(row.data, self._max_seq_length)\n",
    "\n",
    "        true_label = \\\n",
    "            np.fromstring(row.true[1:-1],dtype=float, sep=' ')\n",
    "        \n",
    "        if len(true_label)==10:\n",
    "            true_label = np.append(true_label,0.0)\n",
    "        \n",
    "        fuzzy_label = \\\n",
    "            np.fromstring(row.fuzzy[1:-1],dtype=float, sep=' ')\n",
    "\n",
    "        return {'x_data': ouv_vector,\n",
    "                'y_target': true_label,\n",
    "                'y_fuzzy': fuzzy_label,\n",
    "                'x_length': vec_length,\n",
    "                'id': row.id\n",
    "               }\n",
    "\n",
    "    def get_num_batches(self, batch_size):\n",
    "        \"\"\"Given a batch size, return the number of batches in the dataset\n",
    "        \n",
    "        Args:\n",
    "            batch_size (int)\n",
    "        Returns:\n",
    "            number of batches in the dataset\n",
    "        \"\"\"\n",
    "        return len(self) // batch_size  \n",
    "    \n",
    "def generate_batches(dataset, batch_size, shuffle=True,\n",
    "                     drop_last=True, device=\"cpu\"):\n",
    "    \"\"\"\n",
    "    A generator function which wraps the PyTorch DataLoader. It will \n",
    "      ensure each tensor is on the write device location.\n",
    "    \"\"\"\n",
    "    dataloader = DataLoader(dataset=dataset, batch_size=batch_size,\n",
    "                            shuffle=shuffle, drop_last=drop_last)\n",
    "\n",
    "    for data_dict in dataloader:\n",
    "        out_data_dict = {}\n",
    "        for name, tensor in data_dict.items():\n",
    "            out_data_dict[name] = data_dict[name].to(device)\n",
    "        yield out_data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed_everywhere(seed, cuda):\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    if cuda:\n",
    "        torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "def handle_dirs(dirpath):\n",
    "    if not os.path.exists(dirpath):\n",
    "        os.makedirs(dirpath)\n",
    "\n",
    "def load_glove_from_file(glove_filepath):\n",
    "    \"\"\"\n",
    "    Load the GloVe embeddings \n",
    "    \n",
    "    Args:\n",
    "        glove_filepath (str): path to the glove embeddings file \n",
    "    Returns:\n",
    "        word_to_index (dict), embeddings (numpy.ndarary)\n",
    "    \"\"\"\n",
    "\n",
    "    word_to_index = {}\n",
    "    embeddings = []\n",
    "    with open(glove_filepath, \"r\") as fp:\n",
    "        for index, line in enumerate(fp):\n",
    "            line = line.split(\" \") # each line: word num1 num2 ...\n",
    "            word_to_index[line[0]] = index # word = line[0] \n",
    "            embedding_i = np.array([float(val) for val in line[1:]])\n",
    "            embeddings.append(embedding_i)\n",
    "    return word_to_index, np.stack(embeddings)\n",
    "\n",
    "def make_embedding_matrix(glove_filepath, words):\n",
    "    \"\"\"\n",
    "    Create embedding matrix for a specific set of words.\n",
    "    \n",
    "    Args:\n",
    "        glove_filepath (str): file path to the glove embeddigns\n",
    "        words (list): list of words in the dataset\n",
    "    \"\"\"\n",
    "    word_to_idx, glove_embeddings = load_glove_from_file(glove_filepath)\n",
    "    embedding_size = glove_embeddings.shape[1]\n",
    "    \n",
    "    final_embeddings = np.zeros((len(words), embedding_size))\n",
    "\n",
    "    for i, word in enumerate(words):\n",
    "        if word in word_to_idx:\n",
    "            final_embeddings[i, :] = glove_embeddings[word_to_idx[word]]\n",
    "        else:\n",
    "            embedding_i = torch.ones(1, embedding_size)\n",
    "            torch.nn.init.xavier_uniform_(embedding_i)\n",
    "            final_embeddings[i, :] = embedding_i\n",
    "\n",
    "    return final_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expanded filepaths: \n",
      "\tmodel_storage/ulmfit/vectorizer.json\n",
      "\tmodel_storage/ulmfit/model.pth\n",
      "Using CUDA: True\n"
     ]
    }
   ],
   "source": [
    "args = Namespace(\n",
    "    # Data and Path information\n",
    "    frequency_cutoff=1,\n",
    "    model_state_file='model.pth',\n",
    "    ouv_csv='Data/ouv_with_splits_full.csv',\n",
    "    big_ouv_csv='Data/all_with_splits_full.csv',\n",
    "    prior_csv = 'Data/Coappearance_matrix.csv',\n",
    "    sm_csv = 'Data/Social_media_rome.csv',\n",
    "    save_dir='model_storage/ulmfit/',\n",
    "    vectorizer_file='vectorizer.json',\n",
    "    # Model hyper parameters\n",
    "    glove_filepath='Data/glove/glove.6B.300d.txt', \n",
    "    use_glove=False,\n",
    "    freeze = True,\n",
    "    embedding_size=400, \n",
    "    hidden_dim=64, \n",
    "    bi = False,\n",
    "    # Training hyper parameters\n",
    "    batch_size=64,\n",
    "    early_stopping_criteria=3,\n",
    "    learning_rate=2e-2,\n",
    "    l2=1e-5,\n",
    "    dropout_p=0,\n",
    "    k = 3,\n",
    "    fuzzy = True,\n",
    "    fuzzy_how = 'prior',\n",
    "    fuzzy_lambda = 0.1,\n",
    "    num_epochs=10,\n",
    "    seed=1337,\n",
    "    # Runtime options\n",
    "    catch_keyboard_interrupt=True,\n",
    "    cuda=True,\n",
    "    expand_filepaths_to_save_dir=True,\n",
    "    reload_from_files=False,\n",
    ")\n",
    "\n",
    "classes = ['Criteria i', 'Criteria ii', 'Criteria iii', 'Criteria iv', 'Criteria v', 'Criteria vi', \n",
    "              'Criteria vii', 'Criteria viii', 'Criteria ix', 'Criteria x', 'Others']\n",
    "\n",
    "if args.expand_filepaths_to_save_dir:\n",
    "    args.vectorizer_file = os.path.join(args.save_dir,\n",
    "                                        args.vectorizer_file)\n",
    "\n",
    "    args.model_state_file = os.path.join(args.save_dir,\n",
    "                                         args.model_state_file)\n",
    "    \n",
    "    print(\"Expanded filepaths: \")\n",
    "    print(\"\\t{}\".format(args.vectorizer_file))\n",
    "    print(\"\\t{}\".format(args.model_state_file))\n",
    "    \n",
    "# Check CUDA\n",
    "if not torch.cuda.is_available():\n",
    "    args.cuda = False\n",
    "\n",
    "print(\"Using CUDA: {}\".format(args.cuda))\n",
    "\n",
    "args.device = torch.device(\"cuda\" if args.cuda else \"cpu\")\n",
    "\n",
    "# Set seed for reproducibility\n",
    "set_seed_everywhere(args.seed, args.cuda)\n",
    "\n",
    "# handle dirs\n",
    "handle_dirs(args.save_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train_state(args):\n",
    "    return {'stop_early': False,\n",
    "            'early_stopping_step': 0,\n",
    "            'early_stopping_best_k_acc_val': 0,\n",
    "            'learning_rate': args.learning_rate,\n",
    "            'epoch_index': 0,\n",
    "            'train_loss': [],\n",
    "            'train_1_acc': [],\n",
    "            'train_k_acc': [],\n",
    "            'train_k_jac': [],\n",
    "            'val_loss': [],\n",
    "            'val_1_acc': [],\n",
    "            'val_k_acc': [],\n",
    "            'val_k_jac': [],\n",
    "            'test_loss': -1,\n",
    "            'test_1_acc': -1,\n",
    "            'test_k_acc':-1,\n",
    "            'test_k_jac':-1,\n",
    "            'model_filename': args.model_state_file}\n",
    "\n",
    "def update_train_state(args, model, train_state):\n",
    "    \"\"\"Handle the training state updates.\n",
    "\n",
    "    Components:\n",
    "     - Early Stopping: Prevent overfitting.\n",
    "     - Model Checkpoint: Model is saved if the model is better\n",
    "\n",
    "    :param args: main arguments\n",
    "    :param model: model to train\n",
    "    :param train_state: a dictionary representing the training state values\n",
    "    :returns:\n",
    "        a new train_state\n",
    "    \"\"\"\n",
    "\n",
    "    # Save one model at least\n",
    "    if train_state['epoch_index'] == 0:\n",
    "        torch.save(model.state_dict(), train_state['model_filename'])\n",
    "        train_state['stop_early'] = False\n",
    "\n",
    "    # Save model if performance improved\n",
    "    elif train_state['epoch_index'] >= 1:\n",
    "        acc_tm1, acc_t = train_state['val_k_acc'][-2:]\n",
    "\n",
    "        # If accuracy worsened\n",
    "        if acc_t <= train_state['early_stopping_best_k_acc_val']:\n",
    "            # Update step\n",
    "            train_state['early_stopping_step'] += 1\n",
    "        # Loss decreased\n",
    "        else:\n",
    "            # Save the best model from sklearn\n",
    "            if acc_t > train_state['early_stopping_best_k_acc_val']:\n",
    "                train_state['early_stopping_best_k_acc_val'] = acc_t\n",
    "                torch.save(model.state_dict(), train_state['model_filename'])\n",
    "                \n",
    "            # Reset early stopping step\n",
    "            train_state['early_stopping_step'] = 0\n",
    "\n",
    "        # Stop early ?\n",
    "        train_state['stop_early'] = \\\n",
    "            train_state['early_stopping_step'] >= args.early_stopping_criteria\n",
    "\n",
    "    return train_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cross_entropy(y_pred, y_target):\n",
    "    y_target = y_target.cpu().float()\n",
    "    y_pred = y_pred.cpu().float()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    return criterion(y_target, y_pred)\n",
    "\n",
    "def compute_1_accuracy(y_pred, y_target):\n",
    "    y_target_indices = y_target.max(dim=1)[1]\n",
    "    y_pred_indices = y_pred.max(dim=1)[1]\n",
    "    n_correct = torch.eq(y_pred_indices, y_target_indices).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100\n",
    "\n",
    "def compute_k_accuracy(y_pred, y_target, k=3):\n",
    "    y_pred_indices = y_pred.topk(k, dim=1)[1]\n",
    "    y_target_indices = y_target.max(dim=1)[1]\n",
    "    n_correct = torch.tensor([y_pred_indices[i] in y_target_indices[i] for i in range(len(y_pred))]).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100\n",
    "\n",
    "def compute_k_jaccard_index(y_pred, y_target, k=3):\n",
    "    y_target_indices = y_target.topk(k, dim=1)[1]\n",
    "    y_pred_indices = y_pred.max(dim=1)[1]\n",
    "    jaccard = torch.tensor([len(np.intersect1d(y_target_indices[i], y_pred_indices[i]))/\n",
    "                            len(np.union1d(y_target_indices[i], y_pred_indices[i]))\n",
    "                            for i in range(len(y_pred))]).sum().item()\n",
    "    return jaccard / len(y_pred_indices)\n",
    "\n",
    "def compute_jaccard_index(y_pred, y_target, k=3, multilabel=False):\n",
    "    \n",
    "    threshold = 1.0/(k+1)\n",
    "    threshold_2 = 0.5\n",
    "    \n",
    "    if multilabel:\n",
    "        y_pred_indices = y_pred.gt(threshold_2)\n",
    "    else:\n",
    "        y_pred_indices = y_pred.gt(threshold)\n",
    "    \n",
    "    y_target_indices = y_target.gt(threshold)\n",
    "        \n",
    "    jaccard = ((y_target_indices*y_pred_indices).sum(axis=1)/((y_target_indices+y_pred_indices).sum(axis=1)+1e-8)).sum().item()\n",
    "    return jaccard / len(y_pred_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax_sensitive(T):\n",
    "    T = np.exp(T) - np.exp(0) + 1e-9\n",
    "    if len(T.shape)==1:\n",
    "        return T/T.sum()\n",
    "    return  T/(T.sum(axis=1).unsqueeze(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_entropy(pred, soft_targets):\n",
    "    logsoftmax = nn.LogSoftmax(dim=1)\n",
    "    return torch.mean(torch.sum(- soft_targets * logsoftmax(pred), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert a df to tensor to be used in pytorch\n",
    "def df_to_tensor(df):\n",
    "    device = args.device\n",
    "    return torch.from_numpy(df.values).float().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prior():\n",
    "    prior = pd.read_csv(args.prior_csv,sep=';',names=classes[:-1], skiprows=1)\n",
    "    prior['Others'] = 1\n",
    "    prior = prior.T\n",
    "    prior['Others'] = 1\n",
    "    prior = df_to_tensor(prior)\n",
    "    return(prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_fuzzy_label(y_target, y_fuzzy, fuzzy=False, how='uni', lbd=0):\n",
    "    '''\n",
    "    Using two sets of prediction labels and fuzziness parameters to compute the fuzzy label in the form as \n",
    "    a distribution over classes\n",
    "    \n",
    "    Args:\n",
    "    y_target (torch.Tensor) of shape (n_batch, n_classes): the true label of the ouv description\n",
    "    y_fuzzy (torch.Tensor) of shape (n_batch, n_classes): the fuzzy label of the ouv description\n",
    "    fuzzy (bool): whether or not to turn on the fuzziness option\n",
    "    how (string): the way fuzziness weights are used, one of the options in {'uni', 'prior'}\n",
    "    lbd (float): the scaler applied to the fuzziness of the label\n",
    "    \n",
    "    Returns:\n",
    "    A pytorch Tensor of shape (n_batch, n_classes): The processed label in the form of distribution that add to 1\n",
    "    '''\n",
    "    assert y_target.shape == y_fuzzy.shape, 'target labels must have the same size'\n",
    "    assert how in {'uni', 'prior', 'origin'}, '''how must be one of the two options in {'uni', 'prior'}'''\n",
    "    \n",
    "    if not fuzzy:\n",
    "        return softmax_sensitive(y_target)\n",
    "    \n",
    "    if how == 'uni':\n",
    "        y_label = y_target + lbd * y_fuzzy\n",
    "        return softmax_sensitive(y_label)\n",
    "    \n",
    "    ### TO DO ###\n",
    "    elif how == 'prior':\n",
    "        prior = get_prior()\n",
    "        y_inter = torch.matmul(y_target.float(),prior)\n",
    "        y_inter = y_inter/(y_inter.max(dim=1, keepdim=True)[0])\n",
    "        y_label = y_target + lbd * y_fuzzy * y_inter\n",
    "        return softmax_sensitive(y_label)\n",
    "    \n",
    "    else:\n",
    "        y_label = y_target + lbd\n",
    "        return softmax_sensitive(y_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_encoder(model, file, device=None):\n",
    "        \"Load the encoder `file` from the model directory, optionally ensuring it's on `device`\"\n",
    "        encoder = model[0]\n",
    "        if device is None: device = args.device\n",
    "        if hasattr(encoder, 'module'): encoder = encoder.module\n",
    "        distrib_barrier()\n",
    "        wgts = torch.load(args.save_dir + file+'.pth', map_location=device)\n",
    "        encoder.load_state_dict(clean_raw_keys(wgts))\n",
    "        for param in encoder.parameters():\n",
    "            param.requires_grad = False\n",
    "        #model.freeze()\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ouv_df = pd.read_csv(args.big_ouv_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Due to IPython and Windows limitation, python multiprocessing isn't available now.\n",
      "So `n_workers` has to be changed to 0 to avoid getting stuck\n"
     ]
    }
   ],
   "source": [
    "data_lm = TextDataLoaders.from_df(ouv_df, text_col = 'data', label_col = 'true', path = \"\", is_lm=True)\n",
    "vocab = data_lm.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not using pre-trained embeddings\n"
     ]
    }
   ],
   "source": [
    "if args.reload_from_files:\n",
    "    # training from a checkpoint\n",
    "    dataset = OuvDataset.load_dataset_and_load_vectorizer(args.ouv_csv, args.vectorizer_file, vocab=vocab)\n",
    "\n",
    "else:\n",
    "    # create dataset and vectorizer\n",
    "    dataset = OuvDataset.load_dataset_and_make_vectorizer(args.ouv_csv, cutoff=args.frequency_cutoff, vocab=vocab)\n",
    "    dataset.save_vectorizer(args.vectorizer_file)    \n",
    "\n",
    "vectorizer = dataset.get_vectorizer()\n",
    "set_seed_everywhere(args.seed, args.cuda)\n",
    "\n",
    "# Use GloVe or randomly initialized embeddings\n",
    "if args.use_glove:\n",
    "    words = vectorizer.ouv_vocab._token_to_idx.keys()\n",
    "    embeddings = make_embedding_matrix(glove_filepath=args.glove_filepath, \n",
    "                                       words=words)\n",
    "    print(\"Using pre-trained embeddings\")\n",
    "else:\n",
    "    print(\"Not using pre-trained embeddings\")\n",
    "    embeddings = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a Domain-specific Language Model using whole data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data and Prepare Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ouv_df = pd.read_csv(args.big_ouv_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Due to IPython and Windows limitation, python multiprocessing isn't available now.\n",
      "So `n_workers` has to be changed to 0 to avoid getting stuck\n"
     ]
    }
   ],
   "source": [
    "data_lm = TextDataLoaders.from_df(ouv_df, text_col = 'data', label_col = 'true', path = \"\", is_lm=True)\n",
    "vocab = data_lm.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10536"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Model for Classifier Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = get_text_classifier(AWD_LSTM, len(vocab), len(classes), seq_len=72, config=None, drop_mult=0.5, lin_ftrs=None,\n",
    "                        ps=None, pad_idx=1, max_len=72*20, y_range=None)\n",
    "classifier = load_encoder(classifier, 'finetuned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def freeze_to(model,n):\n",
    "    param_lists = [param for param in model[0].parameters()]\n",
    "    frozen_idx = n if n >= 0 else len(param_lists) + n*4\n",
    "    if frozen_idx >= len(param_lists):\n",
    "        warn(f\"Freezing {frozen_idx} groups; model has {len(param_lists)}; whole model is frozen.\")\n",
    "    for i in range(len(param_lists)):\n",
    "        if i < frozen_idx:\n",
    "            param_lists[i].requires_grad = False\n",
    "        else:\n",
    "            param_lists[i].requires_grad = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Trained Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 1 LS Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(args.save_dir+'hyperdict_fuzzy.p', 'rb') as fp:\n",
    "    hyperdict_fuzzy = pickle.load(fp)\n",
    "    train_state = hyperdict_fuzzy[('prior', 0.1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SequentialRNN(\n",
       "  (0): SentenceEncoder(\n",
       "    (module): AWD_LSTM(\n",
       "      (encoder): Embedding(10536, 400, padding_idx=1)\n",
       "      (encoder_dp): EmbeddingDropout(\n",
       "        (emb): Embedding(10536, 400, padding_idx=1)\n",
       "      )\n",
       "      (rnns): ModuleList(\n",
       "        (0): WeightDropout(\n",
       "          (module): LSTM(400, 1152, batch_first=True)\n",
       "        )\n",
       "        (1): WeightDropout(\n",
       "          (module): LSTM(1152, 1152, batch_first=True)\n",
       "        )\n",
       "        (2): WeightDropout(\n",
       "          (module): LSTM(1152, 400, batch_first=True)\n",
       "        )\n",
       "      )\n",
       "      (input_dp): RNNDropout()\n",
       "      (hidden_dps): ModuleList(\n",
       "        (0): RNNDropout()\n",
       "        (1): RNNDropout()\n",
       "        (2): RNNDropout()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): PoolingLinearClassifier(\n",
       "    (layers): Sequential(\n",
       "      (0): LinBnDrop(\n",
       "        (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (1): Dropout(p=0.2, inplace=False)\n",
       "        (2): Linear(in_features=1200, out_features=50, bias=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "      )\n",
       "      (1): LinBnDrop(\n",
       "        (0): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (1): Dropout(p=0.1, inplace=False)\n",
       "        (2): Linear(in_features=50, out_features=11, bias=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.load_state_dict(torch.load(args.save_dir+'1337/model.pth',map_location=torch.device('cpu')))\n",
    "classifier.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 2 Baseline w/o LS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(args.save_dir+'hyperdict_fuzzy.p', 'rb') as fp:\n",
    "    hyperdict_fuzzy = pickle.load(fp)\n",
    "    train_state = hyperdict_fuzzy[('prior', 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SequentialRNN(\n",
       "  (0): SentenceEncoder(\n",
       "    (module): AWD_LSTM(\n",
       "      (encoder): Embedding(10536, 400, padding_idx=1)\n",
       "      (encoder_dp): EmbeddingDropout(\n",
       "        (emb): Embedding(10536, 400, padding_idx=1)\n",
       "      )\n",
       "      (rnns): ModuleList(\n",
       "        (0): WeightDropout(\n",
       "          (module): LSTM(400, 1152, batch_first=True)\n",
       "        )\n",
       "        (1): WeightDropout(\n",
       "          (module): LSTM(1152, 1152, batch_first=True)\n",
       "        )\n",
       "        (2): WeightDropout(\n",
       "          (module): LSTM(1152, 400, batch_first=True)\n",
       "        )\n",
       "      )\n",
       "      (input_dp): RNNDropout()\n",
       "      (hidden_dps): ModuleList(\n",
       "        (0): RNNDropout()\n",
       "        (1): RNNDropout()\n",
       "        (2): RNNDropout()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): PoolingLinearClassifier(\n",
       "    (layers): Sequential(\n",
       "      (0): LinBnDrop(\n",
       "        (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (1): Dropout(p=0.2, inplace=False)\n",
       "        (2): Linear(in_features=1200, out_features=50, bias=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "      )\n",
       "      (1): LinBnDrop(\n",
       "        (0): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (1): Dropout(p=0.1, inplace=False)\n",
       "        (2): Linear(in_features=50, out_features=11, bias=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.load_state_dict(torch.load(args.save_dir+'baseline/model.pth',map_location=torch.device('cpu')))\n",
    "classifier.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24550730"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters())\n",
    "count_parameters(classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(r\"([.,!?])\", r\" \\1 \", text)\n",
    "    text = re.sub(r\"[^a-zA-Z.,!?]+\", r\" \", text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_rating(text, classifier, vectorizer, classes, k=1):\n",
    "    \"\"\"Predict the rating of a review\n",
    "    \n",
    "    Args:\n",
    "        text (str): the text of the description\n",
    "        classifier (ReviewClassifier): the trained model\n",
    "        vectorizer (ReviewVectorizer): the corresponding vectorizer\n",
    "        classes (list of str): The name of the ouv classes\n",
    "        k (int): show the largest k prediction, default to 1\n",
    "    \"\"\"\n",
    "    ouv = preprocess_text(str(text))\n",
    "    \n",
    "    classifier.eval()\n",
    "    vectorized_ouv = torch.tensor(vectorizer.vectorize(ouv)[0])\n",
    "    X = vectorized_ouv.view(1,-1)\n",
    "    result = classifier(vectorized_ouv.unsqueeze(0))[0]\n",
    "    result = F.softmax(result, dim=1)\n",
    "    \n",
    "    if k==1:\n",
    "        pred_id = result.argmax().item()\n",
    "        return (classes[pred_id], result[0][pred_id])\n",
    "    else:\n",
    "        pred_indices = [i.item() for i in result.topk(k)[1][0]]\n",
    "        output = []\n",
    "        for pred_id in pred_indices:\n",
    "            output.append((classes[pred_id],result[0][pred_id].item()))\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a very old building dating back to 13th century -> Criteria iii with a probability of 0.67\n"
     ]
    }
   ],
   "source": [
    "test_ouv = 'this is a very old building dating back to 13th century'\n",
    "\n",
    "prediction = predict_rating(test_ouv,classifier,vectorizer,classes)\n",
    "print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "this is a very old building dating back to 13th century -> Criteria iii with a probability of 0.67\n",
      "this is a very old building dating back to 13th century -> Criteria iv with a probability of 0.22\n",
      "this is a very old building dating back to 13th century -> Criteria ii with a probability of 0.05\n"
     ]
    }
   ],
   "source": [
    "test_ouv = 'this is a very old building dating back to 13th century'\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "The particular layout of the complex is unique to this site -> Criteria iv with a probability of 0.44\n",
      "The particular layout of the complex is unique to this site -> Criteria iii with a probability of 0.23\n",
      "The particular layout of the complex is unique to this site -> Criteria ii with a probability of 0.11\n"
     ]
    }
   ],
   "source": [
    "test_ouv = 'The particular layout of the complex is unique to this site'\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world from \n",
      "torcellos cathedral to the church of santa maria della salute . the years of the republics extraordinary golden \n",
      "age are represented by monuments of incomparable beauty -> Criteria i with a probability of 0.69\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world from \n",
      "torcellos cathedral to the church of santa maria della salute . the years of the republics extraordinary golden \n",
      "age are represented by monuments of incomparable beauty -> Criteria iv with a probability of 0.12\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world from \n",
      "torcellos cathedral to the church of santa maria della salute . the years of the republics extraordinary golden \n",
      "age are represented by monuments of incomparable beauty -> Criteria ii with a probability of 0.05\n"
     ]
    }
   ],
   "source": [
    "test_ouv = '''the lagoon of venice also has one of the highest concentrations of masterpieces in the world from \n",
    "torcellos cathedral to the church of santa maria della salute . the years of the republics extraordinary golden \n",
    "age are represented by monuments of incomparable beauty'''\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world -> Criteria i with a probability of 0.33\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world -> Criteria vii with a probability of 0.28\n",
      "the lagoon of venice also has one of the highest concentrations of masterpieces in the world -> Criteria iv with a probability of 0.11\n"
     ]
    }
   ],
   "source": [
    "test_ouv = '''the lagoon of venice also has one of the highest concentrations of masterpieces in the world'''\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "from torcellos cathedral to the church of santa maria della salute -> Criteria vi with a probability of 0.38\n",
      "from torcellos cathedral to the church of santa maria della salute -> Criteria i with a probability of 0.20\n",
      "from torcellos cathedral to the church of santa maria della salute -> Criteria ii with a probability of 0.15\n"
     ]
    }
   ],
   "source": [
    "test_ouv = '''from torcellos cathedral to the church of santa maria della salute'''\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions:\n",
      "===================\n",
      "the years of the republics extraordinary golden age are represented by monuments of incomparable beauty -> Criteria i with a probability of 0.25\n",
      "the years of the republics extraordinary golden age are represented by monuments of incomparable beauty -> Criteria iv with a probability of 0.25\n",
      "the years of the republics extraordinary golden age are represented by monuments of incomparable beauty -> Criteria iii with a probability of 0.22\n"
     ]
    }
   ],
   "source": [
    "test_ouv = '''the years of the republics extraordinary golden age are represented by monuments of incomparable beauty'''\n",
    "k=3\n",
    "predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)\n",
    "\n",
    "print(\"Top {} predictions:\".format(k))\n",
    "print(\"===================\")\n",
    "for prediction in predictions:\n",
    "    print('{} -> {} with a probability of {:0.2f}'.format(test_ouv, prediction[0],prediction[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed: 0.019331932067871094\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "class Timer(object):\n",
    "    def __init__(self, name=None):\n",
    "        self.name = name\n",
    "\n",
    "    def __enter__(self):\n",
    "        self.tstart = time.time()\n",
    "\n",
    "    def __exit__(self, type, value, traceback):\n",
    "        if self.name:\n",
    "            print('[%s]' % self.name,)\n",
    "        print('Elapsed: %s' % (time.time() - self.tstart))\n",
    "        \n",
    "set_seed_everywhere(args.seed, args.cuda)        \n",
    "test_ouv = 'The particular layout of the complex is unique to this site'\n",
    "k=3\n",
    "with Timer():\n",
    "    predictions = predict_rating(test_ouv,classifier,vectorizer,classes,k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting scores for Social Media Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def IoU(a,b):\n",
    "    i = set(a).intersection(set(b))\n",
    "    u = set(a).union(set(b))\n",
    "    return float(len(i))/len(u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_dict = {i:classes[i] for i in range(len(classes))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Venezia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = pd.read_csv('../../../Venice_Flickr/Venezia/data_storage/sentences.csv',sep='\\t', index_col = 'Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_all_df = pd.read_csv('../../../Venice_Flickr/Venezia/data_storage/metadata.csv',sep='\\t', index_col = 'Unnamed: 0').reset_index().rename(columns={'level_0':'origin'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_ouv(line, classifier=classifier, vectorizer=vectorizer, classes=classes, k=11):\n",
    "    predictions = predict_rating(line['processed'], classifier, vectorizer, classes, k)\n",
    "    for key, value in predictions:\n",
    "        line[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dict = {}\n",
    "for i in range(len(sm_df)):\n",
    "    new_dict[i]={}\n",
    "    line = sm_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['sentence'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        new_dict[i][key] = value\n",
    "    if len(new_dict)%100==0:\n",
    "        print('{}/{} finished'.format(len(new_dict),len(sm_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/2951 finished\n",
      "200/2951 finished\n",
      "300/2951 finished\n",
      "400/2951 finished\n",
      "500/2951 finished\n",
      "600/2951 finished\n",
      "700/2951 finished\n",
      "800/2951 finished\n",
      "900/2951 finished\n",
      "1000/2951 finished\n",
      "1100/2951 finished\n",
      "1200/2951 finished\n",
      "1300/2951 finished\n",
      "1400/2951 finished\n",
      "1500/2951 finished\n",
      "1600/2951 finished\n",
      "1700/2951 finished\n",
      "1800/2951 finished\n",
      "1900/2951 finished\n",
      "2000/2951 finished\n",
      "2100/2951 finished\n",
      "2200/2951 finished\n",
      "2300/2951 finished\n",
      "2400/2951 finished\n",
      "2500/2951 finished\n",
      "2600/2951 finished\n",
      "2700/2951 finished\n",
      "2800/2951 finished\n",
      "2900/2951 finished\n"
     ]
    }
   ],
   "source": [
    "des_dict = {}\n",
    "for i in range(len(sm_all_df)):\n",
    "    des_dict[i]={}\n",
    "    line = sm_all_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['revised_text'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        des_dict[i][key] = value\n",
    "    if len(des_dict)%100==0:\n",
    "        print('{}/{} finished'.format(len(des_dict),len(sm_all_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.033705</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>0.025328</td>\n",
       "      <td>0.098227</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>0.003724</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.064877</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>0.128423</td>\n",
       "      <td>0.007169</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>0.007108</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.039328</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.106062</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.143317</td>\n",
       "      <td>0.067548</td>\n",
       "      <td>0.348501</td>\n",
       "      <td>0.206013</td>\n",
       "      <td>0.024100</td>\n",
       "      <td>0.161014</td>\n",
       "      <td>0.025110</td>\n",
       "      <td>0.014006</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.005646</td>\n",
       "      <td>0.000413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.012331</td>\n",
       "      <td>0.028577</td>\n",
       "      <td>0.141579</td>\n",
       "      <td>0.024646</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>0.774415</td>\n",
       "      <td>0.004108</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.000626</td>\n",
       "      <td>0.002715</td>\n",
       "      <td>0.000792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4552</th>\n",
       "      <td>0.104771</td>\n",
       "      <td>0.154656</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.145719</td>\n",
       "      <td>0.017407</td>\n",
       "      <td>0.156632</td>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.010011</td>\n",
       "      <td>0.008845</td>\n",
       "      <td>0.011811</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4553</th>\n",
       "      <td>0.104771</td>\n",
       "      <td>0.154656</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.145719</td>\n",
       "      <td>0.017407</td>\n",
       "      <td>0.156632</td>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.010011</td>\n",
       "      <td>0.008845</td>\n",
       "      <td>0.011811</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4554</th>\n",
       "      <td>0.179463</td>\n",
       "      <td>0.122015</td>\n",
       "      <td>0.171377</td>\n",
       "      <td>0.110938</td>\n",
       "      <td>0.009676</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.021827</td>\n",
       "      <td>0.022300</td>\n",
       "      <td>0.009031</td>\n",
       "      <td>0.018681</td>\n",
       "      <td>0.001390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4555</th>\n",
       "      <td>0.126042</td>\n",
       "      <td>0.060049</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.140538</td>\n",
       "      <td>0.012537</td>\n",
       "      <td>0.186152</td>\n",
       "      <td>0.063421</td>\n",
       "      <td>0.059563</td>\n",
       "      <td>0.022375</td>\n",
       "      <td>0.035624</td>\n",
       "      <td>0.000663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>0.134604</td>\n",
       "      <td>0.063405</td>\n",
       "      <td>0.233261</td>\n",
       "      <td>0.138251</td>\n",
       "      <td>0.012058</td>\n",
       "      <td>0.358972</td>\n",
       "      <td>0.020876</td>\n",
       "      <td>0.021326</td>\n",
       "      <td>0.006007</td>\n",
       "      <td>0.010272</td>\n",
       "      <td>0.000969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4557 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.033705     0.300480      0.358030     0.172171    0.025328   \n",
       "1       0.064877     0.203339      0.399754     0.156123    0.022062   \n",
       "2       0.039328     0.304376      0.345548     0.164574    0.026383   \n",
       "3       0.143317     0.067548      0.348501     0.206013    0.024100   \n",
       "4       0.012331     0.028577      0.141579     0.024646    0.009571   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "4552    0.104771     0.154656      0.368583     0.145719    0.017407   \n",
       "4553    0.104771     0.154656      0.368583     0.145719    0.017407   \n",
       "4554    0.179463     0.122015      0.171377     0.110938    0.009676   \n",
       "4555    0.126042     0.060049      0.293035     0.140538    0.012537   \n",
       "4556    0.134604     0.063405      0.233261     0.138251    0.012058   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.098227      0.002784       0.002034     0.003484    0.003724   \n",
       "1        0.128423      0.007169       0.004896     0.006185    0.007108   \n",
       "2        0.106062      0.003234       0.002186     0.003941    0.004332   \n",
       "3        0.161014      0.025110       0.014006     0.004332    0.005646   \n",
       "4        0.774415      0.004108       0.000640     0.000626    0.002715   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "4552     0.156632      0.021453       0.010011     0.008845    0.011811   \n",
       "4553     0.156632      0.021453       0.010011     0.008845    0.011811   \n",
       "4554     0.333300      0.021827       0.022300     0.009031    0.018681   \n",
       "4555     0.186152      0.063421       0.059563     0.022375    0.035624   \n",
       "4556     0.358972      0.020876       0.021326     0.006007    0.010272   \n",
       "\n",
       "        Others  \n",
       "0     0.000033  \n",
       "1     0.000062  \n",
       "2     0.000034  \n",
       "3     0.000413  \n",
       "4     0.000792  \n",
       "...        ...  \n",
       "4552  0.000113  \n",
       "4553  0.000113  \n",
       "4554  0.001390  \n",
       "4555  0.000663  \n",
       "4556  0.000969  \n",
       "\n",
       "[4557 rows x 11 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df = pd.DataFrame(new_dict).reindex(classes).T\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.033705</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>0.025328</td>\n",
       "      <td>0.098227</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>0.003724</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.064877</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>0.128423</td>\n",
       "      <td>0.007169</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>0.007108</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.039328</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.106062</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.053406</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>0.194197</td>\n",
       "      <td>0.032737</td>\n",
       "      <td>0.200104</td>\n",
       "      <td>0.004055</td>\n",
       "      <td>0.003042</td>\n",
       "      <td>0.001213</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>0.000449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.236283</td>\n",
       "      <td>0.188700</td>\n",
       "      <td>0.147055</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>0.029092</td>\n",
       "      <td>0.142973</td>\n",
       "      <td>0.002966</td>\n",
       "      <td>0.002041</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>0.000547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2948</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2949</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2950</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2951 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.033705     0.300480      0.358030     0.172171    0.025328   \n",
       "1       0.064877     0.203339      0.399754     0.156123    0.022062   \n",
       "2       0.039328     0.304376      0.345548     0.164574    0.026383   \n",
       "3       0.053406     0.038182      0.470955     0.194197    0.032737   \n",
       "4       0.236283     0.188700      0.147055     0.247691    0.029092   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "2946    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "2947    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "2948    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "2949    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "2950    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.098227      0.002784       0.002034     0.003484    0.003724   \n",
       "1        0.128423      0.007169       0.004896     0.006185    0.007108   \n",
       "2        0.106062      0.003234       0.002186     0.003941    0.004332   \n",
       "3        0.200104      0.004055       0.003042     0.001213    0.001662   \n",
       "4        0.142973      0.002966       0.002041     0.001215    0.001437   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "2946     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "2947     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "2948     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "2949     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "2950     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "\n",
       "        Others  \n",
       "0     0.000033  \n",
       "1     0.000062  \n",
       "2     0.000034  \n",
       "3     0.000449  \n",
       "4     0.000547  \n",
       "...        ...  \n",
       "2946  0.000025  \n",
       "2947  0.000025  \n",
       "2948  0.000025  \n",
       "2949  0.000025  \n",
       "2950  0.000025  \n",
       "\n",
       "[2951 rows x 11 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_all_df = pd.DataFrame(des_dict).reindex(classes).T\n",
    "score_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = score_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = score_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = score_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_df = pd.concat([score_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.033705</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>0.025328</td>\n",
       "      <td>0.098227</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>0.003724</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.064877</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>0.128423</td>\n",
       "      <td>0.007169</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>0.007108</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.039328</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.106062</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.143317</td>\n",
       "      <td>0.067548</td>\n",
       "      <td>0.348501</td>\n",
       "      <td>0.206013</td>\n",
       "      <td>0.024100</td>\n",
       "      <td>0.161014</td>\n",
       "      <td>0.025110</td>\n",
       "      <td>0.014006</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.005646</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.348501</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.206013</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.161014</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.348501</td>\n",
       "      <td>0.715528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.012331</td>\n",
       "      <td>0.028577</td>\n",
       "      <td>0.141579</td>\n",
       "      <td>0.024646</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>0.774415</td>\n",
       "      <td>0.004108</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.000626</td>\n",
       "      <td>0.002715</td>\n",
       "      <td>0.000792</td>\n",
       "      <td>0.774415</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.141579</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.028577</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.774415</td>\n",
       "      <td>0.944572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4552</th>\n",
       "      <td>0.104771</td>\n",
       "      <td>0.154656</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.145719</td>\n",
       "      <td>0.017407</td>\n",
       "      <td>0.156632</td>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.010011</td>\n",
       "      <td>0.008845</td>\n",
       "      <td>0.011811</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.156632</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.154656</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.679870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4553</th>\n",
       "      <td>0.104771</td>\n",
       "      <td>0.154656</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.145719</td>\n",
       "      <td>0.017407</td>\n",
       "      <td>0.156632</td>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.010011</td>\n",
       "      <td>0.008845</td>\n",
       "      <td>0.011811</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.156632</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.154656</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.679870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4554</th>\n",
       "      <td>0.179463</td>\n",
       "      <td>0.122015</td>\n",
       "      <td>0.171377</td>\n",
       "      <td>0.110938</td>\n",
       "      <td>0.009676</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.021827</td>\n",
       "      <td>0.022300</td>\n",
       "      <td>0.009031</td>\n",
       "      <td>0.018681</td>\n",
       "      <td>0.001390</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.179463</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.171377</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.684141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4555</th>\n",
       "      <td>0.126042</td>\n",
       "      <td>0.060049</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.140538</td>\n",
       "      <td>0.012537</td>\n",
       "      <td>0.186152</td>\n",
       "      <td>0.063421</td>\n",
       "      <td>0.059563</td>\n",
       "      <td>0.022375</td>\n",
       "      <td>0.035624</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.186152</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.140538</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.619725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>0.134604</td>\n",
       "      <td>0.063405</td>\n",
       "      <td>0.233261</td>\n",
       "      <td>0.138251</td>\n",
       "      <td>0.012058</td>\n",
       "      <td>0.358972</td>\n",
       "      <td>0.020876</td>\n",
       "      <td>0.021326</td>\n",
       "      <td>0.006007</td>\n",
       "      <td>0.010272</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.358972</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.233261</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.138251</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358972</td>\n",
       "      <td>0.730484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4557 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.033705     0.300480      0.358030     0.172171    0.025328   \n",
       "1       0.064877     0.203339      0.399754     0.156123    0.022062   \n",
       "2       0.039328     0.304376      0.345548     0.164574    0.026383   \n",
       "3       0.143317     0.067548      0.348501     0.206013    0.024100   \n",
       "4       0.012331     0.028577      0.141579     0.024646    0.009571   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "4552    0.104771     0.154656      0.368583     0.145719    0.017407   \n",
       "4553    0.104771     0.154656      0.368583     0.145719    0.017407   \n",
       "4554    0.179463     0.122015      0.171377     0.110938    0.009676   \n",
       "4555    0.126042     0.060049      0.293035     0.140538    0.012537   \n",
       "4556    0.134604     0.063405      0.233261     0.138251    0.012058   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.098227      0.002784       0.002034     0.003484    0.003724   \n",
       "1        0.128423      0.007169       0.004896     0.006185    0.007108   \n",
       "2        0.106062      0.003234       0.002186     0.003941    0.004332   \n",
       "3        0.161014      0.025110       0.014006     0.004332    0.005646   \n",
       "4        0.774415      0.004108       0.000640     0.000626    0.002715   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "4552     0.156632      0.021453       0.010011     0.008845    0.011811   \n",
       "4553     0.156632      0.021453       0.010011     0.008845    0.011811   \n",
       "4554     0.333300      0.021827       0.022300     0.009031    0.018681   \n",
       "4555     0.186152      0.063421       0.059563     0.022375    0.035624   \n",
       "4556     0.358972      0.020876       0.021326     0.006007    0.010272   \n",
       "\n",
       "        Others  max_1_val     max_1_col  max_2_val     max_2_col  max_3_val  \\\n",
       "0     0.000033   0.358030  Criteria iii   0.300480   Criteria ii   0.172171   \n",
       "1     0.000062   0.399754  Criteria iii   0.203339   Criteria ii   0.156123   \n",
       "2     0.000034   0.345548  Criteria iii   0.304376   Criteria ii   0.164574   \n",
       "3     0.000413   0.348501  Criteria iii   0.206013   Criteria iv   0.161014   \n",
       "4     0.000792   0.774415   Criteria vi   0.141579  Criteria iii   0.028577   \n",
       "...        ...        ...           ...        ...           ...        ...   \n",
       "4552  0.000113   0.368583  Criteria iii   0.156632   Criteria vi   0.154656   \n",
       "4553  0.000113   0.368583  Criteria iii   0.156632   Criteria vi   0.154656   \n",
       "4554  0.001390   0.333300   Criteria vi   0.179463    Criteria i   0.171377   \n",
       "4555  0.000663   0.293035  Criteria iii   0.186152   Criteria vi   0.140538   \n",
       "4556  0.000969   0.358972   Criteria vi   0.233261  Criteria iii   0.138251   \n",
       "\n",
       "         max_3_col     max_1     max_3  \n",
       "0      Criteria iv  0.358030  0.830680  \n",
       "1      Criteria iv  0.399754  0.759217  \n",
       "2      Criteria iv  0.345548  0.814499  \n",
       "3      Criteria vi  0.348501  0.715528  \n",
       "4      Criteria ii  0.774415  0.944572  \n",
       "...            ...       ...       ...  \n",
       "4552   Criteria ii  0.368583  0.679870  \n",
       "4553   Criteria ii  0.368583  0.679870  \n",
       "4554  Criteria iii  0.333300  0.684141  \n",
       "4555   Criteria iv  0.293035  0.619725  \n",
       "4556   Criteria iv  0.358972  0.730484  \n",
       "\n",
       "[4557 rows x 19 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df['max_1'] = score_df['max_1_val']\n",
    "score_df['max_3'] = score_df['max_1_val']+score_df['max_2_val']+score_df['max_3_val']\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = sm_df.merge(score_df, how='left', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = score_all_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = score_all_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = score_all_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_all_df = pd.concat([score_all_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.033705</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>0.025328</td>\n",
       "      <td>0.098227</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>0.003724</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.064877</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>0.128423</td>\n",
       "      <td>0.007169</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>0.007108</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.039328</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.106062</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.053406</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>0.194197</td>\n",
       "      <td>0.032737</td>\n",
       "      <td>0.200104</td>\n",
       "      <td>0.004055</td>\n",
       "      <td>0.003042</td>\n",
       "      <td>0.001213</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.200104</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.194197</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>0.865256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.236283</td>\n",
       "      <td>0.188700</td>\n",
       "      <td>0.147055</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>0.029092</td>\n",
       "      <td>0.142973</td>\n",
       "      <td>0.002966</td>\n",
       "      <td>0.002041</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.236283</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.188700</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>0.672674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2948</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2949</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2950</th>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2951 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.033705     0.300480      0.358030     0.172171    0.025328   \n",
       "1       0.064877     0.203339      0.399754     0.156123    0.022062   \n",
       "2       0.039328     0.304376      0.345548     0.164574    0.026383   \n",
       "3       0.053406     0.038182      0.470955     0.194197    0.032737   \n",
       "4       0.236283     0.188700      0.147055     0.247691    0.029092   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "2946    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "2947    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "2948    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "2949    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "2950    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.098227      0.002784       0.002034     0.003484    0.003724   \n",
       "1        0.128423      0.007169       0.004896     0.006185    0.007108   \n",
       "2        0.106062      0.003234       0.002186     0.003941    0.004332   \n",
       "3        0.200104      0.004055       0.003042     0.001213    0.001662   \n",
       "4        0.142973      0.002966       0.002041     0.001215    0.001437   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "2946     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "2947     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "2948     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "2949     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "2950     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "\n",
       "        Others  max_1_val     max_1_col  max_2_val    max_2_col  max_3_val  \\\n",
       "0     0.000033   0.358030  Criteria iii   0.300480  Criteria ii   0.172171   \n",
       "1     0.000062   0.399754  Criteria iii   0.203339  Criteria ii   0.156123   \n",
       "2     0.000034   0.345548  Criteria iii   0.304376  Criteria ii   0.164574   \n",
       "3     0.000449   0.470955  Criteria iii   0.200104  Criteria vi   0.194197   \n",
       "4     0.000547   0.247691   Criteria iv   0.236283   Criteria i   0.188700   \n",
       "...        ...        ...           ...        ...          ...        ...   \n",
       "2946  0.000025   0.462594  Criteria iii   0.258027  Criteria iv   0.144617   \n",
       "2947  0.000025   0.462594  Criteria iii   0.258027  Criteria iv   0.144617   \n",
       "2948  0.000025   0.462594  Criteria iii   0.258027  Criteria iv   0.144617   \n",
       "2949  0.000025   0.462594  Criteria iii   0.258027  Criteria iv   0.144617   \n",
       "2950  0.000025   0.462594  Criteria iii   0.258027  Criteria iv   0.144617   \n",
       "\n",
       "        max_3_col     max_1     max_3  \n",
       "0     Criteria iv  0.358030  0.830680  \n",
       "1     Criteria iv  0.399754  0.759217  \n",
       "2     Criteria iv  0.345548  0.814499  \n",
       "3     Criteria iv  0.470955  0.865256  \n",
       "4     Criteria ii  0.247691  0.672674  \n",
       "...           ...       ...       ...  \n",
       "2946  Criteria ii  0.462594  0.865238  \n",
       "2947  Criteria ii  0.462594  0.865238  \n",
       "2948  Criteria ii  0.462594  0.865238  \n",
       "2949  Criteria ii  0.462594  0.865238  \n",
       "2950  Criteria ii  0.462594  0.865238  \n",
       "\n",
       "[2951 rows x 19 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_all_df['max_1'] = score_all_df['max_1_val']\n",
    "score_all_df['max_3'] = score_all_df['max_1_val']+score_all_df['max_2_val']+score_all_df['max_3_val']\n",
    "score_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_all_df = sm_all_df.merge(score_all_df, how='left', left_index=True, right_index=True)\n",
    "sm_all_df['valid'] = sm_all_df['revised_text'].apply(lambda x: str(x)!='nan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>candownload</th>\n",
       "      <th>url_c</th>\n",
       "      <th>url_q</th>\n",
       "      <th>url_n</th>\n",
       "      <th>url_largest</th>\n",
       "      <th>owner</th>\n",
       "      <th>owner_loc</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>51870743082</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870743082_d197fdcb50_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870743082_d197fdcb50_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870743082_d197fdcb50_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870743082_26e0f10292_o.jpg</td>\n",
       "      <td>47954272@N06</td>\n",
       "      <td>Tallinn, Estonia</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>51871789478</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51871789478_829f1d0c28_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51871789478_829f1d0c28_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51871789478_829f1d0c28_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51871789478_54105a6b9b_o.jpg</td>\n",
       "      <td>47954272@N06</td>\n",
       "      <td>Tallinn, Estonia</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>51870742942</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870742942_f1a9e654a1_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870742942_f1a9e654a1_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870742942_f1a9e654a1_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870742942_ed1244e911_o.jpg</td>\n",
       "      <td>47954272@N06</td>\n",
       "      <td>Tallinn, Estonia</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>51870380541</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870380541_bedca7dfda_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870380541_bedca7dfda_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870380541_bedca7dfda_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870380541_d6e45411c4_o.jpg</td>\n",
       "      <td>64148082@N02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VENEZIA. CARNEVALE 2019</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.200104</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.194197</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>0.865256</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>51863124066</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51863124066_3064973069_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51863124066_3064973069_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51863124066_3064973069_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51863124066_27314f2f56_o.jpg</td>\n",
       "      <td>186704588@N07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Venice - Ponte dei Sospiri</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.236283</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.188700</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>0.672674</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>4981</td>\n",
       "      <td>51233430558</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233430558_876677cba3_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_145859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>4982</td>\n",
       "      <td>51234286630</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234286630_f490cfba4d_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234286630_f490cfba4d_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234286630_f490cfba4d_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234286630_7794c6246f_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_145745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2948</th>\n",
       "      <td>4983</td>\n",
       "      <td>51234294135</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234294135_11fbdbbab8_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234294135_11fbdbbab8_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234294135_11fbdbbab8_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234294135_71d647f828_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_164727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2949</th>\n",
       "      <td>4984</td>\n",
       "      <td>51234291635</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234291635_a850374644_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_155646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2950</th>\n",
       "      <td>4985</td>\n",
       "      <td>51233434888</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233434888_f389fc41dd_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233434888_f389fc41dd_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233434888_f389fc41dd_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233434888_2c742ac6c6_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_155525</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2951 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index  candownload  \\\n",
       "0          3  51870743082            1   \n",
       "1          4  51871789478            1   \n",
       "2          5  51870742942            1   \n",
       "3          6  51870380541            1   \n",
       "4         13  51863124066            1   \n",
       "...      ...          ...          ...   \n",
       "2946    4981  51233430558            1   \n",
       "2947    4982  51234286630            1   \n",
       "2948    4983  51234294135            1   \n",
       "2949    4984  51234291635            1   \n",
       "2950    4985  51233434888            1   \n",
       "\n",
       "                                                                 url_c  \\\n",
       "0     https://live.staticflickr.com/65535/51870743082_d197fdcb50_c.jpg   \n",
       "1     https://live.staticflickr.com/65535/51871789478_829f1d0c28_c.jpg   \n",
       "2     https://live.staticflickr.com/65535/51870742942_f1a9e654a1_c.jpg   \n",
       "3     https://live.staticflickr.com/65535/51870380541_bedca7dfda_c.jpg   \n",
       "4     https://live.staticflickr.com/65535/51863124066_3064973069_c.jpg   \n",
       "...                                                                ...   \n",
       "2946  https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_c.jpg   \n",
       "2947  https://live.staticflickr.com/65535/51234286630_f490cfba4d_c.jpg   \n",
       "2948  https://live.staticflickr.com/65535/51234294135_11fbdbbab8_c.jpg   \n",
       "2949  https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_c.jpg   \n",
       "2950  https://live.staticflickr.com/65535/51233434888_f389fc41dd_c.jpg   \n",
       "\n",
       "                                                                 url_q  \\\n",
       "0     https://live.staticflickr.com/65535/51870743082_d197fdcb50_q.jpg   \n",
       "1     https://live.staticflickr.com/65535/51871789478_829f1d0c28_q.jpg   \n",
       "2     https://live.staticflickr.com/65535/51870742942_f1a9e654a1_q.jpg   \n",
       "3     https://live.staticflickr.com/65535/51870380541_bedca7dfda_q.jpg   \n",
       "4     https://live.staticflickr.com/65535/51863124066_3064973069_q.jpg   \n",
       "...                                                                ...   \n",
       "2946  https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_q.jpg   \n",
       "2947  https://live.staticflickr.com/65535/51234286630_f490cfba4d_q.jpg   \n",
       "2948  https://live.staticflickr.com/65535/51234294135_11fbdbbab8_q.jpg   \n",
       "2949  https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_q.jpg   \n",
       "2950  https://live.staticflickr.com/65535/51233434888_f389fc41dd_q.jpg   \n",
       "\n",
       "                                                                 url_n  \\\n",
       "0     https://live.staticflickr.com/65535/51870743082_d197fdcb50_n.jpg   \n",
       "1     https://live.staticflickr.com/65535/51871789478_829f1d0c28_n.jpg   \n",
       "2     https://live.staticflickr.com/65535/51870742942_f1a9e654a1_n.jpg   \n",
       "3     https://live.staticflickr.com/65535/51870380541_bedca7dfda_n.jpg   \n",
       "4     https://live.staticflickr.com/65535/51863124066_3064973069_n.jpg   \n",
       "...                                                                ...   \n",
       "2946  https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_n.jpg   \n",
       "2947  https://live.staticflickr.com/65535/51234286630_f490cfba4d_n.jpg   \n",
       "2948  https://live.staticflickr.com/65535/51234294135_11fbdbbab8_n.jpg   \n",
       "2949  https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_n.jpg   \n",
       "2950  https://live.staticflickr.com/65535/51233434888_f389fc41dd_n.jpg   \n",
       "\n",
       "                                                           url_largest  \\\n",
       "0     https://live.staticflickr.com/65535/51870743082_26e0f10292_o.jpg   \n",
       "1     https://live.staticflickr.com/65535/51871789478_54105a6b9b_o.jpg   \n",
       "2     https://live.staticflickr.com/65535/51870742942_ed1244e911_o.jpg   \n",
       "3     https://live.staticflickr.com/65535/51870380541_d6e45411c4_o.jpg   \n",
       "4     https://live.staticflickr.com/65535/51863124066_27314f2f56_o.jpg   \n",
       "...                                                                ...   \n",
       "2946  https://live.staticflickr.com/65535/51233430558_876677cba3_o.jpg   \n",
       "2947  https://live.staticflickr.com/65535/51234286630_7794c6246f_o.jpg   \n",
       "2948  https://live.staticflickr.com/65535/51234294135_71d647f828_o.jpg   \n",
       "2949  https://live.staticflickr.com/65535/51234291635_a850374644_o.jpg   \n",
       "2950  https://live.staticflickr.com/65535/51233434888_2c742ac6c6_o.jpg   \n",
       "\n",
       "              owner         owner_loc                       title  ...  \\\n",
       "0      47954272@N06  Tallinn, Estonia                         ...  ...   \n",
       "1      47954272@N06  Tallinn, Estonia                         ...  ...   \n",
       "2      47954272@N06  Tallinn, Estonia                         ...  ...   \n",
       "3      64148082@N02               NaN     VENEZIA. CARNEVALE 2019  ...   \n",
       "4     186704588@N07               NaN  Venice - Ponte dei Sospiri  ...   \n",
       "...             ...               ...                         ...  ...   \n",
       "2946   83293103@N00     Milano, Italy             20210520_145859  ...   \n",
       "2947   83293103@N00     Milano, Italy             20210520_145745  ...   \n",
       "2948   83293103@N00     Milano, Italy             20210520_164727  ...   \n",
       "2949   83293103@N00     Milano, Italy             20210520_155646  ...   \n",
       "2950   83293103@N00     Milano, Italy             20210520_155525  ...   \n",
       "\n",
       "        Others  max_1_val     max_1_col  max_2_val    max_2_col max_3_val  \\\n",
       "0     0.000033   0.358030  Criteria iii   0.300480  Criteria ii  0.172171   \n",
       "1     0.000062   0.399754  Criteria iii   0.203339  Criteria ii  0.156123   \n",
       "2     0.000034   0.345548  Criteria iii   0.304376  Criteria ii  0.164574   \n",
       "3     0.000449   0.470955  Criteria iii   0.200104  Criteria vi  0.194197   \n",
       "4     0.000547   0.247691   Criteria iv   0.236283   Criteria i  0.188700   \n",
       "...        ...        ...           ...        ...          ...       ...   \n",
       "2946  0.000025   0.462594  Criteria iii   0.258027  Criteria iv  0.144617   \n",
       "2947  0.000025   0.462594  Criteria iii   0.258027  Criteria iv  0.144617   \n",
       "2948  0.000025   0.462594  Criteria iii   0.258027  Criteria iv  0.144617   \n",
       "2949  0.000025   0.462594  Criteria iii   0.258027  Criteria iv  0.144617   \n",
       "2950  0.000025   0.462594  Criteria iii   0.258027  Criteria iv  0.144617   \n",
       "\n",
       "        max_3_col     max_1     max_3 valid  \n",
       "0     Criteria iv  0.358030  0.830680  True  \n",
       "1     Criteria iv  0.399754  0.759217  True  \n",
       "2     Criteria iv  0.345548  0.814499  True  \n",
       "3     Criteria iv  0.470955  0.865256  True  \n",
       "4     Criteria ii  0.247691  0.672674  True  \n",
       "...           ...       ...       ...   ...  \n",
       "2946  Criteria ii  0.462594  0.865238  True  \n",
       "2947  Criteria ii  0.462594  0.865238  True  \n",
       "2948  Criteria ii  0.462594  0.865238  True  \n",
       "2949  Criteria ii  0.462594  0.865238  True  \n",
       "2950  Criteria ii  0.462594  0.865238  True  \n",
       "\n",
       "[2951 rows x 55 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.033705</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>0.025328</td>\n",
       "      <td>0.098227</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>0.003724</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.064877</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>0.128423</td>\n",
       "      <td>0.007169</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>0.007108</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.039328</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.106062</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>0.055417</td>\n",
       "      <td>0.042262</td>\n",
       "      <td>0.328887</td>\n",
       "      <td>0.144357</td>\n",
       "      <td>0.017397</td>\n",
       "      <td>0.371280</td>\n",
       "      <td>0.016876</td>\n",
       "      <td>0.011530</td>\n",
       "      <td>0.004458</td>\n",
       "      <td>0.007134</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0.175743</td>\n",
       "      <td>0.146366</td>\n",
       "      <td>0.192433</td>\n",
       "      <td>0.174829</td>\n",
       "      <td>0.032636</td>\n",
       "      <td>0.182183</td>\n",
       "      <td>0.040901</td>\n",
       "      <td>0.020022</td>\n",
       "      <td>0.018728</td>\n",
       "      <td>0.015696</td>\n",
       "      <td>0.000463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>4981</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>4982</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>4983</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>4984</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>4985</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1761 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin  Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0          3    0.033705     0.300480      0.358030     0.172171    0.025328   \n",
       "1          4    0.064877     0.203339      0.399754     0.156123    0.022062   \n",
       "2          5    0.039328     0.304376      0.345548     0.164574    0.026383   \n",
       "3          6    0.055417     0.042262      0.328887     0.144357    0.017397   \n",
       "4         13    0.175743     0.146366      0.192433     0.174829    0.032636   \n",
       "...      ...         ...          ...           ...          ...         ...   \n",
       "1756    4981    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "1757    4982    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "1758    4983    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "1759    4984    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "1760    4985    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.098227      0.002784       0.002034     0.003484    0.003724   \n",
       "1        0.128423      0.007169       0.004896     0.006185    0.007108   \n",
       "2        0.106062      0.003234       0.002186     0.003941    0.004332   \n",
       "3        0.371280      0.016876       0.011530     0.004458    0.007134   \n",
       "4        0.182183      0.040901       0.020022     0.018728    0.015696   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "1756     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "1757     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "1758     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "1759     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "1760     0.034974      0.001452       0.001590     0.001721    0.001373   \n",
       "\n",
       "        Others  \n",
       "0     0.000033  \n",
       "1     0.000062  \n",
       "2     0.000034  \n",
       "3     0.000402  \n",
       "4     0.000463  \n",
       "...        ...  \n",
       "1756  0.000025  \n",
       "1757  0.000025  \n",
       "1758  0.000025  \n",
       "1759  0.000025  \n",
       "1760  0.000025  \n",
       "\n",
       "[1761 rows x 12 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_agg_df = sm_df[classes+['origin']].groupby('origin').mean().reset_index()\n",
    "sm_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = sm_agg_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = sm_agg_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = sm_agg_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_agg_df = pd.concat([sm_agg_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>...</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.033705</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>0.025328</td>\n",
       "      <td>0.098227</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.064877</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>0.128423</td>\n",
       "      <td>0.007169</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.039328</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.106062</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>0.055417</td>\n",
       "      <td>0.042262</td>\n",
       "      <td>0.328887</td>\n",
       "      <td>0.144357</td>\n",
       "      <td>0.017397</td>\n",
       "      <td>0.371280</td>\n",
       "      <td>0.016876</td>\n",
       "      <td>0.011530</td>\n",
       "      <td>0.004458</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.371280</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.328887</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.144357</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.371280</td>\n",
       "      <td>0.844524</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0.175743</td>\n",
       "      <td>0.146366</td>\n",
       "      <td>0.192433</td>\n",
       "      <td>0.174829</td>\n",
       "      <td>0.032636</td>\n",
       "      <td>0.182183</td>\n",
       "      <td>0.040901</td>\n",
       "      <td>0.020022</td>\n",
       "      <td>0.018728</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>0.192433</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.182183</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.175743</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.192433</td>\n",
       "      <td>0.550358</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>4981</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>4982</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>4983</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>4984</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>4985</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1761 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin  Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0          3    0.033705     0.300480      0.358030     0.172171    0.025328   \n",
       "1          4    0.064877     0.203339      0.399754     0.156123    0.022062   \n",
       "2          5    0.039328     0.304376      0.345548     0.164574    0.026383   \n",
       "3          6    0.055417     0.042262      0.328887     0.144357    0.017397   \n",
       "4         13    0.175743     0.146366      0.192433     0.174829    0.032636   \n",
       "...      ...         ...          ...           ...          ...         ...   \n",
       "1756    4981    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "1757    4982    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "1758    4983    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "1759    4984    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "1760    4985    0.064827     0.144617      0.462594     0.258027    0.028800   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  ...    Others  \\\n",
       "0        0.098227      0.002784       0.002034     0.003484  ...  0.000033   \n",
       "1        0.128423      0.007169       0.004896     0.006185  ...  0.000062   \n",
       "2        0.106062      0.003234       0.002186     0.003941  ...  0.000034   \n",
       "3        0.371280      0.016876       0.011530     0.004458  ...  0.000402   \n",
       "4        0.182183      0.040901       0.020022     0.018728  ...  0.000463   \n",
       "...           ...           ...            ...          ...  ...       ...   \n",
       "1756     0.034974      0.001452       0.001590     0.001721  ...  0.000025   \n",
       "1757     0.034974      0.001452       0.001590     0.001721  ...  0.000025   \n",
       "1758     0.034974      0.001452       0.001590     0.001721  ...  0.000025   \n",
       "1759     0.034974      0.001452       0.001590     0.001721  ...  0.000025   \n",
       "1760     0.034974      0.001452       0.001590     0.001721  ...  0.000025   \n",
       "\n",
       "      max_1_val     max_1_col max_2_val     max_2_col max_3_val    max_3_col  \\\n",
       "0      0.358030  Criteria iii  0.300480   Criteria ii  0.172171  Criteria iv   \n",
       "1      0.399754  Criteria iii  0.203339   Criteria ii  0.156123  Criteria iv   \n",
       "2      0.345548  Criteria iii  0.304376   Criteria ii  0.164574  Criteria iv   \n",
       "3      0.371280   Criteria vi  0.328887  Criteria iii  0.144357  Criteria iv   \n",
       "4      0.192433  Criteria iii  0.182183   Criteria vi  0.175743   Criteria i   \n",
       "...         ...           ...       ...           ...       ...          ...   \n",
       "1756   0.462594  Criteria iii  0.258027   Criteria iv  0.144617  Criteria ii   \n",
       "1757   0.462594  Criteria iii  0.258027   Criteria iv  0.144617  Criteria ii   \n",
       "1758   0.462594  Criteria iii  0.258027   Criteria iv  0.144617  Criteria ii   \n",
       "1759   0.462594  Criteria iii  0.258027   Criteria iv  0.144617  Criteria ii   \n",
       "1760   0.462594  Criteria iii  0.258027   Criteria iv  0.144617  Criteria ii   \n",
       "\n",
       "         max_1     max_3  counts  \n",
       "0     0.358030  0.830680       1  \n",
       "1     0.399754  0.759217       1  \n",
       "2     0.345548  0.814499       1  \n",
       "3     0.371280  0.844524       9  \n",
       "4     0.192433  0.550358      10  \n",
       "...        ...       ...     ...  \n",
       "1756  0.462594  0.865238       1  \n",
       "1757  0.462594  0.865238       1  \n",
       "1758  0.462594  0.865238       1  \n",
       "1759  0.462594  0.865238       1  \n",
       "1760  0.462594  0.865238       1  \n",
       "\n",
       "[1761 rows x 21 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_agg_df['max_1'] = score_agg_df['max_1_val']\n",
    "score_agg_df['max_3'] = score_agg_df['max_1_val']+score_agg_df['max_2_val']+score_agg_df['max_3_val']\n",
    "score_agg_df['counts'] = sm_df[['origin','sentence']].groupby('origin').count().reset_index()['sentence']\n",
    "score_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     1826\n",
       "Criteria vi      1217\n",
       "Criteria i        508\n",
       "Criteria ii       429\n",
       "Criteria iv       271\n",
       "Criteria vii      181\n",
       "Criteria viii      67\n",
       "Criteria x         32\n",
       "Criteria v         19\n",
       "Criteria ix         7\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     3741\n",
       "Criteria vi      2914\n",
       "Criteria iv      2732\n",
       "Criteria ii      1832\n",
       "Criteria i       1515\n",
       "Criteria vii      389\n",
       "Criteria viii     247\n",
       "Criteria x        164\n",
       "Criteria ix        76\n",
       "Criteria v         61\n",
       "dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_df['max_1_col'],score_df['max_2_col'],score_df['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     1161\n",
       "Criteria vi       646\n",
       "Criteria ii       377\n",
       "Criteria iv       130\n",
       "Criteria i        130\n",
       "Criteria viii     125\n",
       "Criteria vii       30\n",
       "Criteria x          8\n",
       "Criteria ix         2\n",
       "Criteria v          1\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_all_df[sm_all_df.valid]['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     2291\n",
       "Criteria vi      2120\n",
       "Criteria iv      1300\n",
       "Criteria ii      1064\n",
       "Criteria i        592\n",
       "Criteria vii      218\n",
       "Criteria viii     148\n",
       "Criteria x         64\n",
       "Criteria v         25\n",
       "Criteria ix         8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_all_df[sm_all_df.valid]['max_1_col'],\n",
    "           score_all_df[sm_all_df.valid]['max_2_col'],\n",
    "           score_all_df[sm_all_df.valid]['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     1027\n",
       "Criteria vi       353\n",
       "Criteria ii       151\n",
       "Criteria i        114\n",
       "Criteria iv        51\n",
       "Criteria vii       42\n",
       "Criteria viii      14\n",
       "Criteria x          8\n",
       "Criteria ix         1\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_agg_df['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     1672\n",
       "Criteria vi      1455\n",
       "Criteria iv       862\n",
       "Criteria ii       595\n",
       "Criteria i        432\n",
       "Criteria vii      185\n",
       "Criteria viii      45\n",
       "Criteria x         29\n",
       "Criteria ix         7\n",
       "Criteria v          1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_agg_df['max_1_col'],score_agg_df['max_2_col'],score_agg_df['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.358030\n",
       "1       0.399754\n",
       "2       0.345548\n",
       "3       0.348501\n",
       "4       0.774415\n",
       "          ...   \n",
       "4552    0.368583\n",
       "4553    0.368583\n",
       "4554    0.333300\n",
       "4555    0.293035\n",
       "4556    0.358972\n",
       "Name: max_1, Length: 4557, dtype: float64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_df['max_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_df.drop_duplicates('sentence')['max_1'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_df.drop_duplicates('sentence')['max_3'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_all_df.drop_duplicates('revised_text')['max_1'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_all_df.drop_duplicates('revised_text')['max_3'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare = score_agg_df.merge(sm_all_df, how = 'left', left_on='origin', right_on='origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare['max_1_eq'] = compare['max_1_col_x']==compare['max_1_col_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8001135718341851"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare['max_1_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7254290171606864"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare[compare['counts']>1]['max_1_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare['max_3_eq'] = compare.apply(lambda x: IoU([x['max_1_col_x'],x['max_2_col_x'],x['max_3_col_x']],\n",
    "                                                 [x['max_1_col_y'],x['max_2_col_y'],x['max_3_col_y']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8347529812606473"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare['max_3_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7730109204368175"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare[compare['counts']>1]['max_3_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1857"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(score_df[(score_df.max_3>0.8) & (score_df.max_1>1./3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_dict_1 = {'max_{}_col_x'.format(i):'max_{}_col_agg'.format(i) for i in range(1,4,1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_1_col_x': 'max_1_col_agg',\n",
       " 'max_2_col_x': 'max_2_col_agg',\n",
       " 'max_3_col_x': 'max_3_col_agg'}"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_dict_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i_x</th>\n",
       "      <th>Criteria ii_x</th>\n",
       "      <th>Criteria iii_x</th>\n",
       "      <th>Criteria iv_x</th>\n",
       "      <th>Criteria v_x</th>\n",
       "      <th>Criteria vi_x</th>\n",
       "      <th>Criteria vii_x</th>\n",
       "      <th>Criteria viii_x</th>\n",
       "      <th>Criteria ix_x</th>\n",
       "      <th>...</th>\n",
       "      <th>max_1_col_y</th>\n",
       "      <th>max_2_val_y</th>\n",
       "      <th>max_2_col_y</th>\n",
       "      <th>max_3_val_y</th>\n",
       "      <th>max_3_col_y</th>\n",
       "      <th>max_1_y</th>\n",
       "      <th>max_3_y</th>\n",
       "      <th>valid</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.033705</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>0.025328</td>\n",
       "      <td>0.098227</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.300480</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.064877</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>0.128423</td>\n",
       "      <td>0.007169</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203339</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.039328</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.106062</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.304376</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>0.055417</td>\n",
       "      <td>0.042262</td>\n",
       "      <td>0.328887</td>\n",
       "      <td>0.144357</td>\n",
       "      <td>0.017397</td>\n",
       "      <td>0.371280</td>\n",
       "      <td>0.016876</td>\n",
       "      <td>0.011530</td>\n",
       "      <td>0.004458</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.200104</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.194197</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>0.865256</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0.175743</td>\n",
       "      <td>0.146366</td>\n",
       "      <td>0.192433</td>\n",
       "      <td>0.174829</td>\n",
       "      <td>0.032636</td>\n",
       "      <td>0.182183</td>\n",
       "      <td>0.040901</td>\n",
       "      <td>0.020022</td>\n",
       "      <td>0.018728</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.236283</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.188700</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>0.672674</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>4981</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>4982</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>4983</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>4984</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>4985</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>0.028800</td>\n",
       "      <td>0.034974</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.258027</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1761 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin  Criteria i_x  Criteria ii_x  Criteria iii_x  Criteria iv_x  \\\n",
       "0          3      0.033705       0.300480        0.358030       0.172171   \n",
       "1          4      0.064877       0.203339        0.399754       0.156123   \n",
       "2          5      0.039328       0.304376        0.345548       0.164574   \n",
       "3          6      0.055417       0.042262        0.328887       0.144357   \n",
       "4         13      0.175743       0.146366        0.192433       0.174829   \n",
       "...      ...           ...            ...             ...            ...   \n",
       "1756    4981      0.064827       0.144617        0.462594       0.258027   \n",
       "1757    4982      0.064827       0.144617        0.462594       0.258027   \n",
       "1758    4983      0.064827       0.144617        0.462594       0.258027   \n",
       "1759    4984      0.064827       0.144617        0.462594       0.258027   \n",
       "1760    4985      0.064827       0.144617        0.462594       0.258027   \n",
       "\n",
       "      Criteria v_x  Criteria vi_x  Criteria vii_x  Criteria viii_x  \\\n",
       "0         0.025328       0.098227        0.002784         0.002034   \n",
       "1         0.022062       0.128423        0.007169         0.004896   \n",
       "2         0.026383       0.106062        0.003234         0.002186   \n",
       "3         0.017397       0.371280        0.016876         0.011530   \n",
       "4         0.032636       0.182183        0.040901         0.020022   \n",
       "...            ...            ...             ...              ...   \n",
       "1756      0.028800       0.034974        0.001452         0.001590   \n",
       "1757      0.028800       0.034974        0.001452         0.001590   \n",
       "1758      0.028800       0.034974        0.001452         0.001590   \n",
       "1759      0.028800       0.034974        0.001452         0.001590   \n",
       "1760      0.028800       0.034974        0.001452         0.001590   \n",
       "\n",
       "      Criteria ix_x  ...   max_1_col_y  max_2_val_y  max_2_col_y max_3_val_y  \\\n",
       "0          0.003484  ...  Criteria iii     0.300480  Criteria ii    0.172171   \n",
       "1          0.006185  ...  Criteria iii     0.203339  Criteria ii    0.156123   \n",
       "2          0.003941  ...  Criteria iii     0.304376  Criteria ii    0.164574   \n",
       "3          0.004458  ...  Criteria iii     0.200104  Criteria vi    0.194197   \n",
       "4          0.018728  ...   Criteria iv     0.236283   Criteria i    0.188700   \n",
       "...             ...  ...           ...          ...          ...         ...   \n",
       "1756       0.001721  ...  Criteria iii     0.258027  Criteria iv    0.144617   \n",
       "1757       0.001721  ...  Criteria iii     0.258027  Criteria iv    0.144617   \n",
       "1758       0.001721  ...  Criteria iii     0.258027  Criteria iv    0.144617   \n",
       "1759       0.001721  ...  Criteria iii     0.258027  Criteria iv    0.144617   \n",
       "1760       0.001721  ...  Criteria iii     0.258027  Criteria iv    0.144617   \n",
       "\n",
       "      max_3_col_y   max_1_y   max_3_y valid  max_1_eq  max_3_eq  \n",
       "0     Criteria iv  0.358030  0.830680  True      True       1.0  \n",
       "1     Criteria iv  0.399754  0.759217  True      True       1.0  \n",
       "2     Criteria iv  0.345548  0.814499  True      True       1.0  \n",
       "3     Criteria iv  0.470955  0.865256  True     False       1.0  \n",
       "4     Criteria ii  0.247691  0.672674  True     False       0.2  \n",
       "...           ...       ...       ...   ...       ...       ...  \n",
       "1756  Criteria ii  0.462594  0.865238  True      True       1.0  \n",
       "1757  Criteria ii  0.462594  0.865238  True      True       1.0  \n",
       "1758  Criteria ii  0.462594  0.865238  True      True       1.0  \n",
       "1759  Criteria ii  0.462594  0.865238  True      True       1.0  \n",
       "1760  Criteria ii  0.462594  0.865238  True      True       1.0  \n",
       "\n",
       "[1761 rows x 77 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare.rename(columns=cp_dict_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_final = sm_all_df.merge(compare.rename(columns=cp_dict_1)[['origin','max_1_col_agg','max_2_col_agg','max_3_col_agg',\n",
    "                                                    'max_1_eq','max_3_eq']], how='left', left_on='origin',right_on='origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>candownload</th>\n",
       "      <th>url_c</th>\n",
       "      <th>url_q</th>\n",
       "      <th>url_n</th>\n",
       "      <th>url_largest</th>\n",
       "      <th>owner</th>\n",
       "      <th>owner_loc</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>valid</th>\n",
       "      <th>max_1_col_agg</th>\n",
       "      <th>max_2_col_agg</th>\n",
       "      <th>max_3_col_agg</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>51870743082</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870743082_d197fdcb50_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870743082_d197fdcb50_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870743082_d197fdcb50_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870743082_26e0f10292_o.jpg</td>\n",
       "      <td>47954272@N06</td>\n",
       "      <td>Tallinn, Estonia</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.172171</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>51871789478</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51871789478_829f1d0c28_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51871789478_829f1d0c28_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51871789478_829f1d0c28_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51871789478_54105a6b9b_o.jpg</td>\n",
       "      <td>47954272@N06</td>\n",
       "      <td>Tallinn, Estonia</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.156123</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>51870742942</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870742942_f1a9e654a1_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870742942_f1a9e654a1_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870742942_f1a9e654a1_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870742942_ed1244e911_o.jpg</td>\n",
       "      <td>47954272@N06</td>\n",
       "      <td>Tallinn, Estonia</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164574</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>51870380541</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870380541_bedca7dfda_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870380541_bedca7dfda_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870380541_bedca7dfda_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51870380541_d6e45411c4_o.jpg</td>\n",
       "      <td>64148082@N02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VENEZIA. CARNEVALE 2019</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194197</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>0.865256</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>51863124066</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51863124066_3064973069_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51863124066_3064973069_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51863124066_3064973069_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51863124066_27314f2f56_o.jpg</td>\n",
       "      <td>186704588@N07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Venice - Ponte dei Sospiri</td>\n",
       "      <td>...</td>\n",
       "      <td>0.188700</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>0.672674</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>4981</td>\n",
       "      <td>51233430558</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233430558_876677cba3_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_145859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>4982</td>\n",
       "      <td>51234286630</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234286630_f490cfba4d_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234286630_f490cfba4d_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234286630_f490cfba4d_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234286630_7794c6246f_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_145745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2948</th>\n",
       "      <td>4983</td>\n",
       "      <td>51234294135</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234294135_11fbdbbab8_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234294135_11fbdbbab8_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234294135_11fbdbbab8_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234294135_71d647f828_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_164727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2949</th>\n",
       "      <td>4984</td>\n",
       "      <td>51234291635</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51234291635_a850374644_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_155646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2950</th>\n",
       "      <td>4985</td>\n",
       "      <td>51233434888</td>\n",
       "      <td>1</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233434888_f389fc41dd_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233434888_f389fc41dd_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233434888_f389fc41dd_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51233434888_2c742ac6c6_o.jpg</td>\n",
       "      <td>83293103@N00</td>\n",
       "      <td>Milano, Italy</td>\n",
       "      <td>20210520_155525</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2951 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index  candownload  \\\n",
       "0          3  51870743082            1   \n",
       "1          4  51871789478            1   \n",
       "2          5  51870742942            1   \n",
       "3          6  51870380541            1   \n",
       "4         13  51863124066            1   \n",
       "...      ...          ...          ...   \n",
       "2946    4981  51233430558            1   \n",
       "2947    4982  51234286630            1   \n",
       "2948    4983  51234294135            1   \n",
       "2949    4984  51234291635            1   \n",
       "2950    4985  51233434888            1   \n",
       "\n",
       "                                                                 url_c  \\\n",
       "0     https://live.staticflickr.com/65535/51870743082_d197fdcb50_c.jpg   \n",
       "1     https://live.staticflickr.com/65535/51871789478_829f1d0c28_c.jpg   \n",
       "2     https://live.staticflickr.com/65535/51870742942_f1a9e654a1_c.jpg   \n",
       "3     https://live.staticflickr.com/65535/51870380541_bedca7dfda_c.jpg   \n",
       "4     https://live.staticflickr.com/65535/51863124066_3064973069_c.jpg   \n",
       "...                                                                ...   \n",
       "2946  https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_c.jpg   \n",
       "2947  https://live.staticflickr.com/65535/51234286630_f490cfba4d_c.jpg   \n",
       "2948  https://live.staticflickr.com/65535/51234294135_11fbdbbab8_c.jpg   \n",
       "2949  https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_c.jpg   \n",
       "2950  https://live.staticflickr.com/65535/51233434888_f389fc41dd_c.jpg   \n",
       "\n",
       "                                                                 url_q  \\\n",
       "0     https://live.staticflickr.com/65535/51870743082_d197fdcb50_q.jpg   \n",
       "1     https://live.staticflickr.com/65535/51871789478_829f1d0c28_q.jpg   \n",
       "2     https://live.staticflickr.com/65535/51870742942_f1a9e654a1_q.jpg   \n",
       "3     https://live.staticflickr.com/65535/51870380541_bedca7dfda_q.jpg   \n",
       "4     https://live.staticflickr.com/65535/51863124066_3064973069_q.jpg   \n",
       "...                                                                ...   \n",
       "2946  https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_q.jpg   \n",
       "2947  https://live.staticflickr.com/65535/51234286630_f490cfba4d_q.jpg   \n",
       "2948  https://live.staticflickr.com/65535/51234294135_11fbdbbab8_q.jpg   \n",
       "2949  https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_q.jpg   \n",
       "2950  https://live.staticflickr.com/65535/51233434888_f389fc41dd_q.jpg   \n",
       "\n",
       "                                                                 url_n  \\\n",
       "0     https://live.staticflickr.com/65535/51870743082_d197fdcb50_n.jpg   \n",
       "1     https://live.staticflickr.com/65535/51871789478_829f1d0c28_n.jpg   \n",
       "2     https://live.staticflickr.com/65535/51870742942_f1a9e654a1_n.jpg   \n",
       "3     https://live.staticflickr.com/65535/51870380541_bedca7dfda_n.jpg   \n",
       "4     https://live.staticflickr.com/65535/51863124066_3064973069_n.jpg   \n",
       "...                                                                ...   \n",
       "2946  https://live.staticflickr.com/65535/51233430558_5be4b3ccc7_n.jpg   \n",
       "2947  https://live.staticflickr.com/65535/51234286630_f490cfba4d_n.jpg   \n",
       "2948  https://live.staticflickr.com/65535/51234294135_11fbdbbab8_n.jpg   \n",
       "2949  https://live.staticflickr.com/65535/51234291635_4cbc7c4ce5_n.jpg   \n",
       "2950  https://live.staticflickr.com/65535/51233434888_f389fc41dd_n.jpg   \n",
       "\n",
       "                                                           url_largest  \\\n",
       "0     https://live.staticflickr.com/65535/51870743082_26e0f10292_o.jpg   \n",
       "1     https://live.staticflickr.com/65535/51871789478_54105a6b9b_o.jpg   \n",
       "2     https://live.staticflickr.com/65535/51870742942_ed1244e911_o.jpg   \n",
       "3     https://live.staticflickr.com/65535/51870380541_d6e45411c4_o.jpg   \n",
       "4     https://live.staticflickr.com/65535/51863124066_27314f2f56_o.jpg   \n",
       "...                                                                ...   \n",
       "2946  https://live.staticflickr.com/65535/51233430558_876677cba3_o.jpg   \n",
       "2947  https://live.staticflickr.com/65535/51234286630_7794c6246f_o.jpg   \n",
       "2948  https://live.staticflickr.com/65535/51234294135_71d647f828_o.jpg   \n",
       "2949  https://live.staticflickr.com/65535/51234291635_a850374644_o.jpg   \n",
       "2950  https://live.staticflickr.com/65535/51233434888_2c742ac6c6_o.jpg   \n",
       "\n",
       "              owner         owner_loc                       title  ...  \\\n",
       "0      47954272@N06  Tallinn, Estonia                         ...  ...   \n",
       "1      47954272@N06  Tallinn, Estonia                         ...  ...   \n",
       "2      47954272@N06  Tallinn, Estonia                         ...  ...   \n",
       "3      64148082@N02               NaN     VENEZIA. CARNEVALE 2019  ...   \n",
       "4     186704588@N07               NaN  Venice - Ponte dei Sospiri  ...   \n",
       "...             ...               ...                         ...  ...   \n",
       "2946   83293103@N00     Milano, Italy             20210520_145859  ...   \n",
       "2947   83293103@N00     Milano, Italy             20210520_145745  ...   \n",
       "2948   83293103@N00     Milano, Italy             20210520_164727  ...   \n",
       "2949   83293103@N00     Milano, Italy             20210520_155646  ...   \n",
       "2950   83293103@N00     Milano, Italy             20210520_155525  ...   \n",
       "\n",
       "     max_3_val    max_3_col     max_1     max_3  valid max_1_col_agg  \\\n",
       "0     0.172171  Criteria iv  0.358030  0.830680   True  Criteria iii   \n",
       "1     0.156123  Criteria iv  0.399754  0.759217   True  Criteria iii   \n",
       "2     0.164574  Criteria iv  0.345548  0.814499   True  Criteria iii   \n",
       "3     0.194197  Criteria iv  0.470955  0.865256   True   Criteria vi   \n",
       "4     0.188700  Criteria ii  0.247691  0.672674   True  Criteria iii   \n",
       "...        ...          ...       ...       ...    ...           ...   \n",
       "2946  0.144617  Criteria ii  0.462594  0.865238   True  Criteria iii   \n",
       "2947  0.144617  Criteria ii  0.462594  0.865238   True  Criteria iii   \n",
       "2948  0.144617  Criteria ii  0.462594  0.865238   True  Criteria iii   \n",
       "2949  0.144617  Criteria ii  0.462594  0.865238   True  Criteria iii   \n",
       "2950  0.144617  Criteria ii  0.462594  0.865238   True  Criteria iii   \n",
       "\n",
       "      max_2_col_agg  max_3_col_agg max_1_eq max_3_eq  \n",
       "0       Criteria ii    Criteria iv     True      1.0  \n",
       "1       Criteria ii    Criteria iv     True      1.0  \n",
       "2       Criteria ii    Criteria iv     True      1.0  \n",
       "3      Criteria iii    Criteria iv    False      1.0  \n",
       "4       Criteria vi     Criteria i    False      0.2  \n",
       "...             ...            ...      ...      ...  \n",
       "2946    Criteria iv    Criteria ii     True      1.0  \n",
       "2947    Criteria iv    Criteria ii     True      1.0  \n",
       "2948    Criteria iv    Criteria ii     True      1.0  \n",
       "2949    Criteria iv    Criteria ii     True      1.0  \n",
       "2950    Criteria iv    Criteria ii     True      1.0  \n",
       "\n",
       "[2951 rows x 60 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df.to_csv('../../../Venice_Flickr/Venezia/data_storage/sentences_ulmfit.csv',sep='\\t', encoding='utf-8-sig')\n",
    "score_final.to_csv('../../../Venice_Flickr/Venezia/data_storage/metadata_ulmfit.csv',sep='\\t', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df=pd.read_csv('../../../Venice_Flickr/Venezia/data_storage/sentences_ulmfit.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')\n",
    "score_final=pd.read_csv('../../../Venice_Flickr/Venezia/data_storage/metadata_ulmfit.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df_bert = pd.read_csv('../../../Venice_Flickr/Venezia/data_storage/sentences_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')\n",
    "score_final_bert = pd.read_csv('../../../Venice_Flickr/Venezia/data_storage/metadata_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent = sm_df.iloc[:,:4]\n",
    "compare_sent['max_1_col_ulmfit'] = sm_df['max_1_col']\n",
    "compare_sent['max_2_col_ulmfit'] = sm_df['max_2_col']\n",
    "compare_sent['max_3_col_ulmfit'] = sm_df['max_3_col']\n",
    "compare_sent['max_1_ulmfit'] = sm_df['max_1']\n",
    "compare_sent['max_3_ulmfit'] = sm_df['max_3']\n",
    "compare_sent['max_1_col_bert'] = sm_df_bert['max_1_col']\n",
    "compare_sent['max_2_col_bert'] = sm_df_bert['max_2_col']\n",
    "compare_sent['max_3_col_bert'] = sm_df_bert['max_3_col']\n",
    "compare_sent['max_1_bert'] = sm_df_bert['max_1']\n",
    "compare_sent['max_3_bert'] = sm_df_bert['max_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['same_1'] = compare_sent['max_1_col_ulmfit']==compare_sent['max_1_col_bert']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['same_3'] = compare_sent.apply(lambda x: IoU(x[['max_1_col_ulmfit','max_2_col_ulmfit','max_3_col_ulmfit']],\n",
    "                                                          x[['max_1_col_bert','max_2_col_bert','max_3_col_bert']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2538951064296686"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_sent['same_1'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6359447004608295"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(compare_sent['same_3']>=0.5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, bridge, water</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.787226</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice</td>\n",
       "      <td>4</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.898092</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of adriaticsea, italy, venice, water</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.812845</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>THE KNIGHT WITH A GRIM LOOK   The Carnival of Venice, if not the most grandiose, is certainly the best known for the charm it exerts and the mystery it continues to possess even now that 900 years have passed since the first document that refers to this famous festival</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.348501</td>\n",
       "      <td>0.715528</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>0.843877</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Who has never heard of it</td>\n",
       "      <td>6</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.774415</td>\n",
       "      <td>0.944572</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.339140</td>\n",
       "      <td>0.709100</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4551</th>\n",
       "      <td>1227</td>\n",
       "      <td>It has tags of venice, murano</td>\n",
       "      <td>4836</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.679870</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4552</th>\n",
       "      <td>1228</td>\n",
       "      <td>It has tags of venice, murano</td>\n",
       "      <td>4837</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.679870</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4553</th>\n",
       "      <td>1229</td>\n",
       "      <td>It has tags of venice, murano</td>\n",
       "      <td>4838</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.368583</td>\n",
       "      <td>0.679870</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.933448</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4555</th>\n",
       "      <td>1231</td>\n",
       "      <td>It has title of Frontage</td>\n",
       "      <td>4857</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.619725</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.543409</td>\n",
       "      <td>0.873502</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>1233</td>\n",
       "      <td>It has title of Venice, at sunset.</td>\n",
       "      <td>4866</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358972</td>\n",
       "      <td>0.730484</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>0.695404</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2898 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  \\\n",
       "0         0   \n",
       "1         1   \n",
       "2         2   \n",
       "3         3   \n",
       "4         4   \n",
       "...     ...   \n",
       "4551   1227   \n",
       "4552   1228   \n",
       "4553   1229   \n",
       "4555   1231   \n",
       "4556   1233   \n",
       "\n",
       "                                                                                                                                                                                                                                                                           sentence  \\\n",
       "0                                                                                                                                                                                                                          It has tags of adriaticsea, italy, venice, bridge, water   \n",
       "1                                                                                                                                                                                                                                         It has tags of adriaticsea, italy, venice   \n",
       "2                                                                                                                                                                                                                                  It has tags of adriaticsea, italy, venice, water   \n",
       "3     THE KNIGHT WITH A GRIM LOOK   The Carnival of Venice, if not the most grandiose, is certainly the best known for the charm it exerts and the mystery it continues to possess even now that 900 years have passed since the first document that refers to this famous festival   \n",
       "4                                                                                                                                                                                                                                                         Who has never heard of it   \n",
       "...                                                                                                                                                                                                                                                                             ...   \n",
       "4551                                                                                                                                                                                                                                                  It has tags of venice, murano   \n",
       "4552                                                                                                                                                                                                                                                  It has tags of venice, murano   \n",
       "4553                                                                                                                                                                                                                                                  It has tags of venice, murano   \n",
       "4555                                                                                                                                                                                                                                                       It has title of Frontage   \n",
       "4556                                                                                                                                                                                                                                             It has title of Venice, at sunset.   \n",
       "\n",
       "      origin   lang max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0          3     en     Criteria iii      Criteria ii      Criteria iv   \n",
       "1          4     en     Criteria iii      Criteria ii      Criteria iv   \n",
       "2          5     en     Criteria iii      Criteria ii      Criteria iv   \n",
       "3          6     en     Criteria iii      Criteria iv      Criteria vi   \n",
       "4          6     en      Criteria vi     Criteria iii      Criteria ii   \n",
       "...      ...    ...              ...              ...              ...   \n",
       "4551    4836  other     Criteria iii      Criteria vi      Criteria ii   \n",
       "4552    4837  other     Criteria iii      Criteria vi      Criteria ii   \n",
       "4553    4838  other     Criteria iii      Criteria vi      Criteria ii   \n",
       "4555    4857  other     Criteria iii      Criteria vi      Criteria iv   \n",
       "4556    4866  other      Criteria vi     Criteria iii      Criteria iv   \n",
       "\n",
       "      max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  \\\n",
       "0         0.358030      0.830680    Criteria ii    Criteria iv     Criteria i   \n",
       "1         0.399754      0.759217    Criteria ii     Criteria i    Criteria iv   \n",
       "2         0.345548      0.814499    Criteria ii     Criteria i    Criteria iv   \n",
       "3         0.348501      0.715528    Criteria vi     Criteria i   Criteria iii   \n",
       "4         0.774415      0.944572     Criteria i   Criteria iii    Criteria vi   \n",
       "...            ...           ...            ...            ...            ...   \n",
       "4551      0.368583      0.679870     Criteria i    Criteria ii   Criteria iii   \n",
       "4552      0.368583      0.679870     Criteria i    Criteria ii   Criteria iii   \n",
       "4553      0.368583      0.679870     Criteria i    Criteria ii   Criteria iii   \n",
       "4555      0.293035      0.619725    Criteria iv     Criteria i   Criteria iii   \n",
       "4556      0.358972      0.730484     Criteria i    Criteria iv   Criteria iii   \n",
       "\n",
       "      max_1_bert  max_3_bert  same_1  same_3  \n",
       "0       0.362388    0.787226   False     0.5  \n",
       "1       0.638658    0.898092   False     0.5  \n",
       "2       0.396352    0.812845   False     0.5  \n",
       "3       0.424027    0.843877   False     0.5  \n",
       "4       0.339140    0.709100   False     0.5  \n",
       "...          ...         ...     ...     ...  \n",
       "4551    0.849580    0.933448   False     0.5  \n",
       "4552    0.849580    0.933448   False     0.5  \n",
       "4553    0.849580    0.933448   False     0.5  \n",
       "4555    0.543409    0.873502   False     0.5  \n",
       "4556    0.308849    0.695404   False     0.5  \n",
       "\n",
       "[2898 rows x 16 columns]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_sent[compare_sent['same_3']>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_sent['same_3'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post = score_final.iloc[:,:2]\n",
    "compare_post['max_1_col_ulmfit'] = score_final['max_1_col']\n",
    "compare_post['max_2_col_ulmfit'] = score_final['max_2_col']\n",
    "compare_post['max_3_col_ulmfit'] = score_final['max_3_col']\n",
    "compare_post['max_1_ulmfit'] = score_final['max_1']\n",
    "compare_post['max_3_ulmfit'] = score_final['max_3']\n",
    "compare_post['max_1_col_bert'] = score_final_bert['max_1_col']\n",
    "compare_post['max_2_col_bert'] = score_final_bert['max_2_col']\n",
    "compare_post['max_3_col_bert'] = score_final_bert['max_3_col']\n",
    "compare_post['max_1_bert'] = score_final_bert['max_1']\n",
    "compare_post['max_3_bert'] = score_final_bert['max_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['same_1'] = compare_post['max_1_col_ulmfit']==compare_post['max_1_col_bert']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['same_3'] = compare_post.apply(lambda x: IoU(x[['max_1_col_ulmfit','max_2_col_ulmfit','max_3_col_ulmfit']],\n",
    "                                                          x[['max_1_col_bert','max_2_col_bert','max_3_col_bert']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20535411724839037"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post['same_1'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37739749237546594"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post['same_3'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4662826160623517"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(compare_post['same_3']>=0.5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_post['same_3'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = cosine_similarity(sm_df[classes], sm_df_bert[classes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['cosine_sim'] = np.diagonal(cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_sent[compare_sent.same_3>=0.5]['cosine_sim'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = cosine_similarity(score_final[classes], score_final_bert[classes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['cosine_sim'] = np.diagonal(cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>51870743082</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.358030</td>\n",
       "      <td>0.830680</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>0.787226</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.812573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>51871789478</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.399754</td>\n",
       "      <td>0.759217</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.898092</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.525624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>51870742942</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.345548</td>\n",
       "      <td>0.814499</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.396352</td>\n",
       "      <td>0.812845</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.756251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>51870380541</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>0.865256</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.823930</td>\n",
       "      <td>0.930945</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.462165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>51863124066</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.247691</td>\n",
       "      <td>0.672674</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.337761</td>\n",
       "      <td>0.871130</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.853685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>4981</td>\n",
       "      <td>51233430558</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>4982</td>\n",
       "      <td>51234286630</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2948</th>\n",
       "      <td>4983</td>\n",
       "      <td>51234294135</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2949</th>\n",
       "      <td>4984</td>\n",
       "      <td>51234291635</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2950</th>\n",
       "      <td>4985</td>\n",
       "      <td>51233434888</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.462594</td>\n",
       "      <td>0.865238</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.571989</td>\n",
       "      <td>0.903156</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.313572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2951 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0          3  51870743082     Criteria iii      Criteria ii      Criteria iv   \n",
       "1          4  51871789478     Criteria iii      Criteria ii      Criteria iv   \n",
       "2          5  51870742942     Criteria iii      Criteria ii      Criteria iv   \n",
       "3          6  51870380541     Criteria iii      Criteria vi      Criteria iv   \n",
       "4         13  51863124066      Criteria iv       Criteria i      Criteria ii   \n",
       "...      ...          ...              ...              ...              ...   \n",
       "2946    4981  51233430558     Criteria iii      Criteria iv      Criteria ii   \n",
       "2947    4982  51234286630     Criteria iii      Criteria iv      Criteria ii   \n",
       "2948    4983  51234294135     Criteria iii      Criteria iv      Criteria ii   \n",
       "2949    4984  51234291635     Criteria iii      Criteria iv      Criteria ii   \n",
       "2950    4985  51233434888     Criteria iii      Criteria iv      Criteria ii   \n",
       "\n",
       "      max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  \\\n",
       "0         0.358030      0.830680    Criteria ii    Criteria iv     Criteria i   \n",
       "1         0.399754      0.759217    Criteria ii     Criteria i    Criteria iv   \n",
       "2         0.345548      0.814499    Criteria ii     Criteria i    Criteria iv   \n",
       "3         0.470955      0.865256    Criteria vi   Criteria iii    Criteria iv   \n",
       "4         0.247691      0.672674    Criteria vi    Criteria iv     Criteria i   \n",
       "...            ...           ...            ...            ...            ...   \n",
       "2946      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "2947      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "2948      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "2949      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "2950      0.462594      0.865238     Criteria i    Criteria ii    Criteria iv   \n",
       "\n",
       "      max_1_bert  max_3_bert  same_1  same_3  cosine_sim  \n",
       "0       0.362388    0.787226   False     0.5    0.812573  \n",
       "1       0.638658    0.898092   False     0.5    0.525624  \n",
       "2       0.396352    0.812845   False     0.5    0.756251  \n",
       "3       0.823930    0.930945   False     1.0    0.462165  \n",
       "4       0.337761    0.871130   False     0.5    0.853685  \n",
       "...          ...         ...     ...     ...         ...  \n",
       "2946    0.571989    0.903156   False     0.5    0.313572  \n",
       "2947    0.571989    0.903156   False     0.5    0.313572  \n",
       "2948    0.571989    0.903156   False     0.5    0.313572  \n",
       "2949    0.571989    0.903156   False     0.5    0.313572  \n",
       "2950    0.571989    0.903156   False     0.5    0.313572  \n",
       "\n",
       "[2951 rows x 15 columns]"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_post[compare_post.same_3>=0.5]['cosine_sim'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent.to_csv('../../../Venice_Flickr/Venezia/data_storage/sentences_compare.csv',sep='\\t', encoding='utf-8-sig')\n",
    "compare_post.to_csv('../../../Venice_Flickr/Venezia/data_storage/metadata_compare.csv',sep='\\t', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amsterdam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = pd.read_csv('../../../Venice_Flickr/Amsterdam/data_storage/sentences.csv',sep='\\t', index_col = 'Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_all_df = pd.read_csv('../../../Venice_Flickr/Amsterdam/data_storage/metadata.csv',sep='\\t', index_col = 'Unnamed: 0').reset_index().rename(columns={'level_0':'origin'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_ouv(line, classifier=classifier, vectorizer=vectorizer, classes=classes, k=11):\n",
    "    predictions = predict_rating(line['processed'], classifier, vectorizer, classes, k)\n",
    "    for key, value in predictions:\n",
    "        line[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/9599 finished\n",
      "200/9599 finished\n",
      "300/9599 finished\n",
      "400/9599 finished\n",
      "500/9599 finished\n",
      "600/9599 finished\n",
      "700/9599 finished\n",
      "800/9599 finished\n",
      "900/9599 finished\n",
      "1000/9599 finished\n",
      "1100/9599 finished\n",
      "1200/9599 finished\n",
      "1300/9599 finished\n",
      "1400/9599 finished\n",
      "1500/9599 finished\n",
      "1600/9599 finished\n",
      "1700/9599 finished\n",
      "1800/9599 finished\n",
      "1900/9599 finished\n",
      "2000/9599 finished\n",
      "2100/9599 finished\n",
      "2200/9599 finished\n",
      "2300/9599 finished\n",
      "2400/9599 finished\n",
      "2500/9599 finished\n",
      "2600/9599 finished\n",
      "2700/9599 finished\n",
      "2800/9599 finished\n",
      "2900/9599 finished\n",
      "3000/9599 finished\n",
      "3100/9599 finished\n",
      "3200/9599 finished\n",
      "3300/9599 finished\n",
      "3400/9599 finished\n",
      "3500/9599 finished\n",
      "3600/9599 finished\n",
      "3700/9599 finished\n",
      "3800/9599 finished\n",
      "3900/9599 finished\n",
      "4000/9599 finished\n",
      "4100/9599 finished\n",
      "4200/9599 finished\n",
      "4300/9599 finished\n",
      "4400/9599 finished\n",
      "4500/9599 finished\n",
      "4600/9599 finished\n",
      "4700/9599 finished\n",
      "4800/9599 finished\n",
      "4900/9599 finished\n",
      "5000/9599 finished\n",
      "5100/9599 finished\n",
      "5200/9599 finished\n",
      "5300/9599 finished\n",
      "5400/9599 finished\n",
      "5500/9599 finished\n",
      "5600/9599 finished\n",
      "5700/9599 finished\n",
      "5800/9599 finished\n",
      "5900/9599 finished\n",
      "6000/9599 finished\n",
      "6100/9599 finished\n",
      "6200/9599 finished\n",
      "6300/9599 finished\n",
      "6400/9599 finished\n",
      "6500/9599 finished\n",
      "6600/9599 finished\n",
      "6700/9599 finished\n",
      "6800/9599 finished\n",
      "6900/9599 finished\n",
      "7000/9599 finished\n",
      "7100/9599 finished\n",
      "7200/9599 finished\n",
      "7300/9599 finished\n",
      "7400/9599 finished\n",
      "7500/9599 finished\n",
      "7600/9599 finished\n",
      "7700/9599 finished\n",
      "7800/9599 finished\n",
      "7900/9599 finished\n",
      "8000/9599 finished\n",
      "8100/9599 finished\n",
      "8200/9599 finished\n",
      "8300/9599 finished\n",
      "8400/9599 finished\n",
      "8500/9599 finished\n",
      "8600/9599 finished\n",
      "8700/9599 finished\n",
      "8800/9599 finished\n",
      "8900/9599 finished\n",
      "9000/9599 finished\n",
      "9100/9599 finished\n",
      "9200/9599 finished\n",
      "9300/9599 finished\n",
      "9400/9599 finished\n",
      "9500/9599 finished\n"
     ]
    }
   ],
   "source": [
    "new_dict = {}\n",
    "for i in range(len(sm_df)):\n",
    "    new_dict[i]={}\n",
    "    line = sm_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['sentence'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        new_dict[i][key] = value\n",
    "    if len(new_dict)%100==0:\n",
    "        print('{}/{} finished'.format(len(new_dict),len(sm_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/3727 finished\n",
      "200/3727 finished\n",
      "300/3727 finished\n",
      "400/3727 finished\n",
      "500/3727 finished\n",
      "600/3727 finished\n",
      "700/3727 finished\n",
      "800/3727 finished\n",
      "900/3727 finished\n",
      "1000/3727 finished\n",
      "1100/3727 finished\n",
      "1200/3727 finished\n",
      "1300/3727 finished\n",
      "1400/3727 finished\n",
      "1500/3727 finished\n",
      "1600/3727 finished\n",
      "1700/3727 finished\n",
      "1800/3727 finished\n",
      "1900/3727 finished\n",
      "2000/3727 finished\n",
      "2100/3727 finished\n",
      "2200/3727 finished\n",
      "2300/3727 finished\n",
      "2400/3727 finished\n",
      "2500/3727 finished\n",
      "2600/3727 finished\n",
      "2700/3727 finished\n",
      "2800/3727 finished\n",
      "2900/3727 finished\n",
      "3000/3727 finished\n",
      "3100/3727 finished\n",
      "3200/3727 finished\n",
      "3300/3727 finished\n",
      "3400/3727 finished\n",
      "3500/3727 finished\n",
      "3600/3727 finished\n",
      "3700/3727 finished\n"
     ]
    }
   ],
   "source": [
    "des_dict = {}\n",
    "for i in range(len(sm_all_df)):\n",
    "    des_dict[i]={}\n",
    "    line = sm_all_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['revised_text'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        des_dict[i][key] = value\n",
    "    if len(des_dict)%100==0:\n",
    "        print('{}/{} finished'.format(len(des_dict),len(sm_all_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.063411</td>\n",
       "      <td>0.183700</td>\n",
       "      <td>0.157456</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.008129</td>\n",
       "      <td>0.410730</td>\n",
       "      <td>0.000902</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.037595</td>\n",
       "      <td>0.129560</td>\n",
       "      <td>0.472180</td>\n",
       "      <td>0.165439</td>\n",
       "      <td>0.041721</td>\n",
       "      <td>0.119189</td>\n",
       "      <td>0.010162</td>\n",
       "      <td>0.005533</td>\n",
       "      <td>0.008944</td>\n",
       "      <td>0.009578</td>\n",
       "      <td>0.000099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.049204</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>0.036770</td>\n",
       "      <td>0.101294</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.006080</td>\n",
       "      <td>0.009712</td>\n",
       "      <td>0.009011</td>\n",
       "      <td>0.000039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.216921</td>\n",
       "      <td>0.188141</td>\n",
       "      <td>0.174296</td>\n",
       "      <td>0.202317</td>\n",
       "      <td>0.048151</td>\n",
       "      <td>0.106487</td>\n",
       "      <td>0.027835</td>\n",
       "      <td>0.009027</td>\n",
       "      <td>0.012432</td>\n",
       "      <td>0.014174</td>\n",
       "      <td>0.000218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.096496</td>\n",
       "      <td>0.037656</td>\n",
       "      <td>0.122139</td>\n",
       "      <td>0.068756</td>\n",
       "      <td>0.009859</td>\n",
       "      <td>0.273391</td>\n",
       "      <td>0.156935</td>\n",
       "      <td>0.131684</td>\n",
       "      <td>0.031384</td>\n",
       "      <td>0.067284</td>\n",
       "      <td>0.004416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9594</th>\n",
       "      <td>0.044658</td>\n",
       "      <td>0.149264</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.172239</td>\n",
       "      <td>0.036680</td>\n",
       "      <td>0.107794</td>\n",
       "      <td>0.014610</td>\n",
       "      <td>0.007455</td>\n",
       "      <td>0.014052</td>\n",
       "      <td>0.015055</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9595</th>\n",
       "      <td>0.044658</td>\n",
       "      <td>0.149264</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.172239</td>\n",
       "      <td>0.036680</td>\n",
       "      <td>0.107794</td>\n",
       "      <td>0.014610</td>\n",
       "      <td>0.007455</td>\n",
       "      <td>0.014052</td>\n",
       "      <td>0.015055</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9596</th>\n",
       "      <td>0.132050</td>\n",
       "      <td>0.063876</td>\n",
       "      <td>0.213784</td>\n",
       "      <td>0.200032</td>\n",
       "      <td>0.013147</td>\n",
       "      <td>0.359905</td>\n",
       "      <td>0.006564</td>\n",
       "      <td>0.005049</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.002852</td>\n",
       "      <td>0.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9597</th>\n",
       "      <td>0.126042</td>\n",
       "      <td>0.060049</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.140538</td>\n",
       "      <td>0.012537</td>\n",
       "      <td>0.186152</td>\n",
       "      <td>0.063421</td>\n",
       "      <td>0.059563</td>\n",
       "      <td>0.022375</td>\n",
       "      <td>0.035624</td>\n",
       "      <td>0.000663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9598</th>\n",
       "      <td>0.126379</td>\n",
       "      <td>0.165190</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>0.017086</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.004861</td>\n",
       "      <td>0.005882</td>\n",
       "      <td>0.000124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9599 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.063411     0.183700      0.157456     0.173913    0.008129   \n",
       "1       0.037595     0.129560      0.472180     0.165439    0.041721   \n",
       "2       0.049204     0.121436      0.441306     0.216375    0.036770   \n",
       "3       0.216921     0.188141      0.174296     0.202317    0.048151   \n",
       "4       0.096496     0.037656      0.122139     0.068756    0.009859   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "9594    0.044658     0.149264      0.438163     0.172239    0.036680   \n",
       "9595    0.044658     0.149264      0.438163     0.172239    0.036680   \n",
       "9596    0.132050     0.063876      0.213784     0.200032    0.013147   \n",
       "9597    0.126042     0.060049      0.293035     0.140538    0.012537   \n",
       "9598    0.126379     0.165190      0.292644     0.203834    0.017086   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.410730      0.000902       0.000622     0.000472    0.000628   \n",
       "1        0.119189      0.010162       0.005533     0.008944    0.009578   \n",
       "2        0.101294      0.008772       0.006080     0.009712    0.009011   \n",
       "3        0.106487      0.027835       0.009027     0.012432    0.014174   \n",
       "4        0.273391      0.156935       0.131684     0.031384    0.067284   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "9594     0.107794      0.014610       0.007455     0.014052    0.015055   \n",
       "9595     0.107794      0.014610       0.007455     0.014052    0.015055   \n",
       "9596     0.359905      0.006564       0.005049     0.002604    0.002852   \n",
       "9597     0.186152      0.063421       0.059563     0.022375    0.035624   \n",
       "9598     0.167321      0.009093       0.007588     0.004861    0.005882   \n",
       "\n",
       "        Others  \n",
       "0     0.000038  \n",
       "1     0.000099  \n",
       "2     0.000039  \n",
       "3     0.000218  \n",
       "4     0.004416  \n",
       "...        ...  \n",
       "9594  0.000029  \n",
       "9595  0.000029  \n",
       "9596  0.000138  \n",
       "9597  0.000663  \n",
       "9598  0.000124  \n",
       "\n",
       "[9599 rows x 11 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df = pd.DataFrame(new_dict).reindex(classes).T\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.029157</td>\n",
       "      <td>0.262568</td>\n",
       "      <td>0.270707</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>0.026330</td>\n",
       "      <td>0.123254</td>\n",
       "      <td>0.001770</td>\n",
       "      <td>0.001524</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>0.002163</td>\n",
       "      <td>0.000043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.049204</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>0.036770</td>\n",
       "      <td>0.101294</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.006080</td>\n",
       "      <td>0.009712</td>\n",
       "      <td>0.009011</td>\n",
       "      <td>0.000039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.137951</td>\n",
       "      <td>0.165506</td>\n",
       "      <td>0.160387</td>\n",
       "      <td>0.228607</td>\n",
       "      <td>0.063707</td>\n",
       "      <td>0.166086</td>\n",
       "      <td>0.025874</td>\n",
       "      <td>0.012238</td>\n",
       "      <td>0.017979</td>\n",
       "      <td>0.021275</td>\n",
       "      <td>0.000388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.013212</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>0.186517</td>\n",
       "      <td>0.199517</td>\n",
       "      <td>0.011723</td>\n",
       "      <td>0.184819</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.000687</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>0.000032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.070444</td>\n",
       "      <td>0.107359</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>0.235703</td>\n",
       "      <td>0.039951</td>\n",
       "      <td>0.190014</td>\n",
       "      <td>0.013431</td>\n",
       "      <td>0.011113</td>\n",
       "      <td>0.011688</td>\n",
       "      <td>0.015853</td>\n",
       "      <td>0.000145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3722</th>\n",
       "      <td>0.063712</td>\n",
       "      <td>0.112252</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>0.216274</td>\n",
       "      <td>0.033205</td>\n",
       "      <td>0.171184</td>\n",
       "      <td>0.009273</td>\n",
       "      <td>0.008810</td>\n",
       "      <td>0.008268</td>\n",
       "      <td>0.011891</td>\n",
       "      <td>0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>0.126379</td>\n",
       "      <td>0.165190</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>0.017086</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.004861</td>\n",
       "      <td>0.005882</td>\n",
       "      <td>0.000124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3724</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726</th>\n",
       "      <td>0.090321</td>\n",
       "      <td>0.143035</td>\n",
       "      <td>0.236535</td>\n",
       "      <td>0.161154</td>\n",
       "      <td>0.014696</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.004833</td>\n",
       "      <td>0.003841</td>\n",
       "      <td>0.004904</td>\n",
       "      <td>0.000081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3727 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.029157     0.262568      0.270707     0.280558    0.026330   \n",
       "1       0.049204     0.121436      0.441306     0.216375    0.036770   \n",
       "2       0.137951     0.165506      0.160387     0.228607    0.063707   \n",
       "3       0.013212     0.401489      0.186517     0.199517    0.011723   \n",
       "4       0.070444     0.107359      0.304299     0.235703    0.039951   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "3722    0.063712     0.112252      0.365005     0.216274    0.033205   \n",
       "3723    0.126379     0.165190      0.292644     0.203834    0.017086   \n",
       "3724    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3725    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3726    0.090321     0.143035      0.236535     0.161154    0.014696   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.123254      0.001770       0.001524     0.001927    0.002163   \n",
       "1        0.101294      0.008772       0.006080     0.009712    0.009011   \n",
       "2        0.166086      0.025874       0.012238     0.017979    0.021275   \n",
       "3        0.184819      0.000431       0.000706     0.000687    0.000867   \n",
       "4        0.190014      0.013431       0.011113     0.011688    0.015853   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "3722     0.171184      0.009273       0.008810     0.008268    0.011891   \n",
       "3723     0.167321      0.009093       0.007588     0.004861    0.005882   \n",
       "3724     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3725     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3726     0.328312      0.012290       0.004833     0.003841    0.004904   \n",
       "\n",
       "        Others  \n",
       "0     0.000043  \n",
       "1     0.000039  \n",
       "2     0.000388  \n",
       "3     0.000032  \n",
       "4     0.000145  \n",
       "...        ...  \n",
       "3722  0.000127  \n",
       "3723  0.000124  \n",
       "3724  0.000030  \n",
       "3725  0.000030  \n",
       "3726  0.000081  \n",
       "\n",
       "[3727 rows x 11 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_all_df = pd.DataFrame(des_dict).reindex(classes).T\n",
    "score_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = score_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = score_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = score_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_df = pd.concat([score_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.063411</td>\n",
       "      <td>0.183700</td>\n",
       "      <td>0.157456</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.008129</td>\n",
       "      <td>0.410730</td>\n",
       "      <td>0.000902</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.410730</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.183700</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.410730</td>\n",
       "      <td>0.768343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.037595</td>\n",
       "      <td>0.129560</td>\n",
       "      <td>0.472180</td>\n",
       "      <td>0.165439</td>\n",
       "      <td>0.041721</td>\n",
       "      <td>0.119189</td>\n",
       "      <td>0.010162</td>\n",
       "      <td>0.005533</td>\n",
       "      <td>0.008944</td>\n",
       "      <td>0.009578</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.472180</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.165439</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.129560</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.472180</td>\n",
       "      <td>0.767179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.049204</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>0.036770</td>\n",
       "      <td>0.101294</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.006080</td>\n",
       "      <td>0.009712</td>\n",
       "      <td>0.009011</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.216921</td>\n",
       "      <td>0.188141</td>\n",
       "      <td>0.174296</td>\n",
       "      <td>0.202317</td>\n",
       "      <td>0.048151</td>\n",
       "      <td>0.106487</td>\n",
       "      <td>0.027835</td>\n",
       "      <td>0.009027</td>\n",
       "      <td>0.012432</td>\n",
       "      <td>0.014174</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.216921</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.202317</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.188141</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.216921</td>\n",
       "      <td>0.607379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.096496</td>\n",
       "      <td>0.037656</td>\n",
       "      <td>0.122139</td>\n",
       "      <td>0.068756</td>\n",
       "      <td>0.009859</td>\n",
       "      <td>0.273391</td>\n",
       "      <td>0.156935</td>\n",
       "      <td>0.131684</td>\n",
       "      <td>0.031384</td>\n",
       "      <td>0.067284</td>\n",
       "      <td>0.004416</td>\n",
       "      <td>0.273391</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.156935</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.131684</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.273391</td>\n",
       "      <td>0.562010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9594</th>\n",
       "      <td>0.044658</td>\n",
       "      <td>0.149264</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.172239</td>\n",
       "      <td>0.036680</td>\n",
       "      <td>0.107794</td>\n",
       "      <td>0.014610</td>\n",
       "      <td>0.007455</td>\n",
       "      <td>0.014052</td>\n",
       "      <td>0.015055</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.172239</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.149264</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.759667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9595</th>\n",
       "      <td>0.044658</td>\n",
       "      <td>0.149264</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.172239</td>\n",
       "      <td>0.036680</td>\n",
       "      <td>0.107794</td>\n",
       "      <td>0.014610</td>\n",
       "      <td>0.007455</td>\n",
       "      <td>0.014052</td>\n",
       "      <td>0.015055</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.172239</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.149264</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.759667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9596</th>\n",
       "      <td>0.132050</td>\n",
       "      <td>0.063876</td>\n",
       "      <td>0.213784</td>\n",
       "      <td>0.200032</td>\n",
       "      <td>0.013147</td>\n",
       "      <td>0.359905</td>\n",
       "      <td>0.006564</td>\n",
       "      <td>0.005049</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.002852</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.359905</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.213784</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.200032</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.359905</td>\n",
       "      <td>0.773720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9597</th>\n",
       "      <td>0.126042</td>\n",
       "      <td>0.060049</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.140538</td>\n",
       "      <td>0.012537</td>\n",
       "      <td>0.186152</td>\n",
       "      <td>0.063421</td>\n",
       "      <td>0.059563</td>\n",
       "      <td>0.022375</td>\n",
       "      <td>0.035624</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.186152</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.140538</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.619725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9598</th>\n",
       "      <td>0.126379</td>\n",
       "      <td>0.165190</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>0.017086</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.004861</td>\n",
       "      <td>0.005882</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.663798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9599 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.063411     0.183700      0.157456     0.173913    0.008129   \n",
       "1       0.037595     0.129560      0.472180     0.165439    0.041721   \n",
       "2       0.049204     0.121436      0.441306     0.216375    0.036770   \n",
       "3       0.216921     0.188141      0.174296     0.202317    0.048151   \n",
       "4       0.096496     0.037656      0.122139     0.068756    0.009859   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "9594    0.044658     0.149264      0.438163     0.172239    0.036680   \n",
       "9595    0.044658     0.149264      0.438163     0.172239    0.036680   \n",
       "9596    0.132050     0.063876      0.213784     0.200032    0.013147   \n",
       "9597    0.126042     0.060049      0.293035     0.140538    0.012537   \n",
       "9598    0.126379     0.165190      0.292644     0.203834    0.017086   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.410730      0.000902       0.000622     0.000472    0.000628   \n",
       "1        0.119189      0.010162       0.005533     0.008944    0.009578   \n",
       "2        0.101294      0.008772       0.006080     0.009712    0.009011   \n",
       "3        0.106487      0.027835       0.009027     0.012432    0.014174   \n",
       "4        0.273391      0.156935       0.131684     0.031384    0.067284   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "9594     0.107794      0.014610       0.007455     0.014052    0.015055   \n",
       "9595     0.107794      0.014610       0.007455     0.014052    0.015055   \n",
       "9596     0.359905      0.006564       0.005049     0.002604    0.002852   \n",
       "9597     0.186152      0.063421       0.059563     0.022375    0.035624   \n",
       "9598     0.167321      0.009093       0.007588     0.004861    0.005882   \n",
       "\n",
       "        Others  max_1_val     max_1_col  max_2_val     max_2_col  max_3_val  \\\n",
       "0     0.000038   0.410730   Criteria vi   0.183700   Criteria ii   0.173913   \n",
       "1     0.000099   0.472180  Criteria iii   0.165439   Criteria iv   0.129560   \n",
       "2     0.000039   0.441306  Criteria iii   0.216375   Criteria iv   0.121436   \n",
       "3     0.000218   0.216921    Criteria i   0.202317   Criteria iv   0.188141   \n",
       "4     0.004416   0.273391   Criteria vi   0.156935  Criteria vii   0.131684   \n",
       "...        ...        ...           ...        ...           ...        ...   \n",
       "9594  0.000029   0.438163  Criteria iii   0.172239   Criteria iv   0.149264   \n",
       "9595  0.000029   0.438163  Criteria iii   0.172239   Criteria iv   0.149264   \n",
       "9596  0.000138   0.359905   Criteria vi   0.213784  Criteria iii   0.200032   \n",
       "9597  0.000663   0.293035  Criteria iii   0.186152   Criteria vi   0.140538   \n",
       "9598  0.000124   0.292644  Criteria iii   0.203834   Criteria iv   0.167321   \n",
       "\n",
       "          max_3_col     max_1     max_3  \n",
       "0       Criteria iv  0.410730  0.768343  \n",
       "1       Criteria ii  0.472180  0.767179  \n",
       "2       Criteria ii  0.441306  0.779117  \n",
       "3       Criteria ii  0.216921  0.607379  \n",
       "4     Criteria viii  0.273391  0.562010  \n",
       "...             ...       ...       ...  \n",
       "9594    Criteria ii  0.438163  0.759667  \n",
       "9595    Criteria ii  0.438163  0.759667  \n",
       "9596    Criteria iv  0.359905  0.773720  \n",
       "9597    Criteria iv  0.293035  0.619725  \n",
       "9598    Criteria vi  0.292644  0.663798  \n",
       "\n",
       "[9599 rows x 19 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df['max_1'] = score_df['max_1_val']\n",
    "score_df['max_3'] = score_df['max_1_val']+score_df['max_2_val']+score_df['max_3_val']\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = sm_df.merge(score_df, how='left', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = score_all_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = score_all_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = score_all_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_all_df = pd.concat([score_all_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.029157</td>\n",
       "      <td>0.262568</td>\n",
       "      <td>0.270707</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>0.026330</td>\n",
       "      <td>0.123254</td>\n",
       "      <td>0.001770</td>\n",
       "      <td>0.001524</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>0.002163</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.270707</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.262568</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>0.813833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.049204</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>0.036770</td>\n",
       "      <td>0.101294</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.006080</td>\n",
       "      <td>0.009712</td>\n",
       "      <td>0.009011</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.137951</td>\n",
       "      <td>0.165506</td>\n",
       "      <td>0.160387</td>\n",
       "      <td>0.228607</td>\n",
       "      <td>0.063707</td>\n",
       "      <td>0.166086</td>\n",
       "      <td>0.025874</td>\n",
       "      <td>0.012238</td>\n",
       "      <td>0.017979</td>\n",
       "      <td>0.021275</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>0.228607</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.166086</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.165506</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.228607</td>\n",
       "      <td>0.560199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.013212</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>0.186517</td>\n",
       "      <td>0.199517</td>\n",
       "      <td>0.011723</td>\n",
       "      <td>0.184819</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.000687</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.199517</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.186517</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>0.787524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.070444</td>\n",
       "      <td>0.107359</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>0.235703</td>\n",
       "      <td>0.039951</td>\n",
       "      <td>0.190014</td>\n",
       "      <td>0.013431</td>\n",
       "      <td>0.011113</td>\n",
       "      <td>0.011688</td>\n",
       "      <td>0.015853</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.235703</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.190014</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>0.730016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3722</th>\n",
       "      <td>0.063712</td>\n",
       "      <td>0.112252</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>0.216274</td>\n",
       "      <td>0.033205</td>\n",
       "      <td>0.171184</td>\n",
       "      <td>0.009273</td>\n",
       "      <td>0.008810</td>\n",
       "      <td>0.008268</td>\n",
       "      <td>0.011891</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.216274</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.171184</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>0.752462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>0.126379</td>\n",
       "      <td>0.165190</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>0.017086</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.004861</td>\n",
       "      <td>0.005882</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.663798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3724</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726</th>\n",
       "      <td>0.090321</td>\n",
       "      <td>0.143035</td>\n",
       "      <td>0.236535</td>\n",
       "      <td>0.161154</td>\n",
       "      <td>0.014696</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.004833</td>\n",
       "      <td>0.003841</td>\n",
       "      <td>0.004904</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.236535</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.161154</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>0.726001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3727 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.029157     0.262568      0.270707     0.280558    0.026330   \n",
       "1       0.049204     0.121436      0.441306     0.216375    0.036770   \n",
       "2       0.137951     0.165506      0.160387     0.228607    0.063707   \n",
       "3       0.013212     0.401489      0.186517     0.199517    0.011723   \n",
       "4       0.070444     0.107359      0.304299     0.235703    0.039951   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "3722    0.063712     0.112252      0.365005     0.216274    0.033205   \n",
       "3723    0.126379     0.165190      0.292644     0.203834    0.017086   \n",
       "3724    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3725    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3726    0.090321     0.143035      0.236535     0.161154    0.014696   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.123254      0.001770       0.001524     0.001927    0.002163   \n",
       "1        0.101294      0.008772       0.006080     0.009712    0.009011   \n",
       "2        0.166086      0.025874       0.012238     0.017979    0.021275   \n",
       "3        0.184819      0.000431       0.000706     0.000687    0.000867   \n",
       "4        0.190014      0.013431       0.011113     0.011688    0.015853   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "3722     0.171184      0.009273       0.008810     0.008268    0.011891   \n",
       "3723     0.167321      0.009093       0.007588     0.004861    0.005882   \n",
       "3724     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3725     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3726     0.328312      0.012290       0.004833     0.003841    0.004904   \n",
       "\n",
       "        Others  max_1_val     max_1_col  max_2_val     max_2_col  max_3_val  \\\n",
       "0     0.000043   0.280558   Criteria iv   0.270707  Criteria iii   0.262568   \n",
       "1     0.000039   0.441306  Criteria iii   0.216375   Criteria iv   0.121436   \n",
       "2     0.000388   0.228607   Criteria iv   0.166086   Criteria vi   0.165506   \n",
       "3     0.000032   0.401489   Criteria ii   0.199517   Criteria iv   0.186517   \n",
       "4     0.000145   0.304299  Criteria iii   0.235703   Criteria iv   0.190014   \n",
       "...        ...        ...           ...        ...           ...        ...   \n",
       "3722  0.000127   0.365005  Criteria iii   0.216274   Criteria iv   0.171184   \n",
       "3723  0.000124   0.292644  Criteria iii   0.203834   Criteria iv   0.167321   \n",
       "3724  0.000030   0.835118  Criteria iii   0.084970   Criteria vi   0.026072   \n",
       "3725  0.000030   0.835118  Criteria iii   0.084970   Criteria vi   0.026072   \n",
       "3726  0.000081   0.328312   Criteria vi   0.236535  Criteria iii   0.161154   \n",
       "\n",
       "         max_3_col     max_1     max_3  \n",
       "0      Criteria ii  0.280558  0.813833  \n",
       "1      Criteria ii  0.441306  0.779117  \n",
       "2      Criteria ii  0.228607  0.560199  \n",
       "3     Criteria iii  0.401489  0.787524  \n",
       "4      Criteria vi  0.304299  0.730016  \n",
       "...            ...       ...       ...  \n",
       "3722   Criteria vi  0.365005  0.752462  \n",
       "3723   Criteria vi  0.292644  0.663798  \n",
       "3724   Criteria iv  0.835118  0.946161  \n",
       "3725   Criteria iv  0.835118  0.946161  \n",
       "3726   Criteria iv  0.328312  0.726001  \n",
       "\n",
       "[3727 rows x 19 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_all_df['max_1'] = score_all_df['max_1_val']\n",
    "score_all_df['max_3'] = score_all_df['max_1_val']+score_all_df['max_2_val']+score_all_df['max_3_val']\n",
    "score_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_all_df = sm_all_df.merge(score_all_df, how='left', left_index=True, right_index=True)\n",
    "sm_all_df['valid'] = sm_all_df['revised_text'].apply(lambda x: str(x)!='nan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>candownload</th>\n",
       "      <th>url_c</th>\n",
       "      <th>url_q</th>\n",
       "      <th>url_n</th>\n",
       "      <th>url_largest</th>\n",
       "      <th>owner</th>\n",
       "      <th>owner_loc</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>51885929339</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51885929339_047ce05277_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51885929339_047ce05277_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51885929339_047ce05277_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51885929339_03ce2e9591_o.jpg</td>\n",
       "      <td>8733637@N04</td>\n",
       "      <td>Haarlem, The Netherlands</td>\n",
       "      <td>Citroën 2CV 1982</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.270707</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.262568</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>0.813833</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>51884357517</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884357517_73d2c871a6_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884357517_73d2c871a6_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884357517_73d2c871a6_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884357517_8eb754431e_o.jpg</td>\n",
       "      <td>150614452@N05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RFO 9901</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>51883860957</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51883860957_1046863951_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51883860957_1046863951_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51883860957_1046863951_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51883860957_391d98249c_o.jpg</td>\n",
       "      <td>195032295@N04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Houseboats, Amsterdam-West</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>0.228607</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.166086</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.165506</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.228607</td>\n",
       "      <td>0.560199</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>51884423594</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884423594_38ed8d7a64_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884423594_38ed8d7a64_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884423594_38ed8d7a64_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884423594_39ace37a79_o.jpg</td>\n",
       "      <td>8733637@N04</td>\n",
       "      <td>Haarlem, The Netherlands</td>\n",
       "      <td>Ford Transit minibus 1978</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.199517</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.186517</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>0.787524</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>51882154188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51882154188_0948af559d_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51882154188_0948af559d_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51882154188_0948af559d_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51882154188_2b1f87ddcf_o.jpg</td>\n",
       "      <td>8733637@N04</td>\n",
       "      <td>Haarlem, The Netherlands</td>\n",
       "      <td>Renault Dauphine 1960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.235703</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.190014</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>0.730016</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3722</th>\n",
       "      <td>4993</td>\n",
       "      <td>51361633644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51361633644_ede3b60e37_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51361633644_ede3b60e37_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51361633644_ede3b60e37_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51361633644_95593e8206_o.jpg</td>\n",
       "      <td>78239672@N00</td>\n",
       "      <td>Kingston upon Hull, UK</td>\n",
       "      <td>Rijksmuseum</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.216274</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.171184</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>0.752462</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>4995</td>\n",
       "      <td>51357996868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51357996868_82e53268b6_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51357996868_82e53268b6_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51357996868_82e53268b6_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51357996868_389074f277_o.jpg</td>\n",
       "      <td>193393816@N06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AMSTERDAM</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.663798</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3724</th>\n",
       "      <td>4996</td>\n",
       "      <td>51356750697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51356750697_ffe1173a00_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51356750697_ffe1173a00_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51356750697_ffe1173a00_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51356750697_fa4927c603_o.jpg</td>\n",
       "      <td>34476863@N00</td>\n",
       "      <td>the netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>4997</td>\n",
       "      <td>51358508460</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51358508460_5ce97afb4d_o.jpg</td>\n",
       "      <td>34476863@N00</td>\n",
       "      <td>the netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726</th>\n",
       "      <td>4999</td>\n",
       "      <td>51368197299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51368197299_49407f9c5c_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51368197299_49407f9c5c_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51368197299_49407f9c5c_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51368197299_9cb63008f8_o.jpg</td>\n",
       "      <td>107735426@N07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DSC04526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.236535</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.161154</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>0.726001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3727 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index  candownload  \\\n",
       "0          2  51885929339          1.0   \n",
       "1          3  51884357517          1.0   \n",
       "2          8  51883860957          1.0   \n",
       "3          9  51884423594          1.0   \n",
       "4         13  51882154188          1.0   \n",
       "...      ...          ...          ...   \n",
       "3722    4993  51361633644          1.0   \n",
       "3723    4995  51357996868          1.0   \n",
       "3724    4996  51356750697          1.0   \n",
       "3725    4997  51358508460          1.0   \n",
       "3726    4999  51368197299          1.0   \n",
       "\n",
       "                                                                 url_c  \\\n",
       "0     https://live.staticflickr.com/65535/51885929339_047ce05277_c.jpg   \n",
       "1     https://live.staticflickr.com/65535/51884357517_73d2c871a6_c.jpg   \n",
       "2     https://live.staticflickr.com/65535/51883860957_1046863951_c.jpg   \n",
       "3     https://live.staticflickr.com/65535/51884423594_38ed8d7a64_c.jpg   \n",
       "4     https://live.staticflickr.com/65535/51882154188_0948af559d_c.jpg   \n",
       "...                                                                ...   \n",
       "3722  https://live.staticflickr.com/65535/51361633644_ede3b60e37_c.jpg   \n",
       "3723  https://live.staticflickr.com/65535/51357996868_82e53268b6_c.jpg   \n",
       "3724  https://live.staticflickr.com/65535/51356750697_ffe1173a00_c.jpg   \n",
       "3725  https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_c.jpg   \n",
       "3726  https://live.staticflickr.com/65535/51368197299_49407f9c5c_c.jpg   \n",
       "\n",
       "                                                                 url_q  \\\n",
       "0     https://live.staticflickr.com/65535/51885929339_047ce05277_q.jpg   \n",
       "1     https://live.staticflickr.com/65535/51884357517_73d2c871a6_q.jpg   \n",
       "2     https://live.staticflickr.com/65535/51883860957_1046863951_q.jpg   \n",
       "3     https://live.staticflickr.com/65535/51884423594_38ed8d7a64_q.jpg   \n",
       "4     https://live.staticflickr.com/65535/51882154188_0948af559d_q.jpg   \n",
       "...                                                                ...   \n",
       "3722  https://live.staticflickr.com/65535/51361633644_ede3b60e37_q.jpg   \n",
       "3723  https://live.staticflickr.com/65535/51357996868_82e53268b6_q.jpg   \n",
       "3724  https://live.staticflickr.com/65535/51356750697_ffe1173a00_q.jpg   \n",
       "3725  https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_q.jpg   \n",
       "3726  https://live.staticflickr.com/65535/51368197299_49407f9c5c_q.jpg   \n",
       "\n",
       "                                                                 url_n  \\\n",
       "0     https://live.staticflickr.com/65535/51885929339_047ce05277_n.jpg   \n",
       "1     https://live.staticflickr.com/65535/51884357517_73d2c871a6_n.jpg   \n",
       "2     https://live.staticflickr.com/65535/51883860957_1046863951_n.jpg   \n",
       "3     https://live.staticflickr.com/65535/51884423594_38ed8d7a64_n.jpg   \n",
       "4     https://live.staticflickr.com/65535/51882154188_0948af559d_n.jpg   \n",
       "...                                                                ...   \n",
       "3722  https://live.staticflickr.com/65535/51361633644_ede3b60e37_n.jpg   \n",
       "3723  https://live.staticflickr.com/65535/51357996868_82e53268b6_n.jpg   \n",
       "3724  https://live.staticflickr.com/65535/51356750697_ffe1173a00_n.jpg   \n",
       "3725  https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_n.jpg   \n",
       "3726  https://live.staticflickr.com/65535/51368197299_49407f9c5c_n.jpg   \n",
       "\n",
       "                                                           url_largest  \\\n",
       "0     https://live.staticflickr.com/65535/51885929339_03ce2e9591_o.jpg   \n",
       "1     https://live.staticflickr.com/65535/51884357517_8eb754431e_o.jpg   \n",
       "2     https://live.staticflickr.com/65535/51883860957_391d98249c_o.jpg   \n",
       "3     https://live.staticflickr.com/65535/51884423594_39ace37a79_o.jpg   \n",
       "4     https://live.staticflickr.com/65535/51882154188_2b1f87ddcf_o.jpg   \n",
       "...                                                                ...   \n",
       "3722  https://live.staticflickr.com/65535/51361633644_95593e8206_o.jpg   \n",
       "3723  https://live.staticflickr.com/65535/51357996868_389074f277_o.jpg   \n",
       "3724  https://live.staticflickr.com/65535/51356750697_fa4927c603_o.jpg   \n",
       "3725  https://live.staticflickr.com/65535/51358508460_5ce97afb4d_o.jpg   \n",
       "3726  https://live.staticflickr.com/65535/51368197299_9cb63008f8_o.jpg   \n",
       "\n",
       "              owner                 owner_loc                       title  \\\n",
       "0       8733637@N04  Haarlem, The Netherlands            Citroën 2CV 1982   \n",
       "1     150614452@N05                       NaN                    RFO 9901   \n",
       "2     195032295@N04                       NaN  Houseboats, Amsterdam-West   \n",
       "3       8733637@N04  Haarlem, The Netherlands   Ford Transit minibus 1978   \n",
       "4       8733637@N04  Haarlem, The Netherlands       Renault Dauphine 1960   \n",
       "...             ...                       ...                         ...   \n",
       "3722   78239672@N00    Kingston upon Hull, UK                 Rijksmuseum   \n",
       "3723  193393816@N06                       NaN                   AMSTERDAM   \n",
       "3724   34476863@N00           the netherlands                         NaN   \n",
       "3725   34476863@N00           the netherlands                         NaN   \n",
       "3726  107735426@N07                       NaN                    DSC04526   \n",
       "\n",
       "      ...    Others  max_1_val     max_1_col  max_2_val     max_2_col  \\\n",
       "0     ...  0.000043   0.280558   Criteria iv   0.270707  Criteria iii   \n",
       "1     ...  0.000039   0.441306  Criteria iii   0.216375   Criteria iv   \n",
       "2     ...  0.000388   0.228607   Criteria iv   0.166086   Criteria vi   \n",
       "3     ...  0.000032   0.401489   Criteria ii   0.199517   Criteria iv   \n",
       "4     ...  0.000145   0.304299  Criteria iii   0.235703   Criteria iv   \n",
       "...   ...       ...        ...           ...        ...           ...   \n",
       "3722  ...  0.000127   0.365005  Criteria iii   0.216274   Criteria iv   \n",
       "3723  ...  0.000124   0.292644  Criteria iii   0.203834   Criteria iv   \n",
       "3724  ...  0.000030   0.835118  Criteria iii   0.084970   Criteria vi   \n",
       "3725  ...  0.000030   0.835118  Criteria iii   0.084970   Criteria vi   \n",
       "3726  ...  0.000081   0.328312   Criteria vi   0.236535  Criteria iii   \n",
       "\n",
       "     max_3_val     max_3_col     max_1     max_3  valid  \n",
       "0     0.262568   Criteria ii  0.280558  0.813833   True  \n",
       "1     0.121436   Criteria ii  0.441306  0.779117   True  \n",
       "2     0.165506   Criteria ii  0.228607  0.560199   True  \n",
       "3     0.186517  Criteria iii  0.401489  0.787524   True  \n",
       "4     0.190014   Criteria vi  0.304299  0.730016   True  \n",
       "...        ...           ...       ...       ...    ...  \n",
       "3722  0.171184   Criteria vi  0.365005  0.752462   True  \n",
       "3723  0.167321   Criteria vi  0.292644  0.663798   True  \n",
       "3724  0.026072   Criteria iv  0.835118  0.946161  False  \n",
       "3725  0.026072   Criteria iv  0.835118  0.946161  False  \n",
       "3726  0.161154   Criteria iv  0.328312  0.726001   True  \n",
       "\n",
       "[3727 rows x 55 columns]"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.050503</td>\n",
       "      <td>0.156630</td>\n",
       "      <td>0.314818</td>\n",
       "      <td>0.169676</td>\n",
       "      <td>0.024925</td>\n",
       "      <td>0.264959</td>\n",
       "      <td>0.005532</td>\n",
       "      <td>0.003077</td>\n",
       "      <td>0.004708</td>\n",
       "      <td>0.005103</td>\n",
       "      <td>0.000069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.049204</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>0.036770</td>\n",
       "      <td>0.101294</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.006080</td>\n",
       "      <td>0.009712</td>\n",
       "      <td>0.009011</td>\n",
       "      <td>0.000039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0.152715</td>\n",
       "      <td>0.136390</td>\n",
       "      <td>0.213535</td>\n",
       "      <td>0.171007</td>\n",
       "      <td>0.031778</td>\n",
       "      <td>0.239042</td>\n",
       "      <td>0.021632</td>\n",
       "      <td>0.010642</td>\n",
       "      <td>0.009947</td>\n",
       "      <td>0.012968</td>\n",
       "      <td>0.000345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>0.058416</td>\n",
       "      <td>0.167559</td>\n",
       "      <td>0.196765</td>\n",
       "      <td>0.150599</td>\n",
       "      <td>0.014248</td>\n",
       "      <td>0.278678</td>\n",
       "      <td>0.053060</td>\n",
       "      <td>0.044576</td>\n",
       "      <td>0.011224</td>\n",
       "      <td>0.023375</td>\n",
       "      <td>0.001499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0.068965</td>\n",
       "      <td>0.117116</td>\n",
       "      <td>0.301381</td>\n",
       "      <td>0.159050</td>\n",
       "      <td>0.020654</td>\n",
       "      <td>0.267579</td>\n",
       "      <td>0.020281</td>\n",
       "      <td>0.016670</td>\n",
       "      <td>0.010699</td>\n",
       "      <td>0.017041</td>\n",
       "      <td>0.000565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2899</th>\n",
       "      <td>4990</td>\n",
       "      <td>0.069137</td>\n",
       "      <td>0.062660</td>\n",
       "      <td>0.252103</td>\n",
       "      <td>0.083146</td>\n",
       "      <td>0.012393</td>\n",
       "      <td>0.401202</td>\n",
       "      <td>0.072347</td>\n",
       "      <td>0.008737</td>\n",
       "      <td>0.009502</td>\n",
       "      <td>0.028410</td>\n",
       "      <td>0.000363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2900</th>\n",
       "      <td>4991</td>\n",
       "      <td>0.067804</td>\n",
       "      <td>0.065300</td>\n",
       "      <td>0.310644</td>\n",
       "      <td>0.234954</td>\n",
       "      <td>0.109906</td>\n",
       "      <td>0.130461</td>\n",
       "      <td>0.025942</td>\n",
       "      <td>0.023376</td>\n",
       "      <td>0.012598</td>\n",
       "      <td>0.018467</td>\n",
       "      <td>0.000548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2901</th>\n",
       "      <td>4993</td>\n",
       "      <td>0.072519</td>\n",
       "      <td>0.100623</td>\n",
       "      <td>0.376549</td>\n",
       "      <td>0.162176</td>\n",
       "      <td>0.028314</td>\n",
       "      <td>0.179604</td>\n",
       "      <td>0.023362</td>\n",
       "      <td>0.019599</td>\n",
       "      <td>0.017496</td>\n",
       "      <td>0.019567</td>\n",
       "      <td>0.000191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>4995</td>\n",
       "      <td>0.126379</td>\n",
       "      <td>0.165190</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>0.017086</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.004861</td>\n",
       "      <td>0.005882</td>\n",
       "      <td>0.000124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2903</th>\n",
       "      <td>4999</td>\n",
       "      <td>0.069545</td>\n",
       "      <td>0.133852</td>\n",
       "      <td>0.279036</td>\n",
       "      <td>0.137033</td>\n",
       "      <td>0.017681</td>\n",
       "      <td>0.290931</td>\n",
       "      <td>0.039999</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>0.007288</td>\n",
       "      <td>0.017682</td>\n",
       "      <td>0.000208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2904 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin  Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0          2    0.050503     0.156630      0.314818     0.169676    0.024925   \n",
       "1          3    0.049204     0.121436      0.441306     0.216375    0.036770   \n",
       "2          8    0.152715     0.136390      0.213535     0.171007    0.031778   \n",
       "3          9    0.058416     0.167559      0.196765     0.150599    0.014248   \n",
       "4         13    0.068965     0.117116      0.301381     0.159050    0.020654   \n",
       "...      ...         ...          ...           ...          ...         ...   \n",
       "2899    4990    0.069137     0.062660      0.252103     0.083146    0.012393   \n",
       "2900    4991    0.067804     0.065300      0.310644     0.234954    0.109906   \n",
       "2901    4993    0.072519     0.100623      0.376549     0.162176    0.028314   \n",
       "2902    4995    0.126379     0.165190      0.292644     0.203834    0.017086   \n",
       "2903    4999    0.069545     0.133852      0.279036     0.137033    0.017681   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.264959      0.005532       0.003077     0.004708    0.005103   \n",
       "1        0.101294      0.008772       0.006080     0.009712    0.009011   \n",
       "2        0.239042      0.021632       0.010642     0.009947    0.012968   \n",
       "3        0.278678      0.053060       0.044576     0.011224    0.023375   \n",
       "4        0.267579      0.020281       0.016670     0.010699    0.017041   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "2899     0.401202      0.072347       0.008737     0.009502    0.028410   \n",
       "2900     0.130461      0.025942       0.023376     0.012598    0.018467   \n",
       "2901     0.179604      0.023362       0.019599     0.017496    0.019567   \n",
       "2902     0.167321      0.009093       0.007588     0.004861    0.005882   \n",
       "2903     0.290931      0.039999       0.006744     0.007288    0.017682   \n",
       "\n",
       "        Others  \n",
       "0     0.000069  \n",
       "1     0.000039  \n",
       "2     0.000345  \n",
       "3     0.001499  \n",
       "4     0.000565  \n",
       "...        ...  \n",
       "2899  0.000363  \n",
       "2900  0.000548  \n",
       "2901  0.000191  \n",
       "2902  0.000124  \n",
       "2903  0.000208  \n",
       "\n",
       "[2904 rows x 12 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_agg_df = sm_df[classes+['origin']].groupby('origin').mean().reset_index()\n",
    "sm_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = sm_agg_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = sm_agg_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = sm_agg_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_agg_df = pd.concat([sm_agg_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>...</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.050503</td>\n",
       "      <td>0.156630</td>\n",
       "      <td>0.314818</td>\n",
       "      <td>0.169676</td>\n",
       "      <td>0.024925</td>\n",
       "      <td>0.264959</td>\n",
       "      <td>0.005532</td>\n",
       "      <td>0.003077</td>\n",
       "      <td>0.004708</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.314818</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.264959</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.169676</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.314818</td>\n",
       "      <td>0.749454</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.049204</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>0.036770</td>\n",
       "      <td>0.101294</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.006080</td>\n",
       "      <td>0.009712</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0.152715</td>\n",
       "      <td>0.136390</td>\n",
       "      <td>0.213535</td>\n",
       "      <td>0.171007</td>\n",
       "      <td>0.031778</td>\n",
       "      <td>0.239042</td>\n",
       "      <td>0.021632</td>\n",
       "      <td>0.010642</td>\n",
       "      <td>0.009947</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.239042</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.213535</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.171007</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.239042</td>\n",
       "      <td>0.623584</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>0.058416</td>\n",
       "      <td>0.167559</td>\n",
       "      <td>0.196765</td>\n",
       "      <td>0.150599</td>\n",
       "      <td>0.014248</td>\n",
       "      <td>0.278678</td>\n",
       "      <td>0.053060</td>\n",
       "      <td>0.044576</td>\n",
       "      <td>0.011224</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>0.278678</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.196765</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.167559</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.278678</td>\n",
       "      <td>0.643003</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0.068965</td>\n",
       "      <td>0.117116</td>\n",
       "      <td>0.301381</td>\n",
       "      <td>0.159050</td>\n",
       "      <td>0.020654</td>\n",
       "      <td>0.267579</td>\n",
       "      <td>0.020281</td>\n",
       "      <td>0.016670</td>\n",
       "      <td>0.010699</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>0.301381</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.267579</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.159050</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.301381</td>\n",
       "      <td>0.728010</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2899</th>\n",
       "      <td>4990</td>\n",
       "      <td>0.069137</td>\n",
       "      <td>0.062660</td>\n",
       "      <td>0.252103</td>\n",
       "      <td>0.083146</td>\n",
       "      <td>0.012393</td>\n",
       "      <td>0.401202</td>\n",
       "      <td>0.072347</td>\n",
       "      <td>0.008737</td>\n",
       "      <td>0.009502</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.401202</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.252103</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.083146</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.401202</td>\n",
       "      <td>0.736451</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2900</th>\n",
       "      <td>4991</td>\n",
       "      <td>0.067804</td>\n",
       "      <td>0.065300</td>\n",
       "      <td>0.310644</td>\n",
       "      <td>0.234954</td>\n",
       "      <td>0.109906</td>\n",
       "      <td>0.130461</td>\n",
       "      <td>0.025942</td>\n",
       "      <td>0.023376</td>\n",
       "      <td>0.012598</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.310644</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.234954</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.130461</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.310644</td>\n",
       "      <td>0.676059</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2901</th>\n",
       "      <td>4993</td>\n",
       "      <td>0.072519</td>\n",
       "      <td>0.100623</td>\n",
       "      <td>0.376549</td>\n",
       "      <td>0.162176</td>\n",
       "      <td>0.028314</td>\n",
       "      <td>0.179604</td>\n",
       "      <td>0.023362</td>\n",
       "      <td>0.019599</td>\n",
       "      <td>0.017496</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.376549</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.179604</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.162176</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.376549</td>\n",
       "      <td>0.718329</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>4995</td>\n",
       "      <td>0.126379</td>\n",
       "      <td>0.165190</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>0.017086</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.004861</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.663798</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2903</th>\n",
       "      <td>4999</td>\n",
       "      <td>0.069545</td>\n",
       "      <td>0.133852</td>\n",
       "      <td>0.279036</td>\n",
       "      <td>0.137033</td>\n",
       "      <td>0.017681</td>\n",
       "      <td>0.290931</td>\n",
       "      <td>0.039999</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>0.007288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.290931</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.279036</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.137033</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.290931</td>\n",
       "      <td>0.707000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2904 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin  Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0          2    0.050503     0.156630      0.314818     0.169676    0.024925   \n",
       "1          3    0.049204     0.121436      0.441306     0.216375    0.036770   \n",
       "2          8    0.152715     0.136390      0.213535     0.171007    0.031778   \n",
       "3          9    0.058416     0.167559      0.196765     0.150599    0.014248   \n",
       "4         13    0.068965     0.117116      0.301381     0.159050    0.020654   \n",
       "...      ...         ...          ...           ...          ...         ...   \n",
       "2899    4990    0.069137     0.062660      0.252103     0.083146    0.012393   \n",
       "2900    4991    0.067804     0.065300      0.310644     0.234954    0.109906   \n",
       "2901    4993    0.072519     0.100623      0.376549     0.162176    0.028314   \n",
       "2902    4995    0.126379     0.165190      0.292644     0.203834    0.017086   \n",
       "2903    4999    0.069545     0.133852      0.279036     0.137033    0.017681   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  ...    Others  \\\n",
       "0        0.264959      0.005532       0.003077     0.004708  ...  0.000069   \n",
       "1        0.101294      0.008772       0.006080     0.009712  ...  0.000039   \n",
       "2        0.239042      0.021632       0.010642     0.009947  ...  0.000345   \n",
       "3        0.278678      0.053060       0.044576     0.011224  ...  0.001499   \n",
       "4        0.267579      0.020281       0.016670     0.010699  ...  0.000565   \n",
       "...           ...           ...            ...          ...  ...       ...   \n",
       "2899     0.401202      0.072347       0.008737     0.009502  ...  0.000363   \n",
       "2900     0.130461      0.025942       0.023376     0.012598  ...  0.000548   \n",
       "2901     0.179604      0.023362       0.019599     0.017496  ...  0.000191   \n",
       "2902     0.167321      0.009093       0.007588     0.004861  ...  0.000124   \n",
       "2903     0.290931      0.039999       0.006744     0.007288  ...  0.000208   \n",
       "\n",
       "      max_1_val     max_1_col max_2_val     max_2_col max_3_val    max_3_col  \\\n",
       "0      0.314818  Criteria iii  0.264959   Criteria vi  0.169676  Criteria iv   \n",
       "1      0.441306  Criteria iii  0.216375   Criteria iv  0.121436  Criteria ii   \n",
       "2      0.239042   Criteria vi  0.213535  Criteria iii  0.171007  Criteria iv   \n",
       "3      0.278678   Criteria vi  0.196765  Criteria iii  0.167559  Criteria ii   \n",
       "4      0.301381  Criteria iii  0.267579   Criteria vi  0.159050  Criteria iv   \n",
       "...         ...           ...       ...           ...       ...          ...   \n",
       "2899   0.401202   Criteria vi  0.252103  Criteria iii  0.083146  Criteria iv   \n",
       "2900   0.310644  Criteria iii  0.234954   Criteria iv  0.130461  Criteria vi   \n",
       "2901   0.376549  Criteria iii  0.179604   Criteria vi  0.162176  Criteria iv   \n",
       "2902   0.292644  Criteria iii  0.203834   Criteria iv  0.167321  Criteria vi   \n",
       "2903   0.290931   Criteria vi  0.279036  Criteria iii  0.137033  Criteria iv   \n",
       "\n",
       "         max_1     max_3  counts  \n",
       "0     0.314818  0.749454       2  \n",
       "1     0.441306  0.779117       1  \n",
       "2     0.239042  0.623584       2  \n",
       "3     0.278678  0.643003       3  \n",
       "4     0.301381  0.728010       3  \n",
       "...        ...       ...     ...  \n",
       "2899  0.401202  0.736451       1  \n",
       "2900  0.310644  0.676059       2  \n",
       "2901  0.376549  0.718329       5  \n",
       "2902  0.292644  0.663798       1  \n",
       "2903  0.290931  0.707000       2  \n",
       "\n",
       "[2904 rows x 21 columns]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_agg_df['max_1'] = score_agg_df['max_1_val']\n",
    "score_agg_df['max_3'] = score_agg_df['max_1_val']+score_agg_df['max_2_val']+score_agg_df['max_3_val']\n",
    "score_agg_df['counts'] = sm_df[['origin','sentence']].groupby('origin').count().reset_index()['sentence']\n",
    "score_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     3754\n",
       "Criteria vi      1819\n",
       "Criteria i       1308\n",
       "Criteria iv      1033\n",
       "Criteria ii       782\n",
       "Criteria vii      391\n",
       "Criteria ix       259\n",
       "Criteria x        174\n",
       "Criteria viii      53\n",
       "Criteria v         26\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      7436\n",
       "Criteria iii     7202\n",
       "Criteria vi      4608\n",
       "Criteria ii      4243\n",
       "Criteria i       2849\n",
       "Criteria x        733\n",
       "Criteria vii      645\n",
       "Criteria viii     564\n",
       "Criteria ix       316\n",
       "Criteria v        201\n",
       "dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_df['max_1_col'],score_df['max_2_col'],score_df['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     932\n",
       "Criteria vi      905\n",
       "Criteria ii      560\n",
       "Criteria iv      475\n",
       "Criteria i       158\n",
       "Criteria vii      61\n",
       "Criteria ix       58\n",
       "Criteria v         8\n",
       "Criteria x         8\n",
       "Criteria viii      7\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_all_df[sm_all_df.valid]['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iv      2619\n",
       "Criteria iii     2492\n",
       "Criteria vi      2010\n",
       "Criteria ii      1562\n",
       "Criteria i        440\n",
       "Criteria vii      114\n",
       "Criteria v         86\n",
       "Criteria x         85\n",
       "Criteria ix        76\n",
       "Criteria viii      32\n",
       "dtype: int64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_all_df[sm_all_df.valid]['max_1_col'],\n",
    "           score_all_df[sm_all_df.valid]['max_2_col'],\n",
    "           score_all_df[sm_all_df.valid]['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     1315\n",
       "Criteria vi      1013\n",
       "Criteria iv       238\n",
       "Criteria i        143\n",
       "Criteria vii       64\n",
       "Criteria ix        57\n",
       "Criteria ii        56\n",
       "Criteria viii      10\n",
       "Criteria x          8\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_agg_df['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     2629\n",
       "Criteria iv      2166\n",
       "Criteria vi      1956\n",
       "Criteria ii       951\n",
       "Criteria i        404\n",
       "Criteria ix       263\n",
       "Criteria vii      144\n",
       "Criteria x         91\n",
       "Criteria viii      54\n",
       "Criteria v         54\n",
       "dtype: int64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_agg_df['max_1_col'],score_agg_df['max_2_col'],score_agg_df['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.410730\n",
       "1       0.472180\n",
       "2       0.441306\n",
       "3       0.216921\n",
       "4       0.273391\n",
       "          ...   \n",
       "9594    0.438163\n",
       "9595    0.438163\n",
       "9596    0.359905\n",
       "9597    0.293035\n",
       "9598    0.292644\n",
       "Name: max_1, Length: 9599, dtype: float64"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_df['max_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGgCAYAAACE80yQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwYElEQVR4nO3de3xUdX7/8feZmUwySYACEgLqLhEMQpVrgqDCIu4iVts+WLs+VgErFQGtUFHgt7isLheRBUSrlkXqtSKP1XphtT5WLtZ2F0VKWK0o4aKQiLJJRIGQ5GQmmTm/P9ikjuQ2k5nJdyav5+ORR8g535nz+TCT5J3vuVmO4zgCAAAwkKujCwAAAGgOQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFieji6gvQoKChQIBNSrV6+OLgUAALTRV199Ja/Xq6KiohbHJf2Mit/vV319fUeXEXeO48jv96szXJ+PXlNXZ+qXXlNTZ+pVim+/9fX18vv9rY5L+hmVnJwcSdLbb7/dwZXEV01NjYqLizVo0CBlZmZ2dDlxRa+pqzP1S6+pqTP1KsW336uuuqpN45J+RgUAAKQuggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqMI5lWfJ6vbIsq6NLAQB0ME9HFwBU23WyA/WNXzvBkLqd01dVdkjVAVuS5PN6lOVL66gSAQAdhKCCDmcH6vXmjiOqrPZLkoLBkGqqq5WZlSW326WuWem69oo8ggoAdEIEFRihstqvE6cbgkpQVadt+UNuud3uDq4MANCROEYFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMaKOKicPHlS9913n8aNG6cRI0boxhtvVFFRUeP66dOna+DAgWEf06ZNa1zv9/u1ZMkSjRkzRsOHD9c999yjb775JjbdAACAlBLxTQnvvvtuffXVV1q7dq169uyp559/Xrfeeqtee+01XXDBBTpw4IB++ctf6oc//GHjY9LS/u+ut7/85S9VVFSkxx57TF6vV/fff7/mzp2rjRs3xqYjAACQMiIKKqWlpXr33Xe1adMmjRw5UpL0i1/8Qn/4wx/0xhtvaOrUqfr66681dOhQ9erV66zHl5eXa/PmzVq/fr0KCgokSWvXrtWkSZP0wQcfaPjw4TFoCQAApIqIgkr37t21YcMGXXLJJY3LLMuSZVmqrKzUgQMHZFmW8vLymnz8nj17JEmjR49uXJaXl6fevXtr9+7dUQcVx3FUU1MT1WOThW3bYZ9ThWVZcoIhBYMhBYNBSWric0hOMCTbtuU4TofVGg+p+ro2pzP1S6+pqTP1KsW3X8dxZFlWq+MiCipdu3bVD37wg7BlW7ZsUWlpqe69914dPHhQXbp00dKlS/Xuu+8qMzNTkyZN0h133CGv16vy8nJ1795d6enpYc+Rk5OjsrKySEoJEwgEVFxcHPXjk0lJSUlHlxBTXq9X3c7pq5rqalWdDv9GsGvOfJ3uCsr216rsy2MKBAIdUWbcpdrr2prO1C+9pqbO1KsUn34DgcBZeaApER+j8m1//OMftWjRIk2cOFHjx4/XvffeK7/fryFDhmj69OkqLi7WqlWrdOzYMa1atUq2bcvr9Z71POnp6fL7/VHX4fV6NWjQoPa0YjzbtlVSUqJ+/frJ5/N1dDkxY1mWquyQMrOy5A+5JZ2ZSbFrbPkyfXK73crMypAvPUO9BgxIyRmVVHxdm9OZ+qXX1NSZepXi229TeaApUQeV7du3a/78+RoxYoTWrFkjSVq6dKn+3//7f+rWrZskKT8/X2lpaZo3b54WLlyojIyMJv8i9vv97foPsCxLmZmZUT8+mfh8vpTrtTpgy+12ye12hy13u91//nDJcrtS+odCKr6uLelM/dJraupMvUrx6bctu32kKK+jsnHjRs2ZM0dXXnml1q9f3zh14/F4GkNKgwsvvFCSVFZWptzcXJ08efKssFJRUaHevXtHUwoAAEhhEQeVTZs2admyZZoyZYrWrl0bNnUzbdo0LVq0KGz83r17lZaWpn79+mnkyJEKhUKNB9VK0pEjR1ReXq7CwsJ2tAEAAFJRRLt+jhw5ohUrVuhHP/qRZs2apePHjzeuy8jI0NVXX60VK1ZoyJAhuuKKK7R3716tWrVKt956q7Kzs5Wdna1rr71Wixcv1ooVK+Tz+XT//fdr1KhRGjZsWKx7AwAASS6ioLJlyxbV1dVp27Zt2rZtW9i6yZMna+XKlbIsS88//7xWrFihXr166ZZbbtHMmTMbxy1btkwrVqzQnXfeKUkaN26cFi9eHINWAABAqokoqMyePVuzZ89uccyUKVM0ZcqUZtdnZmZq+fLlWr58eSSbBgAAnRA3JQQAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMFbEQeXkyZO67777NG7cOI0YMUI33nijioqKGtfv3LlTP/7xjzV06FBNmjRJb775Ztjj/X6/lixZojFjxmj48OG655579M0337S/EwAAkHIiDip33323PvjgA61du1avvPKKBg0apFtvvVWHDx/WZ599plmzZmns2LF69dVX9ZOf/EQLFy7Uzp07Gx//y1/+Ujt27NBjjz2m5557TocPH9bcuXNj2hQAAEgNnkgGl5aW6t1339WmTZs0cuRISdIvfvEL/eEPf9Abb7yhr7/+WgMHDtS8efMkSf3799e+ffv05JNPasyYMSovL9fmzZu1fv16FRQUSJLWrl2rSZMm6YMPPtDw4cNj3B4AAEhmEc2odO/eXRs2bNAll1zSuMyyLFmWpcrKShUVFWnMmDFhjxk9erT27Nkjx3G0Z8+exmUN8vLy1Lt3b+3evbs9fQAAgBQU0YxK165d9YMf/CBs2ZYtW1RaWqp7771Xr732mnJzc8PW5+TkyLZtnThxQuXl5erevbvS09PPGlNWVhZlC5LjOKqpqYn68cnAtu2wz6nCsiw5wZCCwZCCwaAkNfE5JCcYkm3bchynw2qNh1R9XZvTmfql19TUmXqV4tuv4ziyLKvVcREFle/64x//qEWLFmnixIkaP368amtr5fV6w8Y0fB0IBGTb9lnrJSk9PV1+vz/qOgKBgIqLi6N+fDIpKSnp6BJiyuv1qts5fVVTXa2q0+HfCHbNma/TXUHZ/lqVfXlMgUCgI8qMu1R7XVvTmfql19TUmXqV4tNvIBA4a+KiKVEHle3bt2v+/PkaMWKE1qxZI+lM4PjuL5KGr30+nzIyMpr8ReP3++Xz+aItRV6vV4MGDYr68cnAtm2VlJSoX79+7fq/Mo1lWaqyQ8rMypI/5JZ0ZibFrrHly/TJ7XYrMytDvvQM9RowICVnVFLxdW1OZ+qXXlNTZ+pVim+/TU1cNCWqoLJx40Y98MADmjRpkn71q181bqxPnz6qqKgIG1tRUaHMzEx16dJFubm5OnnypAKBQFiBFRUV6t27dzSlSDrzyy4zMzPqxycTn8+Xcr1WB2y53S653e6w5W63+88fLlluV0r/UEjF17Ulnalfek1NnalXKT79tmW3jxTF6cmbNm3SsmXLNGXKFK1duzYscBQUFOh//ud/wsa///77GjFihFwul0aOHKlQKNR4UK0kHTlyROXl5SosLIy0FAAAkOIiCipHjhzRihUr9KMf/UizZs3S8ePH9dVXX+mrr77S6dOnNW3aNH300Udas2aNPvvsMz399NN66623NGPGDElS7969de2112rx4sXatWuXPvroI919990aNWqUhg0bFo/+AABAEoto18+WLVtUV1enbdu2adu2bWHrJk+erJUrV2rdunVavXq1nnvuOZ133nlavXp12CnLy5Yt04oVK3TnnXdKksaNG6fFixfHoBUAAJBqIgoqs2fP1uzZs1scM27cOI0bN67Z9ZmZmVq+fLmWL18eyaYBAEAnxE0JAQCAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWJ6OLgBIpGq7TnagvsUxPq9HWb60BFUEAGgJQQWdih2o15s7jqiy2t/k+q5Z6br2ijyCCgAYgqCCTqey2q8Tp5sOKgAAs3CMCgAAMBZBBQAAGKtdQeWJJ57QtGnTwpYtXrxYAwcODPuYMGFC4/pQKKRHH31UY8eO1bBhw3Tbbbfp6NGj7SkDAACkqKiDygsvvKBHHnnkrOUHDhzQ7NmztWPHjsaPl19+uXH9unXrtGnTJi1btky/+c1vFAqFNGPGDAUCgWhLAQAAKSrioFJeXq7Zs2drzZo16tevX9g6x3H06aef6uKLL1avXr0aP3r06CFJCgQCevrppzV37lyNHz9eF110kR5++GGVlZVp69atMWkIAACkjoiDyieffKK0tDS9/vrrGjp0aNi6zz//XDU1NbrggguafOz+/ftVXV2tMWPGNC7r2rWrBg8erN27d0daCgAASHERn548YcKEsGNOvu3gwYOSpOeff16///3v5XK5NG7cOM2bN09dunRRWVmZJKlPnz5hj8vJyWlcFw3HcVRTUxP145OBbdthn1OFZVlygiEFgyEFg0FJauJzSE4wJNu25ThOTLf1XbHaVlul6uvanM7UL72mps7UqxTffh3HkWVZrY6L6XVUDh48KJfLpZycHK1fv16ff/65Vq1apUOHDum5555rbNTr9YY9Lj09XadOnYp6u4FAQMXFxe2qPVmUlJR0dAkx5fV61e2cvqqprlbV6fBvBLvmzNfprqBsf63KvjzWrmOZWtpWg1htK1Kp9rq2pjP1S6+pqTP1KsWn30AgoPT09FbHxTSo3H777brpppvUvXt3SVJ+fr569eqlG264QXv37lVGRkZjcQ3/liS/3y+fzxf1dr1erwYNGtS+4g1n27ZKSkrUr1+/dv1fmcayLFXZIWVmZckfcks6M5Ni19jyZfrkdruVmZUhX3qGeg0Y0OosR0vp3LIsna4Jhm3ruyLZViyk6uvanM7UL72mps7UqxTffr87adGcmAYVl8vVGFIaXHjhhZKksrKyxl0+FRUV+t73vtc4pqKiQgMHDox6u5ZlKTMzM+rHJxOfz5dyvVYHbLndLrnd4eHB7Xb/+cMly+1q9Zuk2q5TTQv38XG5JMeymtzW/22zbduKtVR8XVvSmfql19TUmXqV4tNvW3b7SDEOKgsXLlRFRYWeffbZxmV79+6VJA0YMEDnn3++srOztWvXrsagUllZqX379mnq1KmxLAWdUGv38Tm3V7YuG9o3wVUBANojpkHl6quv1h133KHHH39cf/M3f6MjR45o6dKluu6669S/f39J0tSpU7VmzRr16NFD5557rlavXq3c3FxNnDgxlqUgxbQxeLd4H5+uWW2bZgQAmCOmQeWqq67SI488og0bNuhf//Vf1aVLF/31X/+17rrrrsYxc+fOVX19vRYvXqza2loVFhbqqaeeUload6tF0zLSPXJZlo6fav6oc5fLUigU/2NKAACJ1a6gsnLlyrOWXXPNNbrmmmuafYzb7daCBQu0YMGC9mwanYjX45K/LqgtO0vZrQMAnUxMZ1SAeGK3DgB0Ptw9GQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBfgOy+roCgAADTwdXQBgkox0j1yWpeOn7FbH+rweZfnSElAVAHReBBXgW7wel/x1QW3ZWarKan+z47pmpevaK/IIKgAQZwQVoAmV1X6dON18UAEAJAbHqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMbydHQBSG3Vdp3sQH2z610uS6GQk8CKAADJhKCCuLID9XpzxxFVVvubXH9ur2xdNrRvgqsyi8fjkWVZHV0GABipXbt+nnjiCU2bNi1sWXFxsaZOnaphw4ZpwoQJ+rd/+7ew9aFQSI8++qjGjh2rYcOG6bbbbtPRo0fbUwYMV1nt14nTTX+crgl0dHkdLqfPeaqyQzp+ym72o9qu6+gyAaBDRD2j8sILL+iRRx5RQUFB47ITJ05o+vTpmjBhgpYsWaIPP/xQS5YsUVZWlq6//npJ0rp167Rp0yatXLlSubm5Wr16tWbMmKE33nhDXq+3/R0BScSyLNUFpbd2HVaV3fQusq5Z6br2ijxl+dISXB0AdLyIg0p5ebnuv/9+7dq1S/369Qtb99JLLyktLU1Lly6Vx+NR//79VVpaqg0bNuj6669XIBDQ008/rfnz52v8+PGSpIcfflhjx47V1q1bdd1118WiJyDpnKoKqLKGWRMA+K6Ig8onn3yitLQ0vf766/qXf/kXffnll43rioqKNGrUKHk8//e0o0eP1hNPPKHjx4/r2LFjqq6u1pgxYxrXd+3aVYMHD9bu3bujDiqO46impiaqxyYL27bDPicDy7LkBEMKBkMKBoNNjgmFQpKjsDHf/dzUmLY8T7zGnKktJCcYkm3bcpzoDwb2+/1/3m6w2e3FalsmSMb3cbToNTV1pl6l+PbrOE6bjs+LOKhMmDBBEyZMaHJdWVmZ8vPzw5bl5ORIkv70pz+prKxMktSnT5+zxjSsi0YgEFBxcXHUj08mJSUlHV1Cm3m9XnU7p69qqqtVdbrpN7ndLU2OE1KtbavqdHX4uhq71TFteZ5Yj5GkdFdQtr9WZV8eUyAQ/XE2Df9HtXZts/9HsdqWSZLpfdxe9JqaOlOvUnz6DQQCSk9Pb3VcTM/6qa2tPes4k4Yi/H5/YyJrasypU6ei3q7X69WgQYOifnwysG1bJSUl6tevn3w+X0eX0yaWZanKDikzK0v+kLvJMb5MnyzLpQyfT9n1Z5J1MBiUXWPLl+mT2+1uckxbnideYyQpMytDvvQM9RowoN0zKqdrQ8rwZSi7mf+jWG3LBMn4Po4WvaamztSrFN9+23pcakyDSkZGxll/8TVMbWdmZiojI0PSmRTV8O+GMe35D7AsS5mZmVE/Ppn4fL6k6rU6YMvtdsntbvqXsMvlkiw1Ocbtdsvtdrc4pi3PE+sxZ2pzyeVxtfsb17Isna6tkcvlltsdanZblrv92zJJsr2P24NeU1Nn6lWKT79tvSxDTINKbm6uKioqwpY1fN27d2/V19c3Lvve974XNmbgwIGxLAWIq4x0j1yWpeOnWt5v6/N6OFsHANohpkGlsLBQv/nNbxQMBhv/Gn3//feVl5ennj17qkuXLsrOztauXbsag0plZaX27dunqVOnxrIUIK68Hpf8dUFt2Vna7MXsOK0YANovpvf6uf7661VVVaWf//zn+vTTT/Xqq6/q2Wef1axZsySd2R81depUrVmzRm+//bb279+vefPmKTc3VxMnToxlKUBCtHQxu+YCDACg7WI6o9KzZ089+eSTeuCBBzR58mT16tVLCxcu1OTJkxvHzJ07V/X19Vq8eLFqa2tVWFiop556Smlp/NUJAADCtSuorFy58qxlQ4YM0YsvvtjsY9xutxYsWKAFCxa0Z9MAAKATiOmuHwAAgFgiqAAAAGMRVAAAgLEIKgAAwFgxPesHnUu1XSc7UN/sepfLUiiU3Jd8BwB0LIIKomYH6vXmjiPNXi/k3F7Zumxo3wRXBQBIJQQVtEvDBc+a0jWrbTecSmWt3crCsixZatv9LgCgMyKoAHHSlvsBWY7DxQ4BoAUEFSBO2nI/oD49M3U5u8cAoFkEFSDOWto9lu3jWxAAWsLpyQAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYizuioUnVdp3sQH2z610uS6GQk8CKAACdEUEFTbID9XpzxxFVVjd9199ze2XrsqF9E1wVAKCzIaigWZXVfp043XRQ6ZrlTXA1AIDOiGNUAACAsQgqAADAWAQVIAlYVkdXAAAdg2NUAMNlpHvksiwdP2W3OM7n9SjLl5agqgAgMQgqgOG8Hpf8dUFt2Vna7FlYXbPSde0VeQQVACmHoAIkiZbOwgKAVEVQATqR1i7kJ7ELCYBZCCqdEFed7bxau5Afu5AAmIag0glx1dnOjV1IAJIJQaWT4qqzAIBkwHVUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFSBHcDwhAKuKsHyAFtOV+QFwfB0AyIqgAKaAt9wPi+jgAkhFBBUghXB8HQKrhGBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgASxrIseb1eWVzvH0AbccG3FFNt18kO1De7nsuoozWxyhBNvRedYEjdzumrKjuk6oAtn9ejLF9abDYIICURVFKMHajXmzuOcBl1RKUt9wyS1KaA0dR7MRgMqaa6WplZWere1adrr8gjqABoEUElBXEZdUSrLfcM6pqV3uaA8d33YjAYVNVpW/6QW243e54BtC7mQaW8vFzjxo07a/mDDz6oH//4xyouLtYDDzygjz/+WD169NAtt9yim2++OdZlAGiHlsIuACRSzIPK/v37lZ6eru3bt4cdMNelSxedOHFC06dP14QJE7RkyRJ9+OGHWrJkibKysnT99dfHuhQAAJDkYh5UDh48qH79+iknJ+esdc8995zS0tK0dOlSeTwe9e/fX6WlpdqwYQNBBQAAnCXmO4kPHDig/v37N7muqKhIo0aNksfzf/lo9OjRKikp0fHjx2NdCgAASHJxmVHp3r27pkyZoiNHjuj73/++br/9do0bN05lZWXKz88PG98w8/KnP/1J55xzTlTbdBxHNTU17a7dZLZth31uimVZcoIhBYMhBYPBJseEQiHJkdFjvvu5o+uJ+/YkhRwzemvLmGAwJCcYkm3bcpymT3Vv7r347de2Lc+TzNryPZsq6DV1xbNfx3HadE2lmAaV+vp6HT58WAMGDNDPfvYzZWdn680339TMmTP1zDPPqLa2Vl5v+Fkn6enpkiS/P/oD9wKBgIqLi9tVe7IoKSlpdp3X61W3c/qqprpaVaebflPZ3dLkOCHV2raqTlcbPcausY2qJx7P5e925syZQK3fiN7aMibdFZTtr1XZl8cUCASaHNPae9GusVWTplafJxW09D2baug1dcWj30Ag0JgBWhLToOLxeLRr1y653W5lZGRIki6++GIdOnRITz31lDIyMs76gdQQUDIzM6Pertfr1aBBg6IvPAnYtq2SkhL169dPPp+vyTGWZanKDikzK0v+kLvJMb5MnyzLpQyfT9n1TSfZjh4TDAZl19jyZfrkdrs7vJ54Pld6xplvUm9GurK7dHxvbRmTmZUhX3qGeg0Y0OKMSlPvxW+/tplZWa0+TzJry/dsqqDX1BXPfr87cdGcmO/6ycrKOmvZhRdeqB07dig3N1cVFRVh6xq+7t27d9TbtCyrXUEnmfh8vhZ7rQ7YcrtdcrubDioul0uylBRj3G633G63MfXEbXuSXJYZvbVljNvtkuV2tfpDq6X34pnXtm3Pk+xa+55NJfSauuLRb1tvpRHTg2kPHTqkESNGaNeuXWHLP/74Yw0YMECFhYXas2dP2D7r999/X3l5eerZs2csSwEQR9yqB0CixHRGpX///rrgggu0dOlSLVmyRN27d9dLL72kDz/8UK+88op69uypJ598Uj//+c81Y8YMffTRR3r22We1ZMmSWJYBII7acpl97ikFIFZiGlRcLpfWr1+vhx56SHfddZcqKys1ePBgPfPMM41n+zz55JN64IEHNHnyZPXq1UsLFy7U5MmTY1kGgDhqy2X2uacUgFiJ+TEq55xzjh588MFm1w8ZMkQvvvhirDcLIMG4pxSAROCuYAAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYK+Y3JQSARKu262QH6lsc4/N6lOVLS1BFAGKFoALAaK2FEJfLUn19SL97r0SV1c3dzTld116RR1ABkhBBBYDR7EC93txxpNkQcm6vbF02tK8qq/06cbrpMQCSF0EliXg8HlmW1dFlADHT1rdzSyGka5Y3hhUBMA1BJYnk9DlPVXZI1QG7yfUul6VQyElwVUB0MtI9clmWjp9q+v0s8Z4GQFBJGpZlqS4ovbXrsKrspvfXN0yBA8nA63HJXxfUlp2lre7WAdB5EVSSzKmqgCpr6ppcxxQ4khG7dQC0hOuoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkElSViWJUtc7A0A0LlwerIhWrufieU4SkvjPiUAgM6FoGKI1u5n0qdnpi7nwlcAgE6GoGKQli58le3jpQIAdD4cowKgU+B+nkBy4s90ACmvLTdAlCSf16MsH8eCASYhqABIeW25AWLXrHRde0UeQQUwDEEFQKfR0nFgEruHABMRVABA7B4CTEVQAQC1fffQdWPZPQQkEkEFAL6lpd1Dkcy6sBcJiA2CCgC0UUQH5XqJKkAsEFQAIEKtHZQLIHYIKgnQ2n18XC5LoZCTwIoAAEgOBJUEaO0+Puf2ytZl3McHAICzEFQSpKWp4q5Z3gRXAyCeLEuyLEter1cWF2cB2oWgAgAx1HBm0Gk7qG7n9FWVHVJ14OyzhLgeC9A2BBUAiKGGM4N+9+5h/an8hDKzsuR2h9//lcv1A21HUAGAODhVFdDXp2z5Q2653e64bae1g/UlZm+Q3AgqAJDEWjtYn9kbJDuCCgAkOa7rglTman0IAABAx2BGBQAMxcUiAYIKABiLi0UCBJV24y8eAPHExSLR2RFU2om/eACYLpEXx+V0acQaQSUG+IsHgKkarpR7/NTZV8f9tliEB8uyVMPp0ogxggoAdIBEzXI0XCl3y87SuIWH8PsaOa2eLs3tjxAJggoAJFhbZjlifXxbLK610txuHScYUrdz+qq6NqTWSk7kDA9SA0EFABKsLbMciT6+rS2zHM0dkxcMhlRTXa38vN66Yvh5LT5HImZ4kFoIKgDQQUw5vi2SGZ6mag4Gg6o6bet0TaDN2+RqumirDgkqoVBIjz/+uP793/9dp0+fVmFhoe677z6df/75HVEOAHRqyTrDg/izLEseT8fOaXTI1tetW6dNmzZp5cqVys3N1erVqzVjxgy98cYb8nrNOUuGa6QA6EySaYbHsqQ0t0uB+lCLz5XIY12S8dTs1mp2giHl9Gl5d168JTyoBAIBPf3005o/f77Gjx8vSXr44Yc1duxYbd26Vdddd12iS2oW10gBgMSLZIantWNdrhvb+rEusQoYrf3O6NU9U1eP/r7sU+3fVqJqzvZ5dFVB3z+f0dUxEh5U9u/fr+rqao0ZM6ZxWdeuXTV48GDt3r3bqKAimfMXBgB0Nm35+dvSmLYee1NfH9Lv3iuJycG9rdXcWgBrS5hpS81tfZ7mjjtqEAy2PGOVCJbjOAndd7F161bNmTNH//u//6uMjIzG5f/0T/+k2tpaPfHEExE93yWXXKJgMKjc3NxYl6qQI9XU1jW7e8fjdikj3cOYOIxxHKcxwZtQT0duL9XGNLy2ptQTzzHBYKjJv0RNrjnaMY7jKM3jNqaeb4+p9derud90brclb5q7xedxuSxlZaTJss70WV9fL4/Hc9ZrG8vfGe2tOZa9+9I9clmK+axKWVmZ3G639u7d2+K4hM+o2PaZZPvdY1HS09N16tSpiJ8vPT1dgUAgLtNSbkvqktn6rAljOucYE2tiDGMYc7a2zIS09Xu+4eJ2TYnl74xY1RzL3mPN4/G06bjUhAeVhlmUQCAQNqPi9/vl8/kifr6ioqKY1QYAAMziSvQG+/TpI0mqqKgIW15RUaHevXsnuhwAAGCwhAeViy66SNnZ2dq1a1fjssrKSu3bt0+FhYWJLgcAABgs4bt+vF6vpk6dqjVr1qhHjx4699xztXr1auXm5mrixImJLgcAABisQy74NnfuXNXX12vx4sWqra1VYWGhnnrqKaWlmXMRHAAA0PESfnoyAABAWyX8GBUAAIC2IqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoGKIUCikRx99VGPHjtWwYcN022236ejRo82OP3TokGbOnKlLL71UY8aM0dy5c3Xs2LEEVhy9SHv95JNP9Pd///caPny4Ro8erfvuu0+nT59OYMXRi7TXb3v99dc1cOBAffHFF3GuMnYi7behx+9+JEPPkfZaV1enhx56qHH81KlTVVxcnMCKoxdJr4899liTr+nAgQO1aNGiBFceuUhf16+//lr33HOPRo8erUsvvVTz5s1TeXl5Aitun0j7LSkp0cyZM1VQUKBx48bp0UcfVX19fXyLdGCExx57zLn00kudd955xykuLnb+4R/+wZk4caLj9/vPGvvNN984l19+uTNnzhznwIEDzt69e50pU6Y411xzjVNbW9sB1Ucmkl6/+uorp7Cw0Fm0aJFz+PBhZ8+ePc5f/dVfOXfccUcHVB65SHr9ti+++MIZOXKkk5+f7xw9ejRB1bZfpP2uWrXKmTp1qlNRURH2UV9fn+DKIxdpr/fee69z2WWXOb///e+dTz/91JkzZ45z+eWXO5WVlQmuPHKR9FpVVXXW6/mrX/3KGTZsmLN///4OqD4ykb6uU6dOdX760586+/btcz755BPnhhtucK6//voEVx29SPo9efKkc9lllzlTp051Pv74Y2f37t3OpEmTnEWLFsW1RoKKAfx+vzN8+HDnhRdeaFx26tQpZ8iQIc4bb7xx1viXXnrJGT58uGPbduOyY8eOOfn5+c57772XkJqjFWmvH374oTNv3jynrq6ucdmzzz7rDB06NBHltkukvTYIBoPOjTfe6Nx8881JFVSi6XfGjBnOsmXLElVizETa6+eff+4MHDjQeeedd8LGX3nllSn3Pftdn3zyifOXf/mXzquvvhrPMmMi0l5PnTrl5OfnO2+//Xbjsu3btzv5+fnOiRMnElFyu0Ta7zPPPOMMGzbM+frrrxuXFRUVxf3nFLt+DLB//35VV1drzJgxjcu6du2qwYMHa/fu3WeNHzNmjNatW6eMjIzGZS7XmZeysrIy/gW3Q6S9Dh06VGvXrpXHc+a2VJ999pl++9vf6vLLL09YzdGKtNcG69evV11dnWbNmpWIMmMmmn4PHDig/v37J6rEmIm013fffVddunTRuHHjwsb/53/+Z9hzmCja93GDpUuXqqCgQJMnT45nmTERaa8ZGRnKysrS5s2bVVVVpaqqKv32t79VXl6eunbtmsjSoxJpv6WlpbrgggvUo0ePxmWDBw+WJBUVFcWtzg65KSHClZWVSZL69OkTtjwnJ6dx3bedd955Ou+888KWbdiwQRkZGSosLIxfoTEQaa/fdvXVV6ukpETnnnuuHn/88bjVGCvR9PrRRx/p6aef1ssvv5xU+7mlyPs9deqUysvLVVRUpE2bNunEiRMaMmSIFixYoLy8vITUHK1Iez1y5IjOP/98bd26VRs2bFB5ebkGDx6sn/3sZ8YHtfZ8z77zzjv64IMPtHnz5niVF1OR9ur1erVy5Urdd999KigokGVZysnJ0caNGxv/eDRZpP3m5OSooqJCwWBQbrdbkvTll19KOnOsTryY/z/ZCdi2LenMm/7b0tPT5ff7W338888/r40bN2r+/PlhSddE7el1zZo1ev7559WzZ0/dfPPNqq6ujludsRBprzU1NZo/f77mz5+vfv36JaLEmIq030OHDkmSHMfRgw8+qEceeUR+v1833XSTjh8/Hv+C2yHSXquqqlRaWqp169bp7rvv1q9//Wt5PB7ddNNNcf0BHwvt+Z595plndOWVV2rQoEFxqy+WIu3VcRwVFxdr+PDheuGFF/Tcc8+pb9++uuOOO1RVVZWQmtsj0n6vueYanTx5Ug8++KBqamp0/PhxLV++XB6PR3V1dXGrk6BigIZdOIFAIGy53++Xz+dr9nGO4+iRRx7R8uXLdfvtt2vatGlxrTMWou1Vki655BKNGjVKjz/+uL744gtt27YtbnXGQqS9Ll++XHl5efrpT3+akPpiLdJ+CwoKtHPnTj300EO6+OKLVVBQoMcff1yhUEivvvpqQmqOVqS9ejweVVVV6eGHH9YVV1yhIUOG6OGHH5Ykvfbaa/EvuB2i/Z49duyYdu3apRtvvDGu9cVSpL3+7ne/08aNG7V69WqNHDlSo0aN0vr16/Xll1/q5ZdfTkjN7RFpv/369dM///M/66233tLIkSN19dVXa/z48erevbu6dOkStzoJKgZomHarqKgIW15RUaHevXs3+Zi6ujotWLBA69ev16JFi3TXXXfFu8yYiLTXw4cP67/+67/ClvXu3Vt/8Rd/YfyukUh7feWVV/Tee+9p+PDhGj58uG677TZJ0nXXXaf169fHv+B2iuZ93KNHD1mW1fi1z+fTeeedl3KvbW5urjweT9hunoyMDJ1//vnGn4odzesqSdu3b1ePHj2S4niyBpH2WlRUpLy8PGVnZzcu69atm/Ly8lRaWhrfYmMgmtd2woQJ2rFjh/77v/9bO3fu1A033KDjx4/r/PPPj1udBBUDXHTRRcrOztauXbsal1VWVmrfvn3NHnOycOFCvfXWW3rooYd0yy23JKjS9ou01/fee09z584NO0j4888/14kTJ4zftx9pr1u3btV//Md/aPPmzdq8ebOWL18u6czxR8kwyxJpvy+++KIuvfRS1dTUNC6rqqpSSUmJBgwYkJCaoxVpr4WFhaqvr9fevXsbl9XW1uro0aP6/ve/n5CaoxXNzyfpzC/xUaNGNR4Inwwi7TU3N1elpaVhu0lqamr0xRdfJMXu20j7LSoq0rRp01RfX6+cnBx5vV5t3bpVPp9PI0aMiF+hcTufCBFZu3atM2rUKGf79u1h57IHAgGnvr7eqaioaDwd+ZVXXnHy8/OdJ5988qzrFXz7lGVTRdLriRMnnLFjxzozZ850Dh486Ozevdv527/9W+fv/u7vkuJaG5H0+l3vv/9+Up2e7DiR9Xvs2DGnoKDA+cd//Efn4MGDzkcffeTccsstzg9/+MOkuB5QpK/tLbfc4lxzzTXO7t27nUOHDjlz5sxxxowZE3aqp6mieR9fddVVzrp16zqo4uhF0mt5ebkzatQoZ/bs2U5xcbFTXFzszJo1yxk7dmxSXB/HcSLr9+uvv3YKCwud5cuXO59//rmzbds2Z+TIkc6vf/3ruNZIUDFEfX29s2rVKmf06NHOsGHDnNtuu63xF9TRo0ed/Px855VXXnEcx3GmT5/u5OfnN/nRMMZkkfTqOI5z+PBhZ+bMmc7IkSOdUaNGOYsWLXJOnTrVUeVHJNJevy0Zg0qk/X788cfO9OnTnZEjRzojRoxw5syZ4xw7dqyjyo9IpL2ePn3auf/++51LL73UGTp0qDN9+nTn0KFDHVV+RKJ5Hw8ZMsTZtGlTR5TbLpH2+umnnzqzZs1yRo0a5YwePdq58847U/p7ds+ePc5PfvITZ8iQIc5VV13lPPPMM3Gv0XIcx4nffA0AAED0OEYFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMb6//+bozXGA9uvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_df.drop_duplicates('sentence')['max_1'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_df.drop_duplicates('sentence')['max_3'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGgCAYAAACE80yQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAw+klEQVR4nO3deXhTdb7H8c9J0rRpEa4gbUHEdvCpgsNOWVQYxRlHR+feh8sdn1FBxQ3cGBfEYeCKC+6oKIjKg4qOcEUHRB0dRRxn5qqg1Os2UhcGiuwVrC1pT5MmOfcPn1YibWnSNPklvF/Pw9P2nF/O+X45Sfrp2WI5juMIAADAQK5UFwAAANASggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLE8qS6gvYYNG6ZgMKju3bunuhQAANBG33zzjbxer8rKylodl/Z7VAKBgEKhUKrLSAjHcRQIBJSp9+DL9P4keswUmd5jpvcn0WM6CIVCCgQCBx2X9ntU8vPzJUlvvvlmiitpv7q6OpWXl6tv377Kzc1NdTkJl+n9SfSYKTK9x0zvT6LHdHDqqae2aVza71EBAACZi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMZqV1B57LHHNHHixKhpf/3rXzV+/HgNHjxYY8eO1d133636+vqm+YFAQLfccotGjRqlwYMH6/rrr9e3337bnjIAAECGijuoLF26VPPmzYuaVlZWpquuukq/+MUv9MILL2j27Nl69dVXdcsttzSNufnmm/X2229r/vz5euqpp7Rp0yZNnTo17gYAAEDmijmo7N69W1OmTNHcuXNVVFQUNe/ZZ5/ViBEjNGXKFBUVFelnP/uZrr32Wr388ssKBoPavXu3Vq1apVmzZmnYsGEaMGCA7r//fq1fv14ffvhhonoCAAAZIuag8tlnnykrK0svvfSSBg4cGDXvoosu0o033hi9ApdLDQ0N8vv9+uCDDyRJI0eObJpfXFysgoICrV+/Pp76AQBABvPE+oCxY8dq7Nixzc7r169f1M8NDQ1asmSJfvrTn6pr167avXu3Dj/8cGVnZ0eNy8/P165du2ItpYnjOKqrq4v78aawbTvqa6awLEvS9+cneb1eBQKBpmnS99svU2TqNtwfPaa/TO9Posd04DhO1O+ClsQcVNoqFApp+vTp+uqrr7R06VJJ3/9ner3eA8ZmZ2crEAjEva5gMKjy8vK4H2+aioqKVJeQEB6PR/k9eqkh/MO0Lkf01L76iPbV/xAss9xS5c5tCoVCKaiyY2TKNmwNPaa/TO9PokeTBYPBA3ZcNKdDgorf79c111yj999/XwsWLNCAAQMkSTk5OQoGgweMDwQC8vl8ca/P6/Wqb9++cT/eFLZtq6KiQkVFRe36/zCFZVny2xG99t4mVfuDikTCqrfrlePLkcvlliR16eTVWSf+RCUlJRmxZyXTtmFz6DH9ZXp/Ej2mg+Z2XDQn4UGlsrJSl156qbZv367HH39cpaWlTfMKCwv13XffKRgMRhVYWVmpgoKCuNdpWZZyc3PbVbdJfD5fxvRTG7Tlt0OqqWtQOByWf5+tThG33O6IJMntdslyu9LyRdaaTNqGLaHH9Jfp/Un0aLK2HPaREnzDt+rqal1wwQX69ttvtXTp0qiQIklDhw5VJBJpOqlWkjZv3qzdu3cfMBYAACChe1TuvPNObd26VYsXL1bXrl31zTffNM3r2rWrCgoKdOaZZ2rWrFm644475PP5NHv2bA0fPlyDBg1KZCkAACADJCyohMNhvfrqq2poaNAFF1xwwPw333xTvXr10m233aY77rhDV111lSRpzJgxmjVrVqLKAAAAGaRdQeWuu+5q+t7tduuTTz456GNyc3M1Z84czZkzpz2rBgAAhwA+lBAAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGCsdgWVxx57TBMnToyaVl5ergkTJmjQoEEaO3asnn766aj5kUhEDz30kEaPHq1Bgwbp0ksv1datW9tTBgAAyFBxB5WlS5dq3rx5UdOqqqo0adIk9e7dWytWrNCVV16puXPnasWKFU1jFi5cqGXLlum2227Ts88+q0gkoksuuUTBYDDuJgAAQGbyxPqA3bt3a/bs2XrvvfdUVFQUNe+5555TVlaWbr31Vnk8HvXp00dbtmzRokWLNH78eAWDQT3xxBOaNm2aTj75ZEnSAw88oNGjR2v16tU666yzEtETAADIEDEHlc8++0xZWVl66aWX9PDDD2v79u1N88rKyjR8+HB5PD8sduTIkXrssce0Z88e7dixQ7W1tRo1alTT/M6dO6tfv35av3593EHFcRzV1dXF9ViT2LYd9TXdWZYlJxxROBxROBxWOByWpKav338fkROOyLZtOY6TqlITJtO2YXPoMf1len8SPaYDx3FkWdZBx8UcVMaOHauxY8c2O2/Xrl0qKSmJmpafny9J2rlzp3bt2iVJ6tGjxwFjGufFIxgMqry8PO7Hm6aioiLVJSSE1+tVlyN6qq62Vv59P7yQ7Lofvs92hWUH6rVr+46MOvyXKduwNfSY/jK9P4keTRYMBpWdnX3QcTEHldbU19fL6/VGTWssIhAINKW+5sZUV1fHvV6v16u+ffvG/XhT2LatiooKFRUVyefzpbqcdrMsS347oty8PAUiboXDYdl1tny5PrndbklSbl6OfNk56n7MMRmzRyWTtmFz6DH9ZXp/Ej2mgx9ngZYkNKjk5OQc8FdxIBCQJOXm5ionJ0fS9ymq8fvGMe35T7YsS7m5uXE/3jQ+ny9j+qkN2nK7XU3BRJLcbnfTz263S5bblZYvstZk0jZsCT2mv0zvT6JHk7XlsI+U4KBSWFioysrKqGmNPxcUFCgUCjVN6927d9SYY489NpGlIMPU2g2yg6FWx/i8HuX5spJUEQAgGRIaVEpLS/Xss88qHA43/cW8bt06FRcXq1u3bjrssMPUqVMnvffee01BpaamRhs2bNCECRMSWQoyjB0M6ZW3N6umNtDs/M552TrzpGKCCgBkmITemXb8+PHy+/2aOXOmNm7cqJUrV2rJkiWaPHmypO+PR02YMEFz587Vm2++qc8//1zXXnutCgsLddpppyWyFGSgmtqAqvY1/6+lAAMASG8J3aPSrVs3LV68WLfffrvGjRun7t27a/r06Ro3blzTmKlTpyoUCmnWrFmqr69XaWmpHn/8cWVl8ZcwAACI1q6gctdddx0wbcCAAVq+fHmLj3G73brhhht0ww03tGfVAADgEMCHEiLl2njiNwDgEJTQQz9ArHKyPXJZlvZUt3xnRZfLUiSS/vdYAQDEjqCClPJ6XAo0hPX62i0tnhB7ZPdOOmFgzyRXBgAwAUEFRmi8oqc5nfPadvdCDiEBQOYhqCAjtOUQksRN4QAg3RBUkBHacgiJm8IBQPohqCCjtHYICQCQfrg8GQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqOKRwUzgASC9cnoxDBjeFA4D0Q1DBIYObwgFA+iGo4JDDTeEAIH1wjgoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLESHlRCoZAefPBBnXLKKRo8eLDOO+88ffTRR03zy8vLNWHCBA0aNEhjx47V008/negSAABAhkh4UHnkkUf0/PPP67bbbtOqVatUXFysSy65RJWVlaqqqtKkSZPUu3dvrVixQldeeaXmzp2rFStWJLoMAACQATyJXuCaNWt01lln6aSTTpIk/f73v9fzzz+vjz76SJs3b1ZWVpZuvfVWeTwe9enTR1u2bNGiRYs0fvz4RJcCAADSXML3qHTr1k1vvfWWtm3bpnA4rOXLl8vr9eq4445TWVmZhg8fLo/nh3w0cuRIVVRUaM+ePYkuBQAApLmE71GZOXOmfve73+nUU0+V2+2Wy+XS/Pnz1bt3b+3atUslJSVR4/Pz8yVJO3fu1BFHHBHXOh3HUV1dXbtrTzXbtqO+pjvLsuSEIwqHIwqHwwqHw5LU9FWSIpGI5KhpTHOSOSYcjkiRiOrr6+U4Tqv9NTc/07Zhc+gx/WV6fxI9pgPHcWRZ1kHHJTyobNy4UYcddpgefvhhFRQU6Pnnn9e0adP0zDPPqL6+Xl6vN2p8dna2JCkQCMS9zmAwqPLy8nbVbZKKiopUl5AQXq9XXY7oqbraWvn3/fBCsuv2+75Llhwnonrbln9fbbPLSeaYw3NdsixLe76rU+QgQSXLLVXu3KZQKHTAvEzZhq2hx/SX6f1J9GiyYDDYlAFak9CgsnPnTl1//fVasmSJhg0bJknq37+/Nm7cqPnz5ysnJ0fBYDDqMY0BJTc3N+71er1e9e3bN/7CDWHbtioqKlRUVCSfz5fqctrNsiz57Yhy8/IUiLgVDodl19ny5frkdrslSb5cnyzLpRyfT51CzSfrZI7p0qWTGsKO3li/XdX+YLNjJKlLJ6/OOvEnKikpidqzkmnbsDn0mP4yvT+JHtPBj3dctCShQeXjjz9WQ0OD+vfvHzV94MCB+sc//qGePXuqsrIyal7jzwUFBXGv17KsdgUd0/h8vozppzZoy+12NQUTSXK73U0/u1wuydIBY/aXijF+O6SauoYW+3K7XbLcrhbfHDJpG7aEHtNfpvcn0aPJ2nLYR0rwybSFhYWSpC+++CJq+pdffqmioiKVlpbqgw8+iDo/YN26dSouLla3bt0SWQoAAMgACQ0qAwYM0NChQ3XjjTdq3bp1qqio0Lx587R27VpddtllGj9+vPx+v2bOnKmNGzdq5cqVWrJkiSZPnpzIMgAAQIZI6KEfl8ulRx55RPPmzdOMGTNUXV2tkpISLVmyRAMHDpQkLV68WLfffrvGjRun7t27a/r06Ro3blwiywAAABki4Vf9dOnSRbNnz9bs2bObnT9gwAAtX7480atFCtTaDbKDB17x0sjlshSJtH7lDAAArUl4UMGhww6G9Mrbm1VT2/yl5Ud276QTBvZMclUAgExCUEG71NQGVLWv+aDSOa9tl54BANCShN9CHwAAIFEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADCWJ9UFwEy1doPsYKjF+S6XpUjESWJFAIBDEUEFzbKDIb3y9mbV1AaanX9k9046YWDPJFcFADjUEFTQopragKr2NR9UOud5k1wNAOBQxDkqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqABxsqzmplnyer2ympsJAIiZJ9UFAOkoJ9sjl2VpT7UdNd0JR9TliJ7y2xHVBm35vB7l+bJSVCUApD+CChAHr8elQENYr6/dopraQNP0cDiiutpa5ebl6fDOPp15UjFBBQDagaACtENNbUBV+/YPKmH599kKRNxyuzmyCgDtxTspAAAwFkEFAAAYi6ACAACMRVABAADG6pCgsmrVKv3qV79S//79deaZZ+ovf/lL07xt27Zp8uTJGjJkiE466STNmzdP4XC4I8oAAABpLuFB5cUXX9TMmTN13nnn6ZVXXtFZZ52l6667Th9++KEaGhp08cUXS5KeffZZ3Xzzzfqf//kfPfzww4kuAwAAZICEXp7sOI4efPBBnX/++TrvvPMkSZdffrnKysr0/vvva/v27dqxY4eee+45denSRSUlJdq7d6/uueceTZkyRV6vN5HlAACANJfQPSqbN2/W9u3b9etf/zpq+uOPP67JkyerrKxMxx9/vLp06dI0b+TIkfL7/SovL09kKQAAIAMkdI/K5s2bJUl1dXW6+OKLtWHDBvXq1UuXX365xo4dq127dqmwsDDqMfn5+ZKknTt3auDAgXGt13Ec1dXVta94A9i2HfU1VSzLkhOOKByOtHj+UCQSkRzFNKZx3P7j41lOqse0Nm7/HsPhiJxwRLZty3GcFpeVbkx5nnakTO8x0/uT6DEdOI7Tps9FS2hQ8fv9kqQbb7xRV111laZNm6bXX39dV1xxhZ588knV19erc+fOUY/Jzs6WJAUCgQOW11bBYDCj9shUVFSkdP1er1ddjuiputpa+fc1/wKwu2TJcSKqt23599XGNMausw86JlHr6ogxbRln19mqy5LsQL12bd+hYDDY4rLSVaqfp8mQ6T1men8SPZosGAw2ZYDWJDSoZGV9/5kmF198scaNGydJ6tu3rzZs2KAnn3xSOTk5B7xhNwaU3NzcuNfr9XrVt2/fuB9vCtu2VVFRoaKiIvl8vpTVYVmW/HZEuXl5CkTczY7x5fpkWS7l+HzqFGo+Ef94TDgcll1ny5frk9vtjns5qR7T2rj9e8zNy5MvO0fdjzkm4/aomPA87UiZ3mOm9yfRYzpo63mpCQ0qBQUFkqSSkpKo6cccc4z+9re/afjw4fryyy+j5lVWVkY9Nh6WZbUr6JjG5/OlvJ/aoC2329UUKH7M5XJJluIa43a7m35uz3JSNaYt477v0SXL7UrLN5C2MOF52tEyvcdM70+iR5O15bCPlOCTaY8//njl5eXp448/jpr+5Zdfqnfv3iotLdWGDRuaDhFJ0rp165SXl6fjjjsukaUAAIAMkNCgkpOTo0suuUQPP/yw/vznP+vrr7/WI488onfeeUeTJk3Sz3/+c3Xv3l3XXHONPv/8c61Zs0b333+/LrroIi5NBgAAB0jooR9JuuKKK+Tz+fTAAw9o9+7d6tOnj+bPn68RI0ZIkhYvXqxbbrlFZ599trp06aJzzz1XV1xxRaLLAIzQxj2bAIAWJDyoSNKkSZM0adKkZucdffTReuKJJzpitYBRcrI9clmW9lS3fumgz+tRni8rSVUBQHrpkKACQPJ6XAo0hPX62i2qqW3+8vvOedk686RiggoAtICgAnSwmtqAqvbFf58gADiUdcinJwMAACQCQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABjLk+oCkHy1doPsYKjF+S6XpUjESWJFOJiDbTNJ8nk9yvNlJakiAEgOgsohyA6G9Mrbm1VTG2h2/pHdO+mEgT2TXBVac7Bt1jkvW2eeVExQAZBxCCqHqJragKr2tfRLz5vkatAWrW0zAMhUnKMCAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGKtDg8rmzZs1ePBgrVy5smlaeXm5JkyYoEGDBmns2LF6+umnO7IEAACQxjosqDQ0NGjatGmqq6trmlZVVaVJkyapd+/eWrFiha688krNnTtXK1as6KgyAABAGvN01ILnz5+vTp06RU177rnnlJWVpVtvvVUej0d9+vTRli1btGjRIo0fP76jSgEAAGmqQ4LK+vXrtXz5cq1atUonn3xy0/SysjINHz5cHs8Pqx05cqQee+wx7dmzR0cccURc63McJ2rPTbqybTvqa0ewLEtOOKJwOKJwONzsmEgkIjlK+JjGcfuP76h1deSY1sbt32NblhUOR+SEI7JtW47jNDumLdusLctJlGQ8T1Mt03vM9P4kekwHjuPIsqyDjkt4UKmpqdH06dM1a9Ys9ejRI2rerl27VFJSEjUtPz9fkrRz5864g0owGFR5eXl8BRuooqKiw5bt9XrV5YieqqutlX9f809uu0uWHCeietuWf19twsfYdfZBxySznljHtGWcXWfLrss56LKyXWHZgXrt2r5DwWCw2TFt2WZtWU6ideTz1BSZ3mOm9yfRo8mCwaCys7MPOi7hQeXmm2/W4MGD9etf//qAefX19fJ6vVHTGosMBAJxr9Pr9apv375xP94Utm2roqJCRUVF8vl8HbIOy7LktyPKzctTIOJudowv1yfLcinH51OnUPNpN54x4XBYdp0tX65Pbre7Q9fVkWNaG7d/j21ZVm5ejnzZOep+zDGt7lE52DZry3ISJRnP01TL9B4zvT+JHtPBj/NASxIaVFatWqWysjK9/PLLzc7Pyck54K+9xoCSm5sb93oty2rX403j8/k6tJ/aoC2329UUFn7M5XJJljpsjNvtbvq5o9fVEWPaMs7tdrdpWW63S5bbddA3mYNts7YuJ5E6+nlqgkzvMdP7k+jRZG057CMlOKisWLFCe/fujTovRZJmz56tV199VYWFhaqsrIya1/hzQUFBIksBAAAZIKFBZe7cuaqvr4+adtppp2nq1Kn693//d7344ot69tlnFQ6Hm/4yXLdunYqLi9WtW7dElgIAADJAQu+jUlBQoKOPPjrqnyR169ZNBQUFGj9+vPx+v2bOnKmNGzdq5cqVWrJkiSZPnpzIMgAAQIZI6i30u3XrpsWLF2vz5s0aN26cFixYoOnTp2vcuHHJLAMAAKSJDrvhW6Mvvvgi6ucBAwZo+fLlHb1aAACQAfhQQiDF2njiOwAckjp8jwqAluVke+SyLO2pbvnOki6XpUikY++NAgCmIqgAKeT1uBRoCOv1tVtUU9v8TQ+P7N5JJwzsmeTKAMAMBBXAADW1AVXtaz6odM5r290bASATcY4KAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEyhGWlugIASDw+lBDIADnZHrksS3uq7VbH+bwe5fmyklQVALQfQQXIAF6PS4GGsF5fu0U1tS19CnO2zjypmKACIK0QVIAMUlMbUNW+5oMKAKQjzlEBAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWJ5UF4DEqrUbZAdDLc53uSxFIk4SKwIAIH4ElQxjB0N65e3NqqkNNDv/yO6ddMLAnkmuCgCA+BBUMlBNbUBV+5oPKp3zvEmuBgCA+HGOCgAAMBZBBQAAGIugAgAAjEVQAQAAxkp4UPnuu+900003acyYMRoyZIjOOecclZWVNc1fu3at/vM//1MDBw7U6aefrldeeSXRJQAAgAyR8KBy3XXX6cMPP9T999+vFStWqG/fvrr44ou1adMm/etf/9LkyZM1evRorVy5Ur/5zW80ffp0rV27NtFlAACADJDQy5O3bNmid955R8uWLdPQoUMlSf/93/+t//3f/9XLL7+svXv36thjj9W1114rSerTp482bNigxYsXa9SoUYksBQAAZICE7lE5/PDDtWjRIvXv379pmmVZsixLNTU1KisrOyCQjBw5Uh988IEch7ulAgCAaAndo9K5c2f97Gc/i5r2+uuva8uWLfrDH/6gF154QYWFhVHz8/PzZdu2qqqq1LVr17jW6ziO6urq4q7bFLZtR31tjmVZrc5zwhGFwxGFw+Fmx0QiEclRSsY0jtt/fCrriXdMa+P279G0usPhiJxwRLZtt+sPg7Y8T9NdpveY6f1J9JgOHMdp9Xdaow69M+3//d//acaMGTrttNN08sknq76+Xl5v9J1RG38OBoNxrycYDKq8vLxdtZqkoqLigGkej0f5PXqpoeXfnfK4XXK5PLLtOvn3NR/c7C5ZcpyI6m1b/n21KRlj19kHHWNazbGOs+ts2XU5RtWd7QrLDtRr1/Yd7Xq9NWrueZppMr3HTO9PokeTBYNBZWdnH3RchwWVNWvWaNq0aRoyZIjmzp0rScrOzj7gDbLxZ5/PF/e6vF6v+vbtG3+xhrBtWxUVFSoqKjrg/8OyLPntiF57b5Oq/c3/kumV30knDeolny9XnULNH9Xz5fpkWS7l+HzqFGo+yXbUmHA4LLvOli/XJ7fbnfJ64h3T2rj9ezSt7ty8HPmyc9T9mGPavUelpedppsj0HjO9P4ke08GPd1y0pEOCyjPPPKPbb79dp59+uu6+++6mYnr06KHKysqosZWVlcrNzdVhhx0W9/osy1Jubm67ajaJz+drtp/aoC2/HVJNXUOzj6utD0mW5Ha7moLAj7lcrpSPcbvdTT+bUE+sY9oyzu12G1e32+2S5XYl7A2tpedpJsn0HjO9P4keTdaWwz5SBwSVZcuW6bbbbtPEiRM1c+bMqEKGDRum999/P2r8unXrNGTIkO/faAFkjFq7QXYw1OoYn9ejPF9WkioCkI4SGlQ2b96sO+64Q7/4xS80efJk7dmzp2leTk6OJk6cqHHjxmnu3LkaN26c/v73v+u1117T4sWLE1kGAAPYwZBeeXuzampb+iTvbJ15UjFBBUCrEhpUXn/9dTU0NOiNN97QG2+8ETVv3Lhxuuuuu7Rw4ULde++9euqpp9SrVy/de++93EMFyFA1tQFV7Ws+qABAWyQ0qEyZMkVTpkxpdcyYMWM0ZsyYRK4WAABkKE4MAQAAxiKoAAAAYxFUAACAsQgqAADAWAQV4BDSxvsrAYAxOvSzfgCYIyfbI5dlaU916x9gxk3YAJiEoAIcIrwelwINYb2+dgs3YQOQNggqwCGGm7ABSCecowIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQApww3oABwMlycDSIm23IDOsqQst0vBUKTVZXGTOiBzEVQApERbbkB3ZPdOOmFgT25SBxzCCCoGsSxLXq9XFvvDkUIHe/pZliWPJ3FvHa3dgK5znvegYwBkNoKKIWrtBtXZEXU5oqf8dkS1wejd4S6XpUjESVF1OFS05XCME44ov0evJFYF4FBGUDGEHQzpz+9s0s7dVcrNy5PbHX2ec+MucKAjteVwTCefR6cO68mePwBJQVAxSLU/qL3VtgIRt9xud9S8xl3gQDK0dqglHG79xFYASCQuTwYAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAxc7ksrvoBkBRc9QMgJr5sj3zZOdpnh+UPNH+/Fe77AyBRCCoAYuLNcikYCuu1tRXy26Fmx3DfHwCJQlABEJdqf1A1dQ3NzuO+PwAShXNUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMxeXJAJBgtXaD7GDz95hp5PN6lOfLSlJFQPoiqABAgtnBkF55e7NqagPNzu+cl60zTyomqABtQFABkPYS/bFDlmXJ6/W26/OMamoDqtrXfFAB0HYElSQ42G5gPhcFiF9Otkcuy9Ke6uY/d6hRWw61NL5WnXBEXY7oKb8dUW3wh+ValpTldikYirS4DF7PQGIRVJLgYLuB+VwUIH5ej0uBhrBeX7ul3YdaGl+rVTW26mprlZuXJ7f7h2sOGl+rra2L1zOQWASVJGltNzCfiwK038EOtbT1KM73y6mXf5+tQMQtt9vdNK/xtcrrGUgeggqAjNeWw0McsgHMRFABkPHacniIQzaAmQgqAA4ZJh2ySfSVSkCmIqgAQJIl8kolINMRVAAgyRJ5pRKQ6QgqAJAiibpSCchkBJVW8HkdAFIl2YeHeL+DqVISVCKRiBYsWKDnn39e+/btU2lpqW666SYdddRRqSinRXxeB4BUSfbhId7vYKqUBJWFCxdq2bJluuuuu1RYWKh7771Xl1xyiV5++WV5vWbdLIldswBSKZmfGcTnE8FESQ8qwWBQTzzxhKZNm6aTTz5ZkvTAAw9o9OjRWr16tc4666xklxQ3biIFAObyeDwKRVytvke35fOb2jJGSs9DY+lwyC/pQeXzzz9XbW2tRo0a1TStc+fO6tevn9avX59WQYWbSAGAuVwulwLBsF5tw3t0W97HM/EqrXQ45Gc5jpPUP/dXr16tq6++Wh9//LFycnKapv/ud79TfX29HnvssZiW179/f4XDYRUWFia6VEUcqa6+ocU9Ih63SznZnoSOCYcjzX60fEesKxVjHMeJ6i/V9cQz5mDjGns0rW7GxDbmx8/VVNfTHJfLUl5OVsyHoB3HUSgUksfjaerxYO938a4rVRzHUTgclstt9rZoj+a2Y6zast1zc7Lk6oC+du3aJbfbrU8//bTVcUnfo2Lb3++C+/G5KNnZ2aquro55ednZ2QoGg3FvpNa4Lemw3IOfM8OYQ3OMiTUx5tAcEyvLsg54D27r+126sCxLLtf3n3xt8rZoj+a2Y6xSud09Hk+b6k96UGncixIMBqP2qAQCAfl8vpiXV1ZWlrDaAACAWVzJXmGPHj0kSZWVlVHTKysrVVBQkOxyAACAwZIeVI477jh16tRJ7733XtO0mpoabdiwQaWlpckuBwAAGCzph368Xq8mTJiguXPnqmvXrjryyCN17733qrCwUKeddlqyywEAAAZLyQ3fpk6dqlAopFmzZqm+vl6lpaV6/PHHlZWVXpd1AQCAjpX0y5MBAADaKunnqAAAALQVQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgElSSKRCJ66KGHNHr0aA0aNEiXXnqptm7d2uL4r776SpdddplGjBihUaNGaerUqdqxY0cSK45NrP199tlnuuCCCzR48GCNHDlSN910k/bt25fEimMXa4/7e+mll3Tsscdq27ZtHVxl+8TaY2NfP/5ncp+x9tjQ0KD77ruvafyECRNUXl6exIpjE0t/8+fPb3b7HXvssZoxY0aSK2+7WLfh3r17df3112vkyJEaMWKErr32Wu3evTuJFccu1h4rKip02WWXadiwYRozZoweeughhUKhJFbcQRwkzfz5850RI0Y4b731llNeXu5cdNFFzmmnneYEAoEDxn777bfOiSee6Fx99dXOF1984Xz66afOeeed55xxxhlOfX19Cqo/uFj6++abb5zS0lJnxowZzqZNm5wPPvjA+dWvfuVcccUVKai87WLpcX/btm1zhg4d6pSUlDhbt25NUrXxibXHe+65x5kwYYJTWVkZ9S8UCiW58raLtcc//OEPzgknnOD84x//cDZu3OhcffXVzoknnujU1NQkufK2iaU/v99/wLa7++67nUGDBjmff/55Cqpvm1i34YQJE5zf/va3zoYNG5zPPvvMOfvss53x48cnuerYxNLjd99955xwwgnOhAkTnH/+85/O+vXrndNPP92ZMWNGCipPLIJKkgQCAWfw4MHO0qVLm6ZVV1c7AwYMcF5++eUDxj/33HPO4MGDHdu2m6bt2LHDKSkpcd59992k1ByLWPv76KOPnGuvvdZpaGhomrZkyRJn4MCBySg3LrH22CgcDjvnnHOOc/755xsfVOLp8ZJLLnFuu+22ZJXYbrH2+PXXXzvHHnus89Zbb0WNP+WUUzLitfhjn332mXP88cc7K1eu7Mgy2yXWHqurq52SkhLnzTffbJq2Zs0ap6SkxKmqqkpGyTGLtccnn3zSGTRokLN3796maWVlZca/57QFh36S5PPPP1dtba1GjRrVNK1z587q16+f1q9ff8D4UaNGaeHChcrJyWma5nJ9v7lqamo6vuAYxdrfwIEDdf/998vj+f7jpv71r3/pxRdf1Iknnpi0mmMVa4+NHn30UTU0NGjy5MnJKLNd4unxiy++UJ8+fZJVYrvF2uM777yjww47TGPGjIka/9e//jVqGaaI93na6NZbb9WwYcM0bty4jiyzXWLtMScnR3l5eVq1apX8fr/8fr9efPFFFRcXq3Pnzsksvc1i7XHLli36yU9+oq5duzZN69evnySprKys4wvuQCn5UMJD0a5duyRJPXr0iJqen5/fNG9/vXr1Uq9evaKmLVq0SDk5OSotLe24QuMUa3/7++Uvf6mKigodeeSRWrBgQYfV2F7x9PjJJ5/oiSee0J/+9Cfjj4dLsfdYXV2t3bt3q6ysTMuWLVNVVZUGDBigG264QcXFxUmpOVax9rh582YdddRRWr16tRYtWqTdu3erX79++v3vf29kQGvPa/Gtt97Shx9+qFWrVnVUeQkRa49er1d33XWXbrrpJg0bNkyWZSk/P1/PPPNM0x+Apom1x/z8fFVWViocDsvtdkuStm/fLun783PSmZlbKAPZti3p+xfM/rKzsxUIBA76+D/+8Y965plnNG3atKjEbIr29Dd37lz98Y9/VLdu3XT++eertra2w+psj1h7rKur07Rp0zRt2jQVFRUlo8R2i7XHr776SpLkOI7uvPNOzZs3T4FAQOeee6727NnT8QXHIdYe/X6/tmzZooULF+q6667TI488Io/Ho3PPPdfIXwDteS0++eSTOuWUU9S3b98Oqy8RYu3RcRyVl5dr8ODBWrp0qZ566in17NlTV1xxhfx+f1JqjlWsPZ5xxhn67rvvdOedd6qurk579uzRnDlz5PF41NDQkJSaOwpBJUkaD+EEg8Go6YFAQD6fr8XHOY6jefPmac6cObr88ss1ceLEDq0zXvH2J0n9+/fX8OHDtWDBAm3btk1vvPFGh9XZHrH2OGfOHBUXF+u3v/1tUupLhFh7HDZsmNauXav77rtPP/3pTzVs2DAtWLBAkUhEK1euTErNsYq1R4/HI7/frwceeEAnnXSSBgwYoAceeECS9MILL3R8wTGK97W4Y8cOvffeezrnnHM6tL5EiLXHv/zlL3rmmWd07733aujQoRo+fLgeffRRbd++XX/605+SUnOsYu2xqKhIDz74oF577TUNHTpUv/zlL3XyySfr8MMP12GHHZaUmjsKQSVJGnffVVZWRk2vrKxUQUFBs49paGjQDTfcoEcffVQzZszQNddc09Flxi3W/jZt2qS//e1vUdMKCgr0b//2b8YeIom1xxUrVujdd9/V4MGDNXjwYF166aWSpLPOOkuPPvpoxxcch3iep127dpVlWU0/+3w+9erVK2O2Y2FhoTweT9RhnpycHB111FFGXoIdzzaUpDVr1qhr165GnyfWKNYey8rKVFxcrE6dOjVN69Kli4qLi7Vly5aOLTZO8WzHsWPH6u2339bf//53rV27Vmeffbb27Nmjo446qsPr7UgElSQ57rjj1KlTJ7333ntN02pqarRhw4YWzzmZPn26XnvtNd1333268MILk1RpfGLt791339XUqVOjTgz++uuvVVVVZeRxfyn2HlevXq0///nPWrVqlVatWqU5c+ZI+v5cI1P3ssTa4/LlyzVixAjV1dU1TfP7/aqoqNAxxxyTlJpjFWuPpaWlCoVC+vTTT5um1dfXa+vWrTr66KOTUnMs4nmvkb7/ZT58+PCmE9xNFmuPhYWF2rJlS9Qhk7q6Om3bts3Yw7Kx9lhWVqaJEycqFAopPz9fXq9Xq1evls/n05AhQ5JZeuKl+KqjQ8r999/vDB8+3FmzZk3UNfHBYNAJhUJOZWVl0+XIK1ascEpKSpzFixcfcI+D/S9ZNkks/VVVVTmjR492LrvsMufLL7901q9f7/zHf/yH81//9V9G338jlh5/bN26dWlxqWAsPe7YscMZNmyYc+WVVzpffvml88knnzgXXnih8/Of/9zY+/04Tuzb8cILL3TOOOMMZ/369c5XX33lXH311c6oUaOiLgU1STzP01NPPdVZuHBhiiqOXSw97t692xk+fLgzZcoUp7y83CkvL3cmT57sjB492th74ThObD3u3bvXKS0tdebMmeN8/fXXzhtvvOEMHTrUeeSRR1LcRfsRVJIoFAo599xzjzNy5Ehn0KBBzqWXXtr0S2vr1q1OSUmJs2LFCsdxHGfSpElOSUlJs/8ax5gmlv4cx3E2bdrkXHbZZc7QoUOd4cOHOzNmzHCqq6tTVX6bxNrj/tIlqMTa4z//+U9n0qRJztChQ50hQ4Y4V199tbNjx45Uld8msfa4b98+Z/bs2c6IESOcgQMHOpMmTXK++uqrVJV/UPE8TwcMGOAsW7YsFeXGJdYeN27c6EyePNkZPny4M3LkSOeqq67KuNfiBx984PzmN79xBgwY4Jx66qnOk08+maLKE8tyHMdJ9V4dAACA5nCOCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACM9f+0bQ+nHkgZtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_all_df.drop_duplicates('revised_text')['max_1'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_all_df.drop_duplicates('revised_text')['max_3'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare = score_agg_df.merge(sm_all_df, how = 'left', left_on='origin', right_on='origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare['max_1_eq'] = compare['max_1_col_x']==compare['max_1_col_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6212121212121212"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare['max_1_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3728620296465222"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare[compare['counts']>1]['max_1_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare['max_3_eq'] = compare.apply(lambda x: IoU([x['max_1_col_x'],x['max_2_col_x'],x['max_3_col_x']],\n",
    "                                                 [x['max_1_col_y'],x['max_2_col_y'],x['max_3_col_y']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8326790633608815"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare['max_3_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.722976054732041"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare[compare['counts']>1]['max_3_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2771"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(score_df[(score_df.max_3>0.8) & (score_df.max_1>1./3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_dict_1 = {'max_{}_col_x'.format(i):'max_{}_col_agg'.format(i) for i in range(1,4,1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_1_col_x': 'max_1_col_agg',\n",
       " 'max_2_col_x': 'max_2_col_agg',\n",
       " 'max_3_col_x': 'max_3_col_agg'}"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_dict_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i_x</th>\n",
       "      <th>Criteria ii_x</th>\n",
       "      <th>Criteria iii_x</th>\n",
       "      <th>Criteria iv_x</th>\n",
       "      <th>Criteria v_x</th>\n",
       "      <th>Criteria vi_x</th>\n",
       "      <th>Criteria vii_x</th>\n",
       "      <th>Criteria viii_x</th>\n",
       "      <th>Criteria ix_x</th>\n",
       "      <th>...</th>\n",
       "      <th>max_1_col_y</th>\n",
       "      <th>max_2_val_y</th>\n",
       "      <th>max_2_col_y</th>\n",
       "      <th>max_3_val_y</th>\n",
       "      <th>max_3_col_y</th>\n",
       "      <th>max_1_y</th>\n",
       "      <th>max_3_y</th>\n",
       "      <th>valid</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.050503</td>\n",
       "      <td>0.156630</td>\n",
       "      <td>0.314818</td>\n",
       "      <td>0.169676</td>\n",
       "      <td>0.024925</td>\n",
       "      <td>0.264959</td>\n",
       "      <td>0.005532</td>\n",
       "      <td>0.003077</td>\n",
       "      <td>0.004708</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.270707</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.262568</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>0.813833</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.049204</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>0.036770</td>\n",
       "      <td>0.101294</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.006080</td>\n",
       "      <td>0.009712</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.216375</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0.152715</td>\n",
       "      <td>0.136390</td>\n",
       "      <td>0.213535</td>\n",
       "      <td>0.171007</td>\n",
       "      <td>0.031778</td>\n",
       "      <td>0.239042</td>\n",
       "      <td>0.021632</td>\n",
       "      <td>0.010642</td>\n",
       "      <td>0.009947</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.166086</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.165506</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.228607</td>\n",
       "      <td>0.560199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>0.058416</td>\n",
       "      <td>0.167559</td>\n",
       "      <td>0.196765</td>\n",
       "      <td>0.150599</td>\n",
       "      <td>0.014248</td>\n",
       "      <td>0.278678</td>\n",
       "      <td>0.053060</td>\n",
       "      <td>0.044576</td>\n",
       "      <td>0.011224</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.199517</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.186517</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>0.787524</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0.068965</td>\n",
       "      <td>0.117116</td>\n",
       "      <td>0.301381</td>\n",
       "      <td>0.159050</td>\n",
       "      <td>0.020654</td>\n",
       "      <td>0.267579</td>\n",
       "      <td>0.020281</td>\n",
       "      <td>0.016670</td>\n",
       "      <td>0.010699</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.235703</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.190014</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>0.730016</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2899</th>\n",
       "      <td>4990</td>\n",
       "      <td>0.069137</td>\n",
       "      <td>0.062660</td>\n",
       "      <td>0.252103</td>\n",
       "      <td>0.083146</td>\n",
       "      <td>0.012393</td>\n",
       "      <td>0.401202</td>\n",
       "      <td>0.072347</td>\n",
       "      <td>0.008737</td>\n",
       "      <td>0.009502</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.252103</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.083146</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.401202</td>\n",
       "      <td>0.736451</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2900</th>\n",
       "      <td>4991</td>\n",
       "      <td>0.067804</td>\n",
       "      <td>0.065300</td>\n",
       "      <td>0.310644</td>\n",
       "      <td>0.234954</td>\n",
       "      <td>0.109906</td>\n",
       "      <td>0.130461</td>\n",
       "      <td>0.025942</td>\n",
       "      <td>0.023376</td>\n",
       "      <td>0.012598</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.269683</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.131053</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>0.415419</td>\n",
       "      <td>0.816155</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2901</th>\n",
       "      <td>4993</td>\n",
       "      <td>0.072519</td>\n",
       "      <td>0.100623</td>\n",
       "      <td>0.376549</td>\n",
       "      <td>0.162176</td>\n",
       "      <td>0.028314</td>\n",
       "      <td>0.179604</td>\n",
       "      <td>0.023362</td>\n",
       "      <td>0.019599</td>\n",
       "      <td>0.017496</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.216274</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.171184</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>0.752462</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>4995</td>\n",
       "      <td>0.126379</td>\n",
       "      <td>0.165190</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>0.017086</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>0.009093</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.004861</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.203834</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.663798</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2903</th>\n",
       "      <td>4999</td>\n",
       "      <td>0.069545</td>\n",
       "      <td>0.133852</td>\n",
       "      <td>0.279036</td>\n",
       "      <td>0.137033</td>\n",
       "      <td>0.017681</td>\n",
       "      <td>0.290931</td>\n",
       "      <td>0.039999</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>0.007288</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.236535</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.161154</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>0.726001</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2904 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin  Criteria i_x  Criteria ii_x  Criteria iii_x  Criteria iv_x  \\\n",
       "0          2      0.050503       0.156630        0.314818       0.169676   \n",
       "1          3      0.049204       0.121436        0.441306       0.216375   \n",
       "2          8      0.152715       0.136390        0.213535       0.171007   \n",
       "3          9      0.058416       0.167559        0.196765       0.150599   \n",
       "4         13      0.068965       0.117116        0.301381       0.159050   \n",
       "...      ...           ...            ...             ...            ...   \n",
       "2899    4990      0.069137       0.062660        0.252103       0.083146   \n",
       "2900    4991      0.067804       0.065300        0.310644       0.234954   \n",
       "2901    4993      0.072519       0.100623        0.376549       0.162176   \n",
       "2902    4995      0.126379       0.165190        0.292644       0.203834   \n",
       "2903    4999      0.069545       0.133852        0.279036       0.137033   \n",
       "\n",
       "      Criteria v_x  Criteria vi_x  Criteria vii_x  Criteria viii_x  \\\n",
       "0         0.024925       0.264959        0.005532         0.003077   \n",
       "1         0.036770       0.101294        0.008772         0.006080   \n",
       "2         0.031778       0.239042        0.021632         0.010642   \n",
       "3         0.014248       0.278678        0.053060         0.044576   \n",
       "4         0.020654       0.267579        0.020281         0.016670   \n",
       "...            ...            ...             ...              ...   \n",
       "2899      0.012393       0.401202        0.072347         0.008737   \n",
       "2900      0.109906       0.130461        0.025942         0.023376   \n",
       "2901      0.028314       0.179604        0.023362         0.019599   \n",
       "2902      0.017086       0.167321        0.009093         0.007588   \n",
       "2903      0.017681       0.290931        0.039999         0.006744   \n",
       "\n",
       "      Criteria ix_x  ...   max_1_col_y  max_2_val_y   max_2_col_y max_3_val_y  \\\n",
       "0          0.004708  ...   Criteria iv     0.270707  Criteria iii    0.262568   \n",
       "1          0.009712  ...  Criteria iii     0.216375   Criteria iv    0.121436   \n",
       "2          0.009947  ...   Criteria iv     0.166086   Criteria vi    0.165506   \n",
       "3          0.011224  ...   Criteria ii     0.199517   Criteria iv    0.186517   \n",
       "4          0.010699  ...  Criteria iii     0.235703   Criteria iv    0.190014   \n",
       "...             ...  ...           ...          ...           ...         ...   \n",
       "2899       0.009502  ...   Criteria vi     0.252103  Criteria iii    0.083146   \n",
       "2900       0.012598  ...  Criteria iii     0.269683   Criteria iv    0.131053   \n",
       "2901       0.017496  ...  Criteria iii     0.216274   Criteria iv    0.171184   \n",
       "2902       0.004861  ...  Criteria iii     0.203834   Criteria iv    0.167321   \n",
       "2903       0.007288  ...   Criteria vi     0.236535  Criteria iii    0.161154   \n",
       "\n",
       "       max_3_col_y   max_1_y   max_3_y valid  max_1_eq  max_3_eq  \n",
       "0      Criteria ii  0.280558  0.813833  True     False       0.5  \n",
       "1      Criteria ii  0.441306  0.779117  True      True       1.0  \n",
       "2      Criteria ii  0.228607  0.560199  True     False       0.5  \n",
       "3     Criteria iii  0.401489  0.787524  True     False       0.5  \n",
       "4      Criteria vi  0.304299  0.730016  True      True       1.0  \n",
       "...            ...       ...       ...   ...       ...       ...  \n",
       "2899   Criteria iv  0.401202  0.736451  True      True       1.0  \n",
       "2900    Criteria v  0.415419  0.816155  True      True       0.5  \n",
       "2901   Criteria vi  0.365005  0.752462  True      True       1.0  \n",
       "2902   Criteria vi  0.292644  0.663798  True      True       1.0  \n",
       "2903   Criteria iv  0.328312  0.726001  True      True       1.0  \n",
       "\n",
       "[2904 rows x 77 columns]"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare.rename(columns=cp_dict_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_final = sm_all_df.merge(compare.rename(columns=cp_dict_1)[['origin','max_1_col_agg','max_2_col_agg','max_3_col_agg',\n",
    "                                                    'max_1_eq','max_3_eq']], how='left', left_on='origin',right_on='origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>candownload</th>\n",
       "      <th>url_c</th>\n",
       "      <th>url_q</th>\n",
       "      <th>url_n</th>\n",
       "      <th>url_largest</th>\n",
       "      <th>owner</th>\n",
       "      <th>owner_loc</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>valid</th>\n",
       "      <th>max_1_col_agg</th>\n",
       "      <th>max_2_col_agg</th>\n",
       "      <th>max_3_col_agg</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>51885929339</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51885929339_047ce05277_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51885929339_047ce05277_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51885929339_047ce05277_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51885929339_03ce2e9591_o.jpg</td>\n",
       "      <td>8733637@N04</td>\n",
       "      <td>Haarlem, The Netherlands</td>\n",
       "      <td>Citroën 2CV 1982</td>\n",
       "      <td>...</td>\n",
       "      <td>0.262568</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>0.813833</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>51884357517</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884357517_73d2c871a6_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884357517_73d2c871a6_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884357517_73d2c871a6_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884357517_8eb754431e_o.jpg</td>\n",
       "      <td>150614452@N05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RFO 9901</td>\n",
       "      <td>...</td>\n",
       "      <td>0.121436</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>51883860957</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51883860957_1046863951_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51883860957_1046863951_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51883860957_1046863951_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51883860957_391d98249c_o.jpg</td>\n",
       "      <td>195032295@N04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Houseboats, Amsterdam-West</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165506</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.228607</td>\n",
       "      <td>0.560199</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>51884423594</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884423594_38ed8d7a64_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884423594_38ed8d7a64_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884423594_38ed8d7a64_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51884423594_39ace37a79_o.jpg</td>\n",
       "      <td>8733637@N04</td>\n",
       "      <td>Haarlem, The Netherlands</td>\n",
       "      <td>Ford Transit minibus 1978</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186517</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>0.787524</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>51882154188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51882154188_0948af559d_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51882154188_0948af559d_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51882154188_0948af559d_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51882154188_2b1f87ddcf_o.jpg</td>\n",
       "      <td>8733637@N04</td>\n",
       "      <td>Haarlem, The Netherlands</td>\n",
       "      <td>Renault Dauphine 1960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190014</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>0.730016</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3722</th>\n",
       "      <td>4993</td>\n",
       "      <td>51361633644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51361633644_ede3b60e37_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51361633644_ede3b60e37_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51361633644_ede3b60e37_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51361633644_95593e8206_o.jpg</td>\n",
       "      <td>78239672@N00</td>\n",
       "      <td>Kingston upon Hull, UK</td>\n",
       "      <td>Rijksmuseum</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171184</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>0.752462</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>4995</td>\n",
       "      <td>51357996868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51357996868_82e53268b6_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51357996868_82e53268b6_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51357996868_82e53268b6_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51357996868_389074f277_o.jpg</td>\n",
       "      <td>193393816@N06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AMSTERDAM</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167321</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.663798</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3724</th>\n",
       "      <td>4996</td>\n",
       "      <td>51356750697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51356750697_ffe1173a00_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51356750697_ffe1173a00_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51356750697_ffe1173a00_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51356750697_fa4927c603_o.jpg</td>\n",
       "      <td>34476863@N00</td>\n",
       "      <td>the netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>4997</td>\n",
       "      <td>51358508460</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51358508460_5ce97afb4d_o.jpg</td>\n",
       "      <td>34476863@N00</td>\n",
       "      <td>the netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726</th>\n",
       "      <td>4999</td>\n",
       "      <td>51368197299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51368197299_49407f9c5c_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51368197299_49407f9c5c_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51368197299_49407f9c5c_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51368197299_9cb63008f8_o.jpg</td>\n",
       "      <td>107735426@N07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DSC04526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161154</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>0.726001</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3727 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index  candownload  \\\n",
       "0          2  51885929339          1.0   \n",
       "1          3  51884357517          1.0   \n",
       "2          8  51883860957          1.0   \n",
       "3          9  51884423594          1.0   \n",
       "4         13  51882154188          1.0   \n",
       "...      ...          ...          ...   \n",
       "3722    4993  51361633644          1.0   \n",
       "3723    4995  51357996868          1.0   \n",
       "3724    4996  51356750697          1.0   \n",
       "3725    4997  51358508460          1.0   \n",
       "3726    4999  51368197299          1.0   \n",
       "\n",
       "                                                                 url_c  \\\n",
       "0     https://live.staticflickr.com/65535/51885929339_047ce05277_c.jpg   \n",
       "1     https://live.staticflickr.com/65535/51884357517_73d2c871a6_c.jpg   \n",
       "2     https://live.staticflickr.com/65535/51883860957_1046863951_c.jpg   \n",
       "3     https://live.staticflickr.com/65535/51884423594_38ed8d7a64_c.jpg   \n",
       "4     https://live.staticflickr.com/65535/51882154188_0948af559d_c.jpg   \n",
       "...                                                                ...   \n",
       "3722  https://live.staticflickr.com/65535/51361633644_ede3b60e37_c.jpg   \n",
       "3723  https://live.staticflickr.com/65535/51357996868_82e53268b6_c.jpg   \n",
       "3724  https://live.staticflickr.com/65535/51356750697_ffe1173a00_c.jpg   \n",
       "3725  https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_c.jpg   \n",
       "3726  https://live.staticflickr.com/65535/51368197299_49407f9c5c_c.jpg   \n",
       "\n",
       "                                                                 url_q  \\\n",
       "0     https://live.staticflickr.com/65535/51885929339_047ce05277_q.jpg   \n",
       "1     https://live.staticflickr.com/65535/51884357517_73d2c871a6_q.jpg   \n",
       "2     https://live.staticflickr.com/65535/51883860957_1046863951_q.jpg   \n",
       "3     https://live.staticflickr.com/65535/51884423594_38ed8d7a64_q.jpg   \n",
       "4     https://live.staticflickr.com/65535/51882154188_0948af559d_q.jpg   \n",
       "...                                                                ...   \n",
       "3722  https://live.staticflickr.com/65535/51361633644_ede3b60e37_q.jpg   \n",
       "3723  https://live.staticflickr.com/65535/51357996868_82e53268b6_q.jpg   \n",
       "3724  https://live.staticflickr.com/65535/51356750697_ffe1173a00_q.jpg   \n",
       "3725  https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_q.jpg   \n",
       "3726  https://live.staticflickr.com/65535/51368197299_49407f9c5c_q.jpg   \n",
       "\n",
       "                                                                 url_n  \\\n",
       "0     https://live.staticflickr.com/65535/51885929339_047ce05277_n.jpg   \n",
       "1     https://live.staticflickr.com/65535/51884357517_73d2c871a6_n.jpg   \n",
       "2     https://live.staticflickr.com/65535/51883860957_1046863951_n.jpg   \n",
       "3     https://live.staticflickr.com/65535/51884423594_38ed8d7a64_n.jpg   \n",
       "4     https://live.staticflickr.com/65535/51882154188_0948af559d_n.jpg   \n",
       "...                                                                ...   \n",
       "3722  https://live.staticflickr.com/65535/51361633644_ede3b60e37_n.jpg   \n",
       "3723  https://live.staticflickr.com/65535/51357996868_82e53268b6_n.jpg   \n",
       "3724  https://live.staticflickr.com/65535/51356750697_ffe1173a00_n.jpg   \n",
       "3725  https://live.staticflickr.com/65535/51358508460_24f6a5d0f9_n.jpg   \n",
       "3726  https://live.staticflickr.com/65535/51368197299_49407f9c5c_n.jpg   \n",
       "\n",
       "                                                           url_largest  \\\n",
       "0     https://live.staticflickr.com/65535/51885929339_03ce2e9591_o.jpg   \n",
       "1     https://live.staticflickr.com/65535/51884357517_8eb754431e_o.jpg   \n",
       "2     https://live.staticflickr.com/65535/51883860957_391d98249c_o.jpg   \n",
       "3     https://live.staticflickr.com/65535/51884423594_39ace37a79_o.jpg   \n",
       "4     https://live.staticflickr.com/65535/51882154188_2b1f87ddcf_o.jpg   \n",
       "...                                                                ...   \n",
       "3722  https://live.staticflickr.com/65535/51361633644_95593e8206_o.jpg   \n",
       "3723  https://live.staticflickr.com/65535/51357996868_389074f277_o.jpg   \n",
       "3724  https://live.staticflickr.com/65535/51356750697_fa4927c603_o.jpg   \n",
       "3725  https://live.staticflickr.com/65535/51358508460_5ce97afb4d_o.jpg   \n",
       "3726  https://live.staticflickr.com/65535/51368197299_9cb63008f8_o.jpg   \n",
       "\n",
       "              owner                 owner_loc                       title  \\\n",
       "0       8733637@N04  Haarlem, The Netherlands            Citroën 2CV 1982   \n",
       "1     150614452@N05                       NaN                    RFO 9901   \n",
       "2     195032295@N04                       NaN  Houseboats, Amsterdam-West   \n",
       "3       8733637@N04  Haarlem, The Netherlands   Ford Transit minibus 1978   \n",
       "4       8733637@N04  Haarlem, The Netherlands       Renault Dauphine 1960   \n",
       "...             ...                       ...                         ...   \n",
       "3722   78239672@N00    Kingston upon Hull, UK                 Rijksmuseum   \n",
       "3723  193393816@N06                       NaN                   AMSTERDAM   \n",
       "3724   34476863@N00           the netherlands                         NaN   \n",
       "3725   34476863@N00           the netherlands                         NaN   \n",
       "3726  107735426@N07                       NaN                    DSC04526   \n",
       "\n",
       "      ... max_3_val     max_3_col     max_1     max_3  valid max_1_col_agg  \\\n",
       "0     ...  0.262568   Criteria ii  0.280558  0.813833   True  Criteria iii   \n",
       "1     ...  0.121436   Criteria ii  0.441306  0.779117   True  Criteria iii   \n",
       "2     ...  0.165506   Criteria ii  0.228607  0.560199   True   Criteria vi   \n",
       "3     ...  0.186517  Criteria iii  0.401489  0.787524   True   Criteria vi   \n",
       "4     ...  0.190014   Criteria vi  0.304299  0.730016   True  Criteria iii   \n",
       "...   ...       ...           ...       ...       ...    ...           ...   \n",
       "3722  ...  0.171184   Criteria vi  0.365005  0.752462   True  Criteria iii   \n",
       "3723  ...  0.167321   Criteria vi  0.292644  0.663798   True  Criteria iii   \n",
       "3724  ...  0.026072   Criteria iv  0.835118  0.946161  False           NaN   \n",
       "3725  ...  0.026072   Criteria iv  0.835118  0.946161  False           NaN   \n",
       "3726  ...  0.161154   Criteria iv  0.328312  0.726001   True   Criteria vi   \n",
       "\n",
       "      max_2_col_agg  max_3_col_agg  max_1_eq max_3_eq  \n",
       "0       Criteria vi    Criteria iv     False      0.5  \n",
       "1       Criteria iv    Criteria ii      True      1.0  \n",
       "2      Criteria iii    Criteria iv     False      0.5  \n",
       "3      Criteria iii    Criteria ii     False      0.5  \n",
       "4       Criteria vi    Criteria iv      True      1.0  \n",
       "...             ...            ...       ...      ...  \n",
       "3722    Criteria vi    Criteria iv      True      1.0  \n",
       "3723    Criteria iv    Criteria vi      True      1.0  \n",
       "3724            NaN            NaN       NaN      NaN  \n",
       "3725            NaN            NaN       NaN      NaN  \n",
       "3726   Criteria iii    Criteria iv      True      1.0  \n",
       "\n",
       "[3727 rows x 60 columns]"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df.to_csv('../../../Venice_Flickr/Amsterdam/data_storage/sentences_ulmfit.csv',sep='\\t', encoding='utf-8-sig')\n",
    "score_final.to_csv('../../../Venice_Flickr/Amsterdam/data_storage/metadata_ulmfit.csv',sep='\\t', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df=pd.read_csv('../../../Venice_Flickr/Amsterdam/data_storage/sentences_ulmfit.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')\n",
    "score_final=pd.read_csv('../../../Venice_Flickr/Amsterdam/data_storage/metadata_ulmfit.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df_bert = pd.read_csv('../../../Venice_Flickr/Amsterdam/data_storage/sentences_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')\n",
    "score_final_bert = pd.read_csv('../../../Venice_Flickr/Amsterdam/data_storage/metadata_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent = sm_df.iloc[:,:4]\n",
    "compare_sent['max_1_col_ulmfit'] = sm_df['max_1_col']\n",
    "compare_sent['max_2_col_ulmfit'] = sm_df['max_2_col']\n",
    "compare_sent['max_3_col_ulmfit'] = sm_df['max_3_col']\n",
    "compare_sent['max_1_ulmfit'] = sm_df['max_1']\n",
    "compare_sent['max_3_ulmfit'] = sm_df['max_3']\n",
    "compare_sent['max_1_col_bert'] = sm_df_bert['max_1_col']\n",
    "compare_sent['max_2_col_bert'] = sm_df_bert['max_2_col']\n",
    "compare_sent['max_3_col_bert'] = sm_df_bert['max_3_col']\n",
    "compare_sent['max_1_bert'] = sm_df_bert['max_1']\n",
    "compare_sent['max_3_bert'] = sm_df_bert['max_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['same_1'] = compare_sent['max_1_col_ulmfit']==compare_sent['max_1_col_bert']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['same_3'] = compare_sent.apply(lambda x: IoU(x[['max_1_col_ulmfit','max_2_col_ulmfit','max_3_col_ulmfit']],\n",
    "                                                          x[['max_1_col_bert','max_2_col_bert','max_3_col_bert']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23773309719762475"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_sent['same_1'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6051672049171789"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(compare_sent['same_3']>=0.5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Amsterdam, the Netherlands.</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.410730</td>\n",
       "      <td>0.768343</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.613584</td>\n",
       "      <td>0.870255</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has tags of citro, citro, eend, geit, deuche, deudeuche, red, rood, rouge, stadhouderskade, amsterdam, noordholland, nederland, holland, netherlands, paysbas, oldtimer, old, classic, french, car, auto, automobile, voiture, ancienne, fran, france, frankrijk, vehicle, outdoor</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.472180</td>\n",
       "      <td>0.767179</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.557452</td>\n",
       "      <td>0.855478</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of xt, rfo, station, amsterdam, whiteframe</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.509236</td>\n",
       "      <td>0.742402</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>It has tags of bt, renault, dauphine, renaultdauphine, red, rood, rouge, r, prinsbernhardplein, amsterdam, noordholland, nederland, holland, netherlands, paysbas, oldtimer, old, classic, french, car, auto, automobile, voiture, ancienne, fran, france, frankrijk, vehicle, outdoor, redcars</td>\n",
       "      <td>13</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.466868</td>\n",
       "      <td>0.755010</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.584791</td>\n",
       "      <td>0.852904</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>It has tags of harveybarrison, hbarrison, amsterdam, netherlands, unescoworldheritage, travelphotography</td>\n",
       "      <td>14</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438731</td>\n",
       "      <td>0.776185</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.308114</td>\n",
       "      <td>0.685591</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9591</th>\n",
       "      <td>2495</td>\n",
       "      <td>It has tags of Holland, Netherlands, Amsterdam</td>\n",
       "      <td>4933</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.759667</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.944148</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9592</th>\n",
       "      <td>2496</td>\n",
       "      <td>It has tags of Holland, Netherlands, Amsterdam</td>\n",
       "      <td>4934</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.759667</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.944148</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9593</th>\n",
       "      <td>2497</td>\n",
       "      <td>It has tags of Holland, Netherlands, Amsterdam</td>\n",
       "      <td>4935</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.759667</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.944148</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9594</th>\n",
       "      <td>2498</td>\n",
       "      <td>It has tags of Holland, Netherlands, Amsterdam</td>\n",
       "      <td>4936</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.759667</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.944148</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9595</th>\n",
       "      <td>2499</td>\n",
       "      <td>It has tags of Holland, Netherlands, Amsterdam</td>\n",
       "      <td>4937</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.438163</td>\n",
       "      <td>0.759667</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.819251</td>\n",
       "      <td>0.944148</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5809 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  \\\n",
       "0         0   \n",
       "1         1   \n",
       "2         2   \n",
       "5         7   \n",
       "7         9   \n",
       "...     ...   \n",
       "9591   2495   \n",
       "9592   2496   \n",
       "9593   2497   \n",
       "9594   2498   \n",
       "9595   2499   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                             sentence  \\\n",
       "0                                                                                                                                                                                                                                                                         Amsterdam, the Netherlands.   \n",
       "1               It has tags of citro, citro, eend, geit, deuche, deudeuche, red, rood, rouge, stadhouderskade, amsterdam, noordholland, nederland, holland, netherlands, paysbas, oldtimer, old, classic, french, car, auto, automobile, voiture, ancienne, fran, france, frankrijk, vehicle, outdoor   \n",
       "2                                                                                                                                                                                                                                              It has tags of xt, rfo, station, amsterdam, whiteframe   \n",
       "5     It has tags of bt, renault, dauphine, renaultdauphine, red, rood, rouge, r, prinsbernhardplein, amsterdam, noordholland, nederland, holland, netherlands, paysbas, oldtimer, old, classic, french, car, auto, automobile, voiture, ancienne, fran, france, frankrijk, vehicle, outdoor, redcars   \n",
       "7                                                                                                                                                                                            It has tags of harveybarrison, hbarrison, amsterdam, netherlands, unescoworldheritage, travelphotography   \n",
       "...                                                                                                                                                                                                                                                                                               ...   \n",
       "9591                                                                                                                                                                                                                                                   It has tags of Holland, Netherlands, Amsterdam   \n",
       "9592                                                                                                                                                                                                                                                   It has tags of Holland, Netherlands, Amsterdam   \n",
       "9593                                                                                                                                                                                                                                                   It has tags of Holland, Netherlands, Amsterdam   \n",
       "9594                                                                                                                                                                                                                                                   It has tags of Holland, Netherlands, Amsterdam   \n",
       "9595                                                                                                                                                                                                                                                   It has tags of Holland, Netherlands, Amsterdam   \n",
       "\n",
       "      origin   lang max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0          2     en      Criteria vi      Criteria ii      Criteria iv   \n",
       "1          2     en     Criteria iii      Criteria iv      Criteria ii   \n",
       "2          3     en     Criteria iii      Criteria iv      Criteria ii   \n",
       "5         13     en     Criteria iii      Criteria iv      Criteria vi   \n",
       "7         14     en     Criteria iii      Criteria iv      Criteria ii   \n",
       "...      ...    ...              ...              ...              ...   \n",
       "9591    4933  other     Criteria iii      Criteria iv      Criteria ii   \n",
       "9592    4934  other     Criteria iii      Criteria iv      Criteria ii   \n",
       "9593    4935  other     Criteria iii      Criteria iv      Criteria ii   \n",
       "9594    4936  other     Criteria iii      Criteria iv      Criteria ii   \n",
       "9595    4937  other     Criteria iii      Criteria iv      Criteria ii   \n",
       "\n",
       "      max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  \\\n",
       "0         0.410730      0.768343    Criteria ii     Criteria i    Criteria iv   \n",
       "1         0.472180      0.767179    Criteria iv    Criteria ii    Criteria vi   \n",
       "2         0.441306      0.779117    Criteria iv   Criteria iii    Criteria vi   \n",
       "5         0.466868      0.755010    Criteria iv    Criteria vi    Criteria ii   \n",
       "7         0.438731      0.776185    Criteria ii     Criteria i    Criteria iv   \n",
       "...            ...           ...            ...            ...            ...   \n",
       "9591      0.438163      0.759667    Criteria ii     Criteria i    Criteria iv   \n",
       "9592      0.438163      0.759667    Criteria ii     Criteria i    Criteria iv   \n",
       "9593      0.438163      0.759667    Criteria ii     Criteria i    Criteria iv   \n",
       "9594      0.438163      0.759667    Criteria ii     Criteria i    Criteria iv   \n",
       "9595      0.438163      0.759667    Criteria ii     Criteria i    Criteria iv   \n",
       "\n",
       "      max_1_bert  max_3_bert  same_1  same_3  \n",
       "0       0.613584    0.870255   False     0.5  \n",
       "1       0.557452    0.855478   False     0.5  \n",
       "2       0.509236    0.742402   False     0.5  \n",
       "5       0.584791    0.852904   False     0.5  \n",
       "7       0.308114    0.685591   False     0.5  \n",
       "...          ...         ...     ...     ...  \n",
       "9591    0.819251    0.944148   False     0.5  \n",
       "9592    0.819251    0.944148   False     0.5  \n",
       "9593    0.819251    0.944148   False     0.5  \n",
       "9594    0.819251    0.944148   False     0.5  \n",
       "9595    0.819251    0.944148   False     0.5  \n",
       "\n",
       "[5809 rows x 16 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_sent[compare_sent['same_3']>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGgCAYAAABfSOayAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAjEUlEQVR4nO3de2zV9f3H8deh19MBIkhbkCENrkCXQQu0tMvK+LGMkYxNkSybs26SUevEdYIFdaIgF8NGuQwzRAYICg0Qi6ibU5Qti1PAFs1cpGyCtANKW262FE7PofT7+4Ocsx5u7altT9/4fCQG+J7P+fDpx097nuf0AC7HcRwBAAAY1S3cCwAAAPgiiBkAAGAaMQMAAEwjZgAAgGnEDAAAMI2YAQAAphEzAADAtMhwL6CjjR49Wj6fT3379g33UgAAQCudOHFC0dHRKi0tbXHsDf/KjNfrVWNjY4fM7TiOvF6v+HsHOxb73DnY587BPncO9rnzdNReNzY2yuv1tmrsDf/KTHx8vCRp165d7T73+fPnVVZWpmHDhikuLq7d58cl7HPnYJ87B/vcOdjnztNRe/2d73yn1WNv+FdmAADAjY2YAQAAphEzAADANGIGAACYRswAAADTiBkAAGAaMQMAAEwjZgAAgGnEDAAAMI2YAQAAphEzAADANGIGAACYRswAAADTiBkAAGAaMQMAAEwjZgCgE7lcLkVFRYV7GcANhZgBYFpTkxPuJYTE7XYrJeXrcrlc4V4KcMOIDPcCAOCL6NbNpcLN+3S0+my4l9IqAxJ6qOCeUbpwIdwrAW4cxAwA845Wn9WhY7XhXgaAMOHbTAAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmNbmmDl8+LDS0tK0ffv2wLWysjLl5OQoNTVV48eP14svvhh0n6amJq1cuVLZ2dlKTU1Vbm6ujhw5EjSmpTkAAACaa1PMXLhwQQUFBTp//nzg2pkzZzR16lQNHDhQxcXFmj59ugoLC1VcXBwYs2rVKhUVFWnBggXasmWLmpqaNG3aNPl8vlbPAQAA0FxkW+707LPPqnv37kHXtm3bpqioKM2fP1+RkZEaPHiwKioqtGbNGk2ZMkU+n0/r169XQUGBxo0bJ0lavny5srOztXPnTk2aNKnFOQAAAC4X8iszJSUl2rp1qxYvXhx0vbS0VBkZGYqM/F8fZWZmqry8XCdPntSBAwd07tw5ZWVlBW7v2bOnUlJSVFJS0qo5AAAALhfSKzN1dXWaPXu25syZo379+gXdVlVVpeTk5KBr8fHxkqTjx4+rqqpKkq64X3x8fOC2lua45ZZbQllugOM4Qd8Say8ejyfoR3QM9rlzWNxnl8slt9sd7mW0idfrleM44V7GDcviebaqo/bacRy5XK5WjQ0pZubNm6e0tDT94Ac/uOK2hoYGRUdHB12LiYmRdOmT1v9BXm1MbW1tq+ZoK5/Pp7KysjbfvyXl5eUdNjf+h33uHJb22e12KyUlJdzLaJPKykoeaDuBpfNsXXvvtc/nCzRAS1odMzt27FBpaalef/31q94eGxsbeCOvnz9A4uLiFBsbG1ic/+f+Mf5nVi3N0VbR0dEaNmxYm+9/LR6PR+Xl5Ro0aJDZZ4cWsM+dw+I+t/ZZW1fUv3//K568of1YPM9WddReh/L50eqYKS4u1qlTpwJv3vWbO3eu3njjDSUmJqqmpiboNv+vExIS1NjYGLg2cODAoDFDhgyRpBbnaCuXy/WFYqglbre7Q+fHJexz52CfO0dMTAwPsp2A89x52nuvQ3my0uqYKSwsVENDQ9C1CRMmKD8/Xz/84Q/16quvasuWLbp48aIiIiIkSXv27FFSUpL69OmjHj16qHv37tq7d28gZurq6rR//37l5ORIktLT0687BwAAwOVa/aeZEhISdNtttwX9J0l9+vRRQkKCpkyZovr6ej3xxBM6ePCgtm/frg0bNigvL0/SpZeLcnJyVFhYqF27dunAgQOaMWOGEhMTNWHCBElqcQ4AAIDLtenvmbmaPn36aO3atVq0aJEmT56svn37avbs2Zo8eXJgTH5+vhobGzVnzhw1NDQoPT1d69atU1RUVKvnAAAAaO4Lxcy///3voF8PHz5cW7duveb4iIgIzZo1S7NmzbrmmJbmAAAAaI5/aBIAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMwISoqSi6XK9zLAAB0QZHhXgDQEpfLpZSUrysyMiLcSwlJU5Ojbt0IMADoaMQMTIiMjFDh5n06Wn023EtplQEJPVRwz6hwLwMAvhSIGZhxtPqsDh2rDfcyAABdDO+ZAQAAphEzAADANGIGAACYRswAAADTiBkAAGAaMQMAAEwjZgAAgGnEDAAAMI2YAQAAphEzAADANGIGAACYRswAAADTiBkAAGAaMQMAAEwjZgAAgGnEDAAAMI2YAQAApoUcM6dOndKsWbOUmZmptLQ03X///Tp06FDg9rKyMuXk5Cg1NVXjx4/Xiy++GHT/pqYmrVy5UtnZ2UpNTVVubq6OHDkSNKalOQAAAPxCjpnp06eroqJCa9as0csvv6zY2Fjdd9998ng8OnPmjKZOnaqBAwequLhY06dPV2FhoYqLiwP3X7VqlYqKirRgwQJt2bJFTU1NmjZtmnw+nyS1ag4AAAC/yFAG19bW6tZbb1VeXp6Sk5MlSQ8++KDuuOMOffrpp9q9e7eioqI0f/58RUZGavDgwYHwmTJlinw+n9avX6+CggKNGzdOkrR8+XJlZ2dr586dmjRpkrZt23bdOQAAAJoL6ZWZm266SUuXLg2EzOnTp7VhwwYlJibq9ttvV2lpqTIyMhQZ+b9GyszMVHl5uU6ePKkDBw7o3LlzysrKCtzes2dPpaSkqKSkRJJanAMAAKC5kF6Zae7JJ5/Utm3bFB0dreeee05xcXGqqqoKhI5ffHy8JOn48eOqqqqSJPXr1++KMf7bWprjlltuCXmtjuPo/PnzId+vJR6PJ+hHdAyfzye32x3uZbSJx+OR4zjhXkarWDzPLpfL7Nnwer1mzoZFFs+zVR21147jyOVytWpsm2Pm5z//uX784x9r8+bNmj59uoqKitTQ0KDo6OigcTExMZIufeL6P9CrjamtrZWkFudoC5/Pp7KysjbdtzXKy8s7bG5IbrdbvXr1Cvcy2uTw4cPmvphaOs9ut1spKSnhXkabVFZWmjsbFlk6z9a19177fL7A439L2hwzt99+uyRp0aJF+uc//6lNmzYpNjY28EZeP3+AxMXFKTY2NrBA/8/9Y/zPrlqaoy2io6M1bNiwNt33ejwej8rLyzVo0CCzzw4tuPw8WJKUlGTm2bfF89zaZ21dUf/+/a944ob2Y/E8W9VRex3K50dIMXP69Gnt3r1b3/ve9wLvaenWrZtuv/121dTUKDExUTU1NUH38f86ISFBjY2NgWsDBw4MGjNkyBBJanGOtnC5XG0OodZwu90dOv+XneUHLItfRDnPnSMmJsbk+bCG89x52nuvQ/naH9IbgE+ePKmZM2dq9+7dgWsXLlzQ/v37NXjwYKWnp2vfvn26ePFi4PY9e/YoKSlJffr00dChQ9W9e3ft3bs3cHtdXZ3279+v9PR0SWpxDgAAgOZCipnk5GSNHTtWCxcuVElJif7zn//oscceU11dne677z5NmTJF9fX1euKJJ3Tw4EFt375dGzZsUF5enqRLLxnl5OSosLBQu3bt0oEDBzRjxgwlJiZqwoQJktTiHAAAAM2F/J6ZZcuWaenSpZoxY4bOnj2r0aNHa/Pmzerfv78kae3atVq0aJEmT56svn37avbs2Zo8eXLg/vn5+WpsbNScOXPU0NCg9PR0rVu3TlFRUZKkPn36tDgHAACAX8gx06NHD82bN0/z5s276u3Dhw/X1q1br3n/iIgIzZo1S7NmzbrmmJbmAAAA8OMfmgQAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmBZyzHz++ed66qmnNHbsWI0cOVJ33323SktLA7fv3r1bd911l0aMGKGJEyfqz3/+c9D9vV6vnn76aWVlZSktLU2PPPKITp8+HTSmpTkAAAD8Qo6ZmTNn6qOPPtKyZctUXFysYcOG6Re/+IU+++wzHTp0SHl5ecrOztb27dv1ox/9SLNnz9bu3bsD9583b57+8Y9/6Nlnn9XGjRv12WefKT8/P3B7a+YAAADwiwxlcEVFhd577z0VFRVp1KhRkqQnn3xS7777rl5//XWdOnVKQ4YM0YwZMyRJgwcP1v79+7V27VplZWWpurpaO3bs0OrVqzV69GhJ0rJlyzRx4kR99NFHSktL08aNG687BwAAQHMhvTJz8803a82aNfrGN74RuOZyueRyuVRXV6fS0tIrgiMzM1P79u2T4zjat29f4JpfUlKSEhISVFJSIkktzgEAANBcSK/M9OzZU9/+9reDrr311luqqKjQb37zG73yyitKTEwMuj0+Pl4ej0dnzpxRdXW1br75ZsXExFwxpqqqSpJUVVV13Tl69+4dypIlSY7j6Pz58yHfryUejyfoR3QMn88nt9sd7mW0icfjMRPhFs+zy+Uyeza8Xq+Zs2GRxfNsVUftteM4crlcrRobUsxc7sMPP9Tjjz+uCRMmaNy4cWpoaFB0dHTQGP+vfT6fPB7PFbdLUkxMjLxeryS1OEdb+Hw+lZWVtem+rVFeXt5hc0Nyu93q1atXuJfRJocPHzb3xdTSeXa73UpJSQn3MtqksrLS3NmwyNJ5tq6999rn813x4se1tDlm3nnnHRUUFGjkyJEqLCyUdClKLg8O/6/dbrdiY2OvGiRerzfw7KqlOdoiOjpaw4YNa9N9r8fj8ai8vFyDBg0y++zQgrZGbFeQlJRk5tm3xfPc2mdtXVH//v2v+uQO7cPiebaqo/Y6lM+PNsXMpk2btGjRIk2cOFG//e1vA79hv379VFNTEzS2pqZGcXFx6tGjhxITE/X555/L5/MFLbKmpkYJCQmtmqMtXC6X4uLi2nTf1nC73R06/5ed5Qcsi19EOc+dIyYmxuT5sIbz3Hnae69D+dof8h/NLioq0oIFC3TPPfdo2bJlQVEyevRoffDBB0Hj9+zZo5EjR6pbt24aNWqUmpqaAm8Eli69DF9dXa309PRWzQEAANBcSHVw+PBhPfPMM/rud7+rvLw8nTx5UidOnNCJEyd09uxZ3Xvvvfr4449VWFioQ4cOaf369XrzzTc1bdo0SVJCQoK+//3va86cOdq7d68+/vhjzZw5UxkZGUpNTZWkFucAAABoLqRvM7311lu6cOGC3n77bb399ttBt02ePFmLFy/WqlWrtGTJEm3cuFEDBgzQkiVLgv6o9YIFC/TMM8/ooYcekiSNHTtWc+bMCdz+ta99rcU5AAAA/EKKmQceeEAPPPDAdceMHTtWY8eOvebtcXFxWrhwoRYuXNjmOQAAAPx4EwoAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAwDRiBgAAmEbMAAAA04gZAABgGjEDAABMI2YAAIBpxAwAADCNmAEAAKYRMwAAoM1cLpeioqLCugZiBgCALqSpyQn3EkLidruVkvJ1uVyusK0hMmy/MwAAuEK3bi4Vbt6no9Vnw72UVhmQ0EMF94zShQvhWwMxAwBAF3O0+qwOHasN9zLM4NtMAADANGIGAACYRswAAADTiBkAAGAaMQMAAEwjZgAAgGnEDAAAMI2YAQAAphEzAADANGIGAACYRswAAADTiBkAAGAaMQMAAEwjZgAAgGnEDAAAMI2YAQAAphEzAADANGIGAACYRswAAADTiBkAAGAaMQMAAEwjZgAAgGlfKGaef/553XvvvUHXysrKlJOTo9TUVI0fP14vvvhi0O1NTU1auXKlsrOzlZqaqtzcXB05ciSkOQAAAPzaHDObN2/WihUrgq6dOXNGU6dO1cCBA1VcXKzp06ersLBQxcXFgTGrVq1SUVGRFixYoC1btqipqUnTpk2Tz+dr9RwAAAB+kaHeobq6WnPnztXevXs1aNCgoNu2bdumqKgozZ8/X5GRkRo8eLAqKiq0Zs0aTZkyRT6fT+vXr1dBQYHGjRsnSVq+fLmys7O1c+dOTZo0qcU5AAAAmgs5Zj755BNFRUXptdde0x/+8AcdO3YscFtpaakyMjIUGfm/aTMzM/X888/r5MmTqqys1Llz55SVlRW4vWfPnkpJSVFJSYkmTZrU4hy33HJLyB+k4zg6f/58yPdricfjCfoRHcPn88ntdod7GW3i8XjkOE64l9EqFs+zy+Uyeza8Xq+Zs2GRxfMscaabcxxHLperVWNDjpnx48dr/PjxV72tqqpKycnJQdfi4+MlScePH1dVVZUkqV+/fleM8d/W0hxtiRmfz6eysrKQ79da5eXlHTY3JLfbrV69eoV7GW1y+PBhc19MLZ1nt9utlJSUcC+jTSorK82dDYssnWeJM92cz+dTTExMq8aGHDPX09DQoOjo6KBr/oV4vd7AB3m1MbW1ta2aoy2io6M1bNiwNt33ejwej8rLyzVo0CCzJW2B//1UFiUlJZl59m3xPLf2WVtX1L9//yu+1qH9WDzPEme6uVDmateYiY2NveKBxx8gcXFxio2NlXTpwcn/c/8Y/2FraY62cLlcbb5va7jd7g6d/8vO8ie3pS+ifpznzhETE2PyfFjDee487X2mQ/na365/z0xiYqJqamqCrvl/nZCQEPj20tXGJCQktGoOAACA5to1ZtLT07Vv3z5dvHgxcG3Pnj1KSkpSnz59NHToUHXv3l179+4N3F5XV6f9+/crPT29VXMAAAA0164xM2XKFNXX1+uJJ57QwYMHtX37dm3YsEF5eXmSLn3/KycnR4WFhdq1a5cOHDigGTNmKDExURMmTGjVHAAAAM2163tm+vTpo7Vr12rRokWaPHmy+vbtq9mzZ2vy5MmBMfn5+WpsbNScOXPU0NCg9PR0rVu3TlFRUa2eAwAAwO8LxczixYuvuDZ8+HBt3br1mveJiIjQrFmzNGvWrGuOaWkOAAAAP/6hSQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBox8wVFRUXJ5XKFexkAAHxpRYZ7AZa5XC6lpHxdkZER4V5KSJqaHHXrRoABAG4MxMwXFBkZocLN+3S0+my4l9IqAxJ6qOCeUeFeBgAA7YaYaQdHq8/q0LHacC8DAIAvJd4zAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJjWJWOmqalJK1euVHZ2tlJTU5Wbm6sjR46Ee1kAAKAL6pIxs2rVKhUVFWnBggXasmWLmpqaNG3aNPl8vnAvDQAAdDFdLmZ8Pp/Wr1+v/Px8jRs3TkOHDtXy5ctVVVWlnTt3hnt5AACgi+lyMXPgwAGdO3dOWVlZgWs9e/ZUSkqKSkpKwrgyAADQFUWGewGXq6qqkiT169cv6Hp8fHzgtlDU1NTo4sWLGj9+fLusrznHcdStWzfV1nvVeNFp9/k7wvEIl77z5xg5jo31+rlcLva5gzmOo4sXLyoiIkIulyvcy2k1zgauxup5luye6aampnbd66qqKkVERLRqbJeLGY/HI0mKjo4Ouh4TE6Pa2tqQ54uJiZHP5+uQw+yf86buMe0+d0ez9sktsc8dzeVyqVu3LvdibatwNnA5y+dZsnmm23u/IyMjr2iBa45t19+5HcTGxkq69N4Z/88lyev1yu12hzxfaWlpu60NAAB0PV0uW/3fXqqpqQm6XlNTo4SEhHAsCQAAdGFdLmaGDh2q7t27a+/evYFrdXV12r9/v9LT08O4MgAA0BV1uW8zRUdHKycnR4WFherdu7duvfVWLVmyRImJiZowYUK4lwcAALqYLhczkpSfn6/GxkbNmTNHDQ0NSk9P17p16xQVFRXupQEAgC7G5fDnAwEAgGFd7j0zAAAAoSBmAACAacQMAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOImetoamrSypUrlZ2drdTUVOXm5urIkSPXHH/mzBk98sgjSk9PV0ZGhp5++unAvwKOawt1nz/99FPdf//9GjNmjLKyspSfn6/KyspOXLFNoe5zc6+99pqGDBmio0ePdvAq7Qt1ny9cuKClS5cGxufk5KisrKwTV2xTqPt86tQpPfLII8rMzNSYMWM0Y8YMVVdXd+KKbwzPP/+87r333uuOCcdjITFzHatWrVJRUZEWLFigLVu2qKmpSdOmTZPP57vq+Pz8fFVUVGjDhg36/e9/r7///e+aN29e5y7aoFD2+cyZM5o6dapiY2P10ksv6Y9//KNOnz6tadOmyev1hmH1doR6nv2OHTum+fPnd9Iq7Qt1n+fNm6ft27frmWeeUXFxsXr37q3c3FydPXu2k1duS6j7/PDDD6uyslIvvPCCXnjhBVVWVmr69OmdvGrbNm/erBUrVrQ4LiyPhQ6uyuv1Omlpac7mzZsD12pra53hw4c7r7/++hXjP/zwQyc5Odk5ePBg4Nq7777rDBkyxKmqquqUNVsU6j5v27bNSUtLczweT+BaZWWlk5yc7Lz//vudsmaLQt1nv4sXLzp3332387Of/cxJTk52jhw50hnLNSvUff7vf//rDBkyxPnb3/4WNP7//u//OM/XEeo+19bWOsnJyc6uXbsC19555x0nOTnZOXPmTGcs2bSqqionLy/PSU1NdSZOnOjk5ORcc2y4Hgt5ZeYaDhw4oHPnzikrKytwrWfPnkpJSVFJSckV40tLS9W3b18NHjw4cC0jI0Mul0v79u3rlDVbFOo+Z2VladWqVYqNjQ1c69bt0jGuq6vr+AUbFeo++61evVoXLlxQXl5eZyzTvFD3+b333lOPHj00duzYoPF//etfg+ZAsFD3OTY2Vl/5yle0Y8cO1dfXq76+Xq+++qqSkpLUs2fPzly6SZ988omioqL02muvacSIEdcdG67Hwi75D012BVVVVZKkfv36BV2Pj48P3NZcdXX1FWOjo6PVq1cvHT9+vOMWalyo+zxgwAANGDAg6NqaNWsUGxur9PT0jluocaHusyR9/PHHWr9+vV5++WXeW9BKoe7z4cOH9dWvflU7d+7UmjVrVF1drZSUFD322GNBDwYIFuo+R0dHa/HixXrqqac0evRouVwuxcfHa9OmTYEnQ7i28ePHa/z48a0aG67HQv4vXoP/zUrR0dFB12NiYq763gyPx3PF2OuNxyWh7vPlXnrpJW3atEkFBQXq3bt3h6zxRhDqPp8/f14FBQUqKCjQoEGDOmOJN4RQ97m+vl4VFRVatWqVZs6cqeeee06RkZH66U9/qlOnTnXKmi0KdZ8dx1FZWZnS0tK0efNmbdy4Uf3799eDDz6o+vr6Tlnzl0W4HguJmWvwfxvj8jeTeb1eud3uq46/2hvPvF6v4uLiOmaRN4BQ99nPcRytWLFCCxcu1C9/+csW313/ZRfqPi9cuFBJSUn6yU9+0inru1GEus+RkZGqr6/X8uXL9a1vfUvDhw/X8uXLJUmvvPJKxy/YqFD3+S9/+Ys2bdqkJUuWaNSoUcrIyNDq1at17Ngxvfzyy52y5i+LcD0WEjPX4H+ZrKamJuh6TU2NEhISrhifmJh4xVifz6fPP/9c8fHxHbdQ40LdZ+nSH2WdNWuWVq9erccff1wPP/xwRy/TvFD3ubi4WO+//77S0tKUlpam3NxcSdKkSZO0evXqjl+wUW35uhEZGRn0LaXY2Fh99atf5Y/BX0eo+1xaWqqkpCR17949cO2mm25SUlKSKioqOnaxXzLheiwkZq5h6NCh6t69u/bu3Ru4VldXp/3791/1vRnp6emqqqoK+sT44IMPJEmjRo3q+AUbFeo+S9Ls2bP15ptvaunSpbrvvvs6aaW2hbrPO3fu1J/+9Cft2LFDO3bs0MKFCyVden8Sr9ZcW1u+bjQ2Nupf//pX4FpDQ4OOHDmi2267rVPWbFGo+5yYmKiKioqgb3OcP39eR48e5duo7Sxcj4W8AfgaoqOjlZOTo8LCQvXu3Vu33nqrlixZosTERE2YMEEXL17U6dOn1aNHD8XGxmrEiBEaOXKkZsyYoXnz5un8+fN66qmndOedd17zFQaEvs/bt2/XG2+8odmzZysjI0MnTpwIzOUfgyuFus+XP5D631TZv39/9erVKwwfgQ2h7vPo0aP1zW9+U48++qjmz5+vXr16aeXKlYqIiNAdd9wR7g+nywp1n++8806tW7dODz/8sH79619LklasWKGYmBjdddddYf5obOsyj4Ud9oe+bwCNjY3O7373OyczM9NJTU11cnNzA3/PxpEjR5zk5GSnuLg4MP7kyZPOr371Kyc1NdUZM2aMM3fuXKehoSFcyzcjlH2eOnWqk5ycfNX/mv+/wJVCPc/N7dmzh79nppVC3eezZ886c+fOdcaMGeOMGDHCmTp1qvPpp5+Ga/lmhLrPBw8edPLy8pyMjAwnMzPTeeihhzjPbfDoo48G/T0zXeWx0OU4jtNxqQQAANCxeM8MAAAwjZgBAACmETMAAMA0YgYAAJhGzAAAANOIGQAAYBoxAwAATCNmAACAacQMAAAwjZgBAACmETMAAMC0/weZZdWFjyrNtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_sent['same_3'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post = score_final.iloc[:,:2]\n",
    "compare_post['max_1_col_ulmfit'] = score_final['max_1_col']\n",
    "compare_post['max_2_col_ulmfit'] = score_final['max_2_col']\n",
    "compare_post['max_3_col_ulmfit'] = score_final['max_3_col']\n",
    "compare_post['max_1_ulmfit'] = score_final['max_1']\n",
    "compare_post['max_3_ulmfit'] = score_final['max_3']\n",
    "compare_post['max_1_col_bert'] = score_final_bert['max_1_col']\n",
    "compare_post['max_2_col_bert'] = score_final_bert['max_2_col']\n",
    "compare_post['max_3_col_bert'] = score_final_bert['max_3_col']\n",
    "compare_post['max_1_bert'] = score_final_bert['max_1']\n",
    "compare_post['max_3_bert'] = score_final_bert['max_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['same_1'] = compare_post['max_1_col_ulmfit']==compare_post['max_1_col_bert']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['same_3'] = compare_post.apply(lambda x: IoU(x[['max_1_col_ulmfit','max_2_col_ulmfit','max_3_col_ulmfit']],\n",
    "                                                          x[['max_1_col_bert','max_2_col_bert','max_3_col_bert']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2015025489669976"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post['same_1'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38553796619264824"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post['same_3'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5186477059297022"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(compare_post['same_3']>=0.5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGgCAYAAABfSOayAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAvJElEQVR4nO3de1zUdb7H8fcgDAyiosZFcw1CQdlW0UTlPNLtUJn7OO4ps0fnbGFlanblyOaliydvpa5Sku1x1fJSig/rpJntLTc7p93alcQ9PXITvHTEGwLmnRhmBH7nDx/MaRZQZpwBvvh6Ph48hO/3+/v24dt3Zt6/mR8zNsuyLAEAABgqpLULAAAAuBqEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAo4W2dgHBNmTIELndbsXExLR2KQAAoJlOnjwpu92uwsLCK45t98/MuFwu1dTUBGVuy7LkcrnE+w4GF+vcMljnlsE6txzWumUEa51ramrkcrmaNbbdPzMTGxsrSdqxY0fA566qqlJRUZH69++vyMjIgM+PS1jnlsE6twzWueWw1i0jWOt82223NXtsu39mBgAAtG+EGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgtKsKMytXrtT48eM9P48fP14pKSmNfm3dulWSVFtbqwEDBjTof/311z3zHDt2TFOmTNHgwYN1yy23KC8vT7W1tVdTKgAAaKf8/mym/Px85eXlaciQIZ62119/XRcvXvT8bFmWcnJydO7cOd1xxx2SpJKSErlcLn3wwQfq3r27Z2z95zlcvHhREydOVEJCgjZt2qQjR47ohRdeUEhIiLKzs/0tFwAAtFM+h5ny8nLNnj1bBQUFSkhI8OqLjo72+nnDhg366quv9MEHH6hjx46SpH379ikqKkr9+vVrdP6PPvpIpaWlevfdd9WlSxclJyfr1KlTWrx4sR577DHZ7XZfSwYAAO2Yzy8zff311woLC9O2bds0cODAJsedPn1aeXl5evzxx3XjjTd62vft26ekpKQmjyssLNQPf/hDdenSxdM2fPhwVVZWqqioyNdyAQBAO+fzMzOZmZnKzMy84rg33nhDERERmjhxolf7/v37VVNTo4kTJ6q4uFhxcXF66KGHdNddd0mSysrKFB8f73VMbGysJOnEiROXDVBNsSxLVVVVPh93JU6n0+tfBAfr3DJY55bBOrcc1rplBGudLcuSzWZr1li/r5m5nMrKSr377rt66qmnFB4e7tV34MAB1dXVKTs7W/Hx8fr000/13HPP6eLFi7r33ntVXV2tzp07ex1TP4fL5fKrHrfbHdRndUpKSoI2Ny4JCwtTeXl5a5fhk5qaGq9ryEzBfm4ZrHPLYa1bRqDX2e12N8gQTQlKmPn444/ldrs1bty4Bn2//vWvVVtb67mGpl+/fiotLdXq1at17733KiIiQm632+uY+hBTf5Gwr+x2u/r37+/XsZfjdDpVUlKihIQEORyOgM+PS1wulzp16qzQ0A6tXYpPauvq5Ha5ZFlWa5fSLOznlsE6txzWumUEa519uUY2aGHmxz/+cYNnWCQpIiKiQVtycrK2bdsmSYqPj9f+/fu9+isqKiRJcXFxftVjs9n8DkLN4XA4gjr/tc5msyk0tINy83frWPmF1i6nWXrFddK0B2428g6U/dwyWOeWw1q3jECvc3NfYpKCFGYKCwv19NNPN2g/f/68br/9dj377LO65557PO179uxR3759JUnp6enaunWrKisrFRUVJUnauXOnOnbs2ORfQOHacKz8gr45fq61ywAAtDEBfwfgEydO6MyZM40Gj86dO2v48OFaunSpPv30U5WUlGjVqlXatm2bJ/zcfvvtiomJ0dSpU1VcXKyPP/5Yr776qh555BH+LBsAADQQ8GdmTp48Kanhe87UW7BggV5//XXNnj1bp06dUlJSkpYtW6YRI0ZIunSx75tvvqm5c+fqvvvuU5cuXXT//ffriSeeCHSpAACgHbiqMLNo0aIGbQMGDNC+ffuaPCYqKkrPPfecnnvuuSbH3HDDDVqzZs3VlAYAAK4RfNAkAAAwGmEGAAAYjTADAACMRpgBAABGI8wAQAuy2WwKCwtr7TKAdoUwA8BodXVmfFxEPYfDodTUH/r07qYALi8o7wAMAC0lJMRm5EddGPgZpECbRZgBYDw+6gK4tvEyEwAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaFcVZlauXKnx48d7tc2aNUspKSleX5mZmZ7+uro6LVu2TCNGjFBaWpomT56so0ePes1RVFSkrKwspaWlKTMzU2+//fbVlAkAANoxv8NMfn6+8vLyGrTv27dPjz32mD777DPP13vvvefpX758uTZu3Kj58+dr06ZNqqur06RJk+R2uyVJZ86c0YQJE9S7d29t3rxZTz75pHJzc7V582Z/SwUAAO1YqK8HlJeXa/bs2SooKFBCQoJXn2VZOnjwoB599FHFxMQ0ONbtdmvNmjWaNm2abr31VknS0qVLNWLECG3fvl1jxozRu+++q7CwMM2bN0+hoaFKSkrS4cOHtWrVKo0bN86vXxIAALRfPj8z8/XXXyssLEzbtm3TwIEDvfqOHDmiqqoq3XjjjY0eW1xcrO+++04ZGRmets6dOys1NVW7du2SJBUWFmro0KEKDf3/nDV8+HCVlJTo22+/9bVcAADQzvn8zExmZqbXNTDft3//fknS+vXr9cc//lEhISEaOXKkcnJy1KlTJ5WVlUmSevTo4XVcbGysp6+srEzJyckN+iXpxIkTuu6663wtWZZlqaqqyufjrsTpdHr9i+Bwu91yOBytXYZfnE6nLMtq7TKaxcT9bLPZjN0bLpfLmL1hKhP3tImCtc6WZclmszVrrM9h5nL279+vkJAQxcbGasWKFTpy5IgWL16sAwcO6K233vL8ona73eu48PBwnTt3TpJUXV3daL906cbvD7fbraKiIr+ObY6SkpKgzQ3J4XAoOjq6tcvwy6FDh4y7IzVpPzscDqWmprZ2GX4pLS01bm+YyqQ9bbJAr7Pb7fY8/l9JQMPM448/rvvvv19du3aVJCUnJysmJkb33Xef9uzZo4iICE+B9d9Ll0JK/dlVRESE52Lg7/dLUmRkpF912e129e/f369jL8fpdKqkpEQJCQnGnh2a4O/3g0kSExONOfs2cT8396ytLerZs2eDEzcElol72kTBWmdfbh8BDTMhISGeIFOvb9++ki69fFT/8lJFRYV69+7tGVNRUaGUlBRJUnx8vCoqKrzmqP85Li7Or7psNpvfQag5HA5HUOe/1pn8gGXiHSj7uWWEh4cbuT9MxJ5uGYFeZ1/u+wP6pnkzZszQww8/7NW2Z88eSVKfPn3Ur18/RUVFqaCgwNN//vx57d27V+np6ZKk9PR07d69W7W1tZ4xO3fuVGJiorp37x7IcgEAQDsQ0DBz55136i9/+Yt++ctf6siRI/r000/1/PPPa8yYMUpKSpLdbldWVpZyc3O1Y8cOFRcXKycnR/Hx8Ro1apQkady4caqsrNQLL7yggwcPasuWLVq3bp2mTJkSyFIBAEA7EdCXmW677Tbl5eVp1apVeuONN9SpUyf99Kc/1dSpUz1jsrOzVVNTo1mzZqm6ulrp6elavXq1wsLCJEndu3fXm2++qZdfflljx45VTEyMZsyYobFjxwayVAAA0E5cVZhZtGhRg7af/OQn+slPftLkMR06dND06dM1ffr0JscMGDBA77zzztWUBgAArhF80CQAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0a4qzKxcuVLjx4/3avvkk080btw4DRo0SJmZmfrFL36h6upqT//u3buVkpLS4KugoMAz5i9/+YvuueceDRw4UKNHj9ZvfvObqykTAAC0Y6H+Hpifn6+8vDwNGTLE01ZYWKinnnpK2dnZGj16tA4fPqwXX3xRZ8+e1cKFCyVJ+/btU+/evbVx40av+bp06SJJ+uabbzRlyhRNmDBBS5Ys0X//939rxowZ6tatmzIyMvwtFwAAtFM+h5ny8nLNnj1bBQUFSkhI8OrbtGmThg0bpscee0ySlJCQoJycHM2aNUtz586V3W7X/v371adPH8XExDQ6/1tvvaWUlBTl5ORIkpKSkrR37169+eabhBkAANCAzy8zff311woLC9O2bds0cOBAr75HHnlEM2fO9P4PhITo4sWLqqyslHTpmZmkpKQm5y8sLGwQWoYPH67du3fLsixfywUAAO2cz8/MZGZmKjMzs9G+1NRUr58vXryodevW6aabblK3bt0kSQcOHFDXrl11zz33qLy8XMnJycrJydGAAQMkSWVlZYqPj/eaJzY2Vk6nU2fOnPHM4wvLslRVVeXzcVfidDq9/kVwuN1uORyO1i7DL06n05gQbuJ+ttlsxu4Nl8tlzN4wlYl72kTBWmfLsmSz2Zo11u9rZq6kpqZGM2bM0IEDB5Sfny9JOnHihC5cuKCqqirNmjVLHTp00IYNG5SVlaUtW7aoT58+qq6ult1u95qr/me32+1XLW63W0VFRVf3C11GSUlJ0OaG5HA4FB0d3dpl+OXQoUPG3ZGatJ8dDkeDkyhTlJaWGrc3TGXSnjZZoNfZ7XYrPDy8WWODEmYqKys1depUffHFF/rlL3/pedalR48e2rVrlxwOh8LCwiRJP/rRj7R3716tX79ec+fOVXh4eIPQUv+zv2dgdrtd/fv3v4rfqHFOp1MlJSVKSEgw9uzQBP6G2LYgMTHRmLNvE/dzc8/a2qKePXs2OHFDYJm4p00UrHX25fYR8DBTUVGhyZMn6/jx41q9erXS09O9+jt37uz1c0hIiJKSklReXi7pUuCpqKhoMGdkZKQ6derkV002m02RkZF+HdscDocjqPNf60x+wDLxDpT93DLCw8ON3B8mYk+3jECvsy/3/QF907xz587poYce0unTp5Wfn98gyPzxj3/UoEGDdPToUU9bTU2NiouL1adPH0nSkCFD9MUXX3gdt3PnTg0ePFghIbzHHwAA8BbQdLBw4UIdPXpUS5YsUbdu3XTy5EnPV21trQYPHqyuXbtq5syZ+tvf/qZ9+/Zp5syZOnv2rB5++GFJ0vjx4/XVV18pNzdX33zzjdasWaPf//73mjRpUiBLBQAA7UTAXmaqra3Vb3/7W128eFEPPfRQg/4dO3aoV69eWrdunXJzczVx4kS5XC7dfPPN2rBhg6677jpJUt++fbV8+XItWbJEb731lnr16qUlS5bwHjMAAKBRVxVmFi1a5Pm+Q4cO+uqrr654TO/evbVs2bLLjhk5cqRGjhx5NaUBAIBrBBehAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIx2VWFm5cqVGj9+vFdbUVGRsrKylJaWpszMTL399tte/XV1dVq2bJlGjBihtLQ0TZ48WUePHvVpDgAAgHp+h5n8/Hzl5eV5tZ05c0YTJkxQ7969tXnzZj355JPKzc3V5s2bPWOWL1+ujRs3av78+dq0aZPq6uo0adIkud3uZs8BAABQL9TXA8rLyzV79mwVFBQoISHBq+/dd99VWFiY5s2bp9DQUCUlJenw4cNatWqVxo0bJ7fbrTVr1mjatGm69dZbJUlLly7ViBEjtH37do0ZM+aKcwAAAHyfz2Hm66+/VlhYmLZt26b/+I//0PHjxz19hYWFGjp0qEJD/3/a4cOHa+XKlfr2229VWlqq7777ThkZGZ7+zp07KzU1Vbt27dKYMWOuOMd1113n8y9pWZaqqqp8Pu5KnE6n178IDrfbLYfD0dpl+MXpdMqyrNYuo1lM3M82m83YveFyuYzZG6YycU+bKFjrbFmWbDZbs8b6HGYyMzOVmZnZaF9ZWZmSk5O92mJjYyVJJ06cUFlZmSSpR48eDcbU911pDn/CjNvtVlFRkc/HNVdJSUnQ5obkcDgUHR3d2mX45dChQ8bdkZq0nx0Oh1JTU1u7DL+UlpYatzdMZdKeNlmg19ntdis8PLxZY30OM5dTXV0tu93u1VZfiMvl8txwGxtz7ty5Zs3hD7vdrv79+/t17OU4nU6VlJQoISHB2LNDE9RfT2WixMREY86+TdzPzT1ra4t69uzZ4L4OgWXinjZRsNbZl9tHQMNMREREgwee+gASGRmpiIgISZcenOq/rx9TvwBXmsMfNpvN72Obw+FwBHX+a53JD1gm3oGyn1tGeHi4kfvDROzplhHodfblvj+g7zMTHx+viooKr7b6n+Pi4jwvLzU2Ji4urllzAAAAfF9Aw0x6erp2796t2tpaT9vOnTuVmJio7t27q1+/foqKilJBQYGn//z589q7d6/S09ObNQcAAMD3BTTMjBs3TpWVlXrhhRd08OBBbdmyRevWrdOUKVMkXXr9KysrS7m5udqxY4eKi4uVk5Oj+Ph4jRo1qllzAAAAfF9Ar5np3r273nzzTb388ssaO3asYmJiNGPGDI0dO9YzJjs7WzU1NZo1a5aqq6uVnp6u1atXKywsrNlzAAAA1LuqMLNo0aIGbQMGDNA777zT5DEdOnTQ9OnTNX369CbHXGkOAACAenzQJAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRQgM5WUFBgR588MFG+3r16qUdO3boV7/6lfLy8hr079u3z/N9fn6+1qxZo5MnT+qmm27SrFmzlJqaGshSAQBAOxHQMDNo0CB99tlnXm1ffvmlnn76aT3xxBOSLoWWu+66S9OnT290jvfff1+LFy/W/PnzlZqaqlWrVmnChAn63e9+p27dugWyXAAA0A4E9GUmu92umJgYz1fHjh21cOFCjR07VuPGjZMk7d+/X6mpqV7jYmJiPHOsWLFCWVlZ+ud//mf16dNHCxYskMPh0H/+538GslQAANBOBPWamRUrVsjpdGrmzJmSJLfbrZKSEt14442Njj916pRKSkqUkZHhaQsNDdWQIUO0a9euYJYKAAAMFdCXmb7v9OnTWrdunZ555hlFR0dLkg4ePKja2lp99NFHevnll+VyuZSenq7p06crNjZWZWVlkqQePXp4zRUbG6vi4mK/a7EsS1VVVX4f3xSn0+n1L4LD7XbL4XC0dhl+cTqdsiyrtctoFhP3s81mM3ZvuFwuY/aGqUzc0yYK1jpbliWbzdassUELMxs3blSnTp30L//yL562/fv3S5IcDodee+01nTp1Sq+++qoefPBBbd261bMQdrvda67w8HC5XC6/a3G73SoqKvL7+CspKSkJ2ty4tF/qA7FpDh06ZNwdqUn72eFwGPvHAaWlpcbtDVOZtKdNFuh1drvdCg8Pb9bYoIWZrVu36u6771ZERISn7e6779bIkSO9LuTt27evRo4cqU8++US9e/eWdOkX+D6Xy3VVZ192u139+/f3+/imOJ1OlZSUKCEhwdizQxP8/X4wSWJiojFn3ybu5+aetbVFPXv2bHDihsAycU+bKFjr7MvtIyhhpri4WEePHtVPf/rTBn1//xdJsbGxio6OVllZmYYNGyZJqqioUFJSkmdMRUWF4uLi/K7HZrMpMjLS7+OvxOFwBHX+a53JD1gm3oGyn1tGeHi4kfvDROzplhHodfblvj8oFwAXFhaqe/fu6tevn1f70qVLdeedd3qdqR47dkxnzpxRnz591L17dyUmJqqgoMDTX1NTo8LCQqWnpwejVAAAYLighJm9e/cqJSWlQfsdd9yh48ePa86cOTp06JB27dqlp59+WoMHD9aIESMkSY888ojWrl2r999/XwcPHtTzzz+v6upq3XvvvcEoFQAAGC4oLzOdPHmy0Qs2b7rpJr3xxht67bXXdM8998hut+u2227TzJkzPU8n3Xfffbpw4YLy8vJ09uxZ3XTTTVq7di1vmAcAABoVlDDzxhtvNNmXkZHh9T4yjZk4caImTpwY6LIAAEA7xAdNAgAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABgt4GGmvLxcKSkpDb62bNkiSSoqKlJWVpbS0tKUmZmpt99+2+v4uro6LVu2TCNGjFBaWpomT56so0ePBrpMAADQToQGesLi4mKFh4fr448/ls1m87R36tRJZ86c0YQJE5SZmam5c+fqyy+/1Ny5c9WxY0eNGzdOkrR8+XJt3LhRixYtUnx8vJYsWaJJkybpww8/lN1uD3S5AADAcAEPM/v371dCQoJiY2Mb9L311lsKCwvTvHnzFBoaqqSkJB0+fFirVq3SuHHj5Ha7tWbNGk2bNk233nqrJGnp0qUaMWKEtm/frjFjxgS6XAAAYLiAv8y0b98+JSUlNdpXWFiooUOHKjT0/zPU8OHDVVJSom+//VbFxcX67rvvlJGR4env3LmzUlNTtWvXrkCXCgAA2oGgPDPTtWtXPfDAAzp06JBuuOEGPf744xo5cqTKysqUnJzsNb7+GZwTJ06orKxMktSjR48GY+r7/GFZlqqqqvw+vilOp9PrXwSH2+2Ww+Fo7TL84nQ6ZVlWa5fRLCbuZ5vNZuzecLlcxuwNU5m4p00UrHW2LMvrcpXLCWiYqamp0f/+7/+qT58+evbZZxUVFaXf/OY3evTRR7V27VpVV1c3uO4lPDxc0qUbdv1CNDbm3LlzftfldrtVVFTk9/FXUlJSErS5ITkcDkVHR7d2GX45dOiQcXekJu1nh8Oh1NTU1i7DL6WlpcbtDVOZtKdNFuh1drvdnoxwJQENM6GhoSooKFCHDh0UEREhSbrpppt04MABrV69WhEREXK73V7HuFwuSVJkZKTnGLfb7fm+fszVnH3Z7Xb179/f7+Ob4nQ6VVJSooSEBGPPDk3w93vGJImJicacfZu4n5t71tYW9ezZkz9qCDIT97SJgrXOvtw+Av4yU8eOHRu09e3bV5999pni4+NVUVHh1Vf/c1xcnGpqajxtvXv39hqTkpLid002m02RkZF+H38lDocjqPNf60x+wDLxDpT93DLCw8ON3B8mYk+3jECvsy/3/QG9APjAgQMaPHiwCgoKvNr/9re/qU+fPkpPT9fu3btVW1vr6du5c6cSExPVvXt39evXT1FRUV7Hnz9/Xnv37lV6enogSwUAAO1EQMNMUlKSbrzxRs2bN0+FhYX65ptvtHDhQn355Zd6/PHHNW7cOFVWVuqFF17QwYMHtWXLFq1bt05TpkyRdOkppaysLOXm5mrHjh0qLi5WTk6O4uPjNWrUqECWCgAA2omAvswUEhKiFStW6JVXXtHUqVN1/vx5paamau3atZ6/YnrzzTf18ssva+zYsYqJidGMGTM0duxYzxzZ2dmqqanRrFmzVF1drfT0dK1evVphYWGBLBUAALQTAb9m5rrrrtPChQub7B8wYIDeeeedJvs7dOig6dOna/r06YEuDQAAtEN80CQAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaAEPM2fPntWLL76okSNHavDgwfrZz36mwsJCT/+ECROUkpLi9TV+/HhPv8vl0ty5c5WRkaFBgwbpmWee0enTpwNdJgAAaCdCAz3hz3/+c508eVKvvvqqunfvrvXr12vixIl6//33deONN2rfvn2aM2eObr/9ds8xYWFhnu/nzJmjwsJCvf7667Lb7Zo9e7ays7O1YcOGQJcKAADagYCGmcOHD+vzzz/Xxo0bdfPNN0uS/v3f/11/+tOf9OGHHyorK0unTp3SwIEDFRMT0+D48vJybd26VStWrNCQIUMkSa+++qpGjx6t//mf/9GgQYMCWS4AAGgHAhpmunbtqlWrVulHP/qRp81ms8lms+n8+fPat2+fbDabEhMTGz1+9+7dkqThw4d72hITExUXF6ddu3b5HWYsy1JVVZVfx16O0+n0+hfB4Xa75XA4WrsMvzidTlmW1dplNIuJ+9lmsxm7N1wulzF7w1Qm7mkTBWudLcuSzWZr1tiAhpnOnTvrxz/+sVfbRx99pMOHD+v555/X/v371alTJ82bN0+ff/65IiMjNXr0aD3xxBOy2+0qLy9X165dFR4e7jVHbGysysrK/K7L7XarqKjI7+OvpKSkJGhzQ3I4HIqOjm7tMvxy6NAh4+5ITdrPDodDqamprV2GX0pLS43bG6YyaU+bLNDr7Ha7G+SBpgT8mpnv++tf/6rnnntOo0aN0q233qrnn39eLpdLAwYM0IQJE1RUVKTFixertLRUixcvltPplN1ubzBPeHi4XC6X33XY7Xb179//an6VRjmdTpWUlCghIcHYs0MTuN3u1i7Bb4mJicacfZu4n5t71tYW9ezZs9H7OwSOiXtaMm9fu1wuHTlyRNdff31A19mX20fQwszHH3+sadOmafDgwcrNzZUkzZs3TzNnzlSXLl0kScnJyQoLC1NOTo5mzJihiIiIRh+4XC7XVS2QzWZTZGSk38dficPhCOr81zrTbtjfZ9IdaD32c8sIDw83cn+YyLQ9XVdnKSTEnPs9h8OhqKhOungxsJcE+HLfH5Qws2HDBr388ssaPXq0fvGLX3jSVWhoqCfI1Ovbt68kqaysTPHx8Tp79qzcbrdXIquoqFBcXFwwSgUAoE0JCbEpN3+3jpVfaO1SmqVXXCdNe+BmXbzYejUEPMxs3LhR8+fP1/jx4/XCCy94Javx48erV69eWrhwoadtz549CgsLU0JCgmJiYlRXV6fdu3crIyND0qVrDsrLy5Wenh7oUgEAaJOOlV/QN8fPtXYZxghomDl06JAWLFigO+64Q1OmTNG3337r6YuIiNCdd96pBQsWaMCAAbrlllu0Z88eLV68WBMnTlRUVJSioqL0T//0T5o1a5YWLFggh8Oh2bNna+jQoUpLSwtkqQAAoJ0IaJj56KOPdPHiRf3hD3/QH/7wB6++sWPHatGiRbLZbFq/fr0WLFigmJgYPfzww3r00Uc94+bPn68FCxboqaeekiSNHDlSs2bNCmSZAACgHQlomHnsscf02GOPXXbMAw88oAceeKDJ/sjISL300kt66aWXAlkaAABop/igSQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaISZqxQWFiabzdbaZQAAcM0Kbe0CTGaz2ZSa+kOFhnZo7VJ8UldnKSSEAAYAaB8IM1cpNLSDcvN361j5hdYupVl6xXXStAdubu0yAAAIGMJMABwrv6Bvjp9r7TIAALgmcc0MAAAwGmEGAAAYrU2Gmbq6Oi1btkwjRoxQWlqaJk+erKNHj7Z2WQAAoA1qk2Fm+fLl2rhxo+bPn69Nmzaprq5OkyZNktvtbu3SAABAG9Pmwozb7daaNWuUnZ2tW2+9Vf369dPSpUtVVlam7du3t3Z5AACgjWlzYaa4uFjfffedMjIyPG2dO3dWamqqdu3a1YqVAQCAtqjN/Wl2WVmZJKlHjx5e7bGxsZ4+X1RUVKi2tlaZmZkBqe/7LMtSSEiIzlW6VFNrBXz+YDjRwabbfhMuyzKj3no2m411DjLLslRbW6sOHToY9a7W7A00hT3dMur3dF1dXUDXuaysTB06NO9NadtcmHE6nZIku93u1R4eHq5z53x/L5fw8HC53e6gbOT6ObtEhQd87mAz6YZdj3UOLpvNppCQNvdkbbOwN9AY9nTLCvRah4aGNsgCTY4N6H85ACIiIiRdunam/ntJcrlccjgcPs9XWFgYsNoAAEDb0+Yia/3LSxUVFV7tFRUViouLa42SAABAG9bmwky/fv0UFRWlgoICT9v58+e1d+9epaent2JlAACgLWpzLzPZ7XZlZWUpNzdX3bp10/XXX68lS5YoPj5eo0aNau3yAABAG9PmwowkZWdnq6amRrNmzVJ1dbXS09O1evVqhYWFtXZpAACgjbFZ/H0gAAAwWJu7ZgYAAMAXhBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZi6jrq5Oy5Yt04gRI5SWlqbJkyfr6NGjTY4/c+aMnnnmGaWnp2vo0KGaO3eu51PA0TRf1/nAgQN69NFHNWzYMGVkZCg7O1ulpaUtWLGZfF3n79u2bZtSUlJ07NixIFdpPl/X+eLFi3rllVc847OyslRUVNSCFZvL17U+deqUnnnmGQ0fPlzDhg1TTk6OysvLW7Bi861cuVLjx4+/7JjWeCwkzFzG8uXLtXHjRs2fP1+bNm1SXV2dJk2aJLfb3ej47OxsHT58WOvWrdNrr72mTz/9VHPmzGnZog3kyzqfOXNGEyZMUEREhNavX6833nhDp0+f1qRJk+RyuVqhenP4up/rHT9+XPPmzWuhKs3n6zrPmTNHW7Zs0YIFC7R582Z169ZNkydP1oULF1q4cvP4utZTp05VaWmp1q5dq7Vr16q0tFRPPvlkC1dtrvz8fOXl5V1xXKs8FlpolMvlsgYNGmTl5+d72s6dO2cNGDDA+vDDDxuM/+tf/2olJydbBw8e9LT96U9/slJSUqyysrIWqdlEvq7zu+++aw0aNMhyOp2ettLSUis5Odn685//3CI1m8jXda5XW1tr/exnP7MefPBBKzk52Tp69GhLlGssX9f5yJEjVkpKivVf//VfXuP/8R//kf18Bb6u9blz56zk5GRrx44dnraPP/7YSk5Ots6cOdMSJRurrKzMmjJlipWWlmaNHj3aysrKanJsaz0W8sxME4qLi/Xdd98pIyPD09a5c2elpqZq165dDcYXFhYqJiZGSUlJnrahQ4fKZrNp9+7dLVKziXxd54yMDC1fvlwRERGetpCQS9v4/PnzwS/YUL6uc70VK1bo4sWLmjJlSkuUaTxf1/nzzz9Xp06dNHLkSK/xn3zyidccaMjXtY6IiFDHjh21detWVVZWqrKyUh988IESExPVuXPnlizdOF9//bXCwsK0bds2DRw48LJjW+uxsE1+0GRbUFZWJknq0aOHV3tsbKyn7/vKy8sbjLXb7YqOjtaJEyeCV6jhfF3nXr16qVevXl5tq1atUkREhNLT04NXqOF8XWdJ+uqrr7RmzRq99957XFfQTL6u86FDh/SDH/xA27dv16pVq1ReXq7U1FQ9++yzXg8GaMjXtbbb7Vq0aJFefPFFDRkyRDabTbGxsdqwYYPnhAiNy8zMVGZmZrPGttZjIf8Hm1B/sZLdbvdqDw8Pb/TaDKfT2WDs5cbjEl/X+e+tX79eGzZs0LRp09StW7eg1Nge+LrOVVVVmjZtmqZNm6aEhISWKLFd8HWdKysrdfjwYS1fvlw///nP9atf/UqhoaG6//77derUqRap2VS+rrVlWSoqKtKgQYOUn5+vt956Sz179tQTTzyhysrKFqn5WtBaj4WEmSbUv4zx9xeSuVwuORyORsc3dtGZy+VSZGRkcIpsB3xd53qWZSkvL08vvfSSHn/88SteXX+t83WdX3rpJSUmJupf//VfW6S+9sLXdQ4NDVVlZaWWLl2qW265RQMGDNDSpUslSe+//37wCzaYr2v9u9/9Ths2bNCSJUt08803a+jQoVqxYoWOHz+u9957r0Vqvha01mMhYaYJ9U+TVVRUeLVXVFQoLi6uwfj4+PgGY91ut86ePavY2NjgFWo4X9dZuvSnrNOnT9eKFSv03HPPaerUqcEu03i+rvPmzZv15z//WYMGDdKgQYM0efJkSdKYMWO0YsWK4BdsKH/uN0JDQ71eUoqIiNAPfvAD/gz+Cnxd68LCQiUmJioqKsrT1qVLFyUmJurw4cPBLfYa0lqPhYSZJvTr109RUVEqKCjwtJ0/f1579+5t9NqM9PR0lZWVed0ovvjiC0nSzTffHPyCDeXrOkvSjBkz9Pvf/16vvPKKHn744Raq1Gy+rvP27dv161//Wlu3btXWrVv10ksvSbp0fRLP1jTNn/uNmpoa7dmzx9NWXV2to0eP6oYbbmiRmk3l61rHx8fr8OHDXi91VFVV6dixY7yUGkCt9VjIBcBNsNvtysrKUm5urrp166brr79eS5YsUXx8vEaNGqXa2lqdPn1anTp1UkREhAYOHKjBgwcrJydHc+bMUVVVlV588UXdfffdTT7DAN/XecuWLfrtb3+rGTNmaOjQoTp58qRnrvoxaMjXdf77B9L6Cyp79uyp6OjoVvgNzODrOg8ZMkT/8A//oJkzZ2revHmKjo7WsmXL1KFDB911112t/eu0ab6u9d13363Vq1dr6tSp+rd/+zdJUl5ensLDw3XPPfe08m9jrjbzWBi0P/puB2pqaqzFixdbw4cPt9LS0qzJkyd73mfj6NGjVnJysrV582bP+G+//dZ6+umnrbS0NGvYsGHW7Nmzrerq6tYq3xi+rPOECROs5OTkRr++//8CDfm6n79v586dvM9MM/m6zhcuXLBmz55tDRs2zBo4cKA1YcIE68CBA61VvlF8XeuDBw9aU6ZMsYYOHWoNHz7ceuqpp9jTPpo5c6bX+8y0lcdCm2VZVvCiEgAAQHBxzQwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjPZ/h0ZbSJhvjvkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_post['same_3'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = cosine_similarity(sm_df[classes], sm_df_bert[classes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['cosine_sim'] = np.diagonal(cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_sent[compare_sent.same_3>=0.5]['cosine_sim'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = cosine_similarity(score_final[classes], score_final_bert[classes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['cosine_sim'] = np.diagonal(cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>51885929339</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.280558</td>\n",
       "      <td>0.813833</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.531683</td>\n",
       "      <td>0.877878</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.805216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>51884357517</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.441306</td>\n",
       "      <td>0.779117</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.509236</td>\n",
       "      <td>0.742402</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.670258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>51883860957</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.228607</td>\n",
       "      <td>0.560199</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.289079</td>\n",
       "      <td>0.687324</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.858258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>51884423594</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.401489</td>\n",
       "      <td>0.787524</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.470036</td>\n",
       "      <td>0.846035</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.705522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>51882154188</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.304299</td>\n",
       "      <td>0.730016</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.440501</td>\n",
       "      <td>0.829711</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.776164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3722</th>\n",
       "      <td>4993</td>\n",
       "      <td>51361633644</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>0.752462</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.461521</td>\n",
       "      <td>0.763086</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.739271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>4995</td>\n",
       "      <td>51357996868</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.292644</td>\n",
       "      <td>0.663798</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.729162</td>\n",
       "      <td>0.905567</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.492865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3724</th>\n",
       "      <td>4996</td>\n",
       "      <td>51356750697</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.271104</td>\n",
       "      <td>0.565897</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.349461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>4997</td>\n",
       "      <td>51358508460</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.271104</td>\n",
       "      <td>0.565897</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.349461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726</th>\n",
       "      <td>4999</td>\n",
       "      <td>51368197299</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.328312</td>\n",
       "      <td>0.726001</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.437944</td>\n",
       "      <td>0.795807</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.883181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3727 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0          2  51885929339      Criteria iv     Criteria iii      Criteria ii   \n",
       "1          3  51884357517     Criteria iii      Criteria iv      Criteria ii   \n",
       "2          8  51883860957      Criteria iv      Criteria vi      Criteria ii   \n",
       "3          9  51884423594      Criteria ii      Criteria iv     Criteria iii   \n",
       "4         13  51882154188     Criteria iii      Criteria iv      Criteria vi   \n",
       "...      ...          ...              ...              ...              ...   \n",
       "3722    4993  51361633644     Criteria iii      Criteria iv      Criteria vi   \n",
       "3723    4995  51357996868     Criteria iii      Criteria iv      Criteria vi   \n",
       "3724    4996  51356750697     Criteria iii      Criteria vi      Criteria iv   \n",
       "3725    4997  51358508460     Criteria iii      Criteria vi      Criteria iv   \n",
       "3726    4999  51368197299      Criteria vi     Criteria iii      Criteria iv   \n",
       "\n",
       "      max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  \\\n",
       "0         0.280558      0.813833    Criteria iv    Criteria ii    Criteria vi   \n",
       "1         0.441306      0.779117    Criteria iv   Criteria iii    Criteria vi   \n",
       "2         0.228607      0.560199     Criteria i    Criteria iv   Criteria iii   \n",
       "3         0.401489      0.787524    Criteria iv    Criteria vi    Criteria ii   \n",
       "4         0.304299      0.730016    Criteria iv    Criteria vi    Criteria ii   \n",
       "...            ...           ...            ...            ...            ...   \n",
       "3722      0.365005      0.752462    Criteria vi   Criteria iii    Criteria iv   \n",
       "3723      0.292644      0.663798    Criteria ii     Criteria i    Criteria iv   \n",
       "3724      0.835118      0.946161     Criteria i     Criteria x   Criteria iii   \n",
       "3725      0.835118      0.946161     Criteria i     Criteria x   Criteria iii   \n",
       "3726      0.328312      0.726001    Criteria vi    Criteria ii    Criteria iv   \n",
       "\n",
       "      max_1_bert  max_3_bert  same_1  same_3  cosine_sim  \n",
       "0       0.531683    0.877878    True     0.5    0.805216  \n",
       "1       0.509236    0.742402   False     0.5    0.670258  \n",
       "2       0.289079    0.687324   False     0.2    0.858258  \n",
       "3       0.470036    0.846035   False     0.5    0.705522  \n",
       "4       0.440501    0.829711   False     0.5    0.776164  \n",
       "...          ...         ...     ...     ...         ...  \n",
       "3722    0.461521    0.763086   False     1.0    0.739271  \n",
       "3723    0.729162    0.905567   False     0.2    0.492865  \n",
       "3724    0.271104    0.565897   False     0.2    0.349461  \n",
       "3725    0.271104    0.565897   False     0.2    0.349461  \n",
       "3726    0.437944    0.795807    True     0.5    0.883181  \n",
       "\n",
       "[3727 rows x 15 columns]"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_post[compare_post.same_3>=0.5]['cosine_sim'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent.to_csv('../../../Venice_Flickr/Amsterdam/data_storage/sentences_compare.csv',sep='\\t', encoding='utf-8-sig')\n",
    "compare_post.to_csv('../../../Venice_Flickr/Amsterdam/data_storage/metadata_compare.csv',sep='\\t', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suzhou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = pd.read_csv('../../../Venice_Flickr/Suzhou/data_storage/sentences.csv',sep='\\t', index_col = 'Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_all_df = pd.read_csv('../../../Venice_Flickr/Suzhou/data_storage/metadata.csv',sep='\\t', index_col = 'Unnamed: 0').reset_index().rename(columns={'level_0':'origin'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_ouv(line, classifier=classifier, vectorizer=vectorizer, classes=classes, k=11):\n",
    "    predictions = predict_rating(line['processed'], classifier, vectorizer, classes, k)\n",
    "    for key, value in predictions:\n",
    "        line[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/984 finished\n",
      "200/984 finished\n",
      "300/984 finished\n",
      "400/984 finished\n",
      "500/984 finished\n",
      "600/984 finished\n",
      "700/984 finished\n",
      "800/984 finished\n",
      "900/984 finished\n"
     ]
    }
   ],
   "source": [
    "new_dict = {}\n",
    "for i in range(len(sm_df)):\n",
    "    new_dict[i]={}\n",
    "    line = sm_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['sentence'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        new_dict[i][key] = value\n",
    "    if len(new_dict)%100==0:\n",
    "        print('{}/{} finished'.format(len(new_dict),len(sm_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/3137 finished\n",
      "200/3137 finished\n",
      "300/3137 finished\n",
      "400/3137 finished\n",
      "500/3137 finished\n",
      "600/3137 finished\n",
      "700/3137 finished\n",
      "800/3137 finished\n",
      "900/3137 finished\n",
      "1000/3137 finished\n",
      "1100/3137 finished\n",
      "1200/3137 finished\n",
      "1300/3137 finished\n",
      "1400/3137 finished\n",
      "1500/3137 finished\n",
      "1600/3137 finished\n",
      "1700/3137 finished\n",
      "1800/3137 finished\n",
      "1900/3137 finished\n",
      "2000/3137 finished\n",
      "2100/3137 finished\n",
      "2200/3137 finished\n",
      "2300/3137 finished\n",
      "2400/3137 finished\n",
      "2500/3137 finished\n",
      "2600/3137 finished\n",
      "2700/3137 finished\n",
      "2800/3137 finished\n",
      "2900/3137 finished\n",
      "3000/3137 finished\n",
      "3100/3137 finished\n"
     ]
    }
   ],
   "source": [
    "des_dict = {}\n",
    "for i in range(len(sm_all_df)):\n",
    "    des_dict[i]={}\n",
    "    line = sm_all_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['revised_text'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        des_dict[i][key] = value\n",
    "    if len(des_dict)%100==0:\n",
    "        print('{}/{} finished'.format(len(des_dict),len(sm_all_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.104594</td>\n",
       "      <td>0.058130</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>0.128036</td>\n",
       "      <td>0.016812</td>\n",
       "      <td>0.279067</td>\n",
       "      <td>0.043005</td>\n",
       "      <td>0.038831</td>\n",
       "      <td>0.016578</td>\n",
       "      <td>0.033540</td>\n",
       "      <td>0.001587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.056032</td>\n",
       "      <td>0.070969</td>\n",
       "      <td>0.168670</td>\n",
       "      <td>0.097677</td>\n",
       "      <td>0.016396</td>\n",
       "      <td>0.331788</td>\n",
       "      <td>0.055129</td>\n",
       "      <td>0.093043</td>\n",
       "      <td>0.033783</td>\n",
       "      <td>0.073110</td>\n",
       "      <td>0.003402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021472</td>\n",
       "      <td>0.106980</td>\n",
       "      <td>0.381616</td>\n",
       "      <td>0.188678</td>\n",
       "      <td>0.094129</td>\n",
       "      <td>0.179596</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>0.004498</td>\n",
       "      <td>0.007748</td>\n",
       "      <td>0.008071</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.126042</td>\n",
       "      <td>0.060049</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.140538</td>\n",
       "      <td>0.012537</td>\n",
       "      <td>0.186152</td>\n",
       "      <td>0.063421</td>\n",
       "      <td>0.059563</td>\n",
       "      <td>0.022375</td>\n",
       "      <td>0.035624</td>\n",
       "      <td>0.000663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.098538</td>\n",
       "      <td>0.030404</td>\n",
       "      <td>0.165419</td>\n",
       "      <td>0.079331</td>\n",
       "      <td>0.011728</td>\n",
       "      <td>0.133529</td>\n",
       "      <td>0.160967</td>\n",
       "      <td>0.207680</td>\n",
       "      <td>0.049797</td>\n",
       "      <td>0.060257</td>\n",
       "      <td>0.002351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.048024</td>\n",
       "      <td>0.136803</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.143459</td>\n",
       "      <td>0.028587</td>\n",
       "      <td>0.143825</td>\n",
       "      <td>0.013213</td>\n",
       "      <td>0.007045</td>\n",
       "      <td>0.011292</td>\n",
       "      <td>0.013941</td>\n",
       "      <td>0.000071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>0.048024</td>\n",
       "      <td>0.136803</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.143459</td>\n",
       "      <td>0.028587</td>\n",
       "      <td>0.143825</td>\n",
       "      <td>0.013213</td>\n",
       "      <td>0.007045</td>\n",
       "      <td>0.011292</td>\n",
       "      <td>0.013941</td>\n",
       "      <td>0.000071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>0.579131</td>\n",
       "      <td>0.127127</td>\n",
       "      <td>0.084413</td>\n",
       "      <td>0.162542</td>\n",
       "      <td>0.003232</td>\n",
       "      <td>0.042198</td>\n",
       "      <td>0.000389</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0.088320</td>\n",
       "      <td>0.053277</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.125195</td>\n",
       "      <td>0.022723</td>\n",
       "      <td>0.069450</td>\n",
       "      <td>0.126014</td>\n",
       "      <td>0.070678</td>\n",
       "      <td>0.050656</td>\n",
       "      <td>0.059995</td>\n",
       "      <td>0.000186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>0.088320</td>\n",
       "      <td>0.053277</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.125195</td>\n",
       "      <td>0.022723</td>\n",
       "      <td>0.069450</td>\n",
       "      <td>0.126014</td>\n",
       "      <td>0.070678</td>\n",
       "      <td>0.050656</td>\n",
       "      <td>0.059995</td>\n",
       "      <td>0.000186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0      0.104594     0.058130      0.279820     0.128036    0.016812   \n",
       "1      0.056032     0.070969      0.168670     0.097677    0.016396   \n",
       "2      0.021472     0.106980      0.381616     0.188678    0.094129   \n",
       "3      0.126042     0.060049      0.293035     0.140538    0.012537   \n",
       "4      0.098538     0.030404      0.165419     0.079331    0.011728   \n",
       "..          ...          ...           ...          ...         ...   \n",
       "979    0.048024     0.136803      0.453741     0.143459    0.028587   \n",
       "980    0.048024     0.136803      0.453741     0.143459    0.028587   \n",
       "981    0.579131     0.127127      0.084413     0.162542    0.003232   \n",
       "982    0.088320     0.053277      0.333507     0.125195    0.022723   \n",
       "983    0.088320     0.053277      0.333507     0.125195    0.022723   \n",
       "\n",
       "     Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0       0.279067      0.043005       0.038831     0.016578    0.033540   \n",
       "1       0.331788      0.055129       0.093043     0.033783    0.073110   \n",
       "2       0.179596      0.007077       0.004498     0.007748    0.008071   \n",
       "3       0.186152      0.063421       0.059563     0.022375    0.035624   \n",
       "4       0.133529      0.160967       0.207680     0.049797    0.060257   \n",
       "..           ...           ...            ...          ...         ...   \n",
       "979     0.143825      0.013213       0.007045     0.011292    0.013941   \n",
       "980     0.143825      0.013213       0.007045     0.011292    0.013941   \n",
       "981     0.042198      0.000389       0.000444     0.000251    0.000213   \n",
       "982     0.069450      0.126014       0.070678     0.050656    0.059995   \n",
       "983     0.069450      0.126014       0.070678     0.050656    0.059995   \n",
       "\n",
       "       Others  \n",
       "0    0.001587  \n",
       "1    0.003402  \n",
       "2    0.000134  \n",
       "3    0.000663  \n",
       "4    0.002351  \n",
       "..        ...  \n",
       "979  0.000071  \n",
       "980  0.000071  \n",
       "981  0.000058  \n",
       "982  0.000186  \n",
       "983  0.000186  \n",
       "\n",
       "[984 rows x 11 columns]"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df = pd.DataFrame(new_dict).reindex(classes).T\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3133</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3134</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>0.209445</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>0.167791</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.237707</td>\n",
       "      <td>0.008434</td>\n",
       "      <td>0.008787</td>\n",
       "      <td>0.002848</td>\n",
       "      <td>0.004234</td>\n",
       "      <td>0.000163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3137 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "1       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "2       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "3       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "4       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "3132    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3133    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3134    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3135    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3136    0.209445     0.090909      0.263014     0.167791    0.006667   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "1        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "2        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "3        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "4        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "3132     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3133     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3134     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3135     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3136     0.237707      0.008434       0.008787     0.002848    0.004234   \n",
       "\n",
       "        Others  \n",
       "0     0.008244  \n",
       "1     0.008244  \n",
       "2     0.008244  \n",
       "3     0.008244  \n",
       "4     0.008244  \n",
       "...        ...  \n",
       "3132  0.000030  \n",
       "3133  0.000030  \n",
       "3134  0.000030  \n",
       "3135  0.000030  \n",
       "3136  0.000163  \n",
       "\n",
       "[3137 rows x 11 columns]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_all_df = pd.DataFrame(des_dict).reindex(classes).T\n",
    "score_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = score_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = score_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = score_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_df = pd.concat([score_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.104594</td>\n",
       "      <td>0.058130</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>0.128036</td>\n",
       "      <td>0.016812</td>\n",
       "      <td>0.279067</td>\n",
       "      <td>0.043005</td>\n",
       "      <td>0.038831</td>\n",
       "      <td>0.016578</td>\n",
       "      <td>0.033540</td>\n",
       "      <td>0.001587</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.279067</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.128036</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>0.686922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.056032</td>\n",
       "      <td>0.070969</td>\n",
       "      <td>0.168670</td>\n",
       "      <td>0.097677</td>\n",
       "      <td>0.016396</td>\n",
       "      <td>0.331788</td>\n",
       "      <td>0.055129</td>\n",
       "      <td>0.093043</td>\n",
       "      <td>0.033783</td>\n",
       "      <td>0.073110</td>\n",
       "      <td>0.003402</td>\n",
       "      <td>0.331788</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.168670</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.097677</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.331788</td>\n",
       "      <td>0.598135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021472</td>\n",
       "      <td>0.106980</td>\n",
       "      <td>0.381616</td>\n",
       "      <td>0.188678</td>\n",
       "      <td>0.094129</td>\n",
       "      <td>0.179596</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>0.004498</td>\n",
       "      <td>0.007748</td>\n",
       "      <td>0.008071</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.381616</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.188678</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.179596</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.381616</td>\n",
       "      <td>0.749891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.126042</td>\n",
       "      <td>0.060049</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.140538</td>\n",
       "      <td>0.012537</td>\n",
       "      <td>0.186152</td>\n",
       "      <td>0.063421</td>\n",
       "      <td>0.059563</td>\n",
       "      <td>0.022375</td>\n",
       "      <td>0.035624</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.186152</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.140538</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.293035</td>\n",
       "      <td>0.619725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.098538</td>\n",
       "      <td>0.030404</td>\n",
       "      <td>0.165419</td>\n",
       "      <td>0.079331</td>\n",
       "      <td>0.011728</td>\n",
       "      <td>0.133529</td>\n",
       "      <td>0.160967</td>\n",
       "      <td>0.207680</td>\n",
       "      <td>0.049797</td>\n",
       "      <td>0.060257</td>\n",
       "      <td>0.002351</td>\n",
       "      <td>0.207680</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.165419</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.160967</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.207680</td>\n",
       "      <td>0.534065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.048024</td>\n",
       "      <td>0.136803</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.143459</td>\n",
       "      <td>0.028587</td>\n",
       "      <td>0.143825</td>\n",
       "      <td>0.013213</td>\n",
       "      <td>0.007045</td>\n",
       "      <td>0.011292</td>\n",
       "      <td>0.013941</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.143825</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.143459</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.741025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>0.048024</td>\n",
       "      <td>0.136803</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.143459</td>\n",
       "      <td>0.028587</td>\n",
       "      <td>0.143825</td>\n",
       "      <td>0.013213</td>\n",
       "      <td>0.007045</td>\n",
       "      <td>0.011292</td>\n",
       "      <td>0.013941</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.143825</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.143459</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.741025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>0.579131</td>\n",
       "      <td>0.127127</td>\n",
       "      <td>0.084413</td>\n",
       "      <td>0.162542</td>\n",
       "      <td>0.003232</td>\n",
       "      <td>0.042198</td>\n",
       "      <td>0.000389</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.579131</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.162542</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.127127</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.579131</td>\n",
       "      <td>0.868801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0.088320</td>\n",
       "      <td>0.053277</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.125195</td>\n",
       "      <td>0.022723</td>\n",
       "      <td>0.069450</td>\n",
       "      <td>0.126014</td>\n",
       "      <td>0.070678</td>\n",
       "      <td>0.050656</td>\n",
       "      <td>0.059995</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.126014</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.125195</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.584716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>0.088320</td>\n",
       "      <td>0.053277</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.125195</td>\n",
       "      <td>0.022723</td>\n",
       "      <td>0.069450</td>\n",
       "      <td>0.126014</td>\n",
       "      <td>0.070678</td>\n",
       "      <td>0.050656</td>\n",
       "      <td>0.059995</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.126014</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.125195</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.584716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0      0.104594     0.058130      0.279820     0.128036    0.016812   \n",
       "1      0.056032     0.070969      0.168670     0.097677    0.016396   \n",
       "2      0.021472     0.106980      0.381616     0.188678    0.094129   \n",
       "3      0.126042     0.060049      0.293035     0.140538    0.012537   \n",
       "4      0.098538     0.030404      0.165419     0.079331    0.011728   \n",
       "..          ...          ...           ...          ...         ...   \n",
       "979    0.048024     0.136803      0.453741     0.143459    0.028587   \n",
       "980    0.048024     0.136803      0.453741     0.143459    0.028587   \n",
       "981    0.579131     0.127127      0.084413     0.162542    0.003232   \n",
       "982    0.088320     0.053277      0.333507     0.125195    0.022723   \n",
       "983    0.088320     0.053277      0.333507     0.125195    0.022723   \n",
       "\n",
       "     Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0       0.279067      0.043005       0.038831     0.016578    0.033540   \n",
       "1       0.331788      0.055129       0.093043     0.033783    0.073110   \n",
       "2       0.179596      0.007077       0.004498     0.007748    0.008071   \n",
       "3       0.186152      0.063421       0.059563     0.022375    0.035624   \n",
       "4       0.133529      0.160967       0.207680     0.049797    0.060257   \n",
       "..           ...           ...            ...          ...         ...   \n",
       "979     0.143825      0.013213       0.007045     0.011292    0.013941   \n",
       "980     0.143825      0.013213       0.007045     0.011292    0.013941   \n",
       "981     0.042198      0.000389       0.000444     0.000251    0.000213   \n",
       "982     0.069450      0.126014       0.070678     0.050656    0.059995   \n",
       "983     0.069450      0.126014       0.070678     0.050656    0.059995   \n",
       "\n",
       "       Others  max_1_val      max_1_col  max_2_val     max_2_col  max_3_val  \\\n",
       "0    0.001587   0.279820   Criteria iii   0.279067   Criteria vi   0.128036   \n",
       "1    0.003402   0.331788    Criteria vi   0.168670  Criteria iii   0.097677   \n",
       "2    0.000134   0.381616   Criteria iii   0.188678   Criteria iv   0.179596   \n",
       "3    0.000663   0.293035   Criteria iii   0.186152   Criteria vi   0.140538   \n",
       "4    0.002351   0.207680  Criteria viii   0.165419  Criteria iii   0.160967   \n",
       "..        ...        ...            ...        ...           ...        ...   \n",
       "979  0.000071   0.453741   Criteria iii   0.143825   Criteria vi   0.143459   \n",
       "980  0.000071   0.453741   Criteria iii   0.143825   Criteria vi   0.143459   \n",
       "981  0.000058   0.579131     Criteria i   0.162542   Criteria iv   0.127127   \n",
       "982  0.000186   0.333507   Criteria iii   0.126014  Criteria vii   0.125195   \n",
       "983  0.000186   0.333507   Criteria iii   0.126014  Criteria vii   0.125195   \n",
       "\n",
       "        max_3_col     max_1     max_3  \n",
       "0     Criteria iv  0.279820  0.686922  \n",
       "1     Criteria iv  0.331788  0.598135  \n",
       "2     Criteria vi  0.381616  0.749891  \n",
       "3     Criteria iv  0.293035  0.619725  \n",
       "4    Criteria vii  0.207680  0.534065  \n",
       "..            ...       ...       ...  \n",
       "979   Criteria iv  0.453741  0.741025  \n",
       "980   Criteria iv  0.453741  0.741025  \n",
       "981   Criteria ii  0.579131  0.868801  \n",
       "982   Criteria iv  0.333507  0.584716  \n",
       "983   Criteria iv  0.333507  0.584716  \n",
       "\n",
       "[984 rows x 19 columns]"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df['max_1'] = score_df['max_1_val']\n",
    "score_df['max_3'] = score_df['max_1_val']+score_df['max_2_val']+score_df['max_3_val']\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = sm_df.merge(score_df, how='left', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = score_all_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = score_all_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = score_all_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_all_df = pd.concat([score_all_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3133</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3134</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>0.024309</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.008615</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>0.209445</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>0.167791</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.237707</td>\n",
       "      <td>0.008434</td>\n",
       "      <td>0.008787</td>\n",
       "      <td>0.002848</td>\n",
       "      <td>0.004234</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.237707</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.209445</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>0.710166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3137 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "1       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "2       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "3       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "4       0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "...          ...          ...           ...          ...         ...   \n",
       "3132    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3133    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3134    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3135    0.024309     0.016332      0.835118     0.026072    0.008615   \n",
       "3136    0.209445     0.090909      0.263014     0.167791    0.006667   \n",
       "\n",
       "      Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "1        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "2        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "3        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "4        0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "...           ...           ...            ...          ...         ...   \n",
       "3132     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3133     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3134     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3135     0.084970      0.002452       0.001664     0.000337    0.000101   \n",
       "3136     0.237707      0.008434       0.008787     0.002848    0.004234   \n",
       "\n",
       "        Others  max_1_val     max_1_col  max_2_val      max_2_col  max_3_val  \\\n",
       "0     0.008244   0.314417    Criteria x   0.293537  Criteria viii   0.191097   \n",
       "1     0.008244   0.314417    Criteria x   0.293537  Criteria viii   0.191097   \n",
       "2     0.008244   0.314417    Criteria x   0.293537  Criteria viii   0.191097   \n",
       "3     0.008244   0.314417    Criteria x   0.293537  Criteria viii   0.191097   \n",
       "4     0.008244   0.314417    Criteria x   0.293537  Criteria viii   0.191097   \n",
       "...        ...        ...           ...        ...            ...        ...   \n",
       "3132  0.000030   0.835118  Criteria iii   0.084970    Criteria vi   0.026072   \n",
       "3133  0.000030   0.835118  Criteria iii   0.084970    Criteria vi   0.026072   \n",
       "3134  0.000030   0.835118  Criteria iii   0.084970    Criteria vi   0.026072   \n",
       "3135  0.000030   0.835118  Criteria iii   0.084970    Criteria vi   0.026072   \n",
       "3136  0.000163   0.263014  Criteria iii   0.237707    Criteria vi   0.209445   \n",
       "\n",
       "         max_3_col     max_1     max_3  \n",
       "0     Criteria vii  0.314417  0.799051  \n",
       "1     Criteria vii  0.314417  0.799051  \n",
       "2     Criteria vii  0.314417  0.799051  \n",
       "3     Criteria vii  0.314417  0.799051  \n",
       "4     Criteria vii  0.314417  0.799051  \n",
       "...            ...       ...       ...  \n",
       "3132   Criteria iv  0.835118  0.946161  \n",
       "3133   Criteria iv  0.835118  0.946161  \n",
       "3134   Criteria iv  0.835118  0.946161  \n",
       "3135   Criteria iv  0.835118  0.946161  \n",
       "3136    Criteria i  0.263014  0.710166  \n",
       "\n",
       "[3137 rows x 19 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_all_df['max_1'] = score_all_df['max_1_val']\n",
    "score_all_df['max_3'] = score_all_df['max_1_val']+score_all_df['max_2_val']+score_all_df['max_3_val']\n",
    "score_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_all_df = sm_all_df.merge(score_all_df, how='left', left_index=True, right_index=True)\n",
    "sm_all_df['valid'] = sm_all_df['revised_text'].apply(lambda x: str(x)!='nan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>candownload</th>\n",
       "      <th>url_c</th>\n",
       "      <th>url_q</th>\n",
       "      <th>url_n</th>\n",
       "      <th>url_largest</th>\n",
       "      <th>owner</th>\n",
       "      <th>owner_loc</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>51859675789</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859675789_2abb735954_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859675789_2abb735954_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859675789_2abb735954_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859675789_0dc586ec53_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>51859339946</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339946_9ca193472a_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339946_9ca193472a_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339946_9ca193472a_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339946_cf01cf0229_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>51858385807</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385807_aeeb29b536_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385807_aeeb29b536_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385807_aeeb29b536_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385807_bf38fd6ae9_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>51859339546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339546_74e167d9a0_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339546_74e167d9a0_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339546_74e167d9a0_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339546_10dd36c744_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>51858385377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385377_9aef1c02a6_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385377_9aef1c02a6_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385377_9aef1c02a6_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385377_a46f863401_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>4225</td>\n",
       "      <td>38397950426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/4582/38397950426_a4b2f17df4_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4582/38397950426_a4b2f17df4_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4582/38397950426_a4b2f17df4_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4582/38397950426_56393c39ef_o.jpg</td>\n",
       "      <td>28571579@N03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20171112_IMG_0515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3133</th>\n",
       "      <td>4226</td>\n",
       "      <td>38397948496</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/4517/38397948496_9ebeca6454_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4517/38397948496_9ebeca6454_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4517/38397948496_9ebeca6454_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4517/38397948496_e059e1dfc9_o.jpg</td>\n",
       "      <td>28571579@N03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20171112_IMG_0518</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3134</th>\n",
       "      <td>4227</td>\n",
       "      <td>38454117381</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/4552/38454117381_40a5e9e096_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4552/38454117381_40a5e9e096_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4552/38454117381_40a5e9e096_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4552/38454117381_11eb0b5276_o.jpg</td>\n",
       "      <td>28571579@N03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20171112_IMG_0531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>4228</td>\n",
       "      <td>38397940256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/4576/38397940256_1e4eb547c5_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4576/38397940256_1e4eb547c5_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4576/38397940256_1e4eb547c5_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4576/38397940256_d2435cc169_o.jpg</td>\n",
       "      <td>28571579@N03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20171112_IMG_0539</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.084970</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>4229</td>\n",
       "      <td>50150116276</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/50150116276_7f89bf7577_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/50150116276_7f89bf7577_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/50150116276_7f89bf7577_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/50150116276_d448db2b5d_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>吴中博物馆</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.237707</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.209445</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>0.710166</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3137 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index  candownload  \\\n",
       "0          8  51859675789          1.0   \n",
       "1          9  51859339946          1.0   \n",
       "2         10  51858385807          1.0   \n",
       "3         11  51859339546          1.0   \n",
       "4         12  51858385377          1.0   \n",
       "...      ...          ...          ...   \n",
       "3132    4225  38397950426          1.0   \n",
       "3133    4226  38397948496          1.0   \n",
       "3134    4227  38454117381          1.0   \n",
       "3135    4228  38397940256          1.0   \n",
       "3136    4229  50150116276          1.0   \n",
       "\n",
       "                                                                 url_c  \\\n",
       "0     https://live.staticflickr.com/65535/51859675789_2abb735954_c.jpg   \n",
       "1     https://live.staticflickr.com/65535/51859339946_9ca193472a_c.jpg   \n",
       "2     https://live.staticflickr.com/65535/51858385807_aeeb29b536_c.jpg   \n",
       "3     https://live.staticflickr.com/65535/51859339546_74e167d9a0_c.jpg   \n",
       "4     https://live.staticflickr.com/65535/51858385377_9aef1c02a6_c.jpg   \n",
       "...                                                                ...   \n",
       "3132   https://live.staticflickr.com/4582/38397950426_a4b2f17df4_c.jpg   \n",
       "3133   https://live.staticflickr.com/4517/38397948496_9ebeca6454_c.jpg   \n",
       "3134   https://live.staticflickr.com/4552/38454117381_40a5e9e096_c.jpg   \n",
       "3135   https://live.staticflickr.com/4576/38397940256_1e4eb547c5_c.jpg   \n",
       "3136  https://live.staticflickr.com/65535/50150116276_7f89bf7577_c.jpg   \n",
       "\n",
       "                                                                 url_q  \\\n",
       "0     https://live.staticflickr.com/65535/51859675789_2abb735954_q.jpg   \n",
       "1     https://live.staticflickr.com/65535/51859339946_9ca193472a_q.jpg   \n",
       "2     https://live.staticflickr.com/65535/51858385807_aeeb29b536_q.jpg   \n",
       "3     https://live.staticflickr.com/65535/51859339546_74e167d9a0_q.jpg   \n",
       "4     https://live.staticflickr.com/65535/51858385377_9aef1c02a6_q.jpg   \n",
       "...                                                                ...   \n",
       "3132   https://live.staticflickr.com/4582/38397950426_a4b2f17df4_q.jpg   \n",
       "3133   https://live.staticflickr.com/4517/38397948496_9ebeca6454_q.jpg   \n",
       "3134   https://live.staticflickr.com/4552/38454117381_40a5e9e096_q.jpg   \n",
       "3135   https://live.staticflickr.com/4576/38397940256_1e4eb547c5_q.jpg   \n",
       "3136  https://live.staticflickr.com/65535/50150116276_7f89bf7577_q.jpg   \n",
       "\n",
       "                                                                 url_n  \\\n",
       "0     https://live.staticflickr.com/65535/51859675789_2abb735954_n.jpg   \n",
       "1     https://live.staticflickr.com/65535/51859339946_9ca193472a_n.jpg   \n",
       "2     https://live.staticflickr.com/65535/51858385807_aeeb29b536_n.jpg   \n",
       "3     https://live.staticflickr.com/65535/51859339546_74e167d9a0_n.jpg   \n",
       "4     https://live.staticflickr.com/65535/51858385377_9aef1c02a6_n.jpg   \n",
       "...                                                                ...   \n",
       "3132   https://live.staticflickr.com/4582/38397950426_a4b2f17df4_n.jpg   \n",
       "3133   https://live.staticflickr.com/4517/38397948496_9ebeca6454_n.jpg   \n",
       "3134   https://live.staticflickr.com/4552/38454117381_40a5e9e096_n.jpg   \n",
       "3135   https://live.staticflickr.com/4576/38397940256_1e4eb547c5_n.jpg   \n",
       "3136  https://live.staticflickr.com/65535/50150116276_7f89bf7577_n.jpg   \n",
       "\n",
       "                                                           url_largest  \\\n",
       "0     https://live.staticflickr.com/65535/51859675789_0dc586ec53_o.jpg   \n",
       "1     https://live.staticflickr.com/65535/51859339946_cf01cf0229_o.jpg   \n",
       "2     https://live.staticflickr.com/65535/51858385807_bf38fd6ae9_o.jpg   \n",
       "3     https://live.staticflickr.com/65535/51859339546_10dd36c744_o.jpg   \n",
       "4     https://live.staticflickr.com/65535/51858385377_a46f863401_o.jpg   \n",
       "...                                                                ...   \n",
       "3132   https://live.staticflickr.com/4582/38397950426_56393c39ef_o.jpg   \n",
       "3133   https://live.staticflickr.com/4517/38397948496_e059e1dfc9_o.jpg   \n",
       "3134   https://live.staticflickr.com/4552/38454117381_11eb0b5276_o.jpg   \n",
       "3135   https://live.staticflickr.com/4576/38397940256_d2435cc169_o.jpg   \n",
       "3136  https://live.staticflickr.com/65535/50150116276_d448db2b5d_o.jpg   \n",
       "\n",
       "             owner       owner_loc              title  ...    Others  \\\n",
       "0     57210421@N04  Beijing, China                灭渡桥  ...  0.008244   \n",
       "1     57210421@N04  Beijing, China                灭渡桥  ...  0.008244   \n",
       "2     57210421@N04  Beijing, China                灭渡桥  ...  0.008244   \n",
       "3     57210421@N04  Beijing, China                灭渡桥  ...  0.008244   \n",
       "4     57210421@N04  Beijing, China                灭渡桥  ...  0.008244   \n",
       "...            ...             ...                ...  ...       ...   \n",
       "3132  28571579@N03             NaN  20171112_IMG_0515  ...  0.000030   \n",
       "3133  28571579@N03             NaN  20171112_IMG_0518  ...  0.000030   \n",
       "3134  28571579@N03             NaN  20171112_IMG_0531  ...  0.000030   \n",
       "3135  28571579@N03             NaN  20171112_IMG_0539  ...  0.000030   \n",
       "3136  57210421@N04  Beijing, China              吴中博物馆  ...  0.000163   \n",
       "\n",
       "      max_1_val     max_1_col  max_2_val      max_2_col max_3_val  \\\n",
       "0      0.314417    Criteria x   0.293537  Criteria viii  0.191097   \n",
       "1      0.314417    Criteria x   0.293537  Criteria viii  0.191097   \n",
       "2      0.314417    Criteria x   0.293537  Criteria viii  0.191097   \n",
       "3      0.314417    Criteria x   0.293537  Criteria viii  0.191097   \n",
       "4      0.314417    Criteria x   0.293537  Criteria viii  0.191097   \n",
       "...         ...           ...        ...            ...       ...   \n",
       "3132   0.835118  Criteria iii   0.084970    Criteria vi  0.026072   \n",
       "3133   0.835118  Criteria iii   0.084970    Criteria vi  0.026072   \n",
       "3134   0.835118  Criteria iii   0.084970    Criteria vi  0.026072   \n",
       "3135   0.835118  Criteria iii   0.084970    Criteria vi  0.026072   \n",
       "3136   0.263014  Criteria iii   0.237707    Criteria vi  0.209445   \n",
       "\n",
       "         max_3_col     max_1     max_3  valid  \n",
       "0     Criteria vii  0.314417  0.799051   True  \n",
       "1     Criteria vii  0.314417  0.799051   True  \n",
       "2     Criteria vii  0.314417  0.799051   True  \n",
       "3     Criteria vii  0.314417  0.799051   True  \n",
       "4     Criteria vii  0.314417  0.799051   True  \n",
       "...            ...       ...       ...    ...  \n",
       "3132   Criteria iv  0.835118  0.946161  False  \n",
       "3133   Criteria iv  0.835118  0.946161  False  \n",
       "3134   Criteria iv  0.835118  0.946161  False  \n",
       "3135   Criteria iv  0.835118  0.946161  False  \n",
       "3136    Criteria i  0.263014  0.710166   True  \n",
       "\n",
       "[3137 rows x 55 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.008244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>0.064325</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.138205</td>\n",
       "      <td>0.009879</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>0.053668</td>\n",
       "      <td>0.049119</td>\n",
       "      <td>0.014844</td>\n",
       "      <td>0.026549</td>\n",
       "      <td>0.000817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>0.064325</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.138205</td>\n",
       "      <td>0.009879</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>0.053668</td>\n",
       "      <td>0.049119</td>\n",
       "      <td>0.014844</td>\n",
       "      <td>0.026549</td>\n",
       "      <td>0.000817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>0.094989</td>\n",
       "      <td>0.110768</td>\n",
       "      <td>0.220466</td>\n",
       "      <td>0.122650</td>\n",
       "      <td>0.008431</td>\n",
       "      <td>0.402479</td>\n",
       "      <td>0.007570</td>\n",
       "      <td>0.014587</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.011958</td>\n",
       "      <td>0.000876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>0.104594</td>\n",
       "      <td>0.058130</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>0.128036</td>\n",
       "      <td>0.016812</td>\n",
       "      <td>0.279067</td>\n",
       "      <td>0.043005</td>\n",
       "      <td>0.038831</td>\n",
       "      <td>0.016578</td>\n",
       "      <td>0.033540</td>\n",
       "      <td>0.001587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>4129</td>\n",
       "      <td>0.301006</td>\n",
       "      <td>0.122344</td>\n",
       "      <td>0.223976</td>\n",
       "      <td>0.162998</td>\n",
       "      <td>0.013824</td>\n",
       "      <td>0.129075</td>\n",
       "      <td>0.025927</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.005951</td>\n",
       "      <td>0.006979</td>\n",
       "      <td>0.000167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>4136</td>\n",
       "      <td>0.007777</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.027598</td>\n",
       "      <td>0.015850</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>4137</td>\n",
       "      <td>0.007777</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.027598</td>\n",
       "      <td>0.015850</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>4139</td>\n",
       "      <td>0.052357</td>\n",
       "      <td>0.029437</td>\n",
       "      <td>0.189260</td>\n",
       "      <td>0.069971</td>\n",
       "      <td>0.014008</td>\n",
       "      <td>0.043776</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>0.165771</td>\n",
       "      <td>0.071021</td>\n",
       "      <td>0.075644</td>\n",
       "      <td>0.000478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>4140</td>\n",
       "      <td>0.052357</td>\n",
       "      <td>0.029437</td>\n",
       "      <td>0.189260</td>\n",
       "      <td>0.069971</td>\n",
       "      <td>0.014008</td>\n",
       "      <td>0.043776</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>0.165771</td>\n",
       "      <td>0.071021</td>\n",
       "      <td>0.075644</td>\n",
       "      <td>0.000478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     origin  Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0         8    0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "1        13    0.179543     0.064325      0.261650     0.138205    0.009879   \n",
       "2        16    0.179543     0.064325      0.261650     0.138205    0.009879   \n",
       "3        23    0.094989     0.110768      0.220466     0.122650    0.008431   \n",
       "4       104    0.104594     0.058130      0.279820     0.128036    0.016812   \n",
       "..      ...         ...          ...           ...          ...         ...   \n",
       "749    4129    0.301006     0.122344      0.223976     0.162998    0.013824   \n",
       "750    4136    0.007777     0.081635      0.602493     0.027598    0.015850   \n",
       "751    4137    0.007777     0.081635      0.602493     0.027598    0.015850   \n",
       "752    4139    0.052357     0.029437      0.189260     0.069971    0.014008   \n",
       "753    4140    0.052357     0.029437      0.189260     0.069971    0.014008   \n",
       "\n",
       "     Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0       0.038865      0.191097       0.293537     0.058757    0.314417   \n",
       "1       0.201400      0.053668       0.049119     0.014844    0.026549   \n",
       "2       0.201400      0.053668       0.049119     0.014844    0.026549   \n",
       "3       0.402479      0.007570       0.014587     0.005225    0.011958   \n",
       "4       0.279067      0.043005       0.038831     0.016578    0.033540   \n",
       "..           ...           ...            ...          ...         ...   \n",
       "749     0.129075      0.025927       0.007752     0.005951    0.006979   \n",
       "750     0.264353      0.000118       0.000057     0.000051    0.000057   \n",
       "751     0.264353      0.000118       0.000057     0.000051    0.000057   \n",
       "752     0.043776      0.288277       0.165771     0.071021    0.075644   \n",
       "753     0.043776      0.288277       0.165771     0.071021    0.075644   \n",
       "\n",
       "       Others  \n",
       "0    0.008244  \n",
       "1    0.000817  \n",
       "2    0.000817  \n",
       "3    0.000876  \n",
       "4    0.001587  \n",
       "..        ...  \n",
       "749  0.000167  \n",
       "750  0.000011  \n",
       "751  0.000011  \n",
       "752  0.000478  \n",
       "753  0.000478  \n",
       "\n",
       "[754 rows x 12 columns]"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_agg_df = sm_df[classes+['origin']].groupby('origin').mean().reset_index()\n",
    "sm_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = sm_agg_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = sm_agg_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = sm_agg_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_agg_df = pd.concat([sm_agg_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>...</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>0.064325</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.138205</td>\n",
       "      <td>0.009879</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>0.053668</td>\n",
       "      <td>0.049119</td>\n",
       "      <td>0.014844</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.642593</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>0.064325</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.138205</td>\n",
       "      <td>0.009879</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>0.053668</td>\n",
       "      <td>0.049119</td>\n",
       "      <td>0.014844</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.642593</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>0.094989</td>\n",
       "      <td>0.110768</td>\n",
       "      <td>0.220466</td>\n",
       "      <td>0.122650</td>\n",
       "      <td>0.008431</td>\n",
       "      <td>0.402479</td>\n",
       "      <td>0.007570</td>\n",
       "      <td>0.014587</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000876</td>\n",
       "      <td>0.402479</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.220466</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.122650</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.402479</td>\n",
       "      <td>0.745595</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>0.104594</td>\n",
       "      <td>0.058130</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>0.128036</td>\n",
       "      <td>0.016812</td>\n",
       "      <td>0.279067</td>\n",
       "      <td>0.043005</td>\n",
       "      <td>0.038831</td>\n",
       "      <td>0.016578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001587</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.279067</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.128036</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>0.686922</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>4129</td>\n",
       "      <td>0.301006</td>\n",
       "      <td>0.122344</td>\n",
       "      <td>0.223976</td>\n",
       "      <td>0.162998</td>\n",
       "      <td>0.013824</td>\n",
       "      <td>0.129075</td>\n",
       "      <td>0.025927</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.005951</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.301006</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.223976</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.162998</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.301006</td>\n",
       "      <td>0.687980</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>4136</td>\n",
       "      <td>0.007777</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.027598</td>\n",
       "      <td>0.015850</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.948481</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>4137</td>\n",
       "      <td>0.007777</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.027598</td>\n",
       "      <td>0.015850</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.948481</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>4139</td>\n",
       "      <td>0.052357</td>\n",
       "      <td>0.029437</td>\n",
       "      <td>0.189260</td>\n",
       "      <td>0.069971</td>\n",
       "      <td>0.014008</td>\n",
       "      <td>0.043776</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>0.165771</td>\n",
       "      <td>0.071021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.189260</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.165771</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>0.643308</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>4140</td>\n",
       "      <td>0.052357</td>\n",
       "      <td>0.029437</td>\n",
       "      <td>0.189260</td>\n",
       "      <td>0.069971</td>\n",
       "      <td>0.014008</td>\n",
       "      <td>0.043776</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>0.165771</td>\n",
       "      <td>0.071021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.189260</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.165771</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>0.643308</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     origin  Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0         8    0.036616     0.006713      0.033030     0.016527    0.002198   \n",
       "1        13    0.179543     0.064325      0.261650     0.138205    0.009879   \n",
       "2        16    0.179543     0.064325      0.261650     0.138205    0.009879   \n",
       "3        23    0.094989     0.110768      0.220466     0.122650    0.008431   \n",
       "4       104    0.104594     0.058130      0.279820     0.128036    0.016812   \n",
       "..      ...         ...          ...           ...          ...         ...   \n",
       "749    4129    0.301006     0.122344      0.223976     0.162998    0.013824   \n",
       "750    4136    0.007777     0.081635      0.602493     0.027598    0.015850   \n",
       "751    4137    0.007777     0.081635      0.602493     0.027598    0.015850   \n",
       "752    4139    0.052357     0.029437      0.189260     0.069971    0.014008   \n",
       "753    4140    0.052357     0.029437      0.189260     0.069971    0.014008   \n",
       "\n",
       "     Criteria vi  Criteria vii  Criteria viii  Criteria ix  ...    Others  \\\n",
       "0       0.038865      0.191097       0.293537     0.058757  ...  0.008244   \n",
       "1       0.201400      0.053668       0.049119     0.014844  ...  0.000817   \n",
       "2       0.201400      0.053668       0.049119     0.014844  ...  0.000817   \n",
       "3       0.402479      0.007570       0.014587     0.005225  ...  0.000876   \n",
       "4       0.279067      0.043005       0.038831     0.016578  ...  0.001587   \n",
       "..           ...           ...            ...          ...  ...       ...   \n",
       "749     0.129075      0.025927       0.007752     0.005951  ...  0.000167   \n",
       "750     0.264353      0.000118       0.000057     0.000051  ...  0.000011   \n",
       "751     0.264353      0.000118       0.000057     0.000051  ...  0.000011   \n",
       "752     0.043776      0.288277       0.165771     0.071021  ...  0.000478   \n",
       "753     0.043776      0.288277       0.165771     0.071021  ...  0.000478   \n",
       "\n",
       "     max_1_val     max_1_col max_2_val      max_2_col max_3_val  \\\n",
       "0     0.314417    Criteria x  0.293537  Criteria viii  0.191097   \n",
       "1     0.261650  Criteria iii  0.201400    Criteria vi  0.179543   \n",
       "2     0.261650  Criteria iii  0.201400    Criteria vi  0.179543   \n",
       "3     0.402479   Criteria vi  0.220466   Criteria iii  0.122650   \n",
       "4     0.279820  Criteria iii  0.279067    Criteria vi  0.128036   \n",
       "..         ...           ...       ...            ...       ...   \n",
       "749   0.301006    Criteria i  0.223976   Criteria iii  0.162998   \n",
       "750   0.602493  Criteria iii  0.264353    Criteria vi  0.081635   \n",
       "751   0.602493  Criteria iii  0.264353    Criteria vi  0.081635   \n",
       "752   0.288277  Criteria vii  0.189260   Criteria iii  0.165771   \n",
       "753   0.288277  Criteria vii  0.189260   Criteria iii  0.165771   \n",
       "\n",
       "         max_3_col     max_1     max_3  counts  \n",
       "0     Criteria vii  0.314417  0.799051       1  \n",
       "1       Criteria i  0.261650  0.642593       1  \n",
       "2       Criteria i  0.261650  0.642593       1  \n",
       "3      Criteria iv  0.402479  0.745595       1  \n",
       "4      Criteria iv  0.279820  0.686922       1  \n",
       "..             ...       ...       ...     ...  \n",
       "749    Criteria iv  0.301006  0.687980       3  \n",
       "750    Criteria ii  0.602493  0.948481       1  \n",
       "751    Criteria ii  0.602493  0.948481       1  \n",
       "752  Criteria viii  0.288277  0.643308       2  \n",
       "753  Criteria viii  0.288277  0.643308       2  \n",
       "\n",
       "[754 rows x 21 columns]"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_agg_df['max_1'] = score_agg_df['max_1_val']\n",
    "score_agg_df['max_3'] = score_agg_df['max_1_val']+score_agg_df['max_2_val']+score_agg_df['max_3_val']\n",
    "score_agg_df['counts'] = sm_df[['origin','sentence']].groupby('origin').count().reset_index()['sentence']\n",
    "score_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     770\n",
       "Criteria vi      117\n",
       "Criteria vii      38\n",
       "Criteria i        32\n",
       "Criteria iv       12\n",
       "Criteria ii        5\n",
       "Criteria x         4\n",
       "Criteria viii      2\n",
       "Criteria v         2\n",
       "Criteria ix        2\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     921\n",
       "Criteria vi      836\n",
       "Criteria ii      620\n",
       "Criteria iv      344\n",
       "Criteria i        82\n",
       "Criteria vii      70\n",
       "Criteria viii     28\n",
       "Criteria x        23\n",
       "Criteria v        16\n",
       "Criteria ix       12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_df['max_1_col'],score_df['max_2_col'],score_df['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii    895\n",
       "Criteria vi     232\n",
       "Criteria i       69\n",
       "Criteria vii     68\n",
       "Criteria x       15\n",
       "Criteria ii       5\n",
       "Criteria iv       3\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_all_df[sm_all_df.valid]['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     1124\n",
       "Criteria vi      1124\n",
       "Criteria ii       613\n",
       "Criteria iv       458\n",
       "Criteria i        269\n",
       "Criteria vii      100\n",
       "Criteria x         79\n",
       "Criteria viii      75\n",
       "Criteria v         10\n",
       "Criteria ix         9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_all_df[sm_all_df.valid]['max_1_col'],\n",
    "           score_all_df[sm_all_df.valid]['max_2_col'],\n",
    "           score_all_df[sm_all_df.valid]['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii    668\n",
       "Criteria vi      57\n",
       "Criteria vii     12\n",
       "Criteria i       11\n",
       "Criteria ii       3\n",
       "Criteria iv       2\n",
       "Criteria x        1\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_agg_df['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     743\n",
       "Criteria vi      715\n",
       "Criteria ii      504\n",
       "Criteria iv      203\n",
       "Criteria vii      44\n",
       "Criteria i        32\n",
       "Criteria x         9\n",
       "Criteria viii      8\n",
       "Criteria v         3\n",
       "Criteria ix        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_agg_df['max_1_col'],score_agg_df['max_2_col'],score_agg_df['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.279820\n",
       "1      0.331788\n",
       "2      0.381616\n",
       "3      0.293035\n",
       "4      0.207680\n",
       "         ...   \n",
       "979    0.453741\n",
       "980    0.453741\n",
       "981    0.579131\n",
       "982    0.333507\n",
       "983    0.333507\n",
       "Name: max_1, Length: 984, dtype: float64"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_df['max_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_df.drop_duplicates('sentence')['max_1'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_df.drop_duplicates('sentence')['max_3'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_all_df.drop_duplicates('revised_text')['max_1'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGgCAYAAACXJAxkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAss0lEQVR4nO3dfXSU9Z3//9c1M5nJTCI5otwWNSgbiZY7DaCIVuMp2626Xeuxp62wPXhTlRVb0QWtdmlrd8UWKwpqsbqy1XjqulqKq1XEutuvrkXiuq6FoLASFoIQcTXJzFwzk5m5fn/4IyUwSWYy18wnmXk+zvFEruuTz+edeV83r8xMEstxHEcAAAAGeEwXAAAAyhdBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADG+EwX0J+GhgYlEgmNGjXKdCkAACBLH330kfx+v5qbmwccO6SfEYnH40omk6bLyMhxHMXjcfH74MyhB+bRA/PogXn04GjJZFLxeDyrsUP6GZHRo0dLkl555RXDlRwtGo2qpaVF9fX1CoVCpsspS/TAPHpgHj0wjx4c7cILL8x67JB+RgQAAJQ2gggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMySuIrF27VgsWLOi1rb29XUuWLFFDQ4Nmz56tm2++Wf/3f/+XV5EAAKA0DTqINDU1adWqVb22JRIJXXnlldq3b59++ctf6uGHH9b27du1bNmyfOsEAAAlKOe/vnvgwAEtX75cmzdvVm1tba99//qv/6q2tja9/PLLOv744yVJt956q374wx8qHA6rurralaIBAEBpyPkZka1bt6qiokIbNmzQtGnTeu177bXXdNZZZ/WEEEk699xztWnTJkIIAAA4Ss7PiDQ2NqqxsTHjvl27dqmhoUEPPPCA1q9fr2Qyqblz5+pv//ZvNWLEiEEV6DiOotHooD63kGzb7vURxUcPzKMH5tEDybKsfvc7jlPQ9enB0RzHGbAvh+QcRPoTDoe1fv16nX322brnnnvU0dGhu+66S4sWLdLjjz+edVGHSyQSamlpcbNMV7W2tpouoezRA/PogXnl2AOfz6fR4yaoO9X/uAqv1P7hXiWTyYLWU4496EsikVAgEMhqrKtBxOfzKRQK6Z577lFFRYUkqaamRpdffrneffddTZ06Nec5/X6/6uvr3SzTFbZtq7W1VbW1tQoGg6bLKUv0wDx6YF4598CyLIXttF7c/IE6womMY2qq/br4nJNVV1dXsGdGyrkHffH7/VmPdTWIjB07Vo7j9IQQSfqzP/szSdLevXsHFUQsy1IoFHKtRrcFg8EhXV85oAfm0QPzyrUHkYStsJ1UZ7Q7436v1yPL6ylKQCjXHmSSyysgrv5Cs5kzZ2r79u2KxWI9295//31J0kknneTmUgAAoAS4GkS+/vWvy+v16uabb9aOHTv01ltv6Y477tDs2bN1+umnu7kUAAAoAa4GkZEjR6qpqUnJZFKXX365rr/+ek2ZMkVr1qxxcxkAAFAi8nqPyIoVK47aVltbq7Vr1+YzLQAAKBP80TsAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMXkFkbVr12rBggV97r/jjjvU2NiYzxIAAKCEDTqINDU1adWqVX3u37Rpk55++unBTg8AAMqAL9dPOHDggJYvX67NmzertrY245j29nZ9//vf16xZs9TW1pZvjQAAoETl/IzI1q1bVVFRoQ0bNmjatGlH7XccR7feequ+8pWvaNasWa4UCQAASlPOz4g0Njb2+76PdevW6aOPPtLPf/5zrV27Nq/ipM+CTTQazXset9m23esjio8emEcPzCv1HliW1e8+J5VWKpVWKpXKOCaVSstJpWXbthzHKUiNpd6DwXAcp9/eHS7nINKf7du3a82aNWpqapLf73dlzkQioZaWFlfmKoTW1lbTJZQ9emAePTCv1Hrg8/k0etwEdWfOF5+N8Xrk8fhk21GFuzJ/wxrwpGTHY9rftk+JRKJA1X6m1HqQj0QioUAgkNVY14JIPB7XLbfcouuvv16TJ092a1r5/X7V19e7Np9bbNtWa2uramtrFQwGTZdTluiBefTAvFLtgWVZCttpvbj5A3WEMweICaOrNXf6BAWDIVUnM7/TIFRVqWCgUqMmTSroMyKl2IN85PJkhGtB5J133tGOHTu0Zs0aPfDAA5Kk7u5uJZNJzZgxQ7/4xS/U0NCQ87yWZSkUCrlVpuuCweCQrq8c0APz6IF5pdiDSMJW2E6qM9qdeX8sKVmS1+uR1+vNOMbr9cjyeooSEEqxB4OV7csykotBZOrUqdq4cWOvbY8//rg2btyoxx9/XGPGjHFrKQAAUCJcCyKVlZU66aSTem2rqamRz+c7ajsAAIDEr3gHAAAG5fWMyIoVK/rdv3jxYi1evDifJQAAQAnjGREAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMXkFkbVr12rBggW9tv3ud7/TZZddphkzZqixsVF33323YrFYXkUCAIDSNOgg0tTUpFWrVvXa1tzcrBtuuEFf/OIX9etf/1rLly/XCy+8oB/+8If51gkAAEpQzkHkwIEDuu6667Ry5UrV1tb22verX/1Ks2fP1nXXXafa2lp94Qtf0E033aTnnntOiUTCrZoBAECJ8OX6CVu3blVFRYU2bNigBx54QG1tbT37rrzySnk8vbONx+NRd3e3wuGwRo4cmX/FAACgZOQcRBobG9XY2Jhx32mnndbr393d3Vq3bp0+//nPDzqEOI6jaDQ6qM8tJNu2e31E8dED8+iBeaXaA8uy5KTSSqXSSqVSGcek02nJUb9jUqm0nFRatm3LcZyC1FqqPciH4ziyLCursTkHkWwlk0ktXbpUO3bsUFNT06DnSSQSamlpcbEyd7W2tpouoezRA/PogXml1gO/36+a48crGoko3JX5Bm/XVMhx0orZtsJdkYxjAp6U7HhM+9v2FfwtAqXWg3wkEgkFAoGsxhYkiITDYX33u9/Vm2++qTVr1mjq1KmDnsvv96u+vt7F6txh27ZaW1tVW1urYDBoupyyRA/MowfmlWoPLMtS2E4rVFWleNqbcUwwFJRleVQZDKo6mfm771BVpYKBSo2aNKmgz4iUYg/y4ff7sx7rehBpb2/XNddco7a2Nj366KOaOXNmXvNZlqVQKORSde4LBoNDur5yQA/MowfmlWIPIglbXq9HXm/mIOLxeCRL/Y7xej2yvJ6iBIRS7MFgZfuyjORyEOno6NC3vvUthcNhNTU16dRTT3VzegAAUGJcDSJ33XWX9uzZo0ceeUQjR47URx991LNv5MiRfSZWAABQnlwLIqlUSi+88IK6u7v1rW9966j9r7zyiiZMmODWcgAAoATkFURWrFjR8/9er1f//d//nXdBAACgfPBH7wAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBABQ0izLdAXoj890AQAGFrG7ZSeS/Y4J+n2qClYUqSJgeKgM+OSxLB3ssPsdx/ljDkEEGAbsRFLPv7ZLnZF4xv0jqgK6aO5ELqTAEfw+j+LdKb30xm7OnyGKIAIME52RuD7pynwhBdA/zp+hi/eIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAmLyCyNq1a7VgwYJe21paWjR//nxNnz5djY2N+uUvf5lXgQAAoHQNOog0NTVp1apVvbZ98sknWrhwoU488UQ988wz+pu/+RutXLlSzzzzTL51AgCAEuTL9RMOHDig5cuXa/Pmzaqtre2175//+Z9VUVGhH/3oR/L5fDrllFO0e/duPfzww7rsssvcqhkAAJSInJ8R2bp1qyoqKrRhwwZNmzat177m5mbNmjVLPt+f8s1ZZ52l1tZWHTx4MP9qAQBAScn5GZHGxkY1NjZm3Ld//37V1dX12jZ69GhJ0ocffqjjjz8+5wIdx1E0Gs358wrNtu1eH1F85dIDy7LkpNJKpdJKpVIZx6RSaTmptGzbluM4RautXHowlJVqD7I57tPptOQo7zH5nj+l2oN8OI4jy7KyGptzEOlPLBaT3+/vtS0QCEiS4vH4oOZMJBJqaWnJu7ZCaW1tNV1C2Sv1Hvj9ftUcP17RSEThrswXuoAnJTse0/62fUokEkWusPR7MByUWg+yOe7tmgo5Tlox21a4KzLoMW6dP6XWg3wkEome+/9AXA0ilZWVRzXxUAAJhUKDmtPv96u+vj7v2txm27ZaW1tVW1urYDBoupyyVC49sCxLYTutUFWV4mlvxjGhqkoFA5UaNWlS0Z8RKYceDGWl2oNsjvtgKCjL8qgyGFR1MvN339mMyff8KdUe5OPIJyX642oQGTt2rNrb23ttO/TvMWPGDGpOy7IGHWKKIRgMDun6ykE59CCSsOX1euT1Zr4ge70eWV6PsYtgOfRgqCvFHgx03Hs8HslS3mPcOn9KsQeDle3LMpLLv9Bs5syZeuutt3q9DveHP/xBEydO1HHHHefmUgAAoAS4GkQuu+wyhcNh3X777dq5c6eeffZZrVu3Ttdee62bywAAgBLhahA57rjj9Mgjj2jXrl269NJLtWbNGi1dulSXXnqpm8sAAIASkdd7RFasWHHUtqlTp+qpp57KZ1oAAFAm+KN3AADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACM8ZkuAABQWiJ2t+xEst8xQb9PVcGKIlWEoYwgAgBwlZ1I6vnXdqkzEs+4f0RVQBfNnUgQgSSCCACgADojcX3SlTmIAIfjPSIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADDG9SCSTCZ133336YILLtCMGTN0xRVX6L/+67/cXgYAAJQA14PIQw89pKefflp33nmn1q9fr4kTJ+rqq69We3u720sBAIBhzvUgsmnTJl188cWaO3euTjrpJN16663q6uriWREAAHAU14PIcccdp1dffVV79+5VKpXSU089Jb/fr8mTJ7u9FAAAGOZ8bk94++236zvf+Y4uvPBCeb1eeTwerV69WieeeOKg5nMcR9Fo1OUq82fbdq+PKL5y6YFlWXJSaaVSaaVSqYxjUqm0nFRatm3LcZyi1TZQDyzL6vfz3ay1mGsNJW6fB9k8jv2Ncet4zWaedDotOcp7TCqVltJpxWKxfo+TvvaVy7UoFwMdJ4dzPYjs3LlTxxxzjB544AGNGTNGTz/9tG655RY98cQTqq+vz3m+RCKhlpYWt8t0TWtrq+kSyl6p98Dv96vm+PGKRiIKd2W+0AU8KdnxmPa37VMikShyhUf3wOfzafS4CerOfN3vUeGV2j/cq2QyOei1i7nWUJbveZDN4+jxWApVVioSteUo803Z5/XI4/HJtqMKd2X+JjKb4zWb496uqZDjpBWzbYW7IoMec2zII8uydPDTqNL9BJGBjqFSvxblIpFIKBAIZDXW1SDy4Ycf6uabb9a6devU0NAgSZoyZYp27typ1atX68EHH8x5Tr/fP6gAU2i2bau1tVW1tbUKBoOmyylL5dIDy7IUttMKVVUpnvZmHBOqqlQwUKlRkyYV/RmRTD04VPOLmz9QRzjzjaam2q+LzzlZdXV1edVczLWGIrfOg2wexwmjqzV3+gRtam4bcEwwGFJ1MvOr/9kcr9kc98FQUJblUWUwqOpk5u++sxlTU1Ot7pSjl7f0/XX1dwyVy7UoF36/P+uxrgaRd955R93d3ZoyZUqv7dOmTdPvf//7Qc1pWZZCoZAb5RVEMBgc0vWVg3LoQSRhy+v1yOvNfEH2ej2yvB5jF8FMPYgkbIXtpDqj3Rk/x82ai7nWUOXGeTDQ4xiJJSVLWY1x43gd6Lj3eDwDrpXLmHyPoXK4FmUr25dlJJffrDp27FhJ0nvvvddr+/vvv6/a2lo3lwIAACXA1SAydepUnXnmmVq2bJn+8Ic/qLW1VatWrdIbb7yhb3/7224uBQAASoCrL814PB499NBDWrVqlW677TZ1dHSorq5O69at07Rp09xcCgAAlADXf2qmpqZGy5cv1/Lly92eGgAAlBj+6B0AADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGN8pgsAUDwRu1t2ItnvmKDfp6pgRZEqKk08zkD2CCJAGbETST3/2i51RuIZ94+oCuiiuRO5QeaJxxnIHkEEKDOdkbg+6cp8g4R7eJyB7PAeEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGFOQILJ+/Xp9+ctf1pQpU3TRRRfpt7/9bSGWAQAAw5zrQeQ3v/mNbr/9dl1xxRV6/vnndfHFF2vJkiV6++233V4KAAAMc64GEcdxdN999+mv//qvdcUVV+jEE0/U9ddfrzlz5ujNN990cykAAFACfG5OtmvXLrW1temSSy7ptf3RRx91cxkAAFAiXA8ikhSNRnXVVVdp27ZtmjBhgq6//no1NjYOak7HcRSNRt0s0xW2bff6iOIrlx5YliUnlVYqlVYqlco4JpVKy0mlZdu2HMcp6DyH66sHhVirL9mupXRasVis37WyrcOyrLzryfZr728tSYrFYpKyOw/yrTudTkuO8h6TTT+KWU++Ncfjcfn9fsXj8YyfW44cxxnw2D3E1SASDoclScuWLdMNN9ygW265RS+99JIWLVqkxx57TGeffXbOcyYSCbW0tLhZpqtaW1tNl1D2Sr0Hfr9fNcePVzQSUbgr880m4EnJjse0v22fEolEQefJ5MgeFHKtI2Wz1rEhjyzL0sFPo0r3c+Ov8ErtH+5VMpnMuN/n82n0uAnqznyv+myM1yOPxyfbjirclfmbqGy+9mzWOlSzz+fr9zxwq267pkKOk1bMthXuigx6TDb9KGY9btRcc/x4dcXSinXb/R5D5SKRSCgQCGQ11tUgUlFRIUm66qqrdOmll0qS6uvrtW3btkEHEb/fr/r6ejfLdIVt22ptbVVtba2CwaDpcspSufTAsiyF7bRCVVWKp70Zx4SqKhUMVGrUpEn9fofpxjyH66sHhVirL9msVVNTre6Uo5e3tKkjnPnGX1Pt18XnnKy6uroBH8MXN3/Q5zwTRldr7vQJCgZDqk5mfhteLv3qb62aar++PKdWHo+n3/PArbqDoaAsy6PKYFDVyczf7WYzJpt+FLOefGtOp1OK2TGNGVWjvzx3Ur/HULnw+/1Zj3U1iIwZM0aSVFdX12v7pEmT9G//9m+DmtOyLIVCoXxLK5hgMDik6ysH5dCDSMKW1+uR15v5Ruv1emR5PQMGMrfmOVKmHhRqrUwGWsvj8UiWFLaT6ox251VPJGH3O08klpQsudavgWr2WJ/dpAc6D9yo+9Dj6NaYoVbPYGtOpVIKd9kKVVW5dkwPd9m+LCO5/FMzp59+uqqqqvTOO+/02v7+++/rxBNPdHMpAABQAlx9RqSyslJXX321HnjgAY0ZM0ZTp07V888/r9dff13r1q1zcykAAFACXA0ikrRo0SIFg0Hde++9OnDggE455RStXr1as2fPdnspAAAwzLkeRCRp4cKFWrhwYSGmBgAAJYQ/egcAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBif6QKAUhaxu2Unkv2OCfp9qgpWFKmi4Wegx9DjsZROO0WsCICbCCJAAdmJpJ5/bZc6I/GM+0dUBXTR3IkEkX4M9Bh+blS15kwbX+SqALiFIAIUWGckrk+6Mt9EkZ3+HsMRVf4iVwPATbxHBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhQ0iOzatUszZszQs88+W8hlAADAMFWwINLd3a1bbrlF0Wi0UEsAAIBhrmBBZPXq1aquri7U9AAAoAQUJIhs2bJFTz31lFasWFGI6QEAQInwuT1hZ2enli5dqjvuuEPjxo3Lez7HcYbkyzu2bff6iOLLpweWZfW733GcQdV05BpOKq1UKq1UKpVxTCqVlpNKy7btPtfMdh6l04rFYgWfR/rs8bEsS/F4XH6/X/F4vNdjWsya0+m05CjvMcWsx82+O3Iy9qAQdTMm85hD/06nU1mfP8VUjOtdpjkHWvcQ14PID37wA82YMUOXXHKJK/MlEgm1tLS4MlchtLa2mi6h7OXSA5/Pp9HjJqg787WmR4VXav9wr5LJ5KDr8vv9qjl+vKKRiMJdmcNSwJOSHY9pf9s+JRKJQc9zbMgjy7J08NOo0n1cVHxejzwen2w7qnBX5nCfzTwej6VQZaUiUVuOHNUcP15dsbS6Yn+a0621spnHrqmQ46QVs22FuyKDHlPMetzsu9fjUVXNqKN6UIi6GdP/GI+TGvAYkty5vmSjmNe7IyUSCQUCgazGuhpE1q9fr+bmZj333HOuzen3+1VfX+/afG6xbVutra2qra1VMBg0XU5ZGkwPLMtS2E7rxc0fqCOc+QZQU+3XxeecrLq6ury+Uzi0VqiqSvG0N+OYUFWlgoFKjZo0qd/vjAeap6amWt0pRy9vaevz65owulpzp09QMBhSdTLzq7K5zLOpuU2fdNqK2TFVBivl8XiPGuPWWv3NEwwFZVkeVQaDqk5m/g4smzHFrMfdvqf13P/7QHYi3asHhaibMZnHpFIp2VFbxxwTGvAYcuv6ko1iXu+O5Pf7sx7rahB55pln9PHHH+v888/vtX358uV64YUX9Mgjj+Q8p2VZCoVCLlXovmAwOKTrKwe59iCSsBW2k+qMdmfc7/V6ZHk9rgTMSMKW1+uR15v5BpHtWgPN4/F4JEv9fl2RWFKy5No8YTupjki3wl22qtNeeb3pgq2VzTxujSlGPa72XZY6umKyk1avHhSibsbkfwy5eX3JRjGvd4fL9mUZyeUgsnLlSsVisV7b5s2bpxtvvFF/+Zd/6eZSAACgBLgaRMaMGZNx+3HHHdfnPgAAUL74Fe8AAMAY139q5kjvvfdeoZcAAADDFM+IAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIABhgWaYrAIYGn+kCgOEqYnfLTiT73O/xWEqnnSJWhOGiMuCTx7J0sMPucwzHD8oFQQQYJDuR1POv7VJnJJ5x/+dGVWvOtPFFrgrDgd/nUbw7pZfe2M3xg7JHEAHy0BmJ65OuzDeSEVX+IleD4YbjB+A9IgAAwCCCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMMb1IPLpp5/q7/7u73TeeefpjDPO0De+8Q01Nze7vQwAACgBrgeRJUuW6O2339bPfvYzPfPMM6qvr9dVV12lDz74wO2lAADAMOdqENm9e7def/11/eAHP1BDQ4MmTpyo73//+xo9erSee+45N5cCAAAlwNUgcuyxx+rhhx/WlClTerZZliXLstTZ2enmUgAAoAT43JxsxIgR+sIXvtBr20svvaTdu3fre9/73qDmdBxH0WjUjfIysixrwPUzsW2710cUx+H9isfj8vv9isfjPdv76tfhn++k0kql0kqlUhnHpFJpKZ1WLBbrc75s5kmn05KjIbNWIcccOdZ0PWU1Rp8dN32NGbJ1l9CYQ//O9px3UmnZtj3g9Sob/d3Dsr3euVnPIY7jDHh/PcTVIHKk//zP/9Rtt92mefPm6fzzzx/UHIlEQi0tLe4WJsnn82n0uAnq7vvclSRVeKX2D/cqmUxm3N/a2up6bThaX/2qOX68umJpdcU+C6sD9cvv96vm+PGKRiIKd2UOkceGPLIsSwc/jSrdx4np83rk8fhk21GFuzIHZbumQo6TVsy2Fe6KGF+rEGPs6P8fyKN2n2OKWU95jvnsuDmyB0O/7tIbE4/FBxwT8KRkx2Pa37ZPiUQi45hsZHMPy+ba4VY9R0okEgoEAlmNLVgQ2bRpk2655RadccYZWrly5aDn8fv9qq+vd7Gyz1iWpbCd1oubP1BHOPODX1Pt18XnnKy6urqjkqJt22ptbVVtba2CwaDr9aG3TP1Kp1OK2TFVBivl8Xj77deR84SqqhRPezOOqampVnfK0ctb2vo8NiaMrtbc6RMUDIZUncz8CmcwFJRleVQZDKo6mfk7g2KuVYgxwXhadtRWMBSU1+sd9DyMyWeM1fP/h/dg6NddOmNSqZTsqK1AZWDAeUJVlQoGKjVq0qS8noHI5h6WzbXDrXqO5Pf7sx5bkCDyxBNP6O///u/1pS99SXfffXdOBR3JsiyFQiEXq/uTSMJW2E6qM9qdcb/X65Hl9fQbNILBYMHqQ29H9iuVSincZas67ZXXm86qX4fm8Xo9fV60PR6PZKnfYyMSS0qWsppnqK1ViDFer7fXeNP1lNUYfXbDO7IHQ77uMh2T7XUqGwPdw7K5drhZz+GyfVlGKsCP7z755JO68847dcUVV+hnP/tZXiEEAACUNlefEdm1a5f+4R/+QV/84hd17bXX6uDBgz37Kisrdcwxx7i5HAAAGOZcDSIvvfSSuru79fLLL+vll1/ute/SSy/VihUr3FwOAAAMc64Gkeuuu07XXXedm1MCAIASxh+9AwAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRlBTLMl0BACAXPtMF4E8idrfsRLLP/ZYlVXg9SiTT/c4T9PtUFawYNmtlO89AKgM+eSxLBzvsPsd4PJbSaSevdQAA7iGIDCF2IqnnX9ulzkg84/7PjarWnGnj9dIbu/scM6IqoIvmThzwpj6U1sp2noH4fR7Fu1P91nzo6wIADA0EkSGmMxLXJ1193bD9A44Zjmu5LZuvCwAwNPAeEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGON6EEmn07r//vt17rnnavr06brmmmu0Z88et5cBAAAlwPUg8uCDD+rJJ5/UnXfeqV/96ldKp9O6+uqrlUgk3F4KAAAMc64GkUQioX/8x3/UjTfeqPPPP1+TJ0/Wvffeq/3792vjxo1uLgUAAEqAq0Fk+/btikQiOvvss3u2jRgxQqeddpq2bNni5lIAAKAEWI7jOG5NtnHjRi1evFjvvPOOKisre7Z/5zvfUSwW09q1a3Oab8qUKUqlUho7dqxbJfaSdqRorFvpdOaHwOOxVFVZIcs6ep/jOEomk/L5fLIyDShAPT6vR5UB36BrHqpr5TOP4zg9j382NTPG/TGH92Ao1FOOYyJ2Qv1dyYdq3aU0xnEcVfi8rl03s+HWdTxUWSGPO7exHvv375fX69W777474Fifmwvbti1J8vv9vbYHAgF1dHTkPF8gEFAikXDtRn8kryUdE/IPPDADy7KO+jqLVc9gax7qa7k1D2MYU25jRlQFBhxT7JoYU3jFvI7nyufzZX2PdDWIHHoWJJFI9HpGJB6PKxgM5jxfc3Oza7UBAIChx9X3iIwbN06S1N7e3mt7e3u7xowZ4+ZSAACgBLgaRCZPnqzq6mpt3ry5Z1tnZ6e2bdummTNnurkUAAAoAa6+NOP3+zV//nytXLlSI0eO1Oc+9zn99Kc/1dixYzVv3jw3lwIAACXA1SAiSTfeeKOSyaTuuOMOxWIxzZw5U48++qgqKircXgoAAAxzrv74LgAAQC74o3cAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGI9CGdTuv+++/Xueeeq+nTp+uaa67Rnj17+hy/YcMGnXrqqUf9t3fv3iJWXVpy7UF3d7fuueeenvHz589XS0tLESsuPbn0YPXq1RnPgVNPPVW33XZbkSsvHbmeBx9//LFuvvlmnXXWWZo9e7ZuuukmHThwoIgVl55ce9Da2qpvf/vbamho0Hnnnaf7779fyWSyiBUPMw4yWr16tTN79mzn1VdfdVpaWpwrr7zSmTdvnhOPxzOO/8lPfuLMnz/faW9v7/VfMpkscuWlI9cefO9733PmzJnj/P73v3d27tzpLF682DnnnHOczs7OIldeOnLpQTgcPur4v/vuu53p06c727dvN1B9acj1PJg/f77z9a9/3dm2bZuzdetW52tf+5pz2WWXFbnq0pJLDz799FNnzpw5zvz5850//vGPzpYtW5wvfelLzm233Wag8uGBIJJBPB53ZsyY4TQ1NfVs6+jocKZOneo899xzGT/n6quvdu68885ilVjycu3B//7v/zqnnnqq8+qrr/Yaf8EFFzj/8R//UYySS85gzoPDbd261Tn99NOdZ599tpBllrRce9DR0eHU1dU5r7zySs+2TZs2OXV1dc4nn3xSjJJLTq49eOyxx5zp06c7H3/8cc+25uZmp66uztmzZ09Rah5ueGkmg+3btysSiejss8/u2TZixAiddtpp2rJlS8bPee+993TKKacUq8SSl2sPXn/9dR1zzDE677zzeo3/3e9+12sOZG8w58HhfvSjH6mhoUGXXnppIcssabn2oLKyUlVVVVq/fr3C4bDC4bB+85vfaOLEiRoxYkQxSy8ZufZg9+7dOvnkkzVy5Miebaeddpokqbm5ufAFD0MEkQz2798vSRo3blyv7aNHj+7Zd7iOjg4dOHBAzc3NuuSSSzR37lwtWrRIu3btKkq9pSjXHuzatUsnnHCCNm7cqK9+9as655xzdM011+h//ud/ilJvKcq1B4d79dVX9fbbb2vZsmUFq68c5NoDv9+vFStW6M0331RDQ4Nmzpypd955R7/4xS/k8XC5H4xcezB69Gi1t7crlUr1bGtra5P02ft3cDSOzAxs25b02Ul9uEAgoHg8ftT4HTt2SJIcx9Fdd92lVatWKR6P65vf/KYOHjxY+IJLUK49CIfD2r17tx588EEtWbJEDz30kHw+n775zW9y8g9Srj043GOPPaYLLrhA9fX1BauvHOTaA8dx1NLSohkzZqipqUn/9E//pPHjx2vRokUKh8NFqbnU5NqDv/iLv9Cnn36qu+66S9FoVAcPHtSPf/xj+Xw+dXd3F6Xm4YYgkkFlZaUkKZFI9Noej8cVDAaPGt/Q0KA33nhD99xzjz7/+c+roaFBa9asUTqd1rPPPluUmktNrj3w+XwKh8O69957NXfuXE2dOlX33nuvJOnXv/514QsuQbn24JB9+/Zp8+bN+sY3vlHQ+spBrj347W9/qyeeeEI//elPdeaZZ2rWrFn6+c9/rra2Nv3Lv/xLUWouNbn2oLa2Vvfdd59efPFFnXnmmfrzP/9znX/++Tr22GN1zDHHFKXm4YYgksGhp+Da29t7bW9vb9eYMWMyfs7IkSNlWVbPv4PBoCZMmMCPzQ1Srj0YO3asfD5fr/fpVFZW6oQTTuBHqAdpMOeBJG3atEkjR47UOeecU9D6ykGuPWhubtbEiRNVXV3ds62mpkYTJ07U7t27C1tsiRrMedDY2KjXXntN//7v/6433nhDX/va13Tw4EGdcMIJBa93OCKIZDB58mRVV1dr8+bNPds6Ozu1bds2zZw586jxTz31lGbPnq1oNNqzLRwOq7W1VZMmTSpKzaUm1x7MnDlTyWRS7777bs+2WCymPXv26KSTTipKzaUm1x4c0tzcrFmzZsnn8xWjzJKWaw/Gjh2r3bt393rJIBqNau/evaqtrS1GySUn1x40NzdrwYIFSiaTGj16tPx+vzZu3KhgMKgzzjijmKUPGwSRDPx+v+bPn6+VK1fqlVde0fbt23XTTTdp7NixmjdvnlKplD766CPFYjFJ0nnnnad0Oq2lS5dqx44devfdd7V48WKNHDlSX/3qVw1/NcNTrj1oaGjQnDlztGzZMjU3N2vnzp1aunSpvF6vvvKVrxj+aoanXHtwyLZt2zR58mRDVZeWXHvwV3/1V5Kk7373u9q+fbu2b9+uJUuWKBAIcC0apFx7cPLJJ+u9997T3XffrT179mjTpk368Y9/rGuvvbbXM1U4jOmfHx6qksmk85Of/MQ566yznOnTpzvXXHNNz8+A79mzx6mrq3OeeeaZnvF//OMfnYULFzpnnnmmc8YZZziLFy929u3bZ6r8kpBrD7q6upzly5c7s2fPdqZNm+YsXLjQ2bFjh6nyS0KuPXAcx5k6darz5JNPmii3JOXag507dzrXXnutM2vWLOess85ybrjhBn5/RZ5y7cFbb73lXH755c7UqVOdCy+80HnssccMVT48WI7jOKbDEAAAKE+8NAMAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMCY/w9/nj9eccxV5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_all_df.drop_duplicates('revised_text')['max_3'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare = score_agg_df.merge(sm_all_df, how = 'left', left_on='origin', right_on='origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare['max_1_eq'] = compare['max_1_col_x']==compare['max_1_col_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9350132625994695"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare['max_1_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.644927536231884"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare[compare['counts']>1]['max_1_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare['max_3_eq'] = compare.apply(lambda x: IoU([x['max_1_col_x'],x['max_2_col_x'],x['max_3_col_x']],\n",
    "                                                 [x['max_1_col_y'],x['max_2_col_y'],x['max_3_col_y']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9594164456233422"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare['max_3_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7782608695652175"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare[compare['counts']>1]['max_3_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "495"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(score_df[(score_df.max_3>0.8) & (score_df.max_1>1./3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_dict_1 = {'max_{}_col_x'.format(i):'max_{}_col_agg'.format(i) for i in range(1,4,1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_1_col_x': 'max_1_col_agg',\n",
       " 'max_2_col_x': 'max_2_col_agg',\n",
       " 'max_3_col_x': 'max_3_col_agg'}"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_dict_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i_x</th>\n",
       "      <th>Criteria ii_x</th>\n",
       "      <th>Criteria iii_x</th>\n",
       "      <th>Criteria iv_x</th>\n",
       "      <th>Criteria v_x</th>\n",
       "      <th>Criteria vi_x</th>\n",
       "      <th>Criteria vii_x</th>\n",
       "      <th>Criteria viii_x</th>\n",
       "      <th>Criteria ix_x</th>\n",
       "      <th>...</th>\n",
       "      <th>max_1_col_y</th>\n",
       "      <th>max_2_val_y</th>\n",
       "      <th>max_2_col_y</th>\n",
       "      <th>max_3_val_y</th>\n",
       "      <th>max_3_col_y</th>\n",
       "      <th>max_1_y</th>\n",
       "      <th>max_3_y</th>\n",
       "      <th>valid</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.036616</td>\n",
       "      <td>0.006713</td>\n",
       "      <td>0.033030</td>\n",
       "      <td>0.016527</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.038865</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>0.058757</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>0.293537</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>0.064325</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.138205</td>\n",
       "      <td>0.009879</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>0.053668</td>\n",
       "      <td>0.049119</td>\n",
       "      <td>0.014844</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.642593</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>0.064325</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.138205</td>\n",
       "      <td>0.009879</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>0.053668</td>\n",
       "      <td>0.049119</td>\n",
       "      <td>0.014844</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.179543</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.642593</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>0.094989</td>\n",
       "      <td>0.110768</td>\n",
       "      <td>0.220466</td>\n",
       "      <td>0.122650</td>\n",
       "      <td>0.008431</td>\n",
       "      <td>0.402479</td>\n",
       "      <td>0.007570</td>\n",
       "      <td>0.014587</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.220466</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.122650</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.402479</td>\n",
       "      <td>0.745595</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>0.104594</td>\n",
       "      <td>0.058130</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>0.128036</td>\n",
       "      <td>0.016812</td>\n",
       "      <td>0.279067</td>\n",
       "      <td>0.043005</td>\n",
       "      <td>0.038831</td>\n",
       "      <td>0.016578</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.279067</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.128036</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.279820</td>\n",
       "      <td>0.686922</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>4129</td>\n",
       "      <td>0.301006</td>\n",
       "      <td>0.122344</td>\n",
       "      <td>0.223976</td>\n",
       "      <td>0.162998</td>\n",
       "      <td>0.013824</td>\n",
       "      <td>0.129075</td>\n",
       "      <td>0.025927</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.005951</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.180936</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.139311</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.404491</td>\n",
       "      <td>0.724738</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>4136</td>\n",
       "      <td>0.007777</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.027598</td>\n",
       "      <td>0.015850</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.948481</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>4137</td>\n",
       "      <td>0.007777</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.027598</td>\n",
       "      <td>0.015850</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.264353</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.081635</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.602493</td>\n",
       "      <td>0.948481</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>4139</td>\n",
       "      <td>0.052357</td>\n",
       "      <td>0.029437</td>\n",
       "      <td>0.189260</td>\n",
       "      <td>0.069971</td>\n",
       "      <td>0.014008</td>\n",
       "      <td>0.043776</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>0.165771</td>\n",
       "      <td>0.071021</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.208068</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.079734</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>0.522356</td>\n",
       "      <td>0.810158</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>4140</td>\n",
       "      <td>0.052357</td>\n",
       "      <td>0.029437</td>\n",
       "      <td>0.189260</td>\n",
       "      <td>0.069971</td>\n",
       "      <td>0.014008</td>\n",
       "      <td>0.043776</td>\n",
       "      <td>0.288277</td>\n",
       "      <td>0.165771</td>\n",
       "      <td>0.071021</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.208068</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.079734</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>0.522356</td>\n",
       "      <td>0.810158</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     origin  Criteria i_x  Criteria ii_x  Criteria iii_x  Criteria iv_x  \\\n",
       "0         8      0.036616       0.006713        0.033030       0.016527   \n",
       "1        13      0.179543       0.064325        0.261650       0.138205   \n",
       "2        16      0.179543       0.064325        0.261650       0.138205   \n",
       "3        23      0.094989       0.110768        0.220466       0.122650   \n",
       "4       104      0.104594       0.058130        0.279820       0.128036   \n",
       "..      ...           ...            ...             ...            ...   \n",
       "749    4129      0.301006       0.122344        0.223976       0.162998   \n",
       "750    4136      0.007777       0.081635        0.602493       0.027598   \n",
       "751    4137      0.007777       0.081635        0.602493       0.027598   \n",
       "752    4139      0.052357       0.029437        0.189260       0.069971   \n",
       "753    4140      0.052357       0.029437        0.189260       0.069971   \n",
       "\n",
       "     Criteria v_x  Criteria vi_x  Criteria vii_x  Criteria viii_x  \\\n",
       "0        0.002198       0.038865        0.191097         0.293537   \n",
       "1        0.009879       0.201400        0.053668         0.049119   \n",
       "2        0.009879       0.201400        0.053668         0.049119   \n",
       "3        0.008431       0.402479        0.007570         0.014587   \n",
       "4        0.016812       0.279067        0.043005         0.038831   \n",
       "..            ...            ...             ...              ...   \n",
       "749      0.013824       0.129075        0.025927         0.007752   \n",
       "750      0.015850       0.264353        0.000118         0.000057   \n",
       "751      0.015850       0.264353        0.000118         0.000057   \n",
       "752      0.014008       0.043776        0.288277         0.165771   \n",
       "753      0.014008       0.043776        0.288277         0.165771   \n",
       "\n",
       "     Criteria ix_x  ...   max_1_col_y  max_2_val_y    max_2_col_y max_3_val_y  \\\n",
       "0         0.058757  ...    Criteria x     0.293537  Criteria viii    0.191097   \n",
       "1         0.014844  ...  Criteria iii     0.201400    Criteria vi    0.179543   \n",
       "2         0.014844  ...  Criteria iii     0.201400    Criteria vi    0.179543   \n",
       "3         0.005225  ...   Criteria vi     0.220466   Criteria iii    0.122650   \n",
       "4         0.016578  ...  Criteria iii     0.279067    Criteria vi    0.128036   \n",
       "..             ...  ...           ...          ...            ...         ...   \n",
       "749       0.005951  ...    Criteria i     0.180936    Criteria iv    0.139311   \n",
       "750       0.000051  ...  Criteria iii     0.264353    Criteria vi    0.081635   \n",
       "751       0.000051  ...  Criteria iii     0.264353    Criteria vi    0.081635   \n",
       "752       0.071021  ...  Criteria vii     0.208068  Criteria viii    0.079734   \n",
       "753       0.071021  ...  Criteria vii     0.208068  Criteria viii    0.079734   \n",
       "\n",
       "      max_3_col_y   max_1_y   max_3_y valid  max_1_eq  max_3_eq  \n",
       "0    Criteria vii  0.314417  0.799051  True      True       1.0  \n",
       "1      Criteria i  0.261650  0.642593  True      True       1.0  \n",
       "2      Criteria i  0.261650  0.642593  True      True       1.0  \n",
       "3     Criteria iv  0.402479  0.745595  True      True       1.0  \n",
       "4     Criteria iv  0.279820  0.686922  True      True       1.0  \n",
       "..            ...       ...       ...   ...       ...       ...  \n",
       "749  Criteria iii  0.404491  0.724738  True      True       1.0  \n",
       "750   Criteria ii  0.602493  0.948481  True      True       1.0  \n",
       "751   Criteria ii  0.602493  0.948481  True      True       1.0  \n",
       "752   Criteria ix  0.522356  0.810158  True      True       0.5  \n",
       "753   Criteria ix  0.522356  0.810158  True      True       0.5  \n",
       "\n",
       "[754 rows x 77 columns]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare.rename(columns=cp_dict_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_final = sm_all_df.merge(compare.rename(columns=cp_dict_1)[['origin','max_1_col_agg','max_2_col_agg','max_3_col_agg',\n",
    "                                                    'max_1_eq','max_3_eq']], how='left', left_on='origin',right_on='origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>candownload</th>\n",
       "      <th>url_c</th>\n",
       "      <th>url_q</th>\n",
       "      <th>url_n</th>\n",
       "      <th>url_largest</th>\n",
       "      <th>owner</th>\n",
       "      <th>owner_loc</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>valid</th>\n",
       "      <th>max_1_col_agg</th>\n",
       "      <th>max_2_col_agg</th>\n",
       "      <th>max_3_col_agg</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>51859675789</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859675789_2abb735954_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859675789_2abb735954_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859675789_2abb735954_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859675789_0dc586ec53_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>51859339946</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339946_9ca193472a_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339946_9ca193472a_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339946_9ca193472a_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339946_cf01cf0229_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>51858385807</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385807_aeeb29b536_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385807_aeeb29b536_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385807_aeeb29b536_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385807_bf38fd6ae9_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>51859339546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339546_74e167d9a0_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339546_74e167d9a0_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339546_74e167d9a0_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51859339546_10dd36c744_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>51858385377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385377_9aef1c02a6_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385377_9aef1c02a6_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385377_9aef1c02a6_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/51858385377_a46f863401_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>灭渡桥</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191097</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>4225</td>\n",
       "      <td>38397950426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/4582/38397950426_a4b2f17df4_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4582/38397950426_a4b2f17df4_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4582/38397950426_a4b2f17df4_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4582/38397950426_56393c39ef_o.jpg</td>\n",
       "      <td>28571579@N03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20171112_IMG_0515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3133</th>\n",
       "      <td>4226</td>\n",
       "      <td>38397948496</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/4517/38397948496_9ebeca6454_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4517/38397948496_9ebeca6454_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4517/38397948496_9ebeca6454_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4517/38397948496_e059e1dfc9_o.jpg</td>\n",
       "      <td>28571579@N03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20171112_IMG_0518</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3134</th>\n",
       "      <td>4227</td>\n",
       "      <td>38454117381</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/4552/38454117381_40a5e9e096_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4552/38454117381_40a5e9e096_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4552/38454117381_40a5e9e096_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4552/38454117381_11eb0b5276_o.jpg</td>\n",
       "      <td>28571579@N03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20171112_IMG_0531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>4228</td>\n",
       "      <td>38397940256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/4576/38397940256_1e4eb547c5_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4576/38397940256_1e4eb547c5_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4576/38397940256_1e4eb547c5_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/4576/38397940256_d2435cc169_o.jpg</td>\n",
       "      <td>28571579@N03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20171112_IMG_0539</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>4229</td>\n",
       "      <td>50150116276</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://live.staticflickr.com/65535/50150116276_7f89bf7577_c.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/50150116276_7f89bf7577_q.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/50150116276_7f89bf7577_n.jpg</td>\n",
       "      <td>https://live.staticflickr.com/65535/50150116276_d448db2b5d_o.jpg</td>\n",
       "      <td>57210421@N04</td>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>吴中博物馆</td>\n",
       "      <td>...</td>\n",
       "      <td>0.209445</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>0.710166</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3137 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index  candownload  \\\n",
       "0          8  51859675789          1.0   \n",
       "1          9  51859339946          1.0   \n",
       "2         10  51858385807          1.0   \n",
       "3         11  51859339546          1.0   \n",
       "4         12  51858385377          1.0   \n",
       "...      ...          ...          ...   \n",
       "3132    4225  38397950426          1.0   \n",
       "3133    4226  38397948496          1.0   \n",
       "3134    4227  38454117381          1.0   \n",
       "3135    4228  38397940256          1.0   \n",
       "3136    4229  50150116276          1.0   \n",
       "\n",
       "                                                                 url_c  \\\n",
       "0     https://live.staticflickr.com/65535/51859675789_2abb735954_c.jpg   \n",
       "1     https://live.staticflickr.com/65535/51859339946_9ca193472a_c.jpg   \n",
       "2     https://live.staticflickr.com/65535/51858385807_aeeb29b536_c.jpg   \n",
       "3     https://live.staticflickr.com/65535/51859339546_74e167d9a0_c.jpg   \n",
       "4     https://live.staticflickr.com/65535/51858385377_9aef1c02a6_c.jpg   \n",
       "...                                                                ...   \n",
       "3132   https://live.staticflickr.com/4582/38397950426_a4b2f17df4_c.jpg   \n",
       "3133   https://live.staticflickr.com/4517/38397948496_9ebeca6454_c.jpg   \n",
       "3134   https://live.staticflickr.com/4552/38454117381_40a5e9e096_c.jpg   \n",
       "3135   https://live.staticflickr.com/4576/38397940256_1e4eb547c5_c.jpg   \n",
       "3136  https://live.staticflickr.com/65535/50150116276_7f89bf7577_c.jpg   \n",
       "\n",
       "                                                                 url_q  \\\n",
       "0     https://live.staticflickr.com/65535/51859675789_2abb735954_q.jpg   \n",
       "1     https://live.staticflickr.com/65535/51859339946_9ca193472a_q.jpg   \n",
       "2     https://live.staticflickr.com/65535/51858385807_aeeb29b536_q.jpg   \n",
       "3     https://live.staticflickr.com/65535/51859339546_74e167d9a0_q.jpg   \n",
       "4     https://live.staticflickr.com/65535/51858385377_9aef1c02a6_q.jpg   \n",
       "...                                                                ...   \n",
       "3132   https://live.staticflickr.com/4582/38397950426_a4b2f17df4_q.jpg   \n",
       "3133   https://live.staticflickr.com/4517/38397948496_9ebeca6454_q.jpg   \n",
       "3134   https://live.staticflickr.com/4552/38454117381_40a5e9e096_q.jpg   \n",
       "3135   https://live.staticflickr.com/4576/38397940256_1e4eb547c5_q.jpg   \n",
       "3136  https://live.staticflickr.com/65535/50150116276_7f89bf7577_q.jpg   \n",
       "\n",
       "                                                                 url_n  \\\n",
       "0     https://live.staticflickr.com/65535/51859675789_2abb735954_n.jpg   \n",
       "1     https://live.staticflickr.com/65535/51859339946_9ca193472a_n.jpg   \n",
       "2     https://live.staticflickr.com/65535/51858385807_aeeb29b536_n.jpg   \n",
       "3     https://live.staticflickr.com/65535/51859339546_74e167d9a0_n.jpg   \n",
       "4     https://live.staticflickr.com/65535/51858385377_9aef1c02a6_n.jpg   \n",
       "...                                                                ...   \n",
       "3132   https://live.staticflickr.com/4582/38397950426_a4b2f17df4_n.jpg   \n",
       "3133   https://live.staticflickr.com/4517/38397948496_9ebeca6454_n.jpg   \n",
       "3134   https://live.staticflickr.com/4552/38454117381_40a5e9e096_n.jpg   \n",
       "3135   https://live.staticflickr.com/4576/38397940256_1e4eb547c5_n.jpg   \n",
       "3136  https://live.staticflickr.com/65535/50150116276_7f89bf7577_n.jpg   \n",
       "\n",
       "                                                           url_largest  \\\n",
       "0     https://live.staticflickr.com/65535/51859675789_0dc586ec53_o.jpg   \n",
       "1     https://live.staticflickr.com/65535/51859339946_cf01cf0229_o.jpg   \n",
       "2     https://live.staticflickr.com/65535/51858385807_bf38fd6ae9_o.jpg   \n",
       "3     https://live.staticflickr.com/65535/51859339546_10dd36c744_o.jpg   \n",
       "4     https://live.staticflickr.com/65535/51858385377_a46f863401_o.jpg   \n",
       "...                                                                ...   \n",
       "3132   https://live.staticflickr.com/4582/38397950426_56393c39ef_o.jpg   \n",
       "3133   https://live.staticflickr.com/4517/38397948496_e059e1dfc9_o.jpg   \n",
       "3134   https://live.staticflickr.com/4552/38454117381_11eb0b5276_o.jpg   \n",
       "3135   https://live.staticflickr.com/4576/38397940256_d2435cc169_o.jpg   \n",
       "3136  https://live.staticflickr.com/65535/50150116276_d448db2b5d_o.jpg   \n",
       "\n",
       "             owner       owner_loc              title  ... max_3_val  \\\n",
       "0     57210421@N04  Beijing, China                灭渡桥  ...  0.191097   \n",
       "1     57210421@N04  Beijing, China                灭渡桥  ...  0.191097   \n",
       "2     57210421@N04  Beijing, China                灭渡桥  ...  0.191097   \n",
       "3     57210421@N04  Beijing, China                灭渡桥  ...  0.191097   \n",
       "4     57210421@N04  Beijing, China                灭渡桥  ...  0.191097   \n",
       "...            ...             ...                ...  ...       ...   \n",
       "3132  28571579@N03             NaN  20171112_IMG_0515  ...  0.026072   \n",
       "3133  28571579@N03             NaN  20171112_IMG_0518  ...  0.026072   \n",
       "3134  28571579@N03             NaN  20171112_IMG_0531  ...  0.026072   \n",
       "3135  28571579@N03             NaN  20171112_IMG_0539  ...  0.026072   \n",
       "3136  57210421@N04  Beijing, China              吴中博物馆  ...  0.209445   \n",
       "\n",
       "         max_3_col     max_1     max_3  valid max_1_col_agg  max_2_col_agg  \\\n",
       "0     Criteria vii  0.314417  0.799051   True    Criteria x  Criteria viii   \n",
       "1     Criteria vii  0.314417  0.799051   True           NaN            NaN   \n",
       "2     Criteria vii  0.314417  0.799051   True           NaN            NaN   \n",
       "3     Criteria vii  0.314417  0.799051   True           NaN            NaN   \n",
       "4     Criteria vii  0.314417  0.799051   True           NaN            NaN   \n",
       "...            ...       ...       ...    ...           ...            ...   \n",
       "3132   Criteria iv  0.835118  0.946161  False           NaN            NaN   \n",
       "3133   Criteria iv  0.835118  0.946161  False           NaN            NaN   \n",
       "3134   Criteria iv  0.835118  0.946161  False           NaN            NaN   \n",
       "3135   Criteria iv  0.835118  0.946161  False           NaN            NaN   \n",
       "3136    Criteria i  0.263014  0.710166   True           NaN            NaN   \n",
       "\n",
       "      max_3_col_agg  max_1_eq max_3_eq  \n",
       "0      Criteria vii      True      1.0  \n",
       "1               NaN       NaN      NaN  \n",
       "2               NaN       NaN      NaN  \n",
       "3               NaN       NaN      NaN  \n",
       "4               NaN       NaN      NaN  \n",
       "...             ...       ...      ...  \n",
       "3132            NaN       NaN      NaN  \n",
       "3133            NaN       NaN      NaN  \n",
       "3134            NaN       NaN      NaN  \n",
       "3135            NaN       NaN      NaN  \n",
       "3136            NaN       NaN      NaN  \n",
       "\n",
       "[3137 rows x 60 columns]"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df.to_csv('../../../Venice_Flickr/Suzhou/data_storage/sentences_ulmfit.csv',sep='\\t', encoding='utf-8-sig')\n",
    "score_final.to_csv('../../../Venice_Flickr/Suzhou/data_storage/metadata_ulmfit.csv',sep='\\t', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df=pd.read_csv('../../../Venice_Flickr/Suzhou/data_storage/sentences_ulmfit.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')\n",
    "score_final=pd.read_csv('../../../Venice_Flickr/Suzhou/data_storage/metadata_ulmfit.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-470-5421ceb0d8b8>:2: DtypeWarning: Columns (824,825,826,827) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  score_final_bert = pd.read_csv('../../../Venice_Flickr/Suzhou/data_storage/metadata_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')\n"
     ]
    }
   ],
   "source": [
    "sm_df_bert = pd.read_csv('../../../Venice_Flickr/Suzhou/data_storage/sentences_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')\n",
    "score_final_bert = pd.read_csv('../../../Venice_Flickr/Suzhou/data_storage/metadata_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent = sm_df.iloc[:,:4]\n",
    "compare_sent['max_1_col_ulmfit'] = sm_df['max_1_col']\n",
    "compare_sent['max_2_col_ulmfit'] = sm_df['max_2_col']\n",
    "compare_sent['max_3_col_ulmfit'] = sm_df['max_3_col']\n",
    "compare_sent['max_1_ulmfit'] = sm_df['max_1']\n",
    "compare_sent['max_3_ulmfit'] = sm_df['max_3']\n",
    "compare_sent['max_1_col_bert'] = sm_df_bert['max_1_col']\n",
    "compare_sent['max_2_col_bert'] = sm_df_bert['max_2_col']\n",
    "compare_sent['max_3_col_bert'] = sm_df_bert['max_3_col']\n",
    "compare_sent['max_1_bert'] = sm_df_bert['max_1']\n",
    "compare_sent['max_3_bert'] = sm_df_bert['max_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['same_1'] = compare_sent['max_1_col_ulmfit']==compare_sent['max_1_col_bert']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['same_3'] = compare_sent.apply(lambda x: IoU(x[['max_1_col_ulmfit','max_2_col_ulmfit','max_3_col_ulmfit']],\n",
    "                                                          x[['max_1_col_bert','max_2_col_bert','max_3_col_bert']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21951219512195122"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_sent['same_1'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7296747967479674"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(compare_sent['same_3']>=0.5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>and a family (in the background)</td>\n",
       "      <td>131</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.381616</td>\n",
       "      <td>0.749891</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>0.330711</td>\n",
       "      <td>0.760191</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>It has title of A streetlamp in SIP</td>\n",
       "      <td>137</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.322159</td>\n",
       "      <td>0.666619</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.491229</td>\n",
       "      <td>0.783081</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>It has title of The classic Soochow-style brunch</td>\n",
       "      <td>138</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.362317</td>\n",
       "      <td>0.734053</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.477917</td>\n",
       "      <td>0.773208</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>It has tags of chenyang, suzhou, jiangsu, peoplesrepublicofchina</td>\n",
       "      <td>349</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.509157</td>\n",
       "      <td>0.825236</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.333751</td>\n",
       "      <td>0.768451</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>It has tags of suzhou, jiangsu, peoplesrepublicofchina, chenyang</td>\n",
       "      <td>350</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.480951</td>\n",
       "      <td>0.821939</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.443253</td>\n",
       "      <td>0.770803</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>1761</td>\n",
       "      <td>It has tags of suzhoushi, jiangsusheng, china, cn</td>\n",
       "      <td>4116</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.741025</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>0.609589</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>1763</td>\n",
       "      <td>It has tags of suzhoushi, jiangsusheng, china, cn</td>\n",
       "      <td>4117</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.453741</td>\n",
       "      <td>0.741025</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.247030</td>\n",
       "      <td>0.609589</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>1764</td>\n",
       "      <td>Canglang Pavilion 20171120 adam0730</td>\n",
       "      <td>4129</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.579131</td>\n",
       "      <td>0.868801</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.619855</td>\n",
       "      <td>0.796199</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>1773</td>\n",
       "      <td>It has tags of chinasuzhou</td>\n",
       "      <td>4139</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.584716</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.398199</td>\n",
       "      <td>0.672273</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>1774</td>\n",
       "      <td>It has tags of cinasuzhouautumn</td>\n",
       "      <td>4140</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.333507</td>\n",
       "      <td>0.584716</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.595125</td>\n",
       "      <td>0.776585</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>718 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                                          sentence  \\\n",
       "2        5                                  and a family (in the background)   \n",
       "6        9                               It has title of A streetlamp in SIP   \n",
       "7       10                  It has title of The classic Soochow-style brunch   \n",
       "8       11  It has tags of chenyang, suzhou, jiangsu, peoplesrepublicofchina   \n",
       "9       12  It has tags of suzhou, jiangsu, peoplesrepublicofchina, chenyang   \n",
       "..     ...                                                               ...   \n",
       "979   1761                 It has tags of suzhoushi, jiangsusheng, china, cn   \n",
       "980   1763                 It has tags of suzhoushi, jiangsusheng, china, cn   \n",
       "981   1764                               Canglang Pavilion 20171120 adam0730   \n",
       "982   1773                                        It has tags of chinasuzhou   \n",
       "983   1774                                   It has tags of cinasuzhouautumn   \n",
       "\n",
       "     origin   lang max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "2       131     en     Criteria iii      Criteria iv      Criteria vi   \n",
       "6       137     en      Criteria vi     Criteria iii      Criteria iv   \n",
       "7       138     en       Criteria i      Criteria iv     Criteria iii   \n",
       "8       349     en     Criteria iii      Criteria vi      Criteria iv   \n",
       "9       350     en     Criteria iii      Criteria vi      Criteria ii   \n",
       "..      ...    ...              ...              ...              ...   \n",
       "979    4116  other     Criteria iii      Criteria vi      Criteria iv   \n",
       "980    4117  other     Criteria iii      Criteria vi      Criteria iv   \n",
       "981    4129  other       Criteria i      Criteria iv      Criteria ii   \n",
       "982    4139  other     Criteria iii     Criteria vii      Criteria iv   \n",
       "983    4140  other     Criteria iii     Criteria vii      Criteria iv   \n",
       "\n",
       "     max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  \\\n",
       "2        0.381616      0.749891    Criteria iv   Criteria iii     Criteria v   \n",
       "6        0.322159      0.666619    Criteria iv     Criteria i   Criteria iii   \n",
       "7        0.362317      0.734053    Criteria vi    Criteria iv   Criteria iii   \n",
       "8        0.509157      0.825236   Criteria iii    Criteria vi    Criteria iv   \n",
       "9        0.480951      0.821939    Criteria vi   Criteria iii    Criteria iv   \n",
       "..            ...           ...            ...            ...            ...   \n",
       "979      0.453741      0.741025   Criteria iii    Criteria ii    Criteria vi   \n",
       "980      0.453741      0.741025   Criteria iii    Criteria ii    Criteria vi   \n",
       "981      0.579131      0.868801     Criteria i    Criteria iv    Criteria ii   \n",
       "982      0.333507      0.584716   Criteria iii    Criteria vi    Criteria iv   \n",
       "983      0.333507      0.584716     Criteria x   Criteria iii   Criteria vii   \n",
       "\n",
       "     max_1_bert  max_3_bert  same_1  same_3  \n",
       "2      0.330711    0.760191   False     0.5  \n",
       "6      0.491229    0.783081   False     0.5  \n",
       "7      0.477917    0.773208   False     0.5  \n",
       "8      0.333751    0.768451    True     1.0  \n",
       "9      0.443253    0.770803   False     0.5  \n",
       "..          ...         ...     ...     ...  \n",
       "979    0.247030    0.609589    True     0.5  \n",
       "980    0.247030    0.609589    True     0.5  \n",
       "981    0.619855    0.796199    True     1.0  \n",
       "982    0.398199    0.672273    True     0.5  \n",
       "983    0.595125    0.776585   False     0.5  \n",
       "\n",
       "[718 rows x 16 columns]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_sent[compare_sent['same_3']>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_sent['same_3'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post = score_final.iloc[:,:2]\n",
    "compare_post['max_1_col_ulmfit'] = score_final['max_1_col']\n",
    "compare_post['max_2_col_ulmfit'] = score_final['max_2_col']\n",
    "compare_post['max_3_col_ulmfit'] = score_final['max_3_col']\n",
    "compare_post['max_1_ulmfit'] = score_final['max_1']\n",
    "compare_post['max_3_ulmfit'] = score_final['max_3']\n",
    "compare_post['max_1_col_bert'] = score_final_bert['max_1_col']\n",
    "compare_post['max_2_col_bert'] = score_final_bert['max_2_col']\n",
    "compare_post['max_3_col_bert'] = score_final_bert['max_3_col']\n",
    "compare_post['max_1_bert'] = score_final_bert['max_1']\n",
    "compare_post['max_3_bert'] = score_final_bert['max_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['same_1'] = compare_post['max_1_col_ulmfit']==compare_post['max_1_col_bert']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['same_3'] = compare_post.apply(lambda x: IoU(x[['max_1_col_ulmfit','max_2_col_ulmfit','max_3_col_ulmfit']],\n",
    "                                                          x[['max_1_col_bert','max_2_col_bert','max_3_col_bert']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10583359897991712"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post['same_1'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3478163850812878"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post['same_3'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3190946764424609"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(compare_post['same_3']>=0.5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_post['same_3'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = cosine_similarity(sm_df[classes], sm_df_bert[classes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['cosine_sim'] = np.diagonal(cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_sent[compare_sent.same_3>=0.5]['cosine_sim'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = cosine_similarity(score_final[classes], score_final_bert[classes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['cosine_sim'] = np.diagonal(cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "      <th>cosine_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>51859675789</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.592425</td>\n",
       "      <td>0.898150</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.455594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>51859339946</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.592425</td>\n",
       "      <td>0.898150</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.455594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>51858385807</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.592425</td>\n",
       "      <td>0.898150</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.455594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>51859339546</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.592425</td>\n",
       "      <td>0.898150</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.455594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>51858385377</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.314417</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>Criteria ix</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria viii</td>\n",
       "      <td>0.592425</td>\n",
       "      <td>0.898150</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.455594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>4225</td>\n",
       "      <td>38397950426</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.270940</td>\n",
       "      <td>0.565778</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.349416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3133</th>\n",
       "      <td>4226</td>\n",
       "      <td>38397948496</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.270940</td>\n",
       "      <td>0.565778</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.349416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3134</th>\n",
       "      <td>4227</td>\n",
       "      <td>38454117381</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.270940</td>\n",
       "      <td>0.565778</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.349416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>4228</td>\n",
       "      <td>38397940256</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.946161</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.270940</td>\n",
       "      <td>0.565778</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.349416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>4229</td>\n",
       "      <td>50150116276</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>0.710166</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.586755</td>\n",
       "      <td>0.842555</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.799728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3137 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin        index max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0          8  51859675789       Criteria x    Criteria viii     Criteria vii   \n",
       "1          9  51859339946       Criteria x    Criteria viii     Criteria vii   \n",
       "2         10  51858385807       Criteria x    Criteria viii     Criteria vii   \n",
       "3         11  51859339546       Criteria x    Criteria viii     Criteria vii   \n",
       "4         12  51858385377       Criteria x    Criteria viii     Criteria vii   \n",
       "...      ...          ...              ...              ...              ...   \n",
       "3132    4225  38397950426     Criteria iii      Criteria vi      Criteria iv   \n",
       "3133    4226  38397948496     Criteria iii      Criteria vi      Criteria iv   \n",
       "3134    4227  38454117381     Criteria iii      Criteria vi      Criteria iv   \n",
       "3135    4228  38397940256     Criteria iii      Criteria vi      Criteria iv   \n",
       "3136    4229  50150116276     Criteria iii      Criteria vi       Criteria i   \n",
       "\n",
       "      max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert max_3_col_bert  \\\n",
       "0         0.314417      0.799051    Criteria ix     Criteria x  Criteria viii   \n",
       "1         0.314417      0.799051    Criteria ix     Criteria x  Criteria viii   \n",
       "2         0.314417      0.799051    Criteria ix     Criteria x  Criteria viii   \n",
       "3         0.314417      0.799051    Criteria ix     Criteria x  Criteria viii   \n",
       "4         0.314417      0.799051    Criteria ix     Criteria x  Criteria viii   \n",
       "...            ...           ...            ...            ...            ...   \n",
       "3132      0.835118      0.946161     Criteria i     Criteria x   Criteria iii   \n",
       "3133      0.835118      0.946161     Criteria i     Criteria x   Criteria iii   \n",
       "3134      0.835118      0.946161     Criteria i     Criteria x   Criteria iii   \n",
       "3135      0.835118      0.946161     Criteria i     Criteria x   Criteria iii   \n",
       "3136      0.263014      0.710166   Criteria iii    Criteria vi    Criteria iv   \n",
       "\n",
       "      max_1_bert  max_3_bert  same_1  same_3  cosine_sim  \n",
       "0       0.592425    0.898150   False     0.5    0.455594  \n",
       "1       0.592425    0.898150   False     0.5    0.455594  \n",
       "2       0.592425    0.898150   False     0.5    0.455594  \n",
       "3       0.592425    0.898150   False     0.5    0.455594  \n",
       "4       0.592425    0.898150   False     0.5    0.455594  \n",
       "...          ...         ...     ...     ...         ...  \n",
       "3132    0.270940    0.565778   False     0.2    0.349416  \n",
       "3133    0.270940    0.565778   False     0.2    0.349416  \n",
       "3134    0.270940    0.565778   False     0.2    0.349416  \n",
       "3135    0.270940    0.565778   False     0.2    0.349416  \n",
       "3136    0.586755    0.842555    True     0.5    0.799728  \n",
       "\n",
       "[3137 rows x 15 columns]"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_post[compare_post.same_3>=0.5]['cosine_sim'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent.to_csv('../../../Venice_Flickr/Suzhou/data_storage/sentences_compare.csv',sep='\\t', encoding='utf-8-sig')\n",
    "compare_post.to_csv('../../../Venice_Flickr/Suzhou/data_storage/metadata_compare.csv',sep='\\t', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Venice_Large"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = pd.read_csv('../../../Venice_Flickr/data_storage/images/sentences.csv',sep='\\t', index_col = 'Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_all_df = pd.read_csv('../../../Venice_Flickr/data_storage/images/metadata.csv',sep='\\t', index_col = 'Unnamed: 0').reset_index().rename(columns={'level_0':'origin'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_ouv(line, classifier=classifier, vectorizer=vectorizer, classes=classes, k=11):\n",
    "    predictions = predict_rating(line['processed'], classifier, vectorizer, classes, k)\n",
    "    for key, value in predictions:\n",
    "        line[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/171024 finished\n",
      "200/171024 finished\n",
      "300/171024 finished\n",
      "400/171024 finished\n",
      "500/171024 finished\n",
      "600/171024 finished\n",
      "700/171024 finished\n",
      "800/171024 finished\n",
      "900/171024 finished\n",
      "1000/171024 finished\n",
      "1100/171024 finished\n",
      "1200/171024 finished\n",
      "1300/171024 finished\n",
      "1400/171024 finished\n",
      "1500/171024 finished\n",
      "1600/171024 finished\n",
      "1700/171024 finished\n",
      "1800/171024 finished\n",
      "1900/171024 finished\n",
      "2000/171024 finished\n",
      "2100/171024 finished\n",
      "2200/171024 finished\n",
      "2300/171024 finished\n",
      "2400/171024 finished\n",
      "2500/171024 finished\n",
      "2600/171024 finished\n",
      "2700/171024 finished\n",
      "2800/171024 finished\n",
      "2900/171024 finished\n",
      "3000/171024 finished\n",
      "3100/171024 finished\n",
      "3200/171024 finished\n",
      "3300/171024 finished\n",
      "3400/171024 finished\n",
      "3500/171024 finished\n",
      "3600/171024 finished\n",
      "3700/171024 finished\n",
      "3800/171024 finished\n",
      "3900/171024 finished\n",
      "4000/171024 finished\n",
      "4100/171024 finished\n",
      "4200/171024 finished\n",
      "4300/171024 finished\n",
      "4400/171024 finished\n",
      "4500/171024 finished\n",
      "4600/171024 finished\n",
      "4700/171024 finished\n",
      "4800/171024 finished\n",
      "4900/171024 finished\n",
      "5000/171024 finished\n",
      "5100/171024 finished\n",
      "5200/171024 finished\n",
      "5300/171024 finished\n",
      "5400/171024 finished\n",
      "5500/171024 finished\n",
      "5600/171024 finished\n",
      "5700/171024 finished\n",
      "5800/171024 finished\n",
      "5900/171024 finished\n",
      "6000/171024 finished\n",
      "6100/171024 finished\n",
      "6200/171024 finished\n",
      "6300/171024 finished\n",
      "6400/171024 finished\n",
      "6500/171024 finished\n",
      "6600/171024 finished\n",
      "6700/171024 finished\n",
      "6800/171024 finished\n",
      "6900/171024 finished\n",
      "7000/171024 finished\n",
      "7100/171024 finished\n",
      "7200/171024 finished\n",
      "7300/171024 finished\n",
      "7400/171024 finished\n",
      "7500/171024 finished\n",
      "7600/171024 finished\n",
      "7700/171024 finished\n",
      "7800/171024 finished\n",
      "7900/171024 finished\n",
      "8000/171024 finished\n",
      "8100/171024 finished\n",
      "8200/171024 finished\n",
      "8300/171024 finished\n",
      "8400/171024 finished\n",
      "8500/171024 finished\n",
      "8600/171024 finished\n",
      "8700/171024 finished\n",
      "8800/171024 finished\n",
      "8900/171024 finished\n",
      "9000/171024 finished\n",
      "9100/171024 finished\n",
      "9200/171024 finished\n",
      "9300/171024 finished\n",
      "9400/171024 finished\n",
      "9500/171024 finished\n",
      "9600/171024 finished\n",
      "9700/171024 finished\n",
      "9800/171024 finished\n",
      "9900/171024 finished\n",
      "10000/171024 finished\n",
      "10100/171024 finished\n",
      "10200/171024 finished\n",
      "10300/171024 finished\n",
      "10400/171024 finished\n",
      "10500/171024 finished\n",
      "10600/171024 finished\n",
      "10700/171024 finished\n",
      "10800/171024 finished\n",
      "10900/171024 finished\n",
      "11000/171024 finished\n",
      "11100/171024 finished\n",
      "11200/171024 finished\n",
      "11300/171024 finished\n",
      "11400/171024 finished\n",
      "11500/171024 finished\n",
      "11600/171024 finished\n",
      "11700/171024 finished\n",
      "11800/171024 finished\n",
      "11900/171024 finished\n",
      "12000/171024 finished\n",
      "12100/171024 finished\n",
      "12200/171024 finished\n",
      "12300/171024 finished\n",
      "12400/171024 finished\n",
      "12500/171024 finished\n",
      "12600/171024 finished\n",
      "12700/171024 finished\n",
      "12800/171024 finished\n",
      "12900/171024 finished\n",
      "13000/171024 finished\n",
      "13100/171024 finished\n",
      "13200/171024 finished\n",
      "13300/171024 finished\n",
      "13400/171024 finished\n",
      "13500/171024 finished\n",
      "13600/171024 finished\n",
      "13700/171024 finished\n",
      "13800/171024 finished\n",
      "13900/171024 finished\n",
      "14000/171024 finished\n",
      "14100/171024 finished\n",
      "14200/171024 finished\n",
      "14300/171024 finished\n",
      "14400/171024 finished\n",
      "14500/171024 finished\n",
      "14600/171024 finished\n",
      "14700/171024 finished\n",
      "14800/171024 finished\n",
      "14900/171024 finished\n",
      "15000/171024 finished\n",
      "15100/171024 finished\n",
      "15200/171024 finished\n",
      "15300/171024 finished\n",
      "15400/171024 finished\n",
      "15500/171024 finished\n",
      "15600/171024 finished\n",
      "15700/171024 finished\n",
      "15800/171024 finished\n",
      "15900/171024 finished\n",
      "16000/171024 finished\n",
      "16100/171024 finished\n",
      "16200/171024 finished\n",
      "16300/171024 finished\n",
      "16400/171024 finished\n",
      "16500/171024 finished\n",
      "16600/171024 finished\n",
      "16700/171024 finished\n",
      "16800/171024 finished\n",
      "16900/171024 finished\n",
      "17000/171024 finished\n",
      "17100/171024 finished\n",
      "17200/171024 finished\n",
      "17300/171024 finished\n",
      "17400/171024 finished\n",
      "17500/171024 finished\n",
      "17600/171024 finished\n",
      "17700/171024 finished\n",
      "17800/171024 finished\n",
      "17900/171024 finished\n",
      "18000/171024 finished\n",
      "18100/171024 finished\n",
      "18200/171024 finished\n",
      "18300/171024 finished\n",
      "18400/171024 finished\n",
      "18500/171024 finished\n",
      "18600/171024 finished\n",
      "18700/171024 finished\n",
      "18800/171024 finished\n",
      "18900/171024 finished\n",
      "19000/171024 finished\n",
      "19100/171024 finished\n",
      "19200/171024 finished\n",
      "19300/171024 finished\n",
      "19400/171024 finished\n",
      "19500/171024 finished\n",
      "19600/171024 finished\n",
      "19700/171024 finished\n",
      "19800/171024 finished\n",
      "19900/171024 finished\n",
      "20000/171024 finished\n",
      "20100/171024 finished\n",
      "20200/171024 finished\n",
      "20300/171024 finished\n",
      "20400/171024 finished\n",
      "20500/171024 finished\n",
      "20600/171024 finished\n",
      "20700/171024 finished\n",
      "20800/171024 finished\n",
      "20900/171024 finished\n",
      "21000/171024 finished\n",
      "21100/171024 finished\n",
      "21200/171024 finished\n",
      "21300/171024 finished\n",
      "21400/171024 finished\n",
      "21500/171024 finished\n",
      "21600/171024 finished\n",
      "21700/171024 finished\n",
      "21800/171024 finished\n",
      "21900/171024 finished\n",
      "22000/171024 finished\n",
      "22100/171024 finished\n",
      "22200/171024 finished\n",
      "22300/171024 finished\n",
      "22400/171024 finished\n",
      "22500/171024 finished\n",
      "22600/171024 finished\n",
      "22700/171024 finished\n",
      "22800/171024 finished\n",
      "22900/171024 finished\n",
      "23000/171024 finished\n",
      "23100/171024 finished\n",
      "23200/171024 finished\n",
      "23300/171024 finished\n",
      "23400/171024 finished\n",
      "23500/171024 finished\n",
      "23600/171024 finished\n",
      "23700/171024 finished\n",
      "23800/171024 finished\n",
      "23900/171024 finished\n",
      "24000/171024 finished\n",
      "24100/171024 finished\n",
      "24200/171024 finished\n",
      "24300/171024 finished\n",
      "24400/171024 finished\n",
      "24500/171024 finished\n",
      "24600/171024 finished\n",
      "24700/171024 finished\n",
      "24800/171024 finished\n",
      "24900/171024 finished\n",
      "25000/171024 finished\n",
      "25100/171024 finished\n",
      "25200/171024 finished\n",
      "25300/171024 finished\n",
      "25400/171024 finished\n",
      "25500/171024 finished\n",
      "25600/171024 finished\n",
      "25700/171024 finished\n",
      "25800/171024 finished\n",
      "25900/171024 finished\n",
      "26000/171024 finished\n",
      "26100/171024 finished\n",
      "26200/171024 finished\n",
      "26300/171024 finished\n",
      "26400/171024 finished\n",
      "26500/171024 finished\n",
      "26600/171024 finished\n",
      "26700/171024 finished\n",
      "26800/171024 finished\n",
      "26900/171024 finished\n",
      "27000/171024 finished\n",
      "27100/171024 finished\n",
      "27200/171024 finished\n",
      "27300/171024 finished\n",
      "27400/171024 finished\n",
      "27500/171024 finished\n",
      "27600/171024 finished\n",
      "27700/171024 finished\n",
      "27800/171024 finished\n",
      "27900/171024 finished\n",
      "28000/171024 finished\n",
      "28100/171024 finished\n",
      "28200/171024 finished\n",
      "28300/171024 finished\n",
      "28400/171024 finished\n",
      "28500/171024 finished\n",
      "28600/171024 finished\n",
      "28700/171024 finished\n",
      "28800/171024 finished\n",
      "28900/171024 finished\n",
      "29000/171024 finished\n",
      "29100/171024 finished\n",
      "29200/171024 finished\n",
      "29300/171024 finished\n",
      "29400/171024 finished\n",
      "29500/171024 finished\n",
      "29600/171024 finished\n",
      "29700/171024 finished\n",
      "29800/171024 finished\n",
      "29900/171024 finished\n",
      "30000/171024 finished\n",
      "30100/171024 finished\n",
      "30200/171024 finished\n",
      "30300/171024 finished\n",
      "30400/171024 finished\n",
      "30500/171024 finished\n",
      "30600/171024 finished\n",
      "30700/171024 finished\n",
      "30800/171024 finished\n",
      "30900/171024 finished\n",
      "31000/171024 finished\n",
      "31100/171024 finished\n",
      "31200/171024 finished\n",
      "31300/171024 finished\n",
      "31400/171024 finished\n",
      "31500/171024 finished\n",
      "31600/171024 finished\n",
      "31700/171024 finished\n",
      "31800/171024 finished\n",
      "31900/171024 finished\n",
      "32000/171024 finished\n",
      "32100/171024 finished\n",
      "32200/171024 finished\n",
      "32300/171024 finished\n",
      "32400/171024 finished\n",
      "32500/171024 finished\n",
      "32600/171024 finished\n",
      "32700/171024 finished\n",
      "32800/171024 finished\n",
      "32900/171024 finished\n",
      "33000/171024 finished\n",
      "33100/171024 finished\n",
      "33200/171024 finished\n",
      "33300/171024 finished\n",
      "33400/171024 finished\n",
      "33500/171024 finished\n",
      "33600/171024 finished\n",
      "33700/171024 finished\n",
      "33800/171024 finished\n",
      "33900/171024 finished\n",
      "34000/171024 finished\n",
      "34100/171024 finished\n",
      "34200/171024 finished\n",
      "34300/171024 finished\n",
      "34400/171024 finished\n",
      "34500/171024 finished\n",
      "34600/171024 finished\n",
      "34700/171024 finished\n",
      "34800/171024 finished\n",
      "34900/171024 finished\n",
      "35000/171024 finished\n",
      "35100/171024 finished\n",
      "35200/171024 finished\n",
      "35300/171024 finished\n",
      "35400/171024 finished\n",
      "35500/171024 finished\n",
      "35600/171024 finished\n",
      "35700/171024 finished\n",
      "35800/171024 finished\n",
      "35900/171024 finished\n",
      "36000/171024 finished\n",
      "36100/171024 finished\n",
      "36200/171024 finished\n",
      "36300/171024 finished\n",
      "36400/171024 finished\n",
      "36500/171024 finished\n",
      "36600/171024 finished\n",
      "36700/171024 finished\n",
      "36800/171024 finished\n",
      "36900/171024 finished\n",
      "37000/171024 finished\n",
      "37100/171024 finished\n",
      "37200/171024 finished\n",
      "37300/171024 finished\n",
      "37400/171024 finished\n",
      "37500/171024 finished\n",
      "37600/171024 finished\n",
      "37700/171024 finished\n",
      "37800/171024 finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37900/171024 finished\n",
      "38000/171024 finished\n",
      "38100/171024 finished\n",
      "38200/171024 finished\n",
      "38300/171024 finished\n",
      "38400/171024 finished\n",
      "38500/171024 finished\n",
      "38600/171024 finished\n",
      "38700/171024 finished\n",
      "38800/171024 finished\n",
      "38900/171024 finished\n",
      "39000/171024 finished\n",
      "39100/171024 finished\n",
      "39200/171024 finished\n",
      "39300/171024 finished\n",
      "39400/171024 finished\n",
      "39500/171024 finished\n",
      "39600/171024 finished\n",
      "39700/171024 finished\n",
      "39800/171024 finished\n",
      "39900/171024 finished\n",
      "40000/171024 finished\n",
      "40100/171024 finished\n",
      "40200/171024 finished\n",
      "40300/171024 finished\n",
      "40400/171024 finished\n",
      "40500/171024 finished\n",
      "40600/171024 finished\n",
      "40700/171024 finished\n",
      "40800/171024 finished\n",
      "40900/171024 finished\n",
      "41000/171024 finished\n",
      "41100/171024 finished\n",
      "41200/171024 finished\n",
      "41300/171024 finished\n",
      "41400/171024 finished\n",
      "41500/171024 finished\n",
      "41600/171024 finished\n",
      "41700/171024 finished\n",
      "41800/171024 finished\n",
      "41900/171024 finished\n",
      "42000/171024 finished\n",
      "42100/171024 finished\n",
      "42200/171024 finished\n",
      "42300/171024 finished\n",
      "42400/171024 finished\n",
      "42500/171024 finished\n",
      "42600/171024 finished\n",
      "42700/171024 finished\n",
      "42800/171024 finished\n",
      "42900/171024 finished\n",
      "43000/171024 finished\n",
      "43100/171024 finished\n",
      "43200/171024 finished\n",
      "43300/171024 finished\n",
      "43400/171024 finished\n",
      "43500/171024 finished\n",
      "43600/171024 finished\n",
      "43700/171024 finished\n",
      "43800/171024 finished\n",
      "43900/171024 finished\n",
      "44000/171024 finished\n",
      "44100/171024 finished\n",
      "44200/171024 finished\n",
      "44300/171024 finished\n",
      "44400/171024 finished\n",
      "44500/171024 finished\n",
      "44600/171024 finished\n",
      "44700/171024 finished\n",
      "44800/171024 finished\n",
      "44900/171024 finished\n",
      "45000/171024 finished\n",
      "45100/171024 finished\n",
      "45200/171024 finished\n",
      "45300/171024 finished\n",
      "45400/171024 finished\n",
      "45500/171024 finished\n",
      "45600/171024 finished\n",
      "45700/171024 finished\n",
      "45800/171024 finished\n",
      "45900/171024 finished\n",
      "46000/171024 finished\n",
      "46100/171024 finished\n",
      "46200/171024 finished\n",
      "46300/171024 finished\n",
      "46400/171024 finished\n",
      "46500/171024 finished\n",
      "46600/171024 finished\n",
      "46700/171024 finished\n",
      "46800/171024 finished\n",
      "46900/171024 finished\n",
      "47000/171024 finished\n",
      "47100/171024 finished\n",
      "47200/171024 finished\n",
      "47300/171024 finished\n",
      "47400/171024 finished\n",
      "47500/171024 finished\n",
      "47600/171024 finished\n",
      "47700/171024 finished\n",
      "47800/171024 finished\n",
      "47900/171024 finished\n",
      "48000/171024 finished\n",
      "48100/171024 finished\n",
      "48200/171024 finished\n",
      "48300/171024 finished\n",
      "48400/171024 finished\n",
      "48500/171024 finished\n",
      "48600/171024 finished\n",
      "48700/171024 finished\n",
      "48800/171024 finished\n",
      "48900/171024 finished\n",
      "49000/171024 finished\n",
      "49100/171024 finished\n",
      "49200/171024 finished\n",
      "49300/171024 finished\n",
      "49400/171024 finished\n",
      "49500/171024 finished\n",
      "49600/171024 finished\n",
      "49700/171024 finished\n",
      "49800/171024 finished\n",
      "49900/171024 finished\n",
      "50000/171024 finished\n",
      "50100/171024 finished\n",
      "50200/171024 finished\n",
      "50300/171024 finished\n",
      "50400/171024 finished\n",
      "50500/171024 finished\n",
      "50600/171024 finished\n",
      "50700/171024 finished\n",
      "50800/171024 finished\n",
      "50900/171024 finished\n",
      "51000/171024 finished\n",
      "51100/171024 finished\n",
      "51200/171024 finished\n",
      "51300/171024 finished\n",
      "51400/171024 finished\n",
      "51500/171024 finished\n",
      "51600/171024 finished\n",
      "51700/171024 finished\n",
      "51800/171024 finished\n",
      "51900/171024 finished\n",
      "52000/171024 finished\n",
      "52100/171024 finished\n",
      "52200/171024 finished\n",
      "52300/171024 finished\n",
      "52400/171024 finished\n",
      "52500/171024 finished\n",
      "52600/171024 finished\n",
      "52700/171024 finished\n",
      "52800/171024 finished\n",
      "52900/171024 finished\n",
      "53000/171024 finished\n",
      "53100/171024 finished\n",
      "53200/171024 finished\n",
      "53300/171024 finished\n",
      "53400/171024 finished\n",
      "53500/171024 finished\n",
      "53600/171024 finished\n",
      "53700/171024 finished\n",
      "53800/171024 finished\n",
      "53900/171024 finished\n",
      "54000/171024 finished\n",
      "54100/171024 finished\n",
      "54200/171024 finished\n",
      "54300/171024 finished\n",
      "54400/171024 finished\n",
      "54500/171024 finished\n",
      "54600/171024 finished\n",
      "54700/171024 finished\n",
      "54800/171024 finished\n",
      "54900/171024 finished\n",
      "55000/171024 finished\n",
      "55100/171024 finished\n",
      "55200/171024 finished\n",
      "55300/171024 finished\n",
      "55400/171024 finished\n",
      "55500/171024 finished\n",
      "55600/171024 finished\n",
      "55700/171024 finished\n",
      "55800/171024 finished\n",
      "55900/171024 finished\n",
      "56000/171024 finished\n",
      "56100/171024 finished\n",
      "56200/171024 finished\n",
      "56300/171024 finished\n",
      "56400/171024 finished\n",
      "56500/171024 finished\n",
      "56600/171024 finished\n",
      "56700/171024 finished\n",
      "56800/171024 finished\n",
      "56900/171024 finished\n",
      "57000/171024 finished\n",
      "57100/171024 finished\n",
      "57200/171024 finished\n",
      "57300/171024 finished\n",
      "57400/171024 finished\n",
      "57500/171024 finished\n",
      "57600/171024 finished\n",
      "57700/171024 finished\n",
      "57800/171024 finished\n",
      "57900/171024 finished\n",
      "58000/171024 finished\n",
      "58100/171024 finished\n",
      "58200/171024 finished\n",
      "58300/171024 finished\n",
      "58400/171024 finished\n",
      "58500/171024 finished\n",
      "58600/171024 finished\n",
      "58700/171024 finished\n",
      "58800/171024 finished\n",
      "58900/171024 finished\n",
      "59000/171024 finished\n",
      "59100/171024 finished\n",
      "59200/171024 finished\n",
      "59300/171024 finished\n",
      "59400/171024 finished\n",
      "59500/171024 finished\n",
      "59600/171024 finished\n",
      "59700/171024 finished\n",
      "59800/171024 finished\n",
      "59900/171024 finished\n",
      "60000/171024 finished\n",
      "60100/171024 finished\n",
      "60200/171024 finished\n",
      "60300/171024 finished\n",
      "60400/171024 finished\n",
      "60500/171024 finished\n",
      "60600/171024 finished\n",
      "60700/171024 finished\n",
      "60800/171024 finished\n",
      "60900/171024 finished\n",
      "61000/171024 finished\n",
      "61100/171024 finished\n",
      "61200/171024 finished\n",
      "61300/171024 finished\n",
      "61400/171024 finished\n",
      "61500/171024 finished\n",
      "61600/171024 finished\n",
      "61700/171024 finished\n",
      "61800/171024 finished\n",
      "61900/171024 finished\n",
      "62000/171024 finished\n",
      "62100/171024 finished\n",
      "62200/171024 finished\n",
      "62300/171024 finished\n",
      "62400/171024 finished\n",
      "62500/171024 finished\n",
      "62600/171024 finished\n",
      "62700/171024 finished\n",
      "62800/171024 finished\n",
      "62900/171024 finished\n",
      "63000/171024 finished\n",
      "63100/171024 finished\n",
      "63200/171024 finished\n",
      "63300/171024 finished\n",
      "63400/171024 finished\n",
      "63500/171024 finished\n",
      "63600/171024 finished\n",
      "63700/171024 finished\n",
      "63800/171024 finished\n",
      "63900/171024 finished\n",
      "64000/171024 finished\n",
      "64100/171024 finished\n",
      "64200/171024 finished\n",
      "64300/171024 finished\n",
      "64400/171024 finished\n",
      "64500/171024 finished\n",
      "64600/171024 finished\n",
      "64700/171024 finished\n",
      "64800/171024 finished\n",
      "64900/171024 finished\n",
      "65000/171024 finished\n",
      "65100/171024 finished\n",
      "65200/171024 finished\n",
      "65300/171024 finished\n",
      "65400/171024 finished\n",
      "65500/171024 finished\n",
      "65600/171024 finished\n",
      "65700/171024 finished\n",
      "65800/171024 finished\n",
      "65900/171024 finished\n",
      "66000/171024 finished\n",
      "66100/171024 finished\n",
      "66200/171024 finished\n",
      "66300/171024 finished\n",
      "66400/171024 finished\n",
      "66500/171024 finished\n",
      "66600/171024 finished\n",
      "66700/171024 finished\n",
      "66800/171024 finished\n",
      "66900/171024 finished\n",
      "67000/171024 finished\n",
      "67100/171024 finished\n",
      "67200/171024 finished\n",
      "67300/171024 finished\n",
      "67400/171024 finished\n",
      "67500/171024 finished\n",
      "67600/171024 finished\n",
      "67700/171024 finished\n",
      "67800/171024 finished\n",
      "67900/171024 finished\n",
      "68000/171024 finished\n",
      "68100/171024 finished\n",
      "68200/171024 finished\n",
      "68300/171024 finished\n",
      "68400/171024 finished\n",
      "68500/171024 finished\n",
      "68600/171024 finished\n",
      "68700/171024 finished\n",
      "68800/171024 finished\n",
      "68900/171024 finished\n",
      "69000/171024 finished\n",
      "69100/171024 finished\n",
      "69200/171024 finished\n",
      "69300/171024 finished\n",
      "69400/171024 finished\n",
      "69500/171024 finished\n",
      "69600/171024 finished\n",
      "69700/171024 finished\n",
      "69800/171024 finished\n",
      "69900/171024 finished\n",
      "70000/171024 finished\n",
      "70100/171024 finished\n",
      "70200/171024 finished\n",
      "70300/171024 finished\n",
      "70400/171024 finished\n",
      "70500/171024 finished\n",
      "70600/171024 finished\n",
      "70700/171024 finished\n",
      "70800/171024 finished\n",
      "70900/171024 finished\n",
      "71000/171024 finished\n",
      "71100/171024 finished\n",
      "71200/171024 finished\n",
      "71300/171024 finished\n",
      "71400/171024 finished\n",
      "71500/171024 finished\n",
      "71600/171024 finished\n",
      "71700/171024 finished\n",
      "71800/171024 finished\n",
      "71900/171024 finished\n",
      "72000/171024 finished\n",
      "72100/171024 finished\n",
      "72200/171024 finished\n",
      "72300/171024 finished\n",
      "72400/171024 finished\n",
      "72500/171024 finished\n",
      "72600/171024 finished\n",
      "72700/171024 finished\n",
      "72800/171024 finished\n",
      "72900/171024 finished\n",
      "73000/171024 finished\n",
      "73100/171024 finished\n",
      "73200/171024 finished\n",
      "73300/171024 finished\n",
      "73400/171024 finished\n",
      "73500/171024 finished\n",
      "73600/171024 finished\n",
      "73700/171024 finished\n",
      "73800/171024 finished\n",
      "73900/171024 finished\n",
      "74000/171024 finished\n",
      "74100/171024 finished\n",
      "74200/171024 finished\n",
      "74300/171024 finished\n",
      "74400/171024 finished\n",
      "74500/171024 finished\n",
      "74600/171024 finished\n",
      "74700/171024 finished\n",
      "74800/171024 finished\n",
      "74900/171024 finished\n",
      "75000/171024 finished\n",
      "75100/171024 finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75200/171024 finished\n",
      "75300/171024 finished\n",
      "75400/171024 finished\n",
      "75500/171024 finished\n",
      "75600/171024 finished\n",
      "75700/171024 finished\n",
      "75800/171024 finished\n",
      "75900/171024 finished\n",
      "76000/171024 finished\n",
      "76100/171024 finished\n",
      "76200/171024 finished\n",
      "76300/171024 finished\n",
      "76400/171024 finished\n",
      "76500/171024 finished\n",
      "76600/171024 finished\n",
      "76700/171024 finished\n",
      "76800/171024 finished\n",
      "76900/171024 finished\n",
      "77000/171024 finished\n",
      "77100/171024 finished\n",
      "77200/171024 finished\n",
      "77300/171024 finished\n",
      "77400/171024 finished\n",
      "77500/171024 finished\n",
      "77600/171024 finished\n",
      "77700/171024 finished\n",
      "77800/171024 finished\n",
      "77900/171024 finished\n",
      "78000/171024 finished\n",
      "78100/171024 finished\n",
      "78200/171024 finished\n",
      "78300/171024 finished\n",
      "78400/171024 finished\n",
      "78500/171024 finished\n",
      "78600/171024 finished\n",
      "78700/171024 finished\n",
      "78800/171024 finished\n",
      "78900/171024 finished\n",
      "79000/171024 finished\n",
      "79100/171024 finished\n",
      "79200/171024 finished\n",
      "79300/171024 finished\n",
      "79400/171024 finished\n",
      "79500/171024 finished\n",
      "79600/171024 finished\n",
      "79700/171024 finished\n",
      "79800/171024 finished\n",
      "79900/171024 finished\n",
      "80000/171024 finished\n",
      "80100/171024 finished\n",
      "80200/171024 finished\n",
      "80300/171024 finished\n",
      "80400/171024 finished\n",
      "80500/171024 finished\n",
      "80600/171024 finished\n",
      "80700/171024 finished\n",
      "80800/171024 finished\n",
      "80900/171024 finished\n",
      "81000/171024 finished\n",
      "81100/171024 finished\n",
      "81200/171024 finished\n",
      "81300/171024 finished\n",
      "81400/171024 finished\n",
      "81500/171024 finished\n",
      "81600/171024 finished\n",
      "81700/171024 finished\n",
      "81800/171024 finished\n",
      "81900/171024 finished\n",
      "82000/171024 finished\n",
      "82100/171024 finished\n",
      "82200/171024 finished\n",
      "82300/171024 finished\n",
      "82400/171024 finished\n",
      "82500/171024 finished\n",
      "82600/171024 finished\n",
      "82700/171024 finished\n",
      "82800/171024 finished\n",
      "82900/171024 finished\n",
      "83000/171024 finished\n",
      "83100/171024 finished\n",
      "83200/171024 finished\n",
      "83300/171024 finished\n",
      "83400/171024 finished\n",
      "83500/171024 finished\n",
      "83600/171024 finished\n",
      "83700/171024 finished\n",
      "83800/171024 finished\n",
      "83900/171024 finished\n",
      "84000/171024 finished\n",
      "84100/171024 finished\n",
      "84200/171024 finished\n",
      "84300/171024 finished\n",
      "84400/171024 finished\n",
      "84500/171024 finished\n",
      "84600/171024 finished\n",
      "84700/171024 finished\n",
      "84800/171024 finished\n",
      "84900/171024 finished\n",
      "85000/171024 finished\n",
      "85100/171024 finished\n",
      "85200/171024 finished\n",
      "85300/171024 finished\n",
      "85400/171024 finished\n",
      "85500/171024 finished\n",
      "85600/171024 finished\n",
      "85700/171024 finished\n",
      "85800/171024 finished\n",
      "85900/171024 finished\n",
      "86000/171024 finished\n",
      "86100/171024 finished\n",
      "86200/171024 finished\n",
      "86300/171024 finished\n",
      "86400/171024 finished\n",
      "86500/171024 finished\n",
      "86600/171024 finished\n",
      "86700/171024 finished\n",
      "86800/171024 finished\n",
      "86900/171024 finished\n",
      "87000/171024 finished\n",
      "87100/171024 finished\n",
      "87200/171024 finished\n",
      "87300/171024 finished\n",
      "87400/171024 finished\n",
      "87500/171024 finished\n",
      "87600/171024 finished\n",
      "87700/171024 finished\n",
      "87800/171024 finished\n",
      "87900/171024 finished\n",
      "88000/171024 finished\n",
      "88100/171024 finished\n",
      "88200/171024 finished\n",
      "88300/171024 finished\n",
      "88400/171024 finished\n",
      "88500/171024 finished\n",
      "88600/171024 finished\n",
      "88700/171024 finished\n",
      "88800/171024 finished\n",
      "88900/171024 finished\n",
      "89000/171024 finished\n",
      "89100/171024 finished\n",
      "89200/171024 finished\n",
      "89300/171024 finished\n",
      "89400/171024 finished\n",
      "89500/171024 finished\n",
      "89600/171024 finished\n",
      "89700/171024 finished\n",
      "89800/171024 finished\n",
      "89900/171024 finished\n",
      "90000/171024 finished\n",
      "90100/171024 finished\n",
      "90200/171024 finished\n",
      "90300/171024 finished\n",
      "90400/171024 finished\n",
      "90500/171024 finished\n",
      "90600/171024 finished\n",
      "90700/171024 finished\n",
      "90800/171024 finished\n",
      "90900/171024 finished\n",
      "91000/171024 finished\n",
      "91100/171024 finished\n",
      "91200/171024 finished\n",
      "91300/171024 finished\n",
      "91400/171024 finished\n",
      "91500/171024 finished\n",
      "91600/171024 finished\n",
      "91700/171024 finished\n",
      "91800/171024 finished\n",
      "91900/171024 finished\n",
      "92000/171024 finished\n",
      "92100/171024 finished\n",
      "92200/171024 finished\n",
      "92300/171024 finished\n",
      "92400/171024 finished\n",
      "92500/171024 finished\n",
      "92600/171024 finished\n",
      "92700/171024 finished\n",
      "92800/171024 finished\n",
      "92900/171024 finished\n",
      "93000/171024 finished\n",
      "93100/171024 finished\n",
      "93200/171024 finished\n",
      "93300/171024 finished\n",
      "93400/171024 finished\n",
      "93500/171024 finished\n",
      "93600/171024 finished\n",
      "93700/171024 finished\n",
      "93800/171024 finished\n",
      "93900/171024 finished\n",
      "94000/171024 finished\n",
      "94100/171024 finished\n",
      "94200/171024 finished\n",
      "94300/171024 finished\n",
      "94400/171024 finished\n",
      "94500/171024 finished\n",
      "94600/171024 finished\n",
      "94700/171024 finished\n",
      "94800/171024 finished\n",
      "94900/171024 finished\n",
      "95000/171024 finished\n",
      "95100/171024 finished\n",
      "95200/171024 finished\n",
      "95300/171024 finished\n",
      "95400/171024 finished\n",
      "95500/171024 finished\n",
      "95600/171024 finished\n",
      "95700/171024 finished\n",
      "95800/171024 finished\n",
      "95900/171024 finished\n",
      "96000/171024 finished\n",
      "96100/171024 finished\n",
      "96200/171024 finished\n",
      "96300/171024 finished\n",
      "96400/171024 finished\n",
      "96500/171024 finished\n",
      "96600/171024 finished\n",
      "96700/171024 finished\n",
      "96800/171024 finished\n",
      "96900/171024 finished\n",
      "97000/171024 finished\n",
      "97100/171024 finished\n",
      "97200/171024 finished\n",
      "97300/171024 finished\n",
      "97400/171024 finished\n",
      "97500/171024 finished\n",
      "97600/171024 finished\n",
      "97700/171024 finished\n",
      "97800/171024 finished\n",
      "97900/171024 finished\n",
      "98000/171024 finished\n",
      "98100/171024 finished\n",
      "98200/171024 finished\n",
      "98300/171024 finished\n",
      "98400/171024 finished\n",
      "98500/171024 finished\n",
      "98600/171024 finished\n",
      "98700/171024 finished\n",
      "98800/171024 finished\n",
      "98900/171024 finished\n",
      "99000/171024 finished\n",
      "99100/171024 finished\n",
      "99200/171024 finished\n",
      "99300/171024 finished\n",
      "99400/171024 finished\n",
      "99500/171024 finished\n",
      "99600/171024 finished\n",
      "99700/171024 finished\n",
      "99800/171024 finished\n",
      "99900/171024 finished\n",
      "100000/171024 finished\n",
      "100100/171024 finished\n",
      "100200/171024 finished\n",
      "100300/171024 finished\n",
      "100400/171024 finished\n",
      "100500/171024 finished\n",
      "100600/171024 finished\n",
      "100700/171024 finished\n",
      "100800/171024 finished\n",
      "100900/171024 finished\n",
      "101000/171024 finished\n",
      "101100/171024 finished\n",
      "101200/171024 finished\n",
      "101300/171024 finished\n",
      "101400/171024 finished\n",
      "101500/171024 finished\n",
      "101600/171024 finished\n",
      "101700/171024 finished\n",
      "101800/171024 finished\n",
      "101900/171024 finished\n",
      "102000/171024 finished\n",
      "102100/171024 finished\n",
      "102200/171024 finished\n",
      "102300/171024 finished\n",
      "102400/171024 finished\n",
      "102500/171024 finished\n",
      "102600/171024 finished\n",
      "102700/171024 finished\n",
      "102800/171024 finished\n",
      "102900/171024 finished\n",
      "103000/171024 finished\n",
      "103100/171024 finished\n",
      "103200/171024 finished\n",
      "103300/171024 finished\n",
      "103400/171024 finished\n",
      "103500/171024 finished\n",
      "103600/171024 finished\n",
      "103700/171024 finished\n",
      "103800/171024 finished\n",
      "103900/171024 finished\n",
      "104000/171024 finished\n",
      "104100/171024 finished\n",
      "104200/171024 finished\n",
      "104300/171024 finished\n",
      "104400/171024 finished\n",
      "104500/171024 finished\n",
      "104600/171024 finished\n",
      "104700/171024 finished\n",
      "104800/171024 finished\n",
      "104900/171024 finished\n",
      "105000/171024 finished\n",
      "105100/171024 finished\n",
      "105200/171024 finished\n",
      "105300/171024 finished\n",
      "105400/171024 finished\n",
      "105500/171024 finished\n",
      "105600/171024 finished\n",
      "105700/171024 finished\n",
      "105800/171024 finished\n",
      "105900/171024 finished\n",
      "106000/171024 finished\n",
      "106100/171024 finished\n",
      "106200/171024 finished\n",
      "106300/171024 finished\n",
      "106400/171024 finished\n",
      "106500/171024 finished\n",
      "106600/171024 finished\n",
      "106700/171024 finished\n",
      "106800/171024 finished\n",
      "106900/171024 finished\n",
      "107000/171024 finished\n",
      "107100/171024 finished\n",
      "107200/171024 finished\n",
      "107300/171024 finished\n",
      "107400/171024 finished\n",
      "107500/171024 finished\n",
      "107600/171024 finished\n",
      "107700/171024 finished\n",
      "107800/171024 finished\n",
      "107900/171024 finished\n",
      "108000/171024 finished\n",
      "108100/171024 finished\n",
      "108200/171024 finished\n",
      "108300/171024 finished\n",
      "108400/171024 finished\n",
      "108500/171024 finished\n",
      "108600/171024 finished\n",
      "108700/171024 finished\n",
      "108800/171024 finished\n",
      "108900/171024 finished\n",
      "109000/171024 finished\n",
      "109100/171024 finished\n",
      "109200/171024 finished\n",
      "109300/171024 finished\n",
      "109400/171024 finished\n",
      "109500/171024 finished\n",
      "109600/171024 finished\n",
      "109700/171024 finished\n",
      "109800/171024 finished\n",
      "109900/171024 finished\n",
      "110000/171024 finished\n",
      "110100/171024 finished\n",
      "110200/171024 finished\n",
      "110300/171024 finished\n",
      "110400/171024 finished\n",
      "110500/171024 finished\n",
      "110600/171024 finished\n",
      "110700/171024 finished\n",
      "110800/171024 finished\n",
      "110900/171024 finished\n",
      "111000/171024 finished\n",
      "111100/171024 finished\n",
      "111200/171024 finished\n",
      "111300/171024 finished\n",
      "111400/171024 finished\n",
      "111500/171024 finished\n",
      "111600/171024 finished\n",
      "111700/171024 finished\n",
      "111800/171024 finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111900/171024 finished\n",
      "112000/171024 finished\n",
      "112100/171024 finished\n",
      "112200/171024 finished\n",
      "112300/171024 finished\n",
      "112400/171024 finished\n",
      "112500/171024 finished\n",
      "112600/171024 finished\n",
      "112700/171024 finished\n",
      "112800/171024 finished\n",
      "112900/171024 finished\n",
      "113000/171024 finished\n",
      "113100/171024 finished\n",
      "113200/171024 finished\n",
      "113300/171024 finished\n",
      "113400/171024 finished\n",
      "113500/171024 finished\n",
      "113600/171024 finished\n",
      "113700/171024 finished\n",
      "113800/171024 finished\n",
      "113900/171024 finished\n",
      "114000/171024 finished\n",
      "114100/171024 finished\n",
      "114200/171024 finished\n",
      "114300/171024 finished\n",
      "114400/171024 finished\n",
      "114500/171024 finished\n",
      "114600/171024 finished\n",
      "114700/171024 finished\n",
      "114800/171024 finished\n",
      "114900/171024 finished\n",
      "115000/171024 finished\n",
      "115100/171024 finished\n",
      "115200/171024 finished\n",
      "115300/171024 finished\n",
      "115400/171024 finished\n",
      "115500/171024 finished\n",
      "115600/171024 finished\n",
      "115700/171024 finished\n",
      "115800/171024 finished\n",
      "115900/171024 finished\n",
      "116000/171024 finished\n",
      "116100/171024 finished\n",
      "116200/171024 finished\n",
      "116300/171024 finished\n",
      "116400/171024 finished\n",
      "116500/171024 finished\n",
      "116600/171024 finished\n",
      "116700/171024 finished\n",
      "116800/171024 finished\n",
      "116900/171024 finished\n",
      "117000/171024 finished\n",
      "117100/171024 finished\n",
      "117200/171024 finished\n",
      "117300/171024 finished\n",
      "117400/171024 finished\n",
      "117500/171024 finished\n",
      "117600/171024 finished\n",
      "117700/171024 finished\n",
      "117800/171024 finished\n",
      "117900/171024 finished\n",
      "118000/171024 finished\n",
      "118100/171024 finished\n",
      "118200/171024 finished\n",
      "118300/171024 finished\n",
      "118400/171024 finished\n",
      "118500/171024 finished\n",
      "118600/171024 finished\n",
      "118700/171024 finished\n",
      "118800/171024 finished\n",
      "118900/171024 finished\n",
      "119000/171024 finished\n",
      "119100/171024 finished\n",
      "119200/171024 finished\n",
      "119300/171024 finished\n",
      "119400/171024 finished\n",
      "119500/171024 finished\n",
      "119600/171024 finished\n",
      "119700/171024 finished\n",
      "119800/171024 finished\n",
      "119900/171024 finished\n",
      "120000/171024 finished\n",
      "120100/171024 finished\n",
      "120200/171024 finished\n",
      "120300/171024 finished\n",
      "120400/171024 finished\n",
      "120500/171024 finished\n",
      "120600/171024 finished\n",
      "120700/171024 finished\n",
      "120800/171024 finished\n",
      "120900/171024 finished\n",
      "121000/171024 finished\n",
      "121100/171024 finished\n",
      "121200/171024 finished\n",
      "121300/171024 finished\n",
      "121400/171024 finished\n",
      "121500/171024 finished\n",
      "121600/171024 finished\n",
      "121700/171024 finished\n",
      "121800/171024 finished\n",
      "121900/171024 finished\n",
      "122000/171024 finished\n",
      "122100/171024 finished\n",
      "122200/171024 finished\n",
      "122300/171024 finished\n",
      "122400/171024 finished\n",
      "122500/171024 finished\n",
      "122600/171024 finished\n",
      "122700/171024 finished\n",
      "122800/171024 finished\n",
      "122900/171024 finished\n",
      "123000/171024 finished\n",
      "123100/171024 finished\n",
      "123200/171024 finished\n",
      "123300/171024 finished\n",
      "123400/171024 finished\n",
      "123500/171024 finished\n",
      "123600/171024 finished\n",
      "123700/171024 finished\n",
      "123800/171024 finished\n",
      "123900/171024 finished\n",
      "124000/171024 finished\n",
      "124100/171024 finished\n",
      "124200/171024 finished\n",
      "124300/171024 finished\n",
      "124400/171024 finished\n",
      "124500/171024 finished\n",
      "124600/171024 finished\n",
      "124700/171024 finished\n",
      "124800/171024 finished\n",
      "124900/171024 finished\n",
      "125000/171024 finished\n",
      "125100/171024 finished\n",
      "125200/171024 finished\n",
      "125300/171024 finished\n",
      "125400/171024 finished\n",
      "125500/171024 finished\n",
      "125600/171024 finished\n",
      "125700/171024 finished\n",
      "125800/171024 finished\n",
      "125900/171024 finished\n",
      "126000/171024 finished\n",
      "126100/171024 finished\n",
      "126200/171024 finished\n",
      "126300/171024 finished\n",
      "126400/171024 finished\n",
      "126500/171024 finished\n",
      "126600/171024 finished\n",
      "126700/171024 finished\n",
      "126800/171024 finished\n",
      "126900/171024 finished\n",
      "127000/171024 finished\n",
      "127100/171024 finished\n",
      "127200/171024 finished\n",
      "127300/171024 finished\n",
      "127400/171024 finished\n",
      "127500/171024 finished\n",
      "127600/171024 finished\n",
      "127700/171024 finished\n",
      "127800/171024 finished\n",
      "127900/171024 finished\n",
      "128000/171024 finished\n",
      "128100/171024 finished\n",
      "128200/171024 finished\n",
      "128300/171024 finished\n",
      "128400/171024 finished\n",
      "128500/171024 finished\n",
      "128600/171024 finished\n",
      "128700/171024 finished\n",
      "128800/171024 finished\n",
      "128900/171024 finished\n",
      "129000/171024 finished\n",
      "129100/171024 finished\n",
      "129200/171024 finished\n",
      "129300/171024 finished\n",
      "129400/171024 finished\n",
      "129500/171024 finished\n",
      "129600/171024 finished\n",
      "129700/171024 finished\n",
      "129800/171024 finished\n",
      "129900/171024 finished\n",
      "130000/171024 finished\n",
      "130100/171024 finished\n",
      "130200/171024 finished\n",
      "130300/171024 finished\n",
      "130400/171024 finished\n",
      "130500/171024 finished\n",
      "130600/171024 finished\n",
      "130700/171024 finished\n",
      "130800/171024 finished\n",
      "130900/171024 finished\n",
      "131000/171024 finished\n",
      "131100/171024 finished\n",
      "131200/171024 finished\n",
      "131300/171024 finished\n",
      "131400/171024 finished\n",
      "131500/171024 finished\n",
      "131600/171024 finished\n",
      "131700/171024 finished\n",
      "131800/171024 finished\n",
      "131900/171024 finished\n",
      "132000/171024 finished\n",
      "132100/171024 finished\n",
      "132200/171024 finished\n",
      "132300/171024 finished\n",
      "132400/171024 finished\n",
      "132500/171024 finished\n",
      "132600/171024 finished\n",
      "132700/171024 finished\n",
      "132800/171024 finished\n",
      "132900/171024 finished\n",
      "133000/171024 finished\n",
      "133100/171024 finished\n",
      "133200/171024 finished\n",
      "133300/171024 finished\n",
      "133400/171024 finished\n",
      "133500/171024 finished\n",
      "133600/171024 finished\n",
      "133700/171024 finished\n",
      "133800/171024 finished\n",
      "133900/171024 finished\n",
      "134000/171024 finished\n",
      "134100/171024 finished\n",
      "134200/171024 finished\n",
      "134300/171024 finished\n",
      "134400/171024 finished\n",
      "134500/171024 finished\n",
      "134600/171024 finished\n",
      "134700/171024 finished\n",
      "134800/171024 finished\n",
      "134900/171024 finished\n",
      "135000/171024 finished\n",
      "135100/171024 finished\n",
      "135200/171024 finished\n",
      "135300/171024 finished\n",
      "135400/171024 finished\n",
      "135500/171024 finished\n",
      "135600/171024 finished\n",
      "135700/171024 finished\n",
      "135800/171024 finished\n",
      "135900/171024 finished\n",
      "136000/171024 finished\n",
      "136100/171024 finished\n",
      "136200/171024 finished\n",
      "136300/171024 finished\n",
      "136400/171024 finished\n",
      "136500/171024 finished\n",
      "136600/171024 finished\n",
      "136700/171024 finished\n",
      "136800/171024 finished\n",
      "136900/171024 finished\n",
      "137000/171024 finished\n",
      "137100/171024 finished\n",
      "137200/171024 finished\n",
      "137300/171024 finished\n",
      "137400/171024 finished\n",
      "137500/171024 finished\n",
      "137600/171024 finished\n",
      "137700/171024 finished\n",
      "137800/171024 finished\n",
      "137900/171024 finished\n",
      "138000/171024 finished\n",
      "138100/171024 finished\n",
      "138200/171024 finished\n",
      "138300/171024 finished\n",
      "138400/171024 finished\n",
      "138500/171024 finished\n",
      "138600/171024 finished\n",
      "138700/171024 finished\n",
      "138800/171024 finished\n",
      "138900/171024 finished\n",
      "139000/171024 finished\n",
      "139100/171024 finished\n",
      "139200/171024 finished\n",
      "139300/171024 finished\n",
      "139400/171024 finished\n",
      "139500/171024 finished\n",
      "139600/171024 finished\n",
      "139700/171024 finished\n",
      "139800/171024 finished\n",
      "139900/171024 finished\n",
      "140000/171024 finished\n",
      "140100/171024 finished\n",
      "140200/171024 finished\n",
      "140300/171024 finished\n",
      "140400/171024 finished\n",
      "140500/171024 finished\n",
      "140600/171024 finished\n",
      "140700/171024 finished\n",
      "140800/171024 finished\n",
      "140900/171024 finished\n",
      "141000/171024 finished\n",
      "141100/171024 finished\n",
      "141200/171024 finished\n",
      "141300/171024 finished\n",
      "141400/171024 finished\n",
      "141500/171024 finished\n",
      "141600/171024 finished\n",
      "141700/171024 finished\n",
      "141800/171024 finished\n",
      "141900/171024 finished\n",
      "142000/171024 finished\n",
      "142100/171024 finished\n",
      "142200/171024 finished\n",
      "142300/171024 finished\n",
      "142400/171024 finished\n",
      "142500/171024 finished\n",
      "142600/171024 finished\n",
      "142700/171024 finished\n",
      "142800/171024 finished\n",
      "142900/171024 finished\n",
      "143000/171024 finished\n",
      "143100/171024 finished\n",
      "143200/171024 finished\n",
      "143300/171024 finished\n",
      "143400/171024 finished\n",
      "143500/171024 finished\n",
      "143600/171024 finished\n",
      "143700/171024 finished\n",
      "143800/171024 finished\n",
      "143900/171024 finished\n",
      "144000/171024 finished\n",
      "144100/171024 finished\n",
      "144200/171024 finished\n",
      "144300/171024 finished\n",
      "144400/171024 finished\n",
      "144500/171024 finished\n",
      "144600/171024 finished\n",
      "144700/171024 finished\n",
      "144800/171024 finished\n",
      "144900/171024 finished\n",
      "145000/171024 finished\n",
      "145100/171024 finished\n",
      "145200/171024 finished\n",
      "145300/171024 finished\n",
      "145400/171024 finished\n",
      "145500/171024 finished\n",
      "145600/171024 finished\n",
      "145700/171024 finished\n",
      "145800/171024 finished\n",
      "145900/171024 finished\n",
      "146000/171024 finished\n",
      "146100/171024 finished\n",
      "146200/171024 finished\n",
      "146300/171024 finished\n",
      "146400/171024 finished\n",
      "146500/171024 finished\n",
      "146600/171024 finished\n",
      "146700/171024 finished\n",
      "146800/171024 finished\n",
      "146900/171024 finished\n",
      "147000/171024 finished\n",
      "147100/171024 finished\n",
      "147200/171024 finished\n",
      "147300/171024 finished\n",
      "147400/171024 finished\n",
      "147500/171024 finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "147600/171024 finished\n",
      "147700/171024 finished\n",
      "147800/171024 finished\n",
      "147900/171024 finished\n",
      "148000/171024 finished\n",
      "148100/171024 finished\n",
      "148200/171024 finished\n",
      "148300/171024 finished\n",
      "148400/171024 finished\n",
      "148500/171024 finished\n",
      "148600/171024 finished\n",
      "148700/171024 finished\n",
      "148800/171024 finished\n",
      "148900/171024 finished\n",
      "149000/171024 finished\n",
      "149100/171024 finished\n",
      "149200/171024 finished\n",
      "149300/171024 finished\n",
      "149400/171024 finished\n",
      "149500/171024 finished\n",
      "149600/171024 finished\n",
      "149700/171024 finished\n",
      "149800/171024 finished\n",
      "149900/171024 finished\n",
      "150000/171024 finished\n",
      "150100/171024 finished\n",
      "150200/171024 finished\n",
      "150300/171024 finished\n",
      "150400/171024 finished\n",
      "150500/171024 finished\n",
      "150600/171024 finished\n",
      "150700/171024 finished\n",
      "150800/171024 finished\n",
      "150900/171024 finished\n",
      "151000/171024 finished\n",
      "151100/171024 finished\n",
      "151200/171024 finished\n",
      "151300/171024 finished\n",
      "151400/171024 finished\n",
      "151500/171024 finished\n",
      "151600/171024 finished\n",
      "151700/171024 finished\n",
      "151800/171024 finished\n",
      "151900/171024 finished\n",
      "152000/171024 finished\n",
      "152100/171024 finished\n",
      "152200/171024 finished\n",
      "152300/171024 finished\n",
      "152400/171024 finished\n",
      "152500/171024 finished\n",
      "152600/171024 finished\n",
      "152700/171024 finished\n",
      "152800/171024 finished\n",
      "152900/171024 finished\n",
      "153000/171024 finished\n",
      "153100/171024 finished\n",
      "153200/171024 finished\n",
      "153300/171024 finished\n",
      "153400/171024 finished\n",
      "153500/171024 finished\n",
      "153600/171024 finished\n",
      "153700/171024 finished\n",
      "153800/171024 finished\n",
      "153900/171024 finished\n",
      "154000/171024 finished\n",
      "154100/171024 finished\n",
      "154200/171024 finished\n",
      "154300/171024 finished\n",
      "154400/171024 finished\n",
      "154500/171024 finished\n",
      "154600/171024 finished\n",
      "154700/171024 finished\n",
      "154800/171024 finished\n",
      "154900/171024 finished\n",
      "155000/171024 finished\n",
      "155100/171024 finished\n",
      "155200/171024 finished\n",
      "155300/171024 finished\n",
      "155400/171024 finished\n",
      "155500/171024 finished\n",
      "155600/171024 finished\n",
      "155700/171024 finished\n",
      "155800/171024 finished\n",
      "155900/171024 finished\n",
      "156000/171024 finished\n",
      "156100/171024 finished\n",
      "156200/171024 finished\n",
      "156300/171024 finished\n",
      "156400/171024 finished\n",
      "156500/171024 finished\n",
      "156600/171024 finished\n",
      "156700/171024 finished\n",
      "156800/171024 finished\n",
      "156900/171024 finished\n",
      "157000/171024 finished\n",
      "157100/171024 finished\n",
      "157200/171024 finished\n",
      "157300/171024 finished\n",
      "157400/171024 finished\n",
      "157500/171024 finished\n",
      "157600/171024 finished\n",
      "157700/171024 finished\n",
      "157800/171024 finished\n",
      "157900/171024 finished\n",
      "158000/171024 finished\n",
      "158100/171024 finished\n",
      "158200/171024 finished\n",
      "158300/171024 finished\n",
      "158400/171024 finished\n",
      "158500/171024 finished\n",
      "158600/171024 finished\n",
      "158700/171024 finished\n",
      "158800/171024 finished\n",
      "158900/171024 finished\n",
      "159000/171024 finished\n",
      "159100/171024 finished\n",
      "159200/171024 finished\n",
      "159300/171024 finished\n",
      "159400/171024 finished\n",
      "159500/171024 finished\n",
      "159600/171024 finished\n",
      "159700/171024 finished\n",
      "159800/171024 finished\n",
      "159900/171024 finished\n",
      "160000/171024 finished\n",
      "160100/171024 finished\n",
      "160200/171024 finished\n",
      "160300/171024 finished\n",
      "160400/171024 finished\n",
      "160500/171024 finished\n",
      "160600/171024 finished\n",
      "160700/171024 finished\n",
      "160800/171024 finished\n",
      "160900/171024 finished\n",
      "161000/171024 finished\n",
      "161100/171024 finished\n",
      "161200/171024 finished\n",
      "161300/171024 finished\n",
      "161400/171024 finished\n",
      "161500/171024 finished\n",
      "161600/171024 finished\n",
      "161700/171024 finished\n",
      "161800/171024 finished\n",
      "161900/171024 finished\n",
      "162000/171024 finished\n",
      "162100/171024 finished\n",
      "162200/171024 finished\n",
      "162300/171024 finished\n",
      "162400/171024 finished\n",
      "162500/171024 finished\n",
      "162600/171024 finished\n",
      "162700/171024 finished\n",
      "162800/171024 finished\n",
      "162900/171024 finished\n",
      "163000/171024 finished\n",
      "163100/171024 finished\n",
      "163200/171024 finished\n",
      "163300/171024 finished\n",
      "163400/171024 finished\n",
      "163500/171024 finished\n",
      "163600/171024 finished\n",
      "163700/171024 finished\n",
      "163800/171024 finished\n",
      "163900/171024 finished\n",
      "164000/171024 finished\n",
      "164100/171024 finished\n",
      "164200/171024 finished\n",
      "164300/171024 finished\n",
      "164400/171024 finished\n",
      "164500/171024 finished\n",
      "164600/171024 finished\n",
      "164700/171024 finished\n",
      "164800/171024 finished\n",
      "164900/171024 finished\n",
      "165000/171024 finished\n",
      "165100/171024 finished\n",
      "165200/171024 finished\n",
      "165300/171024 finished\n",
      "165400/171024 finished\n",
      "165500/171024 finished\n",
      "165600/171024 finished\n",
      "165700/171024 finished\n",
      "165800/171024 finished\n",
      "165900/171024 finished\n",
      "166000/171024 finished\n",
      "166100/171024 finished\n",
      "166200/171024 finished\n",
      "166300/171024 finished\n",
      "166400/171024 finished\n",
      "166500/171024 finished\n",
      "166600/171024 finished\n",
      "166700/171024 finished\n",
      "166800/171024 finished\n",
      "166900/171024 finished\n",
      "167000/171024 finished\n",
      "167100/171024 finished\n",
      "167200/171024 finished\n",
      "167300/171024 finished\n",
      "167400/171024 finished\n",
      "167500/171024 finished\n",
      "167600/171024 finished\n",
      "167700/171024 finished\n",
      "167800/171024 finished\n",
      "167900/171024 finished\n",
      "168000/171024 finished\n",
      "168100/171024 finished\n",
      "168200/171024 finished\n",
      "168300/171024 finished\n",
      "168400/171024 finished\n",
      "168500/171024 finished\n",
      "168600/171024 finished\n",
      "168700/171024 finished\n",
      "168800/171024 finished\n",
      "168900/171024 finished\n",
      "169000/171024 finished\n",
      "169100/171024 finished\n",
      "169200/171024 finished\n",
      "169300/171024 finished\n",
      "169400/171024 finished\n",
      "169500/171024 finished\n",
      "169600/171024 finished\n",
      "169700/171024 finished\n",
      "169800/171024 finished\n",
      "169900/171024 finished\n",
      "170000/171024 finished\n",
      "170100/171024 finished\n",
      "170200/171024 finished\n",
      "170300/171024 finished\n",
      "170400/171024 finished\n",
      "170500/171024 finished\n",
      "170600/171024 finished\n",
      "170700/171024 finished\n",
      "170800/171024 finished\n",
      "170900/171024 finished\n",
      "171000/171024 finished\n"
     ]
    }
   ],
   "source": [
    "new_dict = {}\n",
    "for i in range(len(sm_df)):\n",
    "    new_dict[i]={}\n",
    "    line = sm_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['sentence'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        new_dict[i][key] = value\n",
    "    if len(new_dict)%100==0:\n",
    "        print('{}/{} finished'.format(len(new_dict),len(sm_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/80963 finished\n",
      "200/80963 finished\n",
      "300/80963 finished\n",
      "400/80963 finished\n",
      "500/80963 finished\n",
      "600/80963 finished\n",
      "700/80963 finished\n",
      "800/80963 finished\n",
      "900/80963 finished\n",
      "1000/80963 finished\n",
      "1100/80963 finished\n",
      "1200/80963 finished\n",
      "1300/80963 finished\n",
      "1400/80963 finished\n",
      "1500/80963 finished\n",
      "1600/80963 finished\n",
      "1700/80963 finished\n",
      "1800/80963 finished\n",
      "1900/80963 finished\n",
      "2000/80963 finished\n",
      "2100/80963 finished\n",
      "2200/80963 finished\n",
      "2300/80963 finished\n",
      "2400/80963 finished\n",
      "2500/80963 finished\n",
      "2600/80963 finished\n",
      "2700/80963 finished\n",
      "2800/80963 finished\n",
      "2900/80963 finished\n",
      "3000/80963 finished\n",
      "3100/80963 finished\n",
      "3200/80963 finished\n",
      "3300/80963 finished\n",
      "3400/80963 finished\n",
      "3500/80963 finished\n",
      "3600/80963 finished\n",
      "3700/80963 finished\n",
      "3800/80963 finished\n",
      "3900/80963 finished\n",
      "4000/80963 finished\n",
      "4100/80963 finished\n",
      "4200/80963 finished\n",
      "4300/80963 finished\n",
      "4400/80963 finished\n",
      "4500/80963 finished\n",
      "4600/80963 finished\n",
      "4700/80963 finished\n",
      "4800/80963 finished\n",
      "4900/80963 finished\n",
      "5000/80963 finished\n",
      "5100/80963 finished\n",
      "5200/80963 finished\n",
      "5300/80963 finished\n",
      "5400/80963 finished\n",
      "5500/80963 finished\n",
      "5600/80963 finished\n",
      "5700/80963 finished\n",
      "5800/80963 finished\n",
      "5900/80963 finished\n",
      "6000/80963 finished\n",
      "6100/80963 finished\n",
      "6200/80963 finished\n",
      "6300/80963 finished\n",
      "6400/80963 finished\n",
      "6500/80963 finished\n",
      "6600/80963 finished\n",
      "6700/80963 finished\n",
      "6800/80963 finished\n",
      "6900/80963 finished\n",
      "7000/80963 finished\n",
      "7100/80963 finished\n",
      "7200/80963 finished\n",
      "7300/80963 finished\n",
      "7400/80963 finished\n",
      "7500/80963 finished\n",
      "7600/80963 finished\n",
      "7700/80963 finished\n",
      "7800/80963 finished\n",
      "7900/80963 finished\n",
      "8000/80963 finished\n",
      "8100/80963 finished\n",
      "8200/80963 finished\n",
      "8300/80963 finished\n",
      "8400/80963 finished\n",
      "8500/80963 finished\n",
      "8600/80963 finished\n",
      "8700/80963 finished\n",
      "8800/80963 finished\n",
      "8900/80963 finished\n",
      "9000/80963 finished\n",
      "9100/80963 finished\n",
      "9200/80963 finished\n",
      "9300/80963 finished\n",
      "9400/80963 finished\n",
      "9500/80963 finished\n",
      "9600/80963 finished\n",
      "9700/80963 finished\n",
      "9800/80963 finished\n",
      "9900/80963 finished\n",
      "10000/80963 finished\n",
      "10100/80963 finished\n",
      "10200/80963 finished\n",
      "10300/80963 finished\n",
      "10400/80963 finished\n",
      "10500/80963 finished\n",
      "10600/80963 finished\n",
      "10700/80963 finished\n",
      "10800/80963 finished\n",
      "10900/80963 finished\n",
      "11000/80963 finished\n",
      "11100/80963 finished\n",
      "11200/80963 finished\n",
      "11300/80963 finished\n",
      "11400/80963 finished\n",
      "11500/80963 finished\n",
      "11600/80963 finished\n",
      "11700/80963 finished\n",
      "11800/80963 finished\n",
      "11900/80963 finished\n",
      "12000/80963 finished\n",
      "12100/80963 finished\n",
      "12200/80963 finished\n",
      "12300/80963 finished\n",
      "12400/80963 finished\n",
      "12500/80963 finished\n",
      "12600/80963 finished\n",
      "12700/80963 finished\n",
      "12800/80963 finished\n",
      "12900/80963 finished\n",
      "13000/80963 finished\n",
      "13100/80963 finished\n",
      "13200/80963 finished\n",
      "13300/80963 finished\n",
      "13400/80963 finished\n",
      "13500/80963 finished\n",
      "13600/80963 finished\n",
      "13700/80963 finished\n",
      "13800/80963 finished\n",
      "13900/80963 finished\n",
      "14000/80963 finished\n",
      "14100/80963 finished\n",
      "14200/80963 finished\n",
      "14300/80963 finished\n",
      "14400/80963 finished\n",
      "14500/80963 finished\n",
      "14600/80963 finished\n",
      "14700/80963 finished\n",
      "14800/80963 finished\n",
      "14900/80963 finished\n",
      "15000/80963 finished\n",
      "15100/80963 finished\n",
      "15200/80963 finished\n",
      "15300/80963 finished\n",
      "15400/80963 finished\n",
      "15500/80963 finished\n",
      "15600/80963 finished\n",
      "15700/80963 finished\n",
      "15800/80963 finished\n",
      "15900/80963 finished\n",
      "16000/80963 finished\n",
      "16100/80963 finished\n",
      "16200/80963 finished\n",
      "16300/80963 finished\n",
      "16400/80963 finished\n",
      "16500/80963 finished\n",
      "16600/80963 finished\n",
      "16700/80963 finished\n",
      "16800/80963 finished\n",
      "16900/80963 finished\n",
      "17000/80963 finished\n",
      "17100/80963 finished\n",
      "17200/80963 finished\n",
      "17300/80963 finished\n",
      "17400/80963 finished\n",
      "17500/80963 finished\n",
      "17600/80963 finished\n",
      "17700/80963 finished\n",
      "17800/80963 finished\n",
      "17900/80963 finished\n",
      "18000/80963 finished\n",
      "18100/80963 finished\n",
      "18200/80963 finished\n",
      "18300/80963 finished\n",
      "18400/80963 finished\n",
      "18500/80963 finished\n",
      "18600/80963 finished\n",
      "18700/80963 finished\n",
      "18800/80963 finished\n",
      "18900/80963 finished\n",
      "19000/80963 finished\n",
      "19100/80963 finished\n",
      "19200/80963 finished\n",
      "19300/80963 finished\n",
      "19400/80963 finished\n",
      "19500/80963 finished\n",
      "19600/80963 finished\n",
      "19700/80963 finished\n",
      "19800/80963 finished\n",
      "19900/80963 finished\n",
      "20000/80963 finished\n",
      "20100/80963 finished\n",
      "20200/80963 finished\n",
      "20300/80963 finished\n",
      "20400/80963 finished\n",
      "20500/80963 finished\n",
      "20600/80963 finished\n",
      "20700/80963 finished\n",
      "20800/80963 finished\n",
      "20900/80963 finished\n",
      "21000/80963 finished\n",
      "21100/80963 finished\n",
      "21200/80963 finished\n",
      "21300/80963 finished\n",
      "21400/80963 finished\n",
      "21500/80963 finished\n",
      "21600/80963 finished\n",
      "21700/80963 finished\n",
      "21800/80963 finished\n",
      "21900/80963 finished\n",
      "22000/80963 finished\n",
      "22100/80963 finished\n",
      "22200/80963 finished\n",
      "22300/80963 finished\n",
      "22400/80963 finished\n",
      "22500/80963 finished\n",
      "22600/80963 finished\n",
      "22700/80963 finished\n",
      "22800/80963 finished\n",
      "22900/80963 finished\n",
      "23000/80963 finished\n",
      "23100/80963 finished\n",
      "23200/80963 finished\n",
      "23300/80963 finished\n",
      "23400/80963 finished\n",
      "23500/80963 finished\n",
      "23600/80963 finished\n",
      "23700/80963 finished\n",
      "23800/80963 finished\n",
      "23900/80963 finished\n",
      "24000/80963 finished\n",
      "24100/80963 finished\n",
      "24200/80963 finished\n",
      "24300/80963 finished\n",
      "24400/80963 finished\n",
      "24500/80963 finished\n",
      "24600/80963 finished\n",
      "24700/80963 finished\n",
      "24800/80963 finished\n",
      "24900/80963 finished\n",
      "25000/80963 finished\n",
      "25100/80963 finished\n",
      "25200/80963 finished\n",
      "25300/80963 finished\n",
      "25400/80963 finished\n",
      "25500/80963 finished\n",
      "25600/80963 finished\n",
      "25700/80963 finished\n",
      "25800/80963 finished\n",
      "25900/80963 finished\n",
      "26000/80963 finished\n",
      "26100/80963 finished\n",
      "26200/80963 finished\n",
      "26300/80963 finished\n",
      "26400/80963 finished\n",
      "26500/80963 finished\n",
      "26600/80963 finished\n",
      "26700/80963 finished\n",
      "26800/80963 finished\n",
      "26900/80963 finished\n",
      "27000/80963 finished\n",
      "27100/80963 finished\n",
      "27200/80963 finished\n",
      "27300/80963 finished\n",
      "27400/80963 finished\n",
      "27500/80963 finished\n",
      "27600/80963 finished\n",
      "27700/80963 finished\n",
      "27800/80963 finished\n",
      "27900/80963 finished\n",
      "28000/80963 finished\n",
      "28100/80963 finished\n",
      "28200/80963 finished\n",
      "28300/80963 finished\n",
      "28400/80963 finished\n",
      "28500/80963 finished\n",
      "28600/80963 finished\n",
      "28700/80963 finished\n",
      "28800/80963 finished\n",
      "28900/80963 finished\n",
      "29000/80963 finished\n",
      "29100/80963 finished\n",
      "29200/80963 finished\n",
      "29300/80963 finished\n",
      "29400/80963 finished\n",
      "29500/80963 finished\n",
      "29600/80963 finished\n",
      "29700/80963 finished\n",
      "29800/80963 finished\n",
      "29900/80963 finished\n",
      "30000/80963 finished\n",
      "30100/80963 finished\n",
      "30200/80963 finished\n",
      "30300/80963 finished\n",
      "30400/80963 finished\n",
      "30500/80963 finished\n",
      "30600/80963 finished\n",
      "30700/80963 finished\n",
      "30800/80963 finished\n",
      "30900/80963 finished\n",
      "31000/80963 finished\n",
      "31100/80963 finished\n",
      "31200/80963 finished\n",
      "31300/80963 finished\n",
      "31400/80963 finished\n",
      "31500/80963 finished\n",
      "31600/80963 finished\n",
      "31700/80963 finished\n",
      "31800/80963 finished\n",
      "31900/80963 finished\n",
      "32000/80963 finished\n",
      "32100/80963 finished\n",
      "32200/80963 finished\n",
      "32300/80963 finished\n",
      "32400/80963 finished\n",
      "32500/80963 finished\n",
      "32600/80963 finished\n",
      "32700/80963 finished\n",
      "32800/80963 finished\n",
      "32900/80963 finished\n",
      "33000/80963 finished\n",
      "33100/80963 finished\n",
      "33200/80963 finished\n",
      "33300/80963 finished\n",
      "33400/80963 finished\n",
      "33500/80963 finished\n",
      "33600/80963 finished\n",
      "33700/80963 finished\n",
      "33800/80963 finished\n",
      "33900/80963 finished\n",
      "34000/80963 finished\n",
      "34100/80963 finished\n",
      "34200/80963 finished\n",
      "34300/80963 finished\n",
      "34400/80963 finished\n",
      "34500/80963 finished\n",
      "34600/80963 finished\n",
      "34700/80963 finished\n",
      "34800/80963 finished\n",
      "34900/80963 finished\n",
      "35000/80963 finished\n",
      "35100/80963 finished\n",
      "35200/80963 finished\n",
      "35300/80963 finished\n",
      "35400/80963 finished\n",
      "35500/80963 finished\n",
      "35600/80963 finished\n",
      "35700/80963 finished\n",
      "35800/80963 finished\n",
      "35900/80963 finished\n",
      "36000/80963 finished\n",
      "36100/80963 finished\n",
      "36200/80963 finished\n",
      "36300/80963 finished\n",
      "36400/80963 finished\n",
      "36500/80963 finished\n",
      "36600/80963 finished\n",
      "36700/80963 finished\n",
      "36800/80963 finished\n",
      "36900/80963 finished\n",
      "37000/80963 finished\n",
      "37100/80963 finished\n",
      "37200/80963 finished\n",
      "37300/80963 finished\n",
      "37400/80963 finished\n",
      "37500/80963 finished\n",
      "37600/80963 finished\n",
      "37700/80963 finished\n",
      "37800/80963 finished\n",
      "37900/80963 finished\n",
      "38000/80963 finished\n",
      "38100/80963 finished\n",
      "38200/80963 finished\n",
      "38300/80963 finished\n",
      "38400/80963 finished\n",
      "38500/80963 finished\n",
      "38600/80963 finished\n",
      "38700/80963 finished\n",
      "38800/80963 finished\n",
      "38900/80963 finished\n",
      "39000/80963 finished\n",
      "39100/80963 finished\n",
      "39200/80963 finished\n",
      "39300/80963 finished\n",
      "39400/80963 finished\n",
      "39500/80963 finished\n",
      "39600/80963 finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39700/80963 finished\n",
      "39800/80963 finished\n",
      "39900/80963 finished\n",
      "40000/80963 finished\n",
      "40100/80963 finished\n",
      "40200/80963 finished\n",
      "40300/80963 finished\n",
      "40400/80963 finished\n",
      "40500/80963 finished\n",
      "40600/80963 finished\n",
      "40700/80963 finished\n",
      "40800/80963 finished\n",
      "40900/80963 finished\n",
      "41000/80963 finished\n",
      "41100/80963 finished\n",
      "41200/80963 finished\n",
      "41300/80963 finished\n",
      "41400/80963 finished\n",
      "41500/80963 finished\n",
      "41600/80963 finished\n",
      "41700/80963 finished\n",
      "41800/80963 finished\n",
      "41900/80963 finished\n",
      "42000/80963 finished\n",
      "42100/80963 finished\n",
      "42200/80963 finished\n",
      "42300/80963 finished\n",
      "42400/80963 finished\n",
      "42500/80963 finished\n",
      "42600/80963 finished\n",
      "42700/80963 finished\n",
      "42800/80963 finished\n",
      "42900/80963 finished\n",
      "43000/80963 finished\n",
      "43100/80963 finished\n",
      "43200/80963 finished\n",
      "43300/80963 finished\n",
      "43400/80963 finished\n",
      "43500/80963 finished\n",
      "43600/80963 finished\n",
      "43700/80963 finished\n",
      "43800/80963 finished\n",
      "43900/80963 finished\n",
      "44000/80963 finished\n",
      "44100/80963 finished\n",
      "44200/80963 finished\n",
      "44300/80963 finished\n",
      "44400/80963 finished\n",
      "44500/80963 finished\n",
      "44600/80963 finished\n",
      "44700/80963 finished\n",
      "44800/80963 finished\n",
      "44900/80963 finished\n",
      "45000/80963 finished\n",
      "45100/80963 finished\n",
      "45200/80963 finished\n",
      "45300/80963 finished\n",
      "45400/80963 finished\n",
      "45500/80963 finished\n",
      "45600/80963 finished\n",
      "45700/80963 finished\n",
      "45800/80963 finished\n",
      "45900/80963 finished\n",
      "46000/80963 finished\n",
      "46100/80963 finished\n",
      "46200/80963 finished\n",
      "46300/80963 finished\n",
      "46400/80963 finished\n",
      "46500/80963 finished\n",
      "46600/80963 finished\n",
      "46700/80963 finished\n",
      "46800/80963 finished\n",
      "46900/80963 finished\n",
      "47000/80963 finished\n",
      "47100/80963 finished\n",
      "47200/80963 finished\n",
      "47300/80963 finished\n",
      "47400/80963 finished\n",
      "47500/80963 finished\n",
      "47600/80963 finished\n",
      "47700/80963 finished\n",
      "47800/80963 finished\n",
      "47900/80963 finished\n",
      "48000/80963 finished\n",
      "48100/80963 finished\n",
      "48200/80963 finished\n",
      "48300/80963 finished\n",
      "48400/80963 finished\n",
      "48500/80963 finished\n",
      "48600/80963 finished\n",
      "48700/80963 finished\n",
      "48800/80963 finished\n",
      "48900/80963 finished\n",
      "49000/80963 finished\n",
      "49100/80963 finished\n",
      "49200/80963 finished\n",
      "49300/80963 finished\n",
      "49400/80963 finished\n",
      "49500/80963 finished\n",
      "49600/80963 finished\n",
      "49700/80963 finished\n",
      "49800/80963 finished\n",
      "49900/80963 finished\n",
      "50000/80963 finished\n",
      "50100/80963 finished\n",
      "50200/80963 finished\n",
      "50300/80963 finished\n",
      "50400/80963 finished\n",
      "50500/80963 finished\n",
      "50600/80963 finished\n",
      "50700/80963 finished\n",
      "50800/80963 finished\n",
      "50900/80963 finished\n",
      "51000/80963 finished\n",
      "51100/80963 finished\n",
      "51200/80963 finished\n",
      "51300/80963 finished\n",
      "51400/80963 finished\n",
      "51500/80963 finished\n",
      "51600/80963 finished\n",
      "51700/80963 finished\n",
      "51800/80963 finished\n",
      "51900/80963 finished\n",
      "52000/80963 finished\n",
      "52100/80963 finished\n",
      "52200/80963 finished\n",
      "52300/80963 finished\n",
      "52400/80963 finished\n",
      "52500/80963 finished\n",
      "52600/80963 finished\n",
      "52700/80963 finished\n",
      "52800/80963 finished\n",
      "52900/80963 finished\n",
      "53000/80963 finished\n",
      "53100/80963 finished\n",
      "53200/80963 finished\n",
      "53300/80963 finished\n",
      "53400/80963 finished\n",
      "53500/80963 finished\n",
      "53600/80963 finished\n",
      "53700/80963 finished\n",
      "53800/80963 finished\n",
      "53900/80963 finished\n",
      "54000/80963 finished\n",
      "54100/80963 finished\n",
      "54200/80963 finished\n",
      "54300/80963 finished\n",
      "54400/80963 finished\n",
      "54500/80963 finished\n",
      "54600/80963 finished\n",
      "54700/80963 finished\n",
      "54800/80963 finished\n",
      "54900/80963 finished\n",
      "55000/80963 finished\n",
      "55100/80963 finished\n",
      "55200/80963 finished\n",
      "55300/80963 finished\n",
      "55400/80963 finished\n",
      "55500/80963 finished\n",
      "55600/80963 finished\n",
      "55700/80963 finished\n",
      "55800/80963 finished\n",
      "55900/80963 finished\n",
      "56000/80963 finished\n",
      "56100/80963 finished\n",
      "56200/80963 finished\n",
      "56300/80963 finished\n",
      "56400/80963 finished\n",
      "56500/80963 finished\n",
      "56600/80963 finished\n",
      "56700/80963 finished\n",
      "56800/80963 finished\n",
      "56900/80963 finished\n",
      "57000/80963 finished\n",
      "57100/80963 finished\n",
      "57200/80963 finished\n",
      "57300/80963 finished\n",
      "57400/80963 finished\n",
      "57500/80963 finished\n",
      "57600/80963 finished\n",
      "57700/80963 finished\n",
      "57800/80963 finished\n",
      "57900/80963 finished\n",
      "58000/80963 finished\n",
      "58100/80963 finished\n",
      "58200/80963 finished\n",
      "58300/80963 finished\n",
      "58400/80963 finished\n",
      "58500/80963 finished\n",
      "58600/80963 finished\n",
      "58700/80963 finished\n",
      "58800/80963 finished\n",
      "58900/80963 finished\n",
      "59000/80963 finished\n",
      "59100/80963 finished\n",
      "59200/80963 finished\n",
      "59300/80963 finished\n",
      "59400/80963 finished\n",
      "59500/80963 finished\n",
      "59600/80963 finished\n",
      "59700/80963 finished\n",
      "59800/80963 finished\n",
      "59900/80963 finished\n",
      "60000/80963 finished\n",
      "60100/80963 finished\n",
      "60200/80963 finished\n",
      "60300/80963 finished\n",
      "60400/80963 finished\n",
      "60500/80963 finished\n",
      "60600/80963 finished\n",
      "60700/80963 finished\n",
      "60800/80963 finished\n",
      "60900/80963 finished\n",
      "61000/80963 finished\n",
      "61100/80963 finished\n",
      "61200/80963 finished\n",
      "61300/80963 finished\n",
      "61400/80963 finished\n",
      "61500/80963 finished\n",
      "61600/80963 finished\n",
      "61700/80963 finished\n",
      "61800/80963 finished\n",
      "61900/80963 finished\n",
      "62000/80963 finished\n",
      "62100/80963 finished\n",
      "62200/80963 finished\n",
      "62300/80963 finished\n",
      "62400/80963 finished\n",
      "62500/80963 finished\n",
      "62600/80963 finished\n",
      "62700/80963 finished\n",
      "62800/80963 finished\n",
      "62900/80963 finished\n",
      "63000/80963 finished\n",
      "63100/80963 finished\n",
      "63200/80963 finished\n",
      "63300/80963 finished\n",
      "63400/80963 finished\n",
      "63500/80963 finished\n",
      "63600/80963 finished\n",
      "63700/80963 finished\n",
      "63800/80963 finished\n",
      "63900/80963 finished\n",
      "64000/80963 finished\n",
      "64100/80963 finished\n",
      "64200/80963 finished\n",
      "64300/80963 finished\n",
      "64400/80963 finished\n",
      "64500/80963 finished\n",
      "64600/80963 finished\n",
      "64700/80963 finished\n",
      "64800/80963 finished\n",
      "64900/80963 finished\n",
      "65000/80963 finished\n",
      "65100/80963 finished\n",
      "65200/80963 finished\n",
      "65300/80963 finished\n",
      "65400/80963 finished\n",
      "65500/80963 finished\n",
      "65600/80963 finished\n",
      "65700/80963 finished\n",
      "65800/80963 finished\n",
      "65900/80963 finished\n",
      "66000/80963 finished\n",
      "66100/80963 finished\n",
      "66200/80963 finished\n",
      "66300/80963 finished\n",
      "66400/80963 finished\n",
      "66500/80963 finished\n",
      "66600/80963 finished\n",
      "66700/80963 finished\n",
      "66800/80963 finished\n",
      "66900/80963 finished\n",
      "67000/80963 finished\n",
      "67100/80963 finished\n",
      "67200/80963 finished\n",
      "67300/80963 finished\n",
      "67400/80963 finished\n",
      "67500/80963 finished\n",
      "67600/80963 finished\n",
      "67700/80963 finished\n",
      "67800/80963 finished\n",
      "67900/80963 finished\n",
      "68000/80963 finished\n",
      "68100/80963 finished\n",
      "68200/80963 finished\n",
      "68300/80963 finished\n",
      "68400/80963 finished\n",
      "68500/80963 finished\n",
      "68600/80963 finished\n",
      "68700/80963 finished\n",
      "68800/80963 finished\n",
      "68900/80963 finished\n",
      "69000/80963 finished\n",
      "69100/80963 finished\n",
      "69200/80963 finished\n",
      "69300/80963 finished\n",
      "69400/80963 finished\n",
      "69500/80963 finished\n",
      "69600/80963 finished\n",
      "69700/80963 finished\n",
      "69800/80963 finished\n",
      "69900/80963 finished\n",
      "70000/80963 finished\n",
      "70100/80963 finished\n",
      "70200/80963 finished\n",
      "70300/80963 finished\n",
      "70400/80963 finished\n",
      "70500/80963 finished\n",
      "70600/80963 finished\n",
      "70700/80963 finished\n",
      "70800/80963 finished\n",
      "70900/80963 finished\n",
      "71000/80963 finished\n",
      "71100/80963 finished\n",
      "71200/80963 finished\n",
      "71300/80963 finished\n",
      "71400/80963 finished\n",
      "71500/80963 finished\n",
      "71600/80963 finished\n",
      "71700/80963 finished\n",
      "71800/80963 finished\n",
      "71900/80963 finished\n",
      "72000/80963 finished\n",
      "72100/80963 finished\n",
      "72200/80963 finished\n",
      "72300/80963 finished\n",
      "72400/80963 finished\n",
      "72500/80963 finished\n",
      "72600/80963 finished\n",
      "72700/80963 finished\n",
      "72800/80963 finished\n",
      "72900/80963 finished\n",
      "73000/80963 finished\n",
      "73100/80963 finished\n",
      "73200/80963 finished\n",
      "73300/80963 finished\n",
      "73400/80963 finished\n",
      "73500/80963 finished\n",
      "73600/80963 finished\n",
      "73700/80963 finished\n",
      "73800/80963 finished\n",
      "73900/80963 finished\n",
      "74000/80963 finished\n",
      "74100/80963 finished\n",
      "74200/80963 finished\n",
      "74300/80963 finished\n",
      "74400/80963 finished\n",
      "74500/80963 finished\n",
      "74600/80963 finished\n",
      "74700/80963 finished\n",
      "74800/80963 finished\n",
      "74900/80963 finished\n",
      "75000/80963 finished\n",
      "75100/80963 finished\n",
      "75200/80963 finished\n",
      "75300/80963 finished\n",
      "75400/80963 finished\n",
      "75500/80963 finished\n",
      "75600/80963 finished\n",
      "75700/80963 finished\n",
      "75800/80963 finished\n",
      "75900/80963 finished\n",
      "76000/80963 finished\n",
      "76100/80963 finished\n",
      "76200/80963 finished\n",
      "76300/80963 finished\n",
      "76400/80963 finished\n",
      "76500/80963 finished\n",
      "76600/80963 finished\n",
      "76700/80963 finished\n",
      "76800/80963 finished\n",
      "76900/80963 finished\n",
      "77000/80963 finished\n",
      "77100/80963 finished\n",
      "77200/80963 finished\n",
      "77300/80963 finished\n",
      "77400/80963 finished\n",
      "77500/80963 finished\n",
      "77600/80963 finished\n",
      "77700/80963 finished\n",
      "77800/80963 finished\n",
      "77900/80963 finished\n",
      "78000/80963 finished\n",
      "78100/80963 finished\n",
      "78200/80963 finished\n",
      "78300/80963 finished\n",
      "78400/80963 finished\n",
      "78500/80963 finished\n",
      "78600/80963 finished\n",
      "78700/80963 finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78800/80963 finished\n",
      "78900/80963 finished\n",
      "79000/80963 finished\n",
      "79100/80963 finished\n",
      "79200/80963 finished\n",
      "79300/80963 finished\n",
      "79400/80963 finished\n",
      "79500/80963 finished\n",
      "79600/80963 finished\n",
      "79700/80963 finished\n",
      "79800/80963 finished\n",
      "79900/80963 finished\n",
      "80000/80963 finished\n",
      "80100/80963 finished\n",
      "80200/80963 finished\n",
      "80300/80963 finished\n",
      "80400/80963 finished\n",
      "80500/80963 finished\n",
      "80600/80963 finished\n",
      "80700/80963 finished\n",
      "80800/80963 finished\n",
      "80900/80963 finished\n"
     ]
    }
   ],
   "source": [
    "des_dict = {}\n",
    "for i in range(len(sm_all_df)):\n",
    "    des_dict[i]={}\n",
    "    line = sm_all_df.iloc[i].copy()\n",
    "    predictions = predict_rating(line['revised_text'], classifier, vectorizer, classes, k=11)\n",
    "    for key, value in predictions:\n",
    "        des_dict[i][key] = value\n",
    "    if len(des_dict)%100==0:\n",
    "        print('{}/{} finished'.format(len(des_dict),len(sm_all_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.181625</td>\n",
       "      <td>0.064852</td>\n",
       "      <td>0.250929</td>\n",
       "      <td>0.127421</td>\n",
       "      <td>0.009266</td>\n",
       "      <td>0.196136</td>\n",
       "      <td>0.049570</td>\n",
       "      <td>0.079567</td>\n",
       "      <td>0.016884</td>\n",
       "      <td>0.022925</td>\n",
       "      <td>0.000824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.200316</td>\n",
       "      <td>0.065371</td>\n",
       "      <td>0.280426</td>\n",
       "      <td>0.163337</td>\n",
       "      <td>0.009722</td>\n",
       "      <td>0.211595</td>\n",
       "      <td>0.026202</td>\n",
       "      <td>0.025634</td>\n",
       "      <td>0.007318</td>\n",
       "      <td>0.009731</td>\n",
       "      <td>0.000350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.023386</td>\n",
       "      <td>0.041048</td>\n",
       "      <td>0.650610</td>\n",
       "      <td>0.120257</td>\n",
       "      <td>0.031732</td>\n",
       "      <td>0.123854</td>\n",
       "      <td>0.004549</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001533</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.000028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.080087</td>\n",
       "      <td>0.051694</td>\n",
       "      <td>0.216419</td>\n",
       "      <td>0.101298</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>0.212157</td>\n",
       "      <td>0.123733</td>\n",
       "      <td>0.054316</td>\n",
       "      <td>0.043083</td>\n",
       "      <td>0.097148</td>\n",
       "      <td>0.001515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.011231</td>\n",
       "      <td>0.092816</td>\n",
       "      <td>0.606865</td>\n",
       "      <td>0.129320</td>\n",
       "      <td>0.083559</td>\n",
       "      <td>0.070361</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.001056</td>\n",
       "      <td>0.001579</td>\n",
       "      <td>0.001387</td>\n",
       "      <td>0.000032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171019</th>\n",
       "      <td>0.020026</td>\n",
       "      <td>0.357152</td>\n",
       "      <td>0.331064</td>\n",
       "      <td>0.177906</td>\n",
       "      <td>0.024876</td>\n",
       "      <td>0.083381</td>\n",
       "      <td>0.001122</td>\n",
       "      <td>0.001206</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171020</th>\n",
       "      <td>0.122292</td>\n",
       "      <td>0.060564</td>\n",
       "      <td>0.283526</td>\n",
       "      <td>0.135095</td>\n",
       "      <td>0.010868</td>\n",
       "      <td>0.273299</td>\n",
       "      <td>0.046704</td>\n",
       "      <td>0.035098</td>\n",
       "      <td>0.012026</td>\n",
       "      <td>0.019668</td>\n",
       "      <td>0.000860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171021</th>\n",
       "      <td>0.020823</td>\n",
       "      <td>0.311447</td>\n",
       "      <td>0.365819</td>\n",
       "      <td>0.190683</td>\n",
       "      <td>0.026340</td>\n",
       "      <td>0.077438</td>\n",
       "      <td>0.001554</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.002185</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>0.000060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171022</th>\n",
       "      <td>0.058224</td>\n",
       "      <td>0.054545</td>\n",
       "      <td>0.307528</td>\n",
       "      <td>0.141623</td>\n",
       "      <td>0.015750</td>\n",
       "      <td>0.407372</td>\n",
       "      <td>0.005190</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.003392</td>\n",
       "      <td>0.000310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171023</th>\n",
       "      <td>0.017980</td>\n",
       "      <td>0.406759</td>\n",
       "      <td>0.329012</td>\n",
       "      <td>0.146924</td>\n",
       "      <td>0.021270</td>\n",
       "      <td>0.074011</td>\n",
       "      <td>0.000740</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.001184</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171024 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0         0.181625     0.064852      0.250929     0.127421    0.009266   \n",
       "1         0.200316     0.065371      0.280426     0.163337    0.009722   \n",
       "2         0.023386     0.041048      0.650610     0.120257    0.031732   \n",
       "3         0.080087     0.051694      0.216419     0.101298    0.018550   \n",
       "4         0.011231     0.092816      0.606865     0.129320    0.083559   \n",
       "...            ...          ...           ...          ...         ...   \n",
       "171019    0.020026     0.357152      0.331064     0.177906    0.024876   \n",
       "171020    0.122292     0.060564      0.283526     0.135095    0.010868   \n",
       "171021    0.020823     0.311447      0.365819     0.190683    0.026340   \n",
       "171022    0.058224     0.054545      0.307528     0.141623    0.015750   \n",
       "171023    0.017980     0.406759      0.329012     0.146924    0.021270   \n",
       "\n",
       "        Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0          0.196136      0.049570       0.079567     0.016884    0.022925   \n",
       "1          0.211595      0.026202       0.025634     0.007318    0.009731   \n",
       "2          0.123854      0.004549       0.002000     0.001533    0.001002   \n",
       "3          0.212157      0.123733       0.054316     0.043083    0.097148   \n",
       "4          0.070361      0.001795       0.001056     0.001579    0.001387   \n",
       "...             ...           ...            ...          ...         ...   \n",
       "171019     0.083381      0.001122       0.001206     0.001620    0.001590   \n",
       "171020     0.273299      0.046704       0.035098     0.012026    0.019668   \n",
       "171021     0.077438      0.001554       0.001953     0.002185    0.001698   \n",
       "171022     0.407372      0.005190       0.003834     0.002232    0.003392   \n",
       "171023     0.074011      0.000740       0.000778     0.001184    0.001296   \n",
       "\n",
       "          Others  \n",
       "0       0.000824  \n",
       "1       0.000350  \n",
       "2       0.000028  \n",
       "3       0.001515  \n",
       "4       0.000032  \n",
       "...          ...  \n",
       "171019  0.000056  \n",
       "171020  0.000860  \n",
       "171021  0.000060  \n",
       "171022  0.000310  \n",
       "171023  0.000046  \n",
       "\n",
       "[171024 rows x 11 columns]"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df = pd.DataFrame(new_dict).reindex(classes).T\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.300038</td>\n",
       "      <td>0.105972</td>\n",
       "      <td>0.110439</td>\n",
       "      <td>0.391179</td>\n",
       "      <td>0.042873</td>\n",
       "      <td>0.043235</td>\n",
       "      <td>0.002022</td>\n",
       "      <td>0.001575</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>0.001220</td>\n",
       "      <td>0.000422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.300537</td>\n",
       "      <td>0.105456</td>\n",
       "      <td>0.111514</td>\n",
       "      <td>0.387872</td>\n",
       "      <td>0.043060</td>\n",
       "      <td>0.045290</td>\n",
       "      <td>0.002039</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>0.001015</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.000434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.183531</td>\n",
       "      <td>0.225481</td>\n",
       "      <td>0.083111</td>\n",
       "      <td>0.449886</td>\n",
       "      <td>0.008262</td>\n",
       "      <td>0.042647</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.002684</td>\n",
       "      <td>0.001059</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.085485</td>\n",
       "      <td>0.132468</td>\n",
       "      <td>0.205980</td>\n",
       "      <td>0.378402</td>\n",
       "      <td>0.047569</td>\n",
       "      <td>0.071661</td>\n",
       "      <td>0.040116</td>\n",
       "      <td>0.015467</td>\n",
       "      <td>0.009485</td>\n",
       "      <td>0.013226</td>\n",
       "      <td>0.000141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.045470</td>\n",
       "      <td>0.187583</td>\n",
       "      <td>0.247151</td>\n",
       "      <td>0.351008</td>\n",
       "      <td>0.074293</td>\n",
       "      <td>0.075992</td>\n",
       "      <td>0.007469</td>\n",
       "      <td>0.002897</td>\n",
       "      <td>0.003285</td>\n",
       "      <td>0.004723</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80958</th>\n",
       "      <td>0.014668</td>\n",
       "      <td>0.161494</td>\n",
       "      <td>0.416799</td>\n",
       "      <td>0.236805</td>\n",
       "      <td>0.074207</td>\n",
       "      <td>0.085732</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.002078</td>\n",
       "      <td>0.003267</td>\n",
       "      <td>0.002727</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80959</th>\n",
       "      <td>0.025120</td>\n",
       "      <td>0.297634</td>\n",
       "      <td>0.334180</td>\n",
       "      <td>0.136499</td>\n",
       "      <td>0.015375</td>\n",
       "      <td>0.185791</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>0.001423</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0.001374</td>\n",
       "      <td>0.000083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80960</th>\n",
       "      <td>0.027727</td>\n",
       "      <td>0.222477</td>\n",
       "      <td>0.365697</td>\n",
       "      <td>0.173320</td>\n",
       "      <td>0.017452</td>\n",
       "      <td>0.184820</td>\n",
       "      <td>0.001975</td>\n",
       "      <td>0.002437</td>\n",
       "      <td>0.002078</td>\n",
       "      <td>0.001947</td>\n",
       "      <td>0.000069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80961</th>\n",
       "      <td>0.049469</td>\n",
       "      <td>0.091807</td>\n",
       "      <td>0.350125</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>0.015471</td>\n",
       "      <td>0.322296</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>0.001710</td>\n",
       "      <td>0.001439</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80962</th>\n",
       "      <td>0.017763</td>\n",
       "      <td>0.339121</td>\n",
       "      <td>0.343892</td>\n",
       "      <td>0.131618</td>\n",
       "      <td>0.016615</td>\n",
       "      <td>0.147517</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.000905</td>\n",
       "      <td>0.001081</td>\n",
       "      <td>0.000060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80963 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0        0.300038     0.105972      0.110439     0.391179    0.042873   \n",
       "1        0.300537     0.105456      0.111514     0.387872    0.043060   \n",
       "2        0.183531     0.225481      0.083111     0.449886    0.008262   \n",
       "3        0.085485     0.132468      0.205980     0.378402    0.047569   \n",
       "4        0.045470     0.187583      0.247151     0.351008    0.074293   \n",
       "...           ...          ...           ...          ...         ...   \n",
       "80958    0.014668     0.161494      0.416799     0.236805    0.074207   \n",
       "80959    0.025120     0.297634      0.334180     0.136499    0.015375   \n",
       "80960    0.027727     0.222477      0.365697     0.173320    0.017452   \n",
       "80961    0.049469     0.091807      0.350125     0.163804    0.015471   \n",
       "80962    0.017763     0.339121      0.343892     0.131618    0.016615   \n",
       "\n",
       "       Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0         0.043235      0.002022       0.001575     0.001025    0.001220   \n",
       "1         0.045290      0.002039       0.001567     0.001015    0.001215   \n",
       "2         0.042647      0.002100       0.002684     0.001059    0.001152   \n",
       "3         0.071661      0.040116       0.015467     0.009485    0.013226   \n",
       "4         0.075992      0.007469       0.002897     0.003285    0.004723   \n",
       "...            ...           ...            ...          ...         ...   \n",
       "80958     0.085732      0.002166       0.002078     0.003267    0.002727   \n",
       "80959     0.185791      0.001277       0.001423     0.001244    0.001374   \n",
       "80960     0.184820      0.001975       0.002437     0.002078    0.001947   \n",
       "80961     0.322296      0.002075       0.001710     0.001439    0.001700   \n",
       "80962     0.147517      0.000668       0.000760     0.000905    0.001081   \n",
       "\n",
       "         Others  \n",
       "0      0.000422  \n",
       "1      0.000434  \n",
       "2      0.000087  \n",
       "3      0.000141  \n",
       "4      0.000129  \n",
       "...         ...  \n",
       "80958  0.000056  \n",
       "80959  0.000083  \n",
       "80960  0.000069  \n",
       "80961  0.000105  \n",
       "80962  0.000060  \n",
       "\n",
       "[80963 rows x 11 columns]"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_all_df = pd.DataFrame(des_dict).reindex(classes).T\n",
    "score_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = score_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = score_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = score_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_df = pd.concat([score_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.181625</td>\n",
       "      <td>0.064852</td>\n",
       "      <td>0.250929</td>\n",
       "      <td>0.127421</td>\n",
       "      <td>0.009266</td>\n",
       "      <td>0.196136</td>\n",
       "      <td>0.049570</td>\n",
       "      <td>0.079567</td>\n",
       "      <td>0.016884</td>\n",
       "      <td>0.022925</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.250929</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.196136</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.181625</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.250929</td>\n",
       "      <td>0.628690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.200316</td>\n",
       "      <td>0.065371</td>\n",
       "      <td>0.280426</td>\n",
       "      <td>0.163337</td>\n",
       "      <td>0.009722</td>\n",
       "      <td>0.211595</td>\n",
       "      <td>0.026202</td>\n",
       "      <td>0.025634</td>\n",
       "      <td>0.007318</td>\n",
       "      <td>0.009731</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.280426</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.211595</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.200316</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.280426</td>\n",
       "      <td>0.692337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.023386</td>\n",
       "      <td>0.041048</td>\n",
       "      <td>0.650610</td>\n",
       "      <td>0.120257</td>\n",
       "      <td>0.031732</td>\n",
       "      <td>0.123854</td>\n",
       "      <td>0.004549</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001533</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.650610</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.123854</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.120257</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.650610</td>\n",
       "      <td>0.894722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.080087</td>\n",
       "      <td>0.051694</td>\n",
       "      <td>0.216419</td>\n",
       "      <td>0.101298</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>0.212157</td>\n",
       "      <td>0.123733</td>\n",
       "      <td>0.054316</td>\n",
       "      <td>0.043083</td>\n",
       "      <td>0.097148</td>\n",
       "      <td>0.001515</td>\n",
       "      <td>0.216419</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.212157</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.123733</td>\n",
       "      <td>Criteria vii</td>\n",
       "      <td>0.216419</td>\n",
       "      <td>0.552309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.011231</td>\n",
       "      <td>0.092816</td>\n",
       "      <td>0.606865</td>\n",
       "      <td>0.129320</td>\n",
       "      <td>0.083559</td>\n",
       "      <td>0.070361</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.001056</td>\n",
       "      <td>0.001579</td>\n",
       "      <td>0.001387</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.606865</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.129320</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.092816</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.606865</td>\n",
       "      <td>0.829000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171019</th>\n",
       "      <td>0.020026</td>\n",
       "      <td>0.357152</td>\n",
       "      <td>0.331064</td>\n",
       "      <td>0.177906</td>\n",
       "      <td>0.024876</td>\n",
       "      <td>0.083381</td>\n",
       "      <td>0.001122</td>\n",
       "      <td>0.001206</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.357152</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.331064</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.177906</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.357152</td>\n",
       "      <td>0.866123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171020</th>\n",
       "      <td>0.122292</td>\n",
       "      <td>0.060564</td>\n",
       "      <td>0.283526</td>\n",
       "      <td>0.135095</td>\n",
       "      <td>0.010868</td>\n",
       "      <td>0.273299</td>\n",
       "      <td>0.046704</td>\n",
       "      <td>0.035098</td>\n",
       "      <td>0.012026</td>\n",
       "      <td>0.019668</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.283526</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.273299</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.135095</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.283526</td>\n",
       "      <td>0.691920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171021</th>\n",
       "      <td>0.020823</td>\n",
       "      <td>0.311447</td>\n",
       "      <td>0.365819</td>\n",
       "      <td>0.190683</td>\n",
       "      <td>0.026340</td>\n",
       "      <td>0.077438</td>\n",
       "      <td>0.001554</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.002185</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.365819</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.311447</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.190683</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.365819</td>\n",
       "      <td>0.867949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171022</th>\n",
       "      <td>0.058224</td>\n",
       "      <td>0.054545</td>\n",
       "      <td>0.307528</td>\n",
       "      <td>0.141623</td>\n",
       "      <td>0.015750</td>\n",
       "      <td>0.407372</td>\n",
       "      <td>0.005190</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.003392</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.407372</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.307528</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.141623</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.407372</td>\n",
       "      <td>0.856523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171023</th>\n",
       "      <td>0.017980</td>\n",
       "      <td>0.406759</td>\n",
       "      <td>0.329012</td>\n",
       "      <td>0.146924</td>\n",
       "      <td>0.021270</td>\n",
       "      <td>0.074011</td>\n",
       "      <td>0.000740</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.001184</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.406759</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.329012</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.146924</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.406759</td>\n",
       "      <td>0.882695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171024 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0         0.181625     0.064852      0.250929     0.127421    0.009266   \n",
       "1         0.200316     0.065371      0.280426     0.163337    0.009722   \n",
       "2         0.023386     0.041048      0.650610     0.120257    0.031732   \n",
       "3         0.080087     0.051694      0.216419     0.101298    0.018550   \n",
       "4         0.011231     0.092816      0.606865     0.129320    0.083559   \n",
       "...            ...          ...           ...          ...         ...   \n",
       "171019    0.020026     0.357152      0.331064     0.177906    0.024876   \n",
       "171020    0.122292     0.060564      0.283526     0.135095    0.010868   \n",
       "171021    0.020823     0.311447      0.365819     0.190683    0.026340   \n",
       "171022    0.058224     0.054545      0.307528     0.141623    0.015750   \n",
       "171023    0.017980     0.406759      0.329012     0.146924    0.021270   \n",
       "\n",
       "        Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0          0.196136      0.049570       0.079567     0.016884    0.022925   \n",
       "1          0.211595      0.026202       0.025634     0.007318    0.009731   \n",
       "2          0.123854      0.004549       0.002000     0.001533    0.001002   \n",
       "3          0.212157      0.123733       0.054316     0.043083    0.097148   \n",
       "4          0.070361      0.001795       0.001056     0.001579    0.001387   \n",
       "...             ...           ...            ...          ...         ...   \n",
       "171019     0.083381      0.001122       0.001206     0.001620    0.001590   \n",
       "171020     0.273299      0.046704       0.035098     0.012026    0.019668   \n",
       "171021     0.077438      0.001554       0.001953     0.002185    0.001698   \n",
       "171022     0.407372      0.005190       0.003834     0.002232    0.003392   \n",
       "171023     0.074011      0.000740       0.000778     0.001184    0.001296   \n",
       "\n",
       "          Others  max_1_val     max_1_col  max_2_val     max_2_col  max_3_val  \\\n",
       "0       0.000824   0.250929  Criteria iii   0.196136   Criteria vi   0.181625   \n",
       "1       0.000350   0.280426  Criteria iii   0.211595   Criteria vi   0.200316   \n",
       "2       0.000028   0.650610  Criteria iii   0.123854   Criteria vi   0.120257   \n",
       "3       0.001515   0.216419  Criteria iii   0.212157   Criteria vi   0.123733   \n",
       "4       0.000032   0.606865  Criteria iii   0.129320   Criteria iv   0.092816   \n",
       "...          ...        ...           ...        ...           ...        ...   \n",
       "171019  0.000056   0.357152   Criteria ii   0.331064  Criteria iii   0.177906   \n",
       "171020  0.000860   0.283526  Criteria iii   0.273299   Criteria vi   0.135095   \n",
       "171021  0.000060   0.365819  Criteria iii   0.311447   Criteria ii   0.190683   \n",
       "171022  0.000310   0.407372   Criteria vi   0.307528  Criteria iii   0.141623   \n",
       "171023  0.000046   0.406759   Criteria ii   0.329012  Criteria iii   0.146924   \n",
       "\n",
       "           max_3_col     max_1     max_3  \n",
       "0         Criteria i  0.250929  0.628690  \n",
       "1         Criteria i  0.280426  0.692337  \n",
       "2        Criteria iv  0.650610  0.894722  \n",
       "3       Criteria vii  0.216419  0.552309  \n",
       "4        Criteria ii  0.606865  0.829000  \n",
       "...              ...       ...       ...  \n",
       "171019   Criteria iv  0.357152  0.866123  \n",
       "171020   Criteria iv  0.283526  0.691920  \n",
       "171021   Criteria iv  0.365819  0.867949  \n",
       "171022   Criteria iv  0.407372  0.856523  \n",
       "171023   Criteria iv  0.406759  0.882695  \n",
       "\n",
       "[171024 rows x 19 columns]"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df['max_1'] = score_df['max_1_val']\n",
    "score_df['max_3'] = score_df['max_1_val']+score_df['max_2_val']+score_df['max_3_val']\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df = sm_df.merge(score_df, how='left', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = score_all_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = score_all_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = score_all_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_all_df = pd.concat([score_all_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.300038</td>\n",
       "      <td>0.105972</td>\n",
       "      <td>0.110439</td>\n",
       "      <td>0.391179</td>\n",
       "      <td>0.042873</td>\n",
       "      <td>0.043235</td>\n",
       "      <td>0.002022</td>\n",
       "      <td>0.001575</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>0.001220</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>0.391179</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.300038</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.110439</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.391179</td>\n",
       "      <td>0.801656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.300537</td>\n",
       "      <td>0.105456</td>\n",
       "      <td>0.111514</td>\n",
       "      <td>0.387872</td>\n",
       "      <td>0.043060</td>\n",
       "      <td>0.045290</td>\n",
       "      <td>0.002039</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>0.001015</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.387872</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.300537</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.111514</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.387872</td>\n",
       "      <td>0.799923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.183531</td>\n",
       "      <td>0.225481</td>\n",
       "      <td>0.083111</td>\n",
       "      <td>0.449886</td>\n",
       "      <td>0.008262</td>\n",
       "      <td>0.042647</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.002684</td>\n",
       "      <td>0.001059</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.449886</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.225481</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.183531</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.449886</td>\n",
       "      <td>0.858898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.085485</td>\n",
       "      <td>0.132468</td>\n",
       "      <td>0.205980</td>\n",
       "      <td>0.378402</td>\n",
       "      <td>0.047569</td>\n",
       "      <td>0.071661</td>\n",
       "      <td>0.040116</td>\n",
       "      <td>0.015467</td>\n",
       "      <td>0.009485</td>\n",
       "      <td>0.013226</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.378402</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.205980</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.132468</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.378402</td>\n",
       "      <td>0.716850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.045470</td>\n",
       "      <td>0.187583</td>\n",
       "      <td>0.247151</td>\n",
       "      <td>0.351008</td>\n",
       "      <td>0.074293</td>\n",
       "      <td>0.075992</td>\n",
       "      <td>0.007469</td>\n",
       "      <td>0.002897</td>\n",
       "      <td>0.003285</td>\n",
       "      <td>0.004723</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.351008</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.247151</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.187583</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.351008</td>\n",
       "      <td>0.785742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80958</th>\n",
       "      <td>0.014668</td>\n",
       "      <td>0.161494</td>\n",
       "      <td>0.416799</td>\n",
       "      <td>0.236805</td>\n",
       "      <td>0.074207</td>\n",
       "      <td>0.085732</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.002078</td>\n",
       "      <td>0.003267</td>\n",
       "      <td>0.002727</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.416799</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.236805</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.161494</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.416799</td>\n",
       "      <td>0.815098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80959</th>\n",
       "      <td>0.025120</td>\n",
       "      <td>0.297634</td>\n",
       "      <td>0.334180</td>\n",
       "      <td>0.136499</td>\n",
       "      <td>0.015375</td>\n",
       "      <td>0.185791</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>0.001423</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0.001374</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.334180</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.297634</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.185791</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.334180</td>\n",
       "      <td>0.817606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80960</th>\n",
       "      <td>0.027727</td>\n",
       "      <td>0.222477</td>\n",
       "      <td>0.365697</td>\n",
       "      <td>0.173320</td>\n",
       "      <td>0.017452</td>\n",
       "      <td>0.184820</td>\n",
       "      <td>0.001975</td>\n",
       "      <td>0.002437</td>\n",
       "      <td>0.002078</td>\n",
       "      <td>0.001947</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.365697</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.222477</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.184820</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365697</td>\n",
       "      <td>0.772994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80961</th>\n",
       "      <td>0.049469</td>\n",
       "      <td>0.091807</td>\n",
       "      <td>0.350125</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>0.015471</td>\n",
       "      <td>0.322296</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>0.001710</td>\n",
       "      <td>0.001439</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.350125</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.322296</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.350125</td>\n",
       "      <td>0.836224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80962</th>\n",
       "      <td>0.017763</td>\n",
       "      <td>0.339121</td>\n",
       "      <td>0.343892</td>\n",
       "      <td>0.131618</td>\n",
       "      <td>0.016615</td>\n",
       "      <td>0.147517</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.000905</td>\n",
       "      <td>0.001081</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.343892</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.339121</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.147517</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.343892</td>\n",
       "      <td>0.830531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80963 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0        0.300038     0.105972      0.110439     0.391179    0.042873   \n",
       "1        0.300537     0.105456      0.111514     0.387872    0.043060   \n",
       "2        0.183531     0.225481      0.083111     0.449886    0.008262   \n",
       "3        0.085485     0.132468      0.205980     0.378402    0.047569   \n",
       "4        0.045470     0.187583      0.247151     0.351008    0.074293   \n",
       "...           ...          ...           ...          ...         ...   \n",
       "80958    0.014668     0.161494      0.416799     0.236805    0.074207   \n",
       "80959    0.025120     0.297634      0.334180     0.136499    0.015375   \n",
       "80960    0.027727     0.222477      0.365697     0.173320    0.017452   \n",
       "80961    0.049469     0.091807      0.350125     0.163804    0.015471   \n",
       "80962    0.017763     0.339121      0.343892     0.131618    0.016615   \n",
       "\n",
       "       Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0         0.043235      0.002022       0.001575     0.001025    0.001220   \n",
       "1         0.045290      0.002039       0.001567     0.001015    0.001215   \n",
       "2         0.042647      0.002100       0.002684     0.001059    0.001152   \n",
       "3         0.071661      0.040116       0.015467     0.009485    0.013226   \n",
       "4         0.075992      0.007469       0.002897     0.003285    0.004723   \n",
       "...            ...           ...            ...          ...         ...   \n",
       "80958     0.085732      0.002166       0.002078     0.003267    0.002727   \n",
       "80959     0.185791      0.001277       0.001423     0.001244    0.001374   \n",
       "80960     0.184820      0.001975       0.002437     0.002078    0.001947   \n",
       "80961     0.322296      0.002075       0.001710     0.001439    0.001700   \n",
       "80962     0.147517      0.000668       0.000760     0.000905    0.001081   \n",
       "\n",
       "         Others  max_1_val     max_1_col  max_2_val     max_2_col  max_3_val  \\\n",
       "0      0.000422   0.391179   Criteria iv   0.300038    Criteria i   0.110439   \n",
       "1      0.000434   0.387872   Criteria iv   0.300537    Criteria i   0.111514   \n",
       "2      0.000087   0.449886   Criteria iv   0.225481   Criteria ii   0.183531   \n",
       "3      0.000141   0.378402   Criteria iv   0.205980  Criteria iii   0.132468   \n",
       "4      0.000129   0.351008   Criteria iv   0.247151  Criteria iii   0.187583   \n",
       "...         ...        ...           ...        ...           ...        ...   \n",
       "80958  0.000056   0.416799  Criteria iii   0.236805   Criteria iv   0.161494   \n",
       "80959  0.000083   0.334180  Criteria iii   0.297634   Criteria ii   0.185791   \n",
       "80960  0.000069   0.365697  Criteria iii   0.222477   Criteria ii   0.184820   \n",
       "80961  0.000105   0.350125  Criteria iii   0.322296   Criteria vi   0.163804   \n",
       "80962  0.000060   0.343892  Criteria iii   0.339121   Criteria ii   0.147517   \n",
       "\n",
       "          max_3_col     max_1     max_3  \n",
       "0      Criteria iii  0.391179  0.801656  \n",
       "1      Criteria iii  0.387872  0.799923  \n",
       "2        Criteria i  0.449886  0.858898  \n",
       "3       Criteria ii  0.378402  0.716850  \n",
       "4       Criteria ii  0.351008  0.785742  \n",
       "...             ...       ...       ...  \n",
       "80958   Criteria ii  0.416799  0.815098  \n",
       "80959   Criteria vi  0.334180  0.817606  \n",
       "80960   Criteria vi  0.365697  0.772994  \n",
       "80961   Criteria iv  0.350125  0.836224  \n",
       "80962   Criteria vi  0.343892  0.830531  \n",
       "\n",
       "[80963 rows x 19 columns]"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_all_df['max_1'] = score_all_df['max_1_val']\n",
    "score_all_df['max_3'] = score_all_df['max_1_val']+score_all_df['max_2_val']+score_all_df['max_3_val']\n",
    "score_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_all_df = sm_all_df.merge(score_all_df, how='left', left_index=True, right_index=True)\n",
    "sm_all_df['valid'] = sm_all_df['revised_text'].apply(lambda x: str(x)!='nan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>index.1</th>\n",
       "      <th>owner</th>\n",
       "      <th>owner_loc</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>comments</th>\n",
       "      <th>taken</th>\n",
       "      <th>views</th>\n",
       "      <th>...</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>50770113131</td>\n",
       "      <td>148125504@N04</td>\n",
       "      <td>Tarazona (Zaragoza), España</td>\n",
       "      <td>Venecia - Patio Palacio Minelli</td>\n",
       "      <td>El palacio Contarini del Bovolo (también llamado palacio Contarini dal Bovolo Minelli) es un pequeño palacio de Venecia, muy conocido por su escalera de caracol exterior, que tiene un gran número de arcos, y que es conocida como la Scala Contarini del Bovolo (del caracol).\\n\\nEl palacio data del siglo XV, construido por la familia Contarini y está, al parecer, en no muy buen estado de conservación. La escalera de caracol conduce a una galería, desde donde se ofrece una encantadora vista panorámica sobre algunos de los tejados de la ciudad. El palacio está situado cerca de la calle Campo Ma...</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-03-27 12:47:45</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>0.391179</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.300038</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.110439</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.391179</td>\n",
       "      <td>0.801656</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>50770228312</td>\n",
       "      <td>148125504@N04</td>\n",
       "      <td>Tarazona (Zaragoza), España</td>\n",
       "      <td>Venecia - Palacio Minelli</td>\n",
       "      <td>El palacio Contarini del Bovolo (también llamado palacio Contarini dal Bovolo Minelli) es un pequeño palacio de Venecia, muy conocido por su escalera de caracol exterior, que tiene un gran número de arcos, y que es conocida como la Scala Contarini del Bovolo (del caracol).\\n\\nEl palacio data del siglo XV, construido por la familia Contarini y está, al parecer, en no muy buen estado de conservación. La escalera de caracol conduce a una galería, desde donde se ofrece una encantadora vista panorámica sobre algunos de los tejados de la ciudad. El palacio está situado cerca de la calle Campo Ma...</td>\n",
       "      <td>7</td>\n",
       "      <td>2010-03-27 12:45:28</td>\n",
       "      <td>238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.387872</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.300537</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.111514</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.387872</td>\n",
       "      <td>0.799923</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50766039303</td>\n",
       "      <td>32980384@N05</td>\n",
       "      <td>Berlin, Germany</td>\n",
       "      <td>_DSC7807</td>\n",
       "      <td>Canal Grande on Venetsian pääliikenneväylä. Kuva on otettu Rialton sillalta, joka on yksi Italian tunnetuimmista nähtävyyksistä.</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-08-11 11:48:15</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.449886</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.225481</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.183531</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.449886</td>\n",
       "      <td>0.858898</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>50766893587</td>\n",
       "      <td>32980384@N05</td>\n",
       "      <td>Berlin, Germany</td>\n",
       "      <td>_DSC7805</td>\n",
       "      <td>Corona-rajoitusten aikaan oli hyvä tilaisuus käydä Venetsiassa, joka normaalisti on täynnä valtameriristeilijöiden tuomia turisteja.</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-08-11 11:44:31</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.378402</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.205980</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.132468</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.378402</td>\n",
       "      <td>0.716850</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>50766892772</td>\n",
       "      <td>32980384@N05</td>\n",
       "      <td>Berlin, Germany</td>\n",
       "      <td>_DSC7802</td>\n",
       "      <td>Corona-rajoitusten aikaan oli hyvä tilaisuus käydä Venetsiassa, joka normaalisti on täynnä valtameriristeilijöiden tuomia turisteja.</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-08-11 11:43:19</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.351008</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.247151</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.187583</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.351008</td>\n",
       "      <td>0.785742</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80958</th>\n",
       "      <td>116670</td>\n",
       "      <td>123096</td>\n",
       "      <td>20879329608</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Hier werden die Pakete mit dem Lastkahn zugestellt.</td>\n",
       "      <td>&lt;a href=\"http://www.brusatotrasporti.it/index.php?pg=Ttrasl\" rel=\"noreferrer nofollow\"&gt;www.brusatotrasporti.it/index.php?pg=Ttrasl&lt;/a&gt;</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 11:34:58</td>\n",
       "      <td>244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.416799</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.236805</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.161494</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.416799</td>\n",
       "      <td>0.815098</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80959</th>\n",
       "      <td>116671</td>\n",
       "      <td>123097</td>\n",
       "      <td>21040989736</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Tür No.342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 11:04:10</td>\n",
       "      <td>156</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.334180</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.297634</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.185791</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.334180</td>\n",
       "      <td>0.817606</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80960</th>\n",
       "      <td>116672</td>\n",
       "      <td>123098</td>\n",
       "      <td>20880415099</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Ave Maria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 11:34:25</td>\n",
       "      <td>163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.365697</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.222477</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.184820</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365697</td>\n",
       "      <td>0.772994</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80961</th>\n",
       "      <td>116673</td>\n",
       "      <td>123099</td>\n",
       "      <td>20879281658</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Kirchenportal der Chiesa di Santa Sofia.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 12:46:41</td>\n",
       "      <td>237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.350125</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.322296</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.350125</td>\n",
       "      <td>0.836224</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80962</th>\n",
       "      <td>116674</td>\n",
       "      <td>123100</td>\n",
       "      <td>21074834751</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Tür im jüdischer Bezirk von Venedig.</td>\n",
       "      <td>&lt;a href=\"http://www.venediginformationen.eu/ueberblick/sehenswertes/ghetto-vecchio/ghettovecchio.htm\" rel=\"noreferrer nofollow\"&gt;www.venediginformationen.eu/ueberblick/sehenswertes/ghett...&lt;/a&gt;</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 11:45:53</td>\n",
       "      <td>160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.343892</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.339121</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.147517</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.343892</td>\n",
       "      <td>0.830531</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80963 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       origin   index      index.1          owner  \\\n",
       "0           6       6  50770113131  148125504@N04   \n",
       "1           7       7  50770228312  148125504@N04   \n",
       "2          10      10  50766039303   32980384@N05   \n",
       "3          11      11  50766893587   32980384@N05   \n",
       "4          12      12  50766892772   32980384@N05   \n",
       "...       ...     ...          ...            ...   \n",
       "80958  116670  123096  20879329608   23097900@N02   \n",
       "80959  116671  123097  21040989736   23097900@N02   \n",
       "80960  116672  123098  20880415099   23097900@N02   \n",
       "80961  116673  123099  20879281658   23097900@N02   \n",
       "80962  116674  123100  21074834751   23097900@N02   \n",
       "\n",
       "                         owner_loc  \\\n",
       "0      Tarazona (Zaragoza), España   \n",
       "1      Tarazona (Zaragoza), España   \n",
       "2                  Berlin, Germany   \n",
       "3                  Berlin, Germany   \n",
       "4                  Berlin, Germany   \n",
       "...                            ...   \n",
       "80958                      Germany   \n",
       "80959                      Germany   \n",
       "80960                      Germany   \n",
       "80961                      Germany   \n",
       "80962                      Germany   \n",
       "\n",
       "                                                     title  \\\n",
       "0                          Venecia - Patio Palacio Minelli   \n",
       "1                                Venecia - Palacio Minelli   \n",
       "2                                                 _DSC7807   \n",
       "3                                                 _DSC7805   \n",
       "4                                                 _DSC7802   \n",
       "...                                                    ...   \n",
       "80958  Hier werden die Pakete mit dem Lastkahn zugestellt.   \n",
       "80959                                           Tür No.342   \n",
       "80960                                            Ave Maria   \n",
       "80961             Kirchenportal der Chiesa di Santa Sofia.   \n",
       "80962                 Tür im jüdischer Bezirk von Venedig.   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   description  \\\n",
       "0      El palacio Contarini del Bovolo (también llamado palacio Contarini dal Bovolo Minelli) es un pequeño palacio de Venecia, muy conocido por su escalera de caracol exterior, que tiene un gran número de arcos, y que es conocida como la Scala Contarini del Bovolo (del caracol).\\n\\nEl palacio data del siglo XV, construido por la familia Contarini y está, al parecer, en no muy buen estado de conservación. La escalera de caracol conduce a una galería, desde donde se ofrece una encantadora vista panorámica sobre algunos de los tejados de la ciudad. El palacio está situado cerca de la calle Campo Ma...   \n",
       "1      El palacio Contarini del Bovolo (también llamado palacio Contarini dal Bovolo Minelli) es un pequeño palacio de Venecia, muy conocido por su escalera de caracol exterior, que tiene un gran número de arcos, y que es conocida como la Scala Contarini del Bovolo (del caracol).\\n\\nEl palacio data del siglo XV, construido por la familia Contarini y está, al parecer, en no muy buen estado de conservación. La escalera de caracol conduce a una galería, desde donde se ofrece una encantadora vista panorámica sobre algunos de los tejados de la ciudad. El palacio está situado cerca de la calle Campo Ma...   \n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Canal Grande on Venetsian pääliikenneväylä. Kuva on otettu Rialton sillalta, joka on yksi Italian tunnetuimmista nähtävyyksistä.   \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Corona-rajoitusten aikaan oli hyvä tilaisuus käydä Venetsiassa, joka normaalisti on täynnä valtameriristeilijöiden tuomia turisteja.   \n",
       "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Corona-rajoitusten aikaan oli hyvä tilaisuus käydä Venetsiassa, joka normaalisti on täynnä valtameriristeilijöiden tuomia turisteja.   \n",
       "...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        ...   \n",
       "80958                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <a href=\"http://www.brusatotrasporti.it/index.php?pg=Ttrasl\" rel=\"noreferrer nofollow\">www.brusatotrasporti.it/index.php?pg=Ttrasl</a>   \n",
       "80959                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN   \n",
       "80960                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN   \n",
       "80961                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN   \n",
       "80962                                                                                                                                                                                                                                                                                                                                                                                                                         <a href=\"http://www.venediginformationen.eu/ueberblick/sehenswertes/ghetto-vecchio/ghettovecchio.htm\" rel=\"noreferrer nofollow\">www.venediginformationen.eu/ueberblick/sehenswertes/ghett...</a>   \n",
       "\n",
       "       comments                taken  views  ...    Others max_1_val  \\\n",
       "0             3  2010-03-27 12:47:45     90  ...  0.000422  0.391179   \n",
       "1             7  2010-03-27 12:45:28    238  ...  0.000434  0.387872   \n",
       "2             0  2020-08-11 11:48:15     25  ...  0.000087  0.449886   \n",
       "3             0  2020-08-11 11:44:31     27  ...  0.000141  0.378402   \n",
       "4             0  2020-08-11 11:43:19     20  ...  0.000129  0.351008   \n",
       "...         ...                  ...    ...  ...       ...       ...   \n",
       "80958         0  2015-07-03 11:34:58    244  ...  0.000056  0.416799   \n",
       "80959         0  2015-07-03 11:04:10    156  ...  0.000083  0.334180   \n",
       "80960         0  2015-07-03 11:34:25    163  ...  0.000069  0.365697   \n",
       "80961         0  2015-07-03 12:46:41    237  ...  0.000105  0.350125   \n",
       "80962         0  2015-07-03 11:45:53    160  ...  0.000060  0.343892   \n",
       "\n",
       "          max_1_col  max_2_val     max_2_col max_3_val     max_3_col  \\\n",
       "0       Criteria iv   0.300038    Criteria i  0.110439  Criteria iii   \n",
       "1       Criteria iv   0.300537    Criteria i  0.111514  Criteria iii   \n",
       "2       Criteria iv   0.225481   Criteria ii  0.183531    Criteria i   \n",
       "3       Criteria iv   0.205980  Criteria iii  0.132468   Criteria ii   \n",
       "4       Criteria iv   0.247151  Criteria iii  0.187583   Criteria ii   \n",
       "...             ...        ...           ...       ...           ...   \n",
       "80958  Criteria iii   0.236805   Criteria iv  0.161494   Criteria ii   \n",
       "80959  Criteria iii   0.297634   Criteria ii  0.185791   Criteria vi   \n",
       "80960  Criteria iii   0.222477   Criteria ii  0.184820   Criteria vi   \n",
       "80961  Criteria iii   0.322296   Criteria vi  0.163804   Criteria iv   \n",
       "80962  Criteria iii   0.339121   Criteria ii  0.147517   Criteria vi   \n",
       "\n",
       "          max_1     max_3  valid  \n",
       "0      0.391179  0.801656   True  \n",
       "1      0.387872  0.799923   True  \n",
       "2      0.449886  0.858898   True  \n",
       "3      0.378402  0.716850   True  \n",
       "4      0.351008  0.785742   True  \n",
       "...         ...       ...    ...  \n",
       "80958  0.416799  0.815098   True  \n",
       "80959  0.334180  0.817606   True  \n",
       "80960  0.365697  0.772994   True  \n",
       "80961  0.350125  0.836224   True  \n",
       "80962  0.343892  0.830531   True  \n",
       "\n",
       "[80963 rows x 52 columns]"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>Criteria x</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>0.163718</td>\n",
       "      <td>0.085216</td>\n",
       "      <td>0.280832</td>\n",
       "      <td>0.310720</td>\n",
       "      <td>0.030539</td>\n",
       "      <td>0.108568</td>\n",
       "      <td>0.008998</td>\n",
       "      <td>0.004779</td>\n",
       "      <td>0.003014</td>\n",
       "      <td>0.003499</td>\n",
       "      <td>0.000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0.167033</td>\n",
       "      <td>0.084983</td>\n",
       "      <td>0.267453</td>\n",
       "      <td>0.304577</td>\n",
       "      <td>0.030154</td>\n",
       "      <td>0.122434</td>\n",
       "      <td>0.010389</td>\n",
       "      <td>0.005576</td>\n",
       "      <td>0.003196</td>\n",
       "      <td>0.004054</td>\n",
       "      <td>0.000150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.145369</td>\n",
       "      <td>0.133964</td>\n",
       "      <td>0.213757</td>\n",
       "      <td>0.243041</td>\n",
       "      <td>0.029157</td>\n",
       "      <td>0.108096</td>\n",
       "      <td>0.052678</td>\n",
       "      <td>0.034805</td>\n",
       "      <td>0.018227</td>\n",
       "      <td>0.020588</td>\n",
       "      <td>0.000317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>0.076119</td>\n",
       "      <td>0.089460</td>\n",
       "      <td>0.266471</td>\n",
       "      <td>0.234519</td>\n",
       "      <td>0.053818</td>\n",
       "      <td>0.085219</td>\n",
       "      <td>0.074331</td>\n",
       "      <td>0.058889</td>\n",
       "      <td>0.032048</td>\n",
       "      <td>0.028972</td>\n",
       "      <td>0.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0.057712</td>\n",
       "      <td>0.137347</td>\n",
       "      <td>0.384181</td>\n",
       "      <td>0.192520</td>\n",
       "      <td>0.054644</td>\n",
       "      <td>0.106413</td>\n",
       "      <td>0.035951</td>\n",
       "      <td>0.008720</td>\n",
       "      <td>0.009331</td>\n",
       "      <td>0.013055</td>\n",
       "      <td>0.000126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49818</th>\n",
       "      <td>116670</td>\n",
       "      <td>0.054110</td>\n",
       "      <td>0.117932</td>\n",
       "      <td>0.374972</td>\n",
       "      <td>0.214453</td>\n",
       "      <td>0.053853</td>\n",
       "      <td>0.143065</td>\n",
       "      <td>0.015148</td>\n",
       "      <td>0.010605</td>\n",
       "      <td>0.006706</td>\n",
       "      <td>0.008923</td>\n",
       "      <td>0.000233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49819</th>\n",
       "      <td>116671</td>\n",
       "      <td>0.051298</td>\n",
       "      <td>0.201248</td>\n",
       "      <td>0.309790</td>\n",
       "      <td>0.134733</td>\n",
       "      <td>0.018713</td>\n",
       "      <td>0.177148</td>\n",
       "      <td>0.037058</td>\n",
       "      <td>0.037359</td>\n",
       "      <td>0.011223</td>\n",
       "      <td>0.020023</td>\n",
       "      <td>0.001407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49820</th>\n",
       "      <td>116672</td>\n",
       "      <td>0.071557</td>\n",
       "      <td>0.186005</td>\n",
       "      <td>0.324673</td>\n",
       "      <td>0.162889</td>\n",
       "      <td>0.018604</td>\n",
       "      <td>0.175369</td>\n",
       "      <td>0.024129</td>\n",
       "      <td>0.018525</td>\n",
       "      <td>0.007105</td>\n",
       "      <td>0.010683</td>\n",
       "      <td>0.000460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49821</th>\n",
       "      <td>116673</td>\n",
       "      <td>0.063146</td>\n",
       "      <td>0.063445</td>\n",
       "      <td>0.359193</td>\n",
       "      <td>0.133555</td>\n",
       "      <td>0.022294</td>\n",
       "      <td>0.347638</td>\n",
       "      <td>0.003461</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>0.002171</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.000211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49822</th>\n",
       "      <td>116674</td>\n",
       "      <td>0.038102</td>\n",
       "      <td>0.230652</td>\n",
       "      <td>0.318270</td>\n",
       "      <td>0.144274</td>\n",
       "      <td>0.018510</td>\n",
       "      <td>0.240692</td>\n",
       "      <td>0.002965</td>\n",
       "      <td>0.002306</td>\n",
       "      <td>0.001708</td>\n",
       "      <td>0.002344</td>\n",
       "      <td>0.000178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49823 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       origin  Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0           6    0.163718     0.085216      0.280832     0.310720    0.030539   \n",
       "1           7    0.167033     0.084983      0.267453     0.304577    0.030154   \n",
       "2          10    0.145369     0.133964      0.213757     0.243041    0.029157   \n",
       "3          11    0.076119     0.089460      0.266471     0.234519    0.053818   \n",
       "4          12    0.057712     0.137347      0.384181     0.192520    0.054644   \n",
       "...       ...         ...          ...           ...          ...         ...   \n",
       "49818  116670    0.054110     0.117932      0.374972     0.214453    0.053853   \n",
       "49819  116671    0.051298     0.201248      0.309790     0.134733    0.018713   \n",
       "49820  116672    0.071557     0.186005      0.324673     0.162889    0.018604   \n",
       "49821  116673    0.063146     0.063445      0.359193     0.133555    0.022294   \n",
       "49822  116674    0.038102     0.230652      0.318270     0.144274    0.018510   \n",
       "\n",
       "       Criteria vi  Criteria vii  Criteria viii  Criteria ix  Criteria x  \\\n",
       "0         0.108568      0.008998       0.004779     0.003014    0.003499   \n",
       "1         0.122434      0.010389       0.005576     0.003196    0.004054   \n",
       "2         0.108096      0.052678       0.034805     0.018227    0.020588   \n",
       "3         0.085219      0.074331       0.058889     0.032048    0.028972   \n",
       "4         0.106413      0.035951       0.008720     0.009331    0.013055   \n",
       "...            ...           ...            ...          ...         ...   \n",
       "49818     0.143065      0.015148       0.010605     0.006706    0.008923   \n",
       "49819     0.177148      0.037058       0.037359     0.011223    0.020023   \n",
       "49820     0.175369      0.024129       0.018525     0.007105    0.010683   \n",
       "49821     0.347638      0.003461       0.002547     0.002171    0.002338   \n",
       "49822     0.240692      0.002965       0.002306     0.001708    0.002344   \n",
       "\n",
       "         Others  \n",
       "0      0.000118  \n",
       "1      0.000150  \n",
       "2      0.000317  \n",
       "3      0.000153  \n",
       "4      0.000126  \n",
       "...         ...  \n",
       "49818  0.000233  \n",
       "49819  0.001407  \n",
       "49820  0.000460  \n",
       "49821  0.000211  \n",
       "49822  0.000178  \n",
       "\n",
       "[49823 rows x 12 columns]"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_agg_df = sm_df[classes+['origin']].groupby('origin').mean().reset_index()\n",
    "sm_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = sm_agg_df[classes].to_numpy().argsort()[:, :-4:-1]\n",
    "c = np.array(classes)[a]\n",
    "d = sm_agg_df[classes].to_numpy()[np.arange(a.shape[0])[:, None], a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(c).rename(columns=lambda x : f'max_{x+1}_col')\n",
    "df2 = pd.DataFrame(d).rename(columns=lambda x : f'max_{x+1}_val')\n",
    "c = sm_agg_df.columns.tolist() + [y for x in zip(df2.columns, df1.columns) for y in x]\n",
    "\n",
    "score_agg_df = pd.concat([sm_agg_df, df1, df2], axis=1).reindex(c, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i</th>\n",
       "      <th>Criteria ii</th>\n",
       "      <th>Criteria iii</th>\n",
       "      <th>Criteria iv</th>\n",
       "      <th>Criteria v</th>\n",
       "      <th>Criteria vi</th>\n",
       "      <th>Criteria vii</th>\n",
       "      <th>Criteria viii</th>\n",
       "      <th>Criteria ix</th>\n",
       "      <th>...</th>\n",
       "      <th>Others</th>\n",
       "      <th>max_1_val</th>\n",
       "      <th>max_1_col</th>\n",
       "      <th>max_2_val</th>\n",
       "      <th>max_2_col</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>0.163718</td>\n",
       "      <td>0.085216</td>\n",
       "      <td>0.280832</td>\n",
       "      <td>0.310720</td>\n",
       "      <td>0.030539</td>\n",
       "      <td>0.108568</td>\n",
       "      <td>0.008998</td>\n",
       "      <td>0.004779</td>\n",
       "      <td>0.003014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.310720</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.280832</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.163718</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.310720</td>\n",
       "      <td>0.755270</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0.167033</td>\n",
       "      <td>0.084983</td>\n",
       "      <td>0.267453</td>\n",
       "      <td>0.304577</td>\n",
       "      <td>0.030154</td>\n",
       "      <td>0.122434</td>\n",
       "      <td>0.010389</td>\n",
       "      <td>0.005576</td>\n",
       "      <td>0.003196</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.304577</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.267453</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.167033</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.304577</td>\n",
       "      <td>0.739063</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.145369</td>\n",
       "      <td>0.133964</td>\n",
       "      <td>0.213757</td>\n",
       "      <td>0.243041</td>\n",
       "      <td>0.029157</td>\n",
       "      <td>0.108096</td>\n",
       "      <td>0.052678</td>\n",
       "      <td>0.034805</td>\n",
       "      <td>0.018227</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.243041</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.213757</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.145369</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.243041</td>\n",
       "      <td>0.602167</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>0.076119</td>\n",
       "      <td>0.089460</td>\n",
       "      <td>0.266471</td>\n",
       "      <td>0.234519</td>\n",
       "      <td>0.053818</td>\n",
       "      <td>0.085219</td>\n",
       "      <td>0.074331</td>\n",
       "      <td>0.058889</td>\n",
       "      <td>0.032048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.266471</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.234519</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.089460</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.266471</td>\n",
       "      <td>0.590450</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0.057712</td>\n",
       "      <td>0.137347</td>\n",
       "      <td>0.384181</td>\n",
       "      <td>0.192520</td>\n",
       "      <td>0.054644</td>\n",
       "      <td>0.106413</td>\n",
       "      <td>0.035951</td>\n",
       "      <td>0.008720</td>\n",
       "      <td>0.009331</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.384181</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.192520</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.137347</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.384181</td>\n",
       "      <td>0.714048</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49818</th>\n",
       "      <td>116670</td>\n",
       "      <td>0.054110</td>\n",
       "      <td>0.117932</td>\n",
       "      <td>0.374972</td>\n",
       "      <td>0.214453</td>\n",
       "      <td>0.053853</td>\n",
       "      <td>0.143065</td>\n",
       "      <td>0.015148</td>\n",
       "      <td>0.010605</td>\n",
       "      <td>0.006706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.374972</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.214453</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.143065</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.374972</td>\n",
       "      <td>0.732489</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49819</th>\n",
       "      <td>116671</td>\n",
       "      <td>0.051298</td>\n",
       "      <td>0.201248</td>\n",
       "      <td>0.309790</td>\n",
       "      <td>0.134733</td>\n",
       "      <td>0.018713</td>\n",
       "      <td>0.177148</td>\n",
       "      <td>0.037058</td>\n",
       "      <td>0.037359</td>\n",
       "      <td>0.011223</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.309790</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.201248</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.177148</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.309790</td>\n",
       "      <td>0.688186</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49820</th>\n",
       "      <td>116672</td>\n",
       "      <td>0.071557</td>\n",
       "      <td>0.186005</td>\n",
       "      <td>0.324673</td>\n",
       "      <td>0.162889</td>\n",
       "      <td>0.018604</td>\n",
       "      <td>0.175369</td>\n",
       "      <td>0.024129</td>\n",
       "      <td>0.018525</td>\n",
       "      <td>0.007105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0.324673</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.186005</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.175369</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.324673</td>\n",
       "      <td>0.686047</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49821</th>\n",
       "      <td>116673</td>\n",
       "      <td>0.063146</td>\n",
       "      <td>0.063445</td>\n",
       "      <td>0.359193</td>\n",
       "      <td>0.133555</td>\n",
       "      <td>0.022294</td>\n",
       "      <td>0.347638</td>\n",
       "      <td>0.003461</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>0.002171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.359193</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.347638</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.133555</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.359193</td>\n",
       "      <td>0.840386</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49822</th>\n",
       "      <td>116674</td>\n",
       "      <td>0.038102</td>\n",
       "      <td>0.230652</td>\n",
       "      <td>0.318270</td>\n",
       "      <td>0.144274</td>\n",
       "      <td>0.018510</td>\n",
       "      <td>0.240692</td>\n",
       "      <td>0.002965</td>\n",
       "      <td>0.002306</td>\n",
       "      <td>0.001708</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.318270</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.240692</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.230652</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.318270</td>\n",
       "      <td>0.789614</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49823 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       origin  Criteria i  Criteria ii  Criteria iii  Criteria iv  Criteria v  \\\n",
       "0           6    0.163718     0.085216      0.280832     0.310720    0.030539   \n",
       "1           7    0.167033     0.084983      0.267453     0.304577    0.030154   \n",
       "2          10    0.145369     0.133964      0.213757     0.243041    0.029157   \n",
       "3          11    0.076119     0.089460      0.266471     0.234519    0.053818   \n",
       "4          12    0.057712     0.137347      0.384181     0.192520    0.054644   \n",
       "...       ...         ...          ...           ...          ...         ...   \n",
       "49818  116670    0.054110     0.117932      0.374972     0.214453    0.053853   \n",
       "49819  116671    0.051298     0.201248      0.309790     0.134733    0.018713   \n",
       "49820  116672    0.071557     0.186005      0.324673     0.162889    0.018604   \n",
       "49821  116673    0.063146     0.063445      0.359193     0.133555    0.022294   \n",
       "49822  116674    0.038102     0.230652      0.318270     0.144274    0.018510   \n",
       "\n",
       "       Criteria vi  Criteria vii  Criteria viii  Criteria ix  ...    Others  \\\n",
       "0         0.108568      0.008998       0.004779     0.003014  ...  0.000118   \n",
       "1         0.122434      0.010389       0.005576     0.003196  ...  0.000150   \n",
       "2         0.108096      0.052678       0.034805     0.018227  ...  0.000317   \n",
       "3         0.085219      0.074331       0.058889     0.032048  ...  0.000153   \n",
       "4         0.106413      0.035951       0.008720     0.009331  ...  0.000126   \n",
       "...            ...           ...            ...          ...  ...       ...   \n",
       "49818     0.143065      0.015148       0.010605     0.006706  ...  0.000233   \n",
       "49819     0.177148      0.037058       0.037359     0.011223  ...  0.001407   \n",
       "49820     0.175369      0.024129       0.018525     0.007105  ...  0.000460   \n",
       "49821     0.347638      0.003461       0.002547     0.002171  ...  0.000211   \n",
       "49822     0.240692      0.002965       0.002306     0.001708  ...  0.000178   \n",
       "\n",
       "       max_1_val     max_1_col max_2_val     max_2_col max_3_val    max_3_col  \\\n",
       "0       0.310720   Criteria iv  0.280832  Criteria iii  0.163718   Criteria i   \n",
       "1       0.304577   Criteria iv  0.267453  Criteria iii  0.167033   Criteria i   \n",
       "2       0.243041   Criteria iv  0.213757  Criteria iii  0.145369   Criteria i   \n",
       "3       0.266471  Criteria iii  0.234519   Criteria iv  0.089460  Criteria ii   \n",
       "4       0.384181  Criteria iii  0.192520   Criteria iv  0.137347  Criteria ii   \n",
       "...          ...           ...       ...           ...       ...          ...   \n",
       "49818   0.374972  Criteria iii  0.214453   Criteria iv  0.143065  Criteria vi   \n",
       "49819   0.309790  Criteria iii  0.201248   Criteria ii  0.177148  Criteria vi   \n",
       "49820   0.324673  Criteria iii  0.186005   Criteria ii  0.175369  Criteria vi   \n",
       "49821   0.359193  Criteria iii  0.347638   Criteria vi  0.133555  Criteria iv   \n",
       "49822   0.318270  Criteria iii  0.240692   Criteria vi  0.230652  Criteria ii   \n",
       "\n",
       "          max_1     max_3  counts  \n",
       "0      0.310720  0.755270      12  \n",
       "1      0.304577  0.739063      12  \n",
       "2      0.243041  0.602167       6  \n",
       "3      0.266471  0.590450       4  \n",
       "4      0.384181  0.714048       4  \n",
       "...         ...       ...     ...  \n",
       "49818  0.374972  0.732489       2  \n",
       "49819  0.309790  0.688186       2  \n",
       "49820  0.324673  0.686047       2  \n",
       "49821  0.359193  0.840386       2  \n",
       "49822  0.318270  0.789614       2  \n",
       "\n",
       "[49823 rows x 21 columns]"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_agg_df['max_1'] = score_agg_df['max_1_val']\n",
    "score_agg_df['max_3'] = score_agg_df['max_1_val']+score_agg_df['max_2_val']+score_agg_df['max_3_val']\n",
    "score_agg_df['counts'] = sm_df[['origin','sentence']].groupby('origin').count().reset_index()['sentence']\n",
    "score_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     87926\n",
       "Criteria vi      42247\n",
       "Criteria i       12695\n",
       "Criteria ii      12458\n",
       "Criteria iv       6705\n",
       "Criteria vii      5071\n",
       "Criteria viii     1956\n",
       "Criteria x        1336\n",
       "Criteria ix        381\n",
       "Criteria v         249\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     149952\n",
       "Criteria vi      118103\n",
       "Criteria iv       99539\n",
       "Criteria ii       70318\n",
       "Criteria i        41788\n",
       "Criteria vii      15120\n",
       "Criteria viii      7410\n",
       "Criteria x         5894\n",
       "Criteria ix        2534\n",
       "Criteria v         2414\n",
       "dtype: int64"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_df['max_1_col'],score_df['max_2_col'],score_df['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     34729\n",
       "Criteria vi      15913\n",
       "Criteria ii       8471\n",
       "Criteria iv       4075\n",
       "Criteria i        2280\n",
       "Criteria vii       813\n",
       "Criteria viii      241\n",
       "Criteria x         222\n",
       "Criteria ix         90\n",
       "Criteria v          36\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_all_df[sm_all_df.valid]['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     61502\n",
       "Criteria vi      50542\n",
       "Criteria iv      38325\n",
       "Criteria ii      34552\n",
       "Criteria i        9899\n",
       "Criteria vii      2399\n",
       "Criteria viii     1201\n",
       "Criteria x         826\n",
       "Criteria v         699\n",
       "Criteria ix        665\n",
       "dtype: int64"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_all_df[sm_all_df.valid]['max_1_col'],\n",
    "           score_all_df[sm_all_df.valid]['max_2_col'],\n",
    "           score_all_df[sm_all_df.valid]['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     31778\n",
       "Criteria vi      10196\n",
       "Criteria ii       3684\n",
       "Criteria i        1879\n",
       "Criteria vii       896\n",
       "Criteria iv        876\n",
       "Criteria viii      269\n",
       "Criteria x         193\n",
       "Criteria ix         46\n",
       "Criteria v           6\n",
       "Name: max_1_col, dtype: int64"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_agg_df['max_1_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Criteria iii     48354\n",
       "Criteria vi      38675\n",
       "Criteria iv      25531\n",
       "Criteria ii      21195\n",
       "Criteria i        8125\n",
       "Criteria vii      4144\n",
       "Criteria viii     1569\n",
       "Criteria x         992\n",
       "Criteria ix        531\n",
       "Criteria v         353\n",
       "dtype: int64"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([score_agg_df['max_1_col'],score_agg_df['max_2_col'],score_agg_df['max_3_col']]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.250929\n",
       "1         0.280426\n",
       "2         0.650610\n",
       "3         0.216419\n",
       "4         0.606865\n",
       "            ...   \n",
       "171019    0.357152\n",
       "171020    0.283526\n",
       "171021    0.365819\n",
       "171022    0.407372\n",
       "171023    0.406759\n",
       "Name: max_1, Length: 171024, dtype: float64"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_df['max_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_df.drop_duplicates('sentence')['max_1'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_df.drop_duplicates('sentence')['max_3'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGgCAYAAABfSOayAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA4bklEQVR4nO3de3RU9b3+8WfPTCYzSQiHSyDgpUQwCKdCuARBDaXYIh45q4ty2lNraOEUwUvlVyzQqtQLN6kgeNBFkYMoiiz1CKW1rirqaW1VoITaaiUIKIkoJhGNue7MZGb27w+akYEEMpPJZPbwfq2FMHt/55vPh7nwuK+GZVmWAAAAbMrR1QUAAAB0BGEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYmqurC+hso0ePlt/vV05OTleXAgAA2unTTz+V2+1WSUnJWcem/JYZn8+nQCAgSbIsSz6fT6l8ncBU7zHV+5PoMVXQY2qgx64TCATk8/naNTblt8z06dNHkvTqq6+qsbFRpaWlGjJkiDIyMrq4ss6R6j2men8SPaYKekwN9Nh1rrrqqnaPTfktMwAAILURZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK1FHWY+++wzLViwQGPHjtWIESM0e/Zsvf/+++H1paWlKi4uVkFBgSZOnKgnnngi4vmhUEhr165VUVGRCgoKdMMNN+jo0aMRY842BwAAQIuow8wtt9yi8vJybdiwQc8995w8Ho9mzJgh0zRVXV2tmTNn6sILL9S2bdt0yy23aNWqVdq2bVv4+evWrdPWrVu1ZMkSPf300wqFQpo1a5b8fr8ktWsOAACAFq5oBtfU1Oi8887TnDlzlJ+fL0m6+eab9a1vfUuHDh3Srl27lJaWpsWLF8vlcmngwIHh4DNt2jT5/X5t2rRJ8+fP14QJEyRJa9asUVFRkXbu3KkpU6bo2WefPeMcAAAAJ4tqy0z37t31wAMPhIPM559/rscff1y5ubkaNGiQSkpKNGbMGLlcX2aksWPHqqysTMePH9eBAwfU0NCgcePGhddnZ2dr6NCh2rt3rySddQ4AAICTRbVl5mS/+MUv9Oyzz8rtdutXv/qVMjIyVFFREQ46Lfr06SNJ+uSTT1RRUSFJ6tev32ljWtadbY7evXtHXatlWWpsbJRpmpIU/j0VpXqPiegvEHLI5w+ecUy62ymXI9QpPz/VX0OJHlMFPaaGZO3RsiwZhtGusTGHmR/+8If6z//8Tz311FO65ZZbtHXrVjU1NcntdkeMS09PlyT5fL7wX1RrY2pqaiTprHPEwu/3q7S0NPy4rKwspnnsJNV77Kz+3G63uvfur9/96X19UdfU6ph/6ebRlPEDVXP8WPhYr86Q6q+hRI+pgh5TQ7L16Pf7w//+n03MYWbQoEGSpGXLlunvf/+7tmzZIo/Hc9qXe0sAycjIkMfjCRfY8ueWMV6vV5LOOkcs3G63hgwZItM0VVZWpgEDBoR/XqpJ9R47uz/DMFRvhuQPOeULOVsd4w855U33KGfQIFmWFfcaUv01lOgxVdBjakjWHk/dsHEmUYWZzz//XLt27dLVV18dPqbF4XBo0KBBqqqqUm5urqqqqiKe0/K4b9++CgQC4WUXXnhhxJjBgwdL0lnniIVhGBFByOv1xhyM7CLVe+zM/hr8ppxOh5zO1sOM0+mQ4XR0+oc+1V9DiR5TBT2mhmTrsb27mKQoDwA+fvy4brvtNu3atSu8rLm5Wfv379fAgQNVWFioffv2KRj88niD3bt3Ky8vT7169dIll1yirKws7dmzJ7y+trZW+/fvV2FhoSSddQ4AAICTRRVm8vPzNX78eC1dulR79+7VwYMH9fOf/1y1tbWaMWOGpk2bpvr6et155506fPiwtm/frscff1xz5syRdGKTUXFxsVatWqVXX31VBw4c0Lx585Sbm6tJkyZJ0lnnAAAAOFnUx8ysXr1aDzzwgObNm6e6ujqNHj1aTz31lPr37y9J2rhxo5YtW6apU6cqJydHCxcu1NSpU8PPnzt3rgKBgBYtWqSmpiYVFhbq0UcfVVpamiSpV69eZ50DAACgRdRhplu3brrnnnt0zz33tLp+2LBheuaZZ9p8vtPp1IIFC7RgwYI2x5xtDgAAgBbcaBIAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANha1LczAOyswWyW6Q+0ud7hMBQKWQmsCADQUYQZnFNMf0AvvH5EtQ2+Vtefl5Oly4f3T3BVAICOIMzgnFPb4FN1XethJjvTneBqAAAdxTEzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1ggzAADA1lxdXQAQLw1ms0x/oM31DoehUMhKYEUAgEQgzCBlmP6AXnj9iGobfK2uPy8nS5cP75/gqgAAnY0wg5RS2+BTdV3rYSY7053gagAAicAxMwAAwNYIMwAAwNYIM7AVwzDkdrtlGEZXlwIASBIcMwNbaDlTyQqG1L13f9WbITX4zfB6zlQCgHMXYQa20HKmUnWtqcaGBmVkZsrp/HLDImcqAcC5izAD2zhxplKT6utM+UJOOZ3O8DrOVAKAcxfHzAAAAFsjzAAAAFsjzAAAAFsjzAAAAFuLOsx88cUXuuuuuzR+/HiNHDlS1113nUpKSsLrZ86cqcGDB0f8mj59eni9z+fTvffeq3HjxmnEiBH66U9/qs8//zziZ+zatUvf/va3NXz4cE2ePFkvvPBCB1oEAACpLOqzmW677TZ9+umnWr16tXr16qUnn3xSP/rRj/TrX/9aF110kd577z3dc889+sY3vhF+TlpaWvjP99xzj0pKSvTQQw/J7Xbr7rvv1ty5c7VlyxZJ0vvvv685c+Zo5syZWrlypf74xz9q4cKF6tmzp8aNGxeHlgEAQCqJKsyUl5frjTfe0NatWzVq1ChJ0i9+8Qv9+c9/1vPPP6/i4mJ99tlnGj58uHJyck57fmVlpXbs2KH169dr9OjRkqTVq1dr8uTJeuuttzRixAht3rxZgwcP1rx58yRJAwcO1P79+7Vx40bCDAAAOE1Uu5l69OihDRs26NJLLw0vMwxDhmGotrZW7733ngzDUF5eXqvP37dvnyRp7Nix4WV5eXnq27ev9u7dK0kqKSk5LbSMHTtW+/btk2VxhVcAABApqi0z2dnZ+trXvhax7KWXXlJ5ebnuuOMOHTx4UN26ddPixYv1xhtvKCMjQ5MnT9bNN98st9utyspK9ejRQ+np6RFz9OnTRxUVFZKkiooK5ebmnrbeNE1VV1erZ8+eUTdpWZYaGxtlmicuf9/yeypKxR4Nw5AVDCkYDCkYDEpS+PcWoVBIshQx5lTxGhMMhmQFQzJNs1MCdiq+hqeix9RAj6khWXu0LKvd9+Hr0BWA//rXv+r222/XpEmTNGHCBN1xxx3y+XwaNmyYZs6cqdLSUt1///06duyY7r//fpmmKbf79Cu1pqeny+fzSZKamppOG9Py2O/3x1Sn3+9XaWlp+HFZWVlM89hJKvXodrvVvXd/NTY0yGz854euMfJDZ3ZPk2WF1GSaqq9raHWeeI1JdwRl+ppU8fGxmN+T7ZFKr2Fb6DE10GNqSLYe/X7/aRs/2hJzmHnllVc0f/58jRw5UqtWrZIkLV68WD/72c/UvXt3SVJ+fr7S0tI0b948LVy4UB6Pp9Uvf5/PJ6/XK+lEsDl1TMvjljHRcrvdGjJkiEzTVFlZmQYMGBDzXMkuFXs0DEP1ZkgZmZlqbD4RZLwZ3ojbGXgzvDIMhzxer7ICrSf5eI3JyPTIm+5RzqBBnbZlJtVew1PRY2qgx9SQrD22tvGjLTGFmS1btmjZsmWaPHmyfvnLX4Z/oMvlCgeZFhdffLGkL3cfffHFF/L7/RFFVlVVqW/fvpKkfv36qaqqKmKOqqoqZWRkqFu3brGUK8MwlJGREX7s9XojHqeiVOuxwW/K6XSEA4zTGXlvJofDIRmKGHOqeI1xOh1yuByd/qFPtdewNfSYGugxNSRbj+3dxSTFEGa2bt2qJUuWaPr06brzzjsjftj06dN1/vnn67777gsve+edd5SWlqYBAwYoJydHoVBI+/btCx/ke+TIEVVWVqqwsFCSNHr0aP3lL3+J+Jm7d+/WyJEjT/xDA3QxT7pLDsPQ8Zoz71/2ul3K9KadcQwAoOOiCjNHjhzR8uXL9c1vflNz5szR8ePHw+s8Ho+uvvpqLV++XMOGDdOVV16pd955R/fff79+9KMfKSsrS1lZWbr22mu1aNEiLV++XF6vV3fffbfGjBmjgoICSScC0dSpU7Vq1SpNnTpVr732ml588UVt3Lgxro0DsXK7HPI1B/XSrnLVNvhaHZOdma5rr8wjzABAAkQVZl566SU1Nzfr5Zdf1ssvvxyxburUqVqxYoUMw9CTTz6p5cuXKycnRzNmzNDs2bPD45YsWaLly5frxz/+sSRp/PjxWrRoUXj9xRdfrHXr1mnlypXavHmzzj//fK1cuZJrzCDp1Db4VF3XepgBACROVGHmxhtv1I033njGMddff72uv/76NtdnZGRo6dKlWrp0aZtjxo8fr/Hjx0dTGgAAOEdxEAoAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1V1cXADSYzTL9gTbXOxyGQiErgRXFh2F0dQUAcG4gzKDLmf6AXnj9iGobfK2uPy8nS5cP75/gqjrGk+6SwzB0vMY84ziv26VMb1qCqgKA1ESYQVKobfCpuq71MJOd6U5wNR3ndjnkaw7qpV3lbYa07Mx0XXtlHmEGADqIMAN0ojOFNABAfHAAMAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsDXCDAAAsLWow8wXX3yhu+66S+PHj9fIkSN13XXXqaSkJLx+165d+va3v63hw4dr8uTJeuGFFyKe7/P5dO+992rcuHEaMWKEfvrTn+rzzz+PGHO2OQAAAFpEHWZuu+02vfXWW1q9erW2bdumIUOG6Ec/+pE++OADvf/++5ozZ46Kioq0fft2fec739HChQu1a9eu8PPvuecevf7663rooYe0efNmffDBB5o7d254fXvmAAAAaOGKZnB5ebneeOMNbd26VaNGjZIk/eIXv9Cf//xnPf/88/rss880ePBgzZs3T5I0cOBA7d+/Xxs3btS4ceNUWVmpHTt2aP369Ro9erQkafXq1Zo8ebLeeustjRgxQps3bz7jHAAAACeLastMjx49tGHDBl166aXhZYZhyDAM1dbWqqSk5LTAMXbsWO3bt0+WZWnfvn3hZS3y8vLUt29f7d27V5LOOgcAAMDJotoyk52dra997WsRy1566SWVl5frjjvu0K9//Wvl5uZGrO/Tp49M01R1dbUqKyvVo0cPpaennzamoqJCklRRUXHGOXr27BlNyZIky7LU2Ngo0zQlKfx7KrJbj4ZhyAqGFAyGFAwGWx0TCoUkSxFjTh3b2pj2zNOVY4LBkBQKqampKSKo+3w+ud1u+Xy+E38/KRji7fY+jQU9pgZ67DqWZckwjHaNjSrMnOqvf/2rbr/9dk2aNEkTJkxQU1OT3G53xJiWx36/X6ZpnrZektLT0+Xz+STprHPEwu/3q7S0NPy4rKwspnnsxC49ut1ude/dX40NDaqva/2DZHZPk2WF1GSaMhv/+aFrNNscU1/XcNZ5kmFMjwyHDMPQ8S8aFTolsHTv3V91TSHVNTUqzSlVffKRAoFAq/PYmV3epx1Bj6mBHhPP7/eftvGjLTGHmVdeeUXz58/XyJEjtWrVKkknQsmpgaPlsdfrlcfjaTWQ+Hw+eb3eds0RC7fbrSFDhsg0TZWVlWnAgAExz5Xs7NajYRiqN0PKyMyUL+RsdYw3wyvDcMjj9crrC8lsNOXN8MrpdLY6JivQepJPtjHdu2epOWjp5b0fq6b+y/d8KBRUk9kkj9ejHtleTbniIuXn56fUFhq7vU9jQY+pgR67TmsbP9oSU5jZsmWLli1bpsmTJ+uXv/xl+Af269dPVVVVEWOrqqqUkZGhbt26KTc3V1988YX8fn9EkVVVVerbt2+75oiFYRjKyMgIP/Z6vRGPU5Gdemzwm3I6HRHh5GQOh0MyFDHG6XRGjG9tTHvmSYYx9WZAtY3N4eXBYFD1daayQk6lpaXJcDqS6gsmnuz0Po0VPaYGeky89u5ikmI4NXvr1q1asmSJrr/+eq1evToilIwePVp/+ctfIsbv3r1bI0eOlMPh0KhRoxQKhcIHAkvSkSNHVFlZqcLCwnbNAQAAcLKo0sGRI0e0fPlyffOb39ScOXN0/Phxffrpp/r0009VV1en6dOn6+2339aqVav0/vvva9OmTXrxxRc1a9YsSVLfvn117bXXatGiRdqzZ4/efvtt3XbbbRozZowKCgok6axzAAAAnCyq3UwvvfSSmpub9fLLL+vll1+OWDd16lStWLFC69at08qVK7V582adf/75WrlyZcSp1kuWLNHy5cv14x//WJI0fvx4LVq0KLz+4osvPuscAAAALaIKMzfeeKNuvPHGM44ZP368xo8f3+b6jIwMLV26VEuXLo15DgAAgBYchAIAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMkOcPo6goAILm5uroAAG3zpLvkMAwdrzHPOM7rdinTm5agqgAguRBmgCTmdjnkaw7qpV3lqm3wtTomOzNd116ZR5gBcM4izAA2UNvgU3Vd62EGAM51HDMDAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsrUNh5pFHHtH06dMjli1atEiDBw+O+DVx4sTw+lAopLVr16qoqEgFBQW64YYbdPTo0Yg5SktLVVxcrIKCAk2cOFFPPPFER8oEAAApLOYw89RTT+nBBx88bfl7772nG2+8Ua+//nr413PPPRdev27dOm3dulVLlizR008/rVAopFmzZsnv90uSqqurNXPmTF144YXatm2bbrnlFq1atUrbtm2LtVQAAJDCXNE+obKyUnfffbf27NmjAQMGRKyzLEuHDx/W7NmzlZOTc9pz/X6/Nm3apPnz52vChAmSpDVr1qioqEg7d+7UlClT9OyzzyotLU2LFy+Wy+XSwIEDVV5erg0bNmjatGkxNQmkOsPo6goAoOtEvWXm3XffVVpamn77299q+PDhEes+/PBDNTY26qKLLmr1uQcOHFBDQ4PGjRsXXpadna2hQ4dq7969kqSSkhKNGTNGLteXOWvs2LEqKyvT8ePHoy0XSHmedJcchqHjNeYZfzWYzV1dKgB0iqi3zEycODHiGJiTHTx4UJL05JNP6k9/+pMcDofGjx+vefPmqVu3bqqoqJAk9evXL+J5ffr0Ca+rqKhQfn7+aesl6ZNPPlHv3r2jLVmWZamxsVGmaUpS+PdUZLceDcOQFQwpGAwpGAy2OiYUCkmWIsacOra1Me2ZJxnHnNxje+ZxOSSfP6jfv/mBaur9rY7pnuXWlCsukkMBWZbV6phEstv7NBb0mBrosetYliWjnZudow4zZ3Lw4EE5HA716dNH69ev14cffqj7779fhw4d0ubNm8N/UW63O+J56enpqqmpkSQ1NTW1ul6SfD5fTHX5/X6VlpaGH5eVlcU0j53YpUe3263uvfursaFB9XWtf5DM7mmyrJCaTFNm4z8/dI1mm2Pq6xrOOo8dxpiNpsxGT7vnqfy0Rp9Wtz6msbtXpq9JFR8fCx+flgzs8j7tCHpMDfSYeH6/P/zv/9nENczcdNNN+v73v68ePXpIkvLz85WTk6Pvfve7euedd+TxeMIFtvxZOhFSvF6vJMnj8Zz2ZdsSYjIyMmKqy+12a8iQITJNU2VlZRowYED456Uau/VoGIbqzZAyMjPlCzlbHePN8MowHPJ4vfL6QjIbTXkzvHI6na2OyQq0nuTtMiYYDIZ7jNfPysj0yJvuUc6gQUmzZcZO79NY0GNqoMeuc+qGjTOJa5hxOBzhINPi4osvlnRi91HL7qWqqipdeOGF4TFVVVUaPHiwJCk3N1dVVVURc7Q87tu3b0x1GYYREYS8Xm/Mwcgu7NRjg9+U0+mICCcnczgckqGIMU6nM2J8a2PaM08yj3E6nXH7WU6nQ4bTkVRfVJK93qexosfUQI+J195dTFKcL5q3cOFCzZgxI2LZO++8I0kaNGiQLrnkEmVlZWnPnj3h9bW1tdq/f78KCwslSYWFhdq3b1/E8QG7d+9WXl6eevXqFc9yAQBACohrmLn66qu1a9cuPfzww/rwww/12muv6Y477tCUKVM0cOBAud1uFRcXa9WqVXr11Vd14MABzZs3T7m5uZo0aZIkadq0aaqvr9edd96pw4cPa/v27Xr88cc1Z86ceJYKAABSRFx3M1111VV68MEHtWHDBv3P//yPunXrpn//93/XT37yk/CYuXPnKhAIaNGiRWpqalJhYaEeffRRpaWlSZJ69eqljRs3atmyZZo6dapycnK0cOFCTZ06NZ6lAgCAFNGhMLNixYrTll1zzTW65ppr2nyO0+nUggULtGDBgjbHDBs2TM8880xHSgMAAOcIbjQJAABsjTADAABsLa7HzACnajCbZfoDba53OAyFQl1/3RMAgH0RZtCpTH9AL7x+RLUNrV+9+bycLF0+vH+CqwIApBLCDDpdbYNP1XWth5nszPZf4REAgNZwzAwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1V1cXACB5NJjNMv2BM47xul3K9KYlqCIAODvCDIAw0x/QC68fUW2Dr9X12ZnpuvbKPMIMgKRCmAEQobbBp+q61sMMACQjjpkBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBzhGG0dUVAEDn4DozwDnAk+6SwzB0vMZsc4zDYSgUshJYFQDEB2EGOAe4XQ75moN6aVd5m1f3PS8nS5cP75/gygCg4wgzwDnkTFf3zc50J7gaAIgPjpkBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC21qEw88gjj2j69OkRy0pLS1VcXKyCggJNnDhRTzzxRMT6UCiktWvXqqioSAUFBbrhhht09OjRqOYAAABoEXOYeeqpp/Tggw9GLKuurtbMmTN14YUXatu2bbrlllu0atUqbdu2LTxm3bp12rp1q5YsWaKnn35aoVBIs2bNkt/vb/ccAAAALVzRPqGyslJ333239uzZowEDBkSse/bZZ5WWlqbFixfL5XJp4MCBKi8v14YNGzRt2jT5/X5t2rRJ8+fP14QJEyRJa9asUVFRkXbu3KkpU6acdQ4AAICTRb1l5t1331VaWpp++9vfavjw4RHrSkpKNGbMGLlcX2aksWPHqqysTMePH9eBAwfU0NCgcePGhddnZ2dr6NCh2rt3b7vmAAAAOFnUW2YmTpyoiRMntrquoqJC+fn5Ecv69OkjSfrkk09UUVEhSerXr99pY1rWnW2O3r17R1uyLMtSY2OjTNOUpPDvqSiZejQMQ1YwpGAwpGAw2OqYUCgkWYp6zKljY50nGcec3GMy1HOyYDAkKxiSaZqyLKvVMe2RTO/TzkKPqYEeu45lWTIMo11jow4zZ9LU1CS32x2xLD09XZLk8/nCf1GtjampqWnXHLHw+/0qLS0NPy4rK4tpHjtJhh7dbre69+6vxoYG1de1/iExu6fJskJqMk3V1zWcdYzZ+M8PXaPZ5pj2zGOHMWajKbPRkzT1SFK6IyjT16SKj4+Fj3PriGR4n3Y2ekwN9Jh4fr8//O//2cQ1zHg8ntO+4FoCSEZGhjweT7jAlj+3jPF6ve2aIxZut1tDhgyRaZoqKyvTgAEDwj8v1SS6xzOlZsMwVNcYVEZmpnwhZ6tjvBleGYZDHq9XWYHW5zp5jNcXktloypvhldPpjHmeZB4TDAbDPSZDPSfLyPTIm+5RzqBBHd4yw2fR/ugxNSRrj6du2DiTuIaZ3NxcVVVVRSxredy3b18FAoHwsgsvvDBizODBg9s1RywMw4gIQl6vN+ZgZBeJ6LHBbFajP9DmeodDsgxDTqcjInhEjnFIhqIe43Q6I8bHOk8yj3E6nUlVz4maHDKcjrh94fFZTA30mBqSrcf27mKS4hxmCgsL9fTTTysYDIa/DHfv3q28vDz16tVL3bp1U1ZWlvbs2RMOM7W1tdq/f7+Ki4vbNQeSh+kP6IXXj6i2ofXdf+flZOny4f0TXBUA4FwT1ysAT5s2TfX19brzzjt1+PBhbd++XY8//rjmzJkj6cQmo+LiYq1atUqvvvqqDhw4oHnz5ik3N1eTJk1q1xxILrUNPlXXtf6rrrHjx1QAAHA2cd0y06tXL23cuFHLli3T1KlTlZOTo4ULF2rq1KnhMXPnzlUgENCiRYvU1NSkwsJCPfroo0pLS2v3HAAAAC06FGZWrFhx2rJhw4bpmWeeafM5TqdTCxYs0IIFC9occ7Y5AAAAWnCjSQAAYGuEGQAAYGuEGQAAYGuEGQAAYGuEGQAAYGuEGQAAYGuEGQAAYGuEGQAAYGuEGQBRieLebwCQEHG9nQGA1OZJd8lhGDpeY55xnNftUqY3LUFVATjXEWYAtJvb5ZCvOaiXdpW3ebf07Mx0XXtlHmEGQMIQZgBEreVu6QCQDDhmBgAA2BphBgAA2BphBgAA2BphBgAA2BphBkDcne1aNIZhyOXi/AMA8cG3CYC4as+1aKxgSH36nZ/AqgCkMsIMgLhqz7VosrwuXTW6vwwuJwwgDggzADrFma5FEwyGElwNgFTGMTMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAuoTDYXCjSQBxwY0mASScN90lb7pHdWZQ9T6z7XFulzK9aQmsDIAdEWYAJJw7zSF/IKgXd5Wp3gy0OiY7M13XXplHmAFwVoQZtKrBbJbpb/0fGenELoJQyEpgRUhFNfV+1TY2d3UZAGyOMINWmf6AXnj9iGobfK2uPy8nS5cP75/gqgAAOB1hBm2qbfCpuq71MJOd6U5wNQAAtI6zmQAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAkLW7dBKA9uM4MgKTkSXfJYRg6XtP2vZsk7t8EgDADIEm5XQ75moN6aVd5m1ei5v5NACTCDIAkd6YrUQOA1AnHzFRWVmrw4MGn/dq+fbskqbS0VMXFxSooKNDEiRP1xBNPRDw/FApp7dq1KioqUkFBgW644QYdPXo03mUCAIAUEfctMwcOHFB6erpeeeUVGScdvdetWzdVV1dr5syZmjhxou6991797W9/07333qvMzExNmzZNkrRu3Tpt3bpVK1asUG5urlauXKlZs2bp+eefl9vN/YAAAECkuIeZgwcPasCAAerTp89p6zZv3qy0tDQtXrxYLpdLAwcOVHl5uTZs2KBp06bJ7/dr06ZNmj9/viZMmCBJWrNmjYqKirRz505NmTIl3uUCAACbi3uYee+99zRw4MBW15WUlGjMmDFyub78sWPHjtUjjzyi48eP69ixY2poaNC4cePC67OzszV06FDt3bs35jBjWZYaGxtlmifOimj5PRXFo0fDMGQFQwoGQwoGg62OCYVCkqUuG3Pq2K6uJ55jTu4xGerptDGSQlbH5gkGQ7KCIZmmKcuyWh3TVfi+SQ302HUsy4rYw3MmnbJlpkePHrr++ut15MgRfeUrX9FNN92k8ePHq6KiQvn5+RHjW7bgfPLJJ6qoqJAk9evX77QxLeti4ff7VVpaGn5cVlYW81x20ZEe3W63uvfur8aGBtXXtf7mNrunybJCajJN1dc1JGyM2fjPD12j2eaYRNbTmWPMRlNmoydp6onnGF/3E2cf+Zt8HZon3RGU6WtSxcfH5Pf7Wx3T1fi+SQ30mHh+v1/p6entGhvXMBMIBPTBBx9o0KBB+vnPf66srCy98MILmj17th577DE1NTWddtxLS6E+ny+cClsbU1NTE3NdbrdbQ4YMkWmaKisr04ABA+T1emOeL5nFo0fDMFRvhpSRmSlfyNnqGG+GV4bhkMfrVVag9eTcGWO8vpDMRlPeDK+cTmfM8yTzmGAwGO4xGerpjDHpnhOfe7cnXVndYp8nI9Mjb7pHOYMGJeWWGb5v7I8eu040x8nGNcy4XC7t2bNHTqdTHo9HkvTVr35Vhw4d0qOPPiqPx3Pa/z35fCdOuczIyAg/x+/3h//cMqYjf8GGYSgjIyP82Ov1RjxORR3tscFvyul0RASGkzkcDslQl41xOp0R47u6ns4Y43Q6k6qeuI+R5DA6No/T6ZDhdCTVF/Cp+L5JDfSYeO3dxSR1wqnZmZmZEUFEki6++GJVVlYqNzdXVVVVEetaHvft2ze8e6m1MX379o13qQAAIAXENcwcOnRII0eO1J49eyKW/+Mf/9CgQYNUWFioffv2RRzMt3v3buXl5alXr1665JJLlJWVFfH82tpa7d+/X4WFhfEsFQAApIi4hpmBAwfqoosu0uLFi1VSUqL3339f9913n/72t7/ppptu0rRp01RfX68777xThw8f1vbt2/X4449rzpw5kk7sHysuLtaqVav06quv6sCBA5o3b55yc3M1adKkeJYKAABSRFyPmXE4HFq/fr0eeOAB/eQnP1Ftba2GDh2qxx57LHwW08aNG7Vs2TJNnTpVOTk5WrhwoaZOnRqeY+7cuQoEAlq0aJGamppUWFioRx99VGlp3HsFAACcLu6nZvfu3Vv33Xdfm+uHDRumZ555ps31TqdTCxYs0IIFC+JdGoAUFMUxggBSFDeaBGBbnnSXHIah4zVnvtiX1+3iztpACiPMALAtt8shX3NQL+0qV21D63fWzs5M17VX5hFmgBRGmAFge7UNPlXXtR5mAKS+uF9nBgAAIJEIMwAAwNYIMwAAwNY4ZuYc1GA2y/QH2lzvcBgKhZLrpn0AALSFMHMOMv0BvfD6kTbP/jgvJ0uXD++f4KoAAIgNYeYcdaazP7Iz23/bdQAAuhrHzAAAAFsjzAAAAFsjzAAAAFsjzABIedyMEkhtHAAMIKVxM0og9RFmAKQ0bkYJpD7CDIBzAjejBFIXx8wAAABbI8wAgDhIGLAzdjMBOOdxkDBgb4QZAOc8DhIG7I0wAwD/dLaDhNkVBSQnwkwKcrvdMvjWBeKKXVFA8iLMpJhAyKHuvfur3gypwX/6l67DYSgUsrqgMsDe2BUFJC/CTIrx+YP63Z/elz/klNN5+slq5+Vk6fLh/bugMiA1cL0aIPkQZlLQF3VN8oWccjqdp63LznR3QUUAAHQerjMDAABsjTADAABsjTADAABsjTADAABsjTADAHHUcoknwzC45hOQIJzNBABxcvKF9axgqM1rPnFhPSC+CDMAECcnX1ivutZUY0ODMjIzI675lJ2ZrilFXFgPiCfCDADE2YkL6zWpvs487ZpP3BYBiD/CDAAkELdFAOKPMAMAXYDbIgDxw9lMAADA1ggzAADA1ggzAJCEuDwN0H4cM2MjDWazTH+gzfUOhyFLkuEwpFDi6gIQX5zxBESHMGMjpj+gF14/0uYZEOflZOnyYf1liP+lA+ysvWc8cb0a4ATCjM2c6QyI7Ex3gqsB0JnO9Hln6w3wJcIMANgQW2+ALxFmAMDGOrr1xjCkNKdD/sCZD7RrbQsPN9NEsiDMAECKas/Wm/NysnT58P5nHJPTI0NXj/2KzJrIExBOvZkmu7TQVQgzAJDi2nOs3dnGtBaKgsFQ+Gaaub2zWg08pyLwoDMQZpJEe067DoWsBFYEAJFODTzBYDB8M80e2R6O4UGXScowEwqF9PDDD+t///d/VVdXp8LCQt1111264IILurq0TtOu066H909wVQAQHc7AQldIyjCzbt06bd26VStWrFBubq5WrlypWbNm6fnnn5fbnbqnH3PaNYBUZsc7hrcc5IzklnRhxu/3a9OmTZo/f74mTJggSVqzZo2Kioq0c+dOTZkypWsLjAG7kADgS/G4Y/jZvlel9m3hOds8LQc5B0Lc/SeZJV2YOXDggBoaGjRu3LjwsuzsbA0dOlR79+61ZZhhFxIAtF97zvQ+2/dqW2dgnczhMBQIhPT7N8vanCcYDMntCOq7k/61XbWjaxiWZSXVJoGdO3fq1ltv1d///nd5PJ7w8v/3//6fmpqa9Mgjj0Q136WXXqpgMKjc3FxZlqXm5malpaXF7boIlqQz/Q0a/xzT2NTc5tYXl9MhT7orbmMaTH+bNcX7Z3XFGMuyTnv9kr3maMe09Jgs9TCG92qi3qtOp0MZ6S6d6R+maL5Xm3yBNr8PnU5D7jTnGeeRToSeTE/aWWuScfZ/D5JpjPTP4GhZCgQCcrlcSXXNoIqKCjmdTr3zzjtnHZt0W2ZM88SBYafuo0xPT1dNTU3U86Wnp8vv98swDBmGofT09LjU2cII/+fMumWcfZ9rvMZkZ569x0TWwxjGMIYx0Y5pj/bM055jb+JVj6R2/XuQfGOS87ggl8vV7rqSLsy0bI3x+/0RW2Z8Pp+8Xm/U85WUlMStNgAAkHyS7oimfv36SZKqqqoilldVValv375dURIAAEhiSRdmLrnkEmVlZWnPnj3hZbW1tdq/f78KCwu7sDIAAJCMkm43k9vtVnFxsVatWqWePXvqvPPO08qVK5Wbm6tJkyZ1dXkAACDJJF2YkaS5c+cqEAho0aJFampqUmFhoR599FGlpSXHRZQAAEDySLpTswEAAKKRdMfMAAAARIMwAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbC2lwkwoFNLatWtVVFSkgoIC3XDDDTp69Gi7njdr1iw99NBDCaiyY6Lt8dChQ5o9e7Yuu+wyjRs3TnPnztWxY8cSWHF0ou3v3Xff1Q9/+EONGDFCY8eO1V133aW6uroEVhy9WN+nkvTb3/5WgwcP1kcffdTJVXZMtD229HXqr2TuM9oem5ub9cADD4THFxcXq7S0NIEVRy+aHh966KFWX8PBgwfr9ttvT3Dl7Rft6/jZZ5/ppz/9qcaOHavLLrtM8+bNU2VlZQIrjl60PZaVlWn27NkaPXq0xo8fr7Vr1yoQCCSw4hhYKeShhx6yLrvsMusPf/iDVVpaav3Xf/2XNWnSJMvn87X5HJ/PZ/3sZz+z8vPzrbVr1yaw2thE0+Pnn39uXXHFFdatt95qvffee9Y777xjXX/99dY111xjNTU1dUH1ZxdNf59++qlVWFho3X777dYHH3xg7du3z/q3f/s36+abb+6CytsvlvepZVnWRx99ZI0aNcrKz8+3jh49mqBqYxNtj/fff79VXFxsVVVVRfwKBAIJrrz9ou3xjjvusC6//HLrT3/6k3X48GHr1ltvta644gqrtrY2wZW3XzQ91tfXn/b6/fKXv7QKCgqsAwcOdEH17RPt61hcXGx973vfs/bv32+9++671ne/+11r2rRpCa46OtH0+MUXX1iXX365VVxcbP3jH/+w9u7da02ePNm6/fbbu6Dy9kuZMOPz+awRI0ZYTz31VHhZTU2NNWzYMOv5559v9Tn79u2zrr32Wuuqq66yRo8enfRhJtoen332WWvEiBGWaZrhZceOHbPy8/OtN998MyE1RyPa/v72t79Z8+bNs5qbm8PLHn/8cWv48OGJKDcmsbxPLcuygsGgdd1111k/+MEPkj7MxNLjrFmzrCVLliSqxA6LtscPP/zQGjx4sPWHP/whYvzXv/71pPwsWlbs79UW7777rvWv//qv1vbt2zuzzA6JtseamhorPz/fevXVV8PLXnnlFSs/P9+qrq5ORMlRi7bHxx57zCooKLA+++yz8LKSkpKk/95Jmd1MBw4cUENDg8aNGxdelp2draFDh2rv3r2tPue1115TUVGRduzYoW7duiWq1JhF2+O4ceO0bt06eTye8DKH48RLXltb2/kFRyna/oYPH67Vq1fL5Tpxi7H3339fv/nNb3TFFVckrOZoxfI+laT169erublZc+bMSUSZHRJLj++9954GDhyYqBI7LNoe33jjDXXr1k3jx4+PGP9///d/EXMkk1jfqy0WL16s0aNHa+rUqZ1ZZodE26PH41FmZqZ27Nih+vp61dfX6ze/+Y3y8vKUnZ2dyNLbLdoey8vLddFFF6lnz57hZUOHDpUklZSUdH7BMUrKG03GoqKiQpLUr1+/iOV9+vQJrzvVvHnzOr2ueIq2x/PPP1/nn39+xLINGzbI4/GosLCw8wqNUSyvYYurr75aZWVlOu+88/Twww93Wo0dFUuPb7/9tjZt2qTnnnsu6ffNS9H3WFNTo8rKSpWUlGjr1q2qrq7WsGHDtGDBAuXl5SWk5mhF2+ORI0d0wQUXaOfOndqwYYMqKys1dOhQ/fznP0/aENeRz+Mf/vAHvfXWW9qxY0dnlRcX0fbodru1YsUK3XXXXRo9erQMw1CfPn20ZcuW8P8oJptoe+zTp4+qqqoUDAbldDolSR9//LGkE8cLJavk/NuPgWmakk682U6Wnp4un8/XFSXFXUd7fPLJJ7VlyxbNnz8/InUni470t2rVKj355JPq1auXfvCDH6ihoaHT6uyIaHtsbGzU/PnzNX/+fA0YMCARJXZYtD0eOnRIkmRZlu677z49+OCD8vl8+v73v6/jx493fsExiLbH+vp6lZeXa926dbrtttv0q1/9Si6XS9///veT9h+IjnweH3vsMX3961/XkCFDOq2+eIi2R8uyVFpaqhEjRuipp57S5s2b1b9/f918882qr69PSM3RirbHa665Rl988YXuu+8+NTY26vjx41q6dKlcLpeam5sTUnMsUibMtOxK8fv9Ect9Pp+8Xm9XlBR3sfZoWZYefPBBLV26VDfddJOmT5/eqXXGqiOv4aWXXqoxY8bo4Ycf1kcffaSXX3650+rsiGh7XLp0qfLy8vS9730vIfXFQ7Q9jh49Wrt27dIDDzygr371qxo9erQefvhhhUIhbd++PSE1RyvaHl0ul+rr67VmzRpdeeWVGjZsmNasWSNJ+vWvf935Bccg1s/jsWPHtGfPHl133XWdWl88RNvj73//e23ZskUrV67UqFGjNGbMGK1fv14ff/yxnnvuuYTUHK1oexwwYID++7//Wy+++KJGjRqlq6++WhMmTFCPHj2S+nCMlAkzLZvQqqqqIpZXVVWpb9++XVFS3MXSY3NzsxYsWKD169fr9ttv109+8pPOLjNm0fb3wQcf6I9//GPEsr59++pf/uVfknZ3TLQ9btu2TW+++aZGjBihESNG6IYbbpAkTZkyRevXr+/8gmMQy/u0Z8+eMgwj/Njr9er8889PmdcxNzdXLpcrYpeSx+PRBRdckLSnn8f6nfrKK6+oZ8+eSX3sWotoeywpKVFeXp6ysrLCy7p37668vDyVl5d3brExiuV1nDhxol5//XW99tpr2rVrl7773e/q+PHjuuCCCzq93lilTJi55JJLlJWVpT179oSX1dbWav/+/Ul5fEgsYulx4cKFevHFF/XAAw9oxowZCao0NtH29+abb2ru3LkRBzN/+OGHqq6uTtrjEKLtcefOnfrd736nHTt2aMeOHVq6dKmkE8c+JevWmmh7fOaZZ3TZZZepsbExvKy+vl5lZWUaNGhQQmqOVrQ9FhYWKhAI6J133gkva2pq0tGjR/WVr3wlITVHK9bv1JKSEo0ZMyZ8YH4yi7bH3NxclZeXR+yeaWxs1EcffZS0u4Gj7bGkpETTp09XIBBQnz595Ha7tXPnTnm9Xo0cOTKRpUeni8+miqvVq1dbY8aMsV555ZWIc+n9fr8VCASsqqqqiNOUT/b1r3896U/Ntqzoety2bZuVn59vbdy48bTrP7T199DVoumvurraKioqsmbPnm0dPHjQ2rt3r/Wtb33L+o//+I+kvj5JR96nu3fvTvpTJC0ruh6PHTtmjR492rrlllusgwcPWm+//bY1Y8YM6xvf+EbSXg/JsqJ/HWfMmGFdc8011t69e61Dhw5Zt956qzVu3LiIU2CTTSzv1auuuspat25dF1UcvWh6rKystMaMGWPdeOONVmlpqVVaWmrNmTPHKioqSurrBUXT42effWYVFhZaS5cutT788EPr5ZdftkaNGmX96le/6uIuziylwkwgELDuv/9+a+zYsVZBQYF1ww03hL/0jx49auXn51vbtm1r9bl2CTPR9Dhz5kwrPz+/1V9t/T10tWhfww8++MCaPXu2NWrUKGvMmDHW7bffbtXU1HRV+e3SkfepXcJMtD3+4x//sGbOnGmNGjXKGjlypHXrrbdax44d66ry2yXaHuvq6qy7777buuyyy6zhw4dbM2fOtA4dOtRV5bdLLO/VYcOGWVu3bu2KcmMSbY+HDx+25syZY40ZM8YaO3as9eMf/zjlPo/79u2zvvOd71jDhg2zrrrqKuuxxx7rosrbz7Asy+rqrUMAAACxSpljZgAAwLmJMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGzt/wM0HT3TKf4PFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_all_df.drop_duplicates('revised_text')['max_1'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGgCAYAAABfSOayAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAuR0lEQVR4nO3df3RU9Z3/8dedmcyPJMSCJAT8RQT5tS2EH0GgwmLsUj2yZ0s59VQb96y7ILqtrijQqqyuihUFxVUPi6yiuMqpXaGurt9WKutpSwVKqF1/kCgoifgjxLgxPyaTmczM/f5BEx0zSWbCZGY+yfNxTgTu/dyb97ydmbxy72futWzbtgUAAGAoR6YLAAAAOBWEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAo7kyXcBAmzVrlkKhkAoLCzNdCgAASNCnn34qt9utysrKPscO+iMzwWBQ4XA402XEsG1bwWBQXK+wd/Spb/QoMfSpb/Sob/QoManqUzgcVjAYTGjsoD8yU1RUJEnas2dPhiv5Qltbm6qqqjR58mTl5uZmupysRZ/6Ro8SQ5/6Ro/6Ro8Sk6o+XXTRRQmPHfRHZgAAwOBGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYzZXpAgAAA8cf6FAgFO51jM/tkpWmeoCBQJgBgEEsEArrpb3H1OwPxl1fkOfRpReUKM9NnIG5CDMAMMg1+4NqbIkfZoDBgDkzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAwBBnWZJlWXK73bIsrjcD83CdGQAYwrwelxyWpZZARKeNHKPWQFT+UKDbOJ/bpTxfTgYqBPpGmAGAIcztcijYEdEvf/++PjnRqNy8PDmdsQftu64STJhBliLMAADU1BrSZ00BBaNOOZ3OTJcDJIU5MwAAwGgcmQEAQ/V1R2yHw1I0aqexIiAzCDMAYKi+7oh9RmG+5k0bk+aqgPQjzACAwXq7I3ZBnjvN1QCZwZwZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0rgAMAFmI+y4BiSPMAEAW4r5LQOIIMwCQpbLpvkuWldZvBySFMAMA6JXX45LDstTQFOh1nM/tUp4vJ01VAV8gzAAAeuV2ORTsiOjlfbU9nvYqyPPo0gtKCDPICMIMACAhvZ32AjKJj2YDAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGC3pMPP555/rtttu04IFCzRjxgxdfvnlqqys7Fq/b98+ffe739W0adN08cUX66WXXorZPhgM6o477tDcuXM1ffp03XTTTfq///u/mDF97QMAAKBT0mHmxhtv1Ouvv64HHnhAO3fu1OTJk/UP//APev/99/Xee+9pxYoVmj9/vnbt2qXvfe97WrNmjfbt29e1/b/8y79o7969evjhh7V9+3a9//77uv7667vWJ7IPAMhW/kCHGpoCvX75Ax2ZLhMYVFzJDK6trdXvf/977dixQzNnzpQk/fM//7N+97vf6cUXX9Rnn32miRMnauXKlZKkcePG6fDhw3rsscc0d+5cnThxQs8//7y2bNmiWbNmSZIeeOABXXzxxXr99dc1ffp0bd++vdd9AEA2C4TCemnvMTX7g3HXF+R5dOkFJcrz5aS5MmDwSirMDB8+XFu3btU3vvGNrmWWZcmyLDU3N6uyslLf+ta3YraZM2eO7r77btm2rUOHDnUt61RSUqJRo0bp4MGDmj59ep/7sCwr6Qdp27ba2tqS3m6gBAKBmD8RH33qGz1KTLr6ZFmW7EhUjc0BNba0xx0TiURlR6IKBAKybbvX/UQiUUUikbhjotGoZCtlY6J29M/1dR+XyH4SeVym4/WWmFT1KZmf+UmFmYKCAv3lX/5lzLKXX35ZtbW1uuWWW/SLX/xCxcXFMeuLiooUCATU2NioEydOaPjw4fJ4PN3G1NXVSZLq6up63ceIESOSKVmSFAqFVFVVlfR2A62mpibTJRiBPvWNHiVmoPvkdrt12sgxavP71doS/43c44go2BHU/336qUKhUI/7yS0o7HU/gdNyZNtRtQcCam3xn/KYUPvJI0mBtu7fL5H9eBwRBYLtqvvo4x4f12DB6y0xp9qnUCjULS/0JKkw81V//OMfdfPNN2vRokVauHCh2tvb5Xa7Y8Z0/jsUCikQCHRbL0kej0fB4MkXUl/76A+3263Jkyf3a9uBEAgEVFNTo7Fjx8rn82W6nKxFn/pGjxKTrj5ZlqXWQFS5eXkKRp1xx5w+Il95vlx53GdKPRzAcDgsRaJ2r/vx5fpkWQ55fT7lh+P/9prMGLfXI8kvX65PTqcz6f3k5nnl83hVOH78oD4yw+utb6nqU7y80JN+h5lXXnlFq1at0owZM7Rx40ZJJ0PJVwNH5799Pp+8Xm/cQBIMBrsecF/76A/LspSbm9uvbQeSz+fLyrqyDX3qGz1KTDr65A8F5HQ6ugWCTl6PS8FwRC/vq+1xXs0ZhfmaN21Mr/txOBySpZSNcVgnPw/idDq7jU1kP06nQ5bTMSR+yPN6S8yp9imZaSX9CjNPP/207r77bl188cW69957u9LT6NGjVV9fHzO2vr5eubm5GjZsmIqLi/X5558rFArFJK76+nqNGjUqoX0AwGDQ7A+qsaWnScKJ/0YKoB8fzd6xY4fuuusu/eAHP9ADDzwQE0pmzZqlP/zhDzHj9+/frxkzZsjhcGjmzJmKRqNdE4El6dixYzpx4oTKysoS2gcAAMCXJZUOjh07pp/+9Kf6q7/6K61YsUINDQ369NNP9emnn6qlpUVXXnml3njjDW3cuFHvvfeetm3bpl/96ldatmyZJGnUqFG69NJLtXbtWh04cEBvvPGGbrzxRs2ePVulpaWS1Oc+AAAAviyp00wvv/yyOjo69Otf/1q//vWvY9YtWbJE69ev1+bNm7VhwwZt375dZ555pjZs2BBzfZi77rpLP/3pT/WjH/1IkrRgwQKtXbu2a/15553X5z4AAAA6JRVmrrnmGl1zzTW9jlmwYIEWLFjQ4/rc3FytW7dO69at6/c+AAAAOjEJBQAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaKd0o0kAGCz8gQ4FQuFex/jcLuX5ctJUEYBEEWYAQFIgFNZLe4/1ePPHgjyPLr2ghDADZCHCDAD8WW83fwSQvZgzAwAAjEaYAQAARiPMAABSwrIyXQGGKubMAABOmdfjksOy1NAU6HUcnwjDQCDMAABOmdvlULAjopf31fKJMKQdYQYAkDJ8IgyZwJwZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAJA2lpXpCjAYuTJdAABgaPB6XHJYlhqaAr2O87ldyvPlpKkqDAaEGQBAWrhdDgU7Inp5X62a/cG4YwryPLr0ghLCDJJCmAEApFWzP6jGlvhhBugP5swAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQBIkGVlugIA8XCjSQBIgNfjksOy1NAU6HGMw2EpGrXTWBUAiTADAAlxuxwKdkT08r5aNfvj3/H5jMJ8zZs2Js2VDT4cAUOyCDMAkIRmf1CNLfHDTEGeO83VDD6JHAGTJJ/bpTxfTpqqQrYjzAAAskYiR8AK8jy69IISwgy6EGYAAFmntyNgwFfxaSYAAGC0Uwozjz76qK688sqYZWvXrtXEiRNjvsrLy7vWR6NRPfTQQ5o/f75KS0u1fPlyHT9+PGYfVVVVqqioUGlpqcrLy/XUU0+dSpkAAGAQ63eYeeaZZ/Tggw92W/7OO+/ommuu0d69e7u+nnvuua71mzdv1o4dO3TXXXfpZz/7maLRqJYtW6ZQKCRJamxs1FVXXaWzzz5bO3fu1A9/+ENt3LhRO3fu7G+pAABgEEt6zsyJEyd0++2368CBAxo7dmzMOtu2dfToUV199dUqLCzstm0oFNK2bdu0atUqLVy4UJK0adMmzZ8/X7t379bixYv185//XDk5Obrzzjvlcrk0btw41dbWauvWrVq6dGm/HiQAABi8kj4y8/bbbysnJ0cvvPCCpk2bFrPugw8+UFtbm84999y421ZXV8vv92vu3LldywoKCjRlyhQdPHhQklRZWanZs2fL5foiZ82ZM0c1NTVqaGhItlwAADDIJX1kpry8PGYOzJe9++67kqT/+I//0G9/+1s5HA4tWLBAK1eu1LBhw1RXVydJGj16dMx2RUVFXevq6uo0YcKEbusl6ZNPPtHIkSOTLVm2bautrS3p7QZKIBCI+RPx0ae+0aPE9NUny7JkR6KKRKKKRCJxx0SjUcnWoB0TtaOSFHdcttUciURlR6IKBAKy7fRdcZnXW2JS1SfbtmUleAXFlH40+91335XD4VBRUZG2bNmiDz74QPfdd5+OHDmi7du3dz0wtzv2wlIej0dNTU2SpPb29rjrJSkY7N/H9EKhkKqqqvq17UCqqanJdAlGoE99o0eJ6alPbrdbp40coza/X60t8d+AA6flyLajag8E1NriH3RjQu0n318Dbd0ff7bV7HFEFAi2q+6jj7vmW6YTr7fEnGqfQqFQ18//vqQ0zFx77bW64oorNHz4cEnShAkTVFhYqMsuu0xvvvmmvF5vV4Gdf5dOhhSfzydJ8nq93Z6cnSEmNze3X3W53W5Nnjy5X9sOhEAgoJqaGo0dO7brcaM7+tQ3epSYvvpkWZZaA1Hl5uUpGHXG3Ycv1yfLcsjr8yk/HP+3RZPHuL0eSX75cn1yOp393k86xuTmeeXzeFU4fnzaj8zweutbqvr01QMbvUlpmHE4HF1BptN5550n6eTpo87TS/X19Tr77LO7xtTX12vixImSpOLiYtXX18fso/Pfo0aN6lddlmX1OwgNJJ/Pl5V1ZRv61Dd6lJje+uQPBeR0Orr9IO/kcDgkS4N2jMM6OYXS6XR2G5ttNTudDllOR8YCBa+3xJxqnxI9xSSl+KJ5a9as0d/93d/FLHvzzTclSePHj9ekSZOUn5+vAwcOdK1vbm7W4cOHVVZWJkkqKyvToUOHYs6V7t+/XyUlJTr99NNTWS4AABgEUhpmvv3tb2vfvn165JFH9MEHH+g3v/mNbrnlFi1evFjjxo2T2+1WRUWFNm7cqD179qi6ulorV65UcXGxFi1aJElaunSpWltbdeutt+ro0aPatWuXnnzySa1YsSKVpQIAgEEipaeZLrroIj344IPaunWr/v3f/13Dhg3TX//1X+uGG27oGnP99dcrHA5r7dq1am9vV1lZmR5//HHl5Jy8Ydjpp5+uxx57THfffbeWLFmiwsJCrVmzRkuWLEllqQAAYJA4pTCzfv36bssuueQSXXLJJT1u43Q6tXr1aq1evbrHMVOnTtWzzz57KqUBAIAhghtNAgAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDIAhwe12y7KsTJcBYAC4Ml0AAAy0cNSh00aOUWsgKn8o0G29w2EpGrUzUBmAVCDMABj0gqGI/vu37ykUdcrp7H5A+ozCfM2bNiYDlQFIBcIMgCHh85Z2BaNOOZ3ObusK8twZqAhAqjBnBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAIBxLCvTFSCbuDJdAAAAyfB6XHJYlhqaAr2O87ldyvPlpKkqZBJhBgBgFLfLoWBHRC/vq1WzPxh3TEGeR5deUEKYGSIIMwAAIzX7g2psiR9mMLQQZgAYzR/oUCAU7nG9w2HJlmQ5LCmavroApA9hBoDRAqGwXtp7rMfTDWcU5mve1DGyxIxRYLAizAAwXm+nGwry3GmuBkC68dFsAABgNMIMAAAw2imFmUcffVRXXnllzLKqqipVVFSotLRU5eXleuqpp2LWR6NRPfTQQ5o/f75KS0u1fPlyHT9+PKl9AAAAdOp3mHnmmWf04IMPxixrbGzUVVddpbPPPls7d+7UD3/4Q23cuFE7d+7sGrN582bt2LFDd911l372s58pGo1q2bJlCoVCCe8DAACgU9ITgE+cOKHbb79dBw4c0NixY2PW/fznP1dOTo7uvPNOuVwujRs3TrW1tdq6dauWLl2qUCikbdu2adWqVVq4cKEkadOmTZo/f752796txYsX97kPAACAL0v6yMzbb7+tnJwcvfDCC5o2bVrMusrKSs2ePVsu1xcZac6cOaqpqVFDQ4Oqq6vl9/s1d+7crvUFBQWaMmWKDh48mNA+AAAAvizpIzPl5eUqLy+Pu66urk4TJkyIWVZUVCRJ+uSTT1RXVydJGj16dLcxnev62sfIkSOTLVm2bautrS3p7QZKIBCI+RPx0ae+DfUeWZYlOxJVJBJVJBKJOyYajUqyJan3Mbb63s8gHhO1T15RMN64bK25tzGRSFR2JKpAICDbtuOOSdZQf70lKlV9sm1bVoJ3FE3pdWba29vldsde08Hj8UiSgsFg1wOLN6apqSmhffRHKBRSVVVVv7YdSDU1NZkuwQj0qW9DtUdut1unjRyjNr9frS3x3zgDp+V0/TALtPU2Jqr2QECtLf4hOSbUfvL9NV6PsrXm3sZ4HBEFgu2q++jjrjmZqTJUX2/JOtU+hUKhrp//fUlpmPF6vd2eNJ0BJDc3V16vt6vAzr93jvH5fAntoz/cbrcmT57cr20HQiAQUE1NjcaOHdv1uNEdferbUO+RZVlqDUSVm5enYNQZd4wv19f1250v1yens/u4k2Mc8vp8yg/H/01wsI9xez2S/HF7lK019zYmN88rn8erwvHjU3pkZii/3hKVqj599cBGb1IaZoqLi1VfXx+zrPPfo0aNUjgc7lp29tlnx4yZOHFiQvvoD8uy+h2EBpLP58vKurINferbUO6RPxSQ0+mIG1IkyeFwSH++lYHT6Yw7zuFwSJb63s8gHuOwTk6hjNejbK25tzFOp0OW0zEgoWMov96Scap9SvQUk5Tii+aVlZXp0KFDMecw9+/fr5KSEp1++umaNGmS8vPzdeDAga71zc3NOnz4sMrKyhLaBwAAwJelNMwsXbpUra2tuvXWW3X06FHt2rVLTz75pFasWCHp5CGjiooKbdy4UXv27FF1dbVWrlyp4uJiLVq0KKF9AAAAfFlKTzOdfvrpeuyxx3T33XdryZIlKiws1Jo1a7RkyZKuMddff73C4bDWrl2r9vZ2lZWV6fHHH1dOTk7C+wAAAOh0SmFm/fr13ZZNnTpVzz77bI/bOJ1OrV69WqtXr+5xTF/7AAAA6MSNJgEAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARkvpRfMAIFH+QIcCoXCvY3xul/J8OWmqCICpCDMAMiIQCuulvcfU7A/GXV+Q59GlF5QQZgD0iTADIGOa/UE1tsQPMwCQKObMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAWcuyMl0BABNwo0kAWcnrcclhWWpoCvQ4xuGwFI3aaawKQDYizADISm6XQ8GOiF7eV6tmf/w7a59RmK9508akuTIA2YYwAyCrNfuDamyJH2YK8txprgZANmLODAAAMBphBgAwKDGBfOjgNBMAYNBJZAK5JPncLuX5ctJUFQYKYQYAMOgkMoG8IM+jSy8oIcwMAoQZAMCg1dsEcgwezJkBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAYMiyrExXgFRwZboAAIOPP9ChQCjc43qHw1I0aqexIqA7r8clh2WpoSnQ51if2yVyT/YizABIuUAorJf2HlOzPxh3/RmF+Zo3bUyaqwJiuV0OBTsienlfbY/PVUkqyPPo0gtKlOcmzmQrwgyAAdHsD6qxJf4PiII8d5qrAXrW23MVZmDODAAAMFrKw8yJEyc0ceLEbl+7du2SJFVVVamiokKlpaUqLy/XU089FbN9NBrVQw89pPnz56u0tFTLly/X8ePHU10mAAAYJFJ+mqm6uloej0evvPKKrC9NEx82bJgaGxt11VVXqby8XHfccYf+9Kc/6Y477lBeXp6WLl0qSdq8ebN27Nih9evXq7i4WBs2bNCyZcv04osvyu3m0DQAAIiV8jDz7rvvauzYsSoqKuq2bvv27crJydGdd94pl8ulcePGqba2Vlu3btXSpUsVCoW0bds2rVq1SgsXLpQkbdq0SfPnz9fu3bu1ePHiVJcLAAAMl/Iw884772jcuHFx11VWVmr27Nlyub74tnPmzNGjjz6qhoYGffzxx/L7/Zo7d27X+oKCAk2ZMkUHDx7sd5ixbVttbW392nYgBAKBmD8RH33qWzb2yLIs2ZGoIpGoIpFI3DHRaFSylb4xOvkx8KypJwvHRO2opPg9ytaa0zFGOrnejkTV3h6SlF2vt2yUqvcl27ZjzvD0ZkCOzAwfPlw/+MEPdOzYMZ1zzjm69tprtWDBAtXV1WnChAkx4zuP4HzyySeqq6uTJI0ePbrbmM51/REKhVRVVdXv7QdKTU1NpkswAn3qWzb1yO1267SRY9Tm96u1Jf6bWeC0HNl2VO2BgFpb/GkYczLMBNqypZ7sGxNqP/lpnng9ytaa0zFGkjyOiALBdjU1fCwpu15v2exU+xQKheTxeBIam9IwEw6H9f7772v8+PH6yU9+ovz8fL300ku6+uqr9cQTT6i9vb3bvJfOQoPBYFeKizemqamp33W53W5Nnjy539unWiAQUE1NjcaOHSufz5fpcrIWfepbpnrU229LlmWppS2i3Lw8BaPOuGN8uT5ZlkNen0/54fj7Su0Yq+vvTmf3mtJfT/aNcXs9kvxxe5StNadjjCTl5nnl83g17Mwz9f777/Oe1IdUvS8lM082pWHG5XLpwIEDcjqd8nq9kqSvf/3rOnLkiB5//HF5vV6FQqGYbYLBk78N5Obmdm0TCoW6/t455lQaYlmWcnNz+739QPH5fFlZV7ahT31LZ4/8gQ619Xp1X8m2LDmdjrjB4eQYh2QpfWP+fO1Wp9MZd1za68nCMQ7r5Idb4/UoW2tOxxjp5HrL6ZDXffLnEu9JiTnVPiV6ikkagNNMeXl53Zadd9552rt3r4qLi1VfXx+zrvPfo0aNUjgc7lp29tlnx4yZOHFiqksF0A9c3RdAtknpdWaOHDmiGTNm6MCBAzHL33rrLY0fP15lZWU6dOhQzESr/fv3q6SkRKeffromTZqk/Pz8mO2bm5t1+PBhlZWVpbJUAKeg84qp8b5a2kJ97wAAUiilYWbcuHE699xzdeedd6qyslLvvfee7rnnHv3pT3/Stddeq6VLl6q1tVW33nqrjh49ql27dunJJ5/UihUrJJ08P1ZRUaGNGzdqz549qq6u1sqVK1VcXKxFixalslQAADBIpPQ0k8Ph0JYtW3T//ffrhhtuUHNzs6ZMmaInnnii61NMjz32mO6++24tWbJEhYWFWrNmjZYsWdK1j+uvv17hcFhr165Ve3u7ysrK9PjjjysnJyeVpQIAgEEi5XNmRo4cqXvuuafH9VOnTtWzzz7b43qn06nVq1dr9erVqS4NAAAMQtxoEgAAGI0wAwAAjEaYAQAARiPMAADQB8s6eRE3t9ud1MXckB4pnwAMIDv5Ax0K9HLlXknyuV3K8/HJQeDLvB6XHJallkBEp40co9ZAVP5Q93tY8frJHMIMMET0deXegjyPLr2ghDdj4CvcLoeCHRH98vfv65MTjcrNy5PTGXtig9dPZhFmgCGk88q9AJLX1BrSZ00BBaPx7/GFzGHODAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAHThWmAATMRHswFI+uLCYA1N3S8G1snhsBSN2mmsCgD6RpgBIOmLC4O9vK+2xwvrnVGYr3nTxqS5MgDoHWEGQIzeLqxXkOdOczUA0DfmzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNG40CRjO5XIpHHWooSnQ4xiHw1I0aqexKmDosaxMVzB0EWYAwzkcDgVDEf2/fbVq9se/2/UZhfmaN21MmisDhg6vxyWHZfX6S4Uk+dwu5fly0lTV0EGYAQaJZn9QjS3xw0xBnjvN1QBDi9vlULAjopd7+aWiIM+jSy8oIcwMAMIMYDDLsuR2E1SAbNHbLxUYOIQZIIP8gQ4FQuEe11uWlON0KBSOxl1vR6IaNrxIzIYBMJQRZoAMCoTCemnvsT7nuvR06DoSiWrkMJe+NXf8QJcKAFmLMANkWCJzXXoaE4lE5HZw/h3A0MZ1ZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAACQJtxZe2Bw0TygH/q6DYHE3XEBxOLO2gOHMAN8RV9BxeGwFA5H9cvXanq8DUHh8Fx9e845CjT1vp9olLsqAUMFd9YeOIQZ4CsSvV9SX7ch6OtNq3M/AIYW7qydeoQZII5E7peUzv0AAHrGBGAAAGA0wgwAADAap5kwpCQyuZdJuQBgFsIMhpREJ/cCAMxBmMGQw6RcABhcmDMDAACMRpgBACCLcMuD5HGaCQCALMEtD/qHMINBg08qATAdtzzoH8IMBg0+qQRgsOCWB8khzGBQ4ZNKADD0EGZgBE4hAcAXmCQcizCDrGVZltxutxwOh1qDnEICAIlJwvEQZpBxPR11sSNRnTZyjPztUUVtTiEBgMQk4XgIM8i4nibuRiJRtfn9mlAyShdMPzND1QFAdmKS8BcIMxhQic51ifeijEQiam0JqKUtNNBlAgAMlpVhJhqN6pFHHtF//ud/qqWlRWVlZbrtttt01llnZbo0JImPSwNAZgylScJZGWY2b96sHTt2aP369SouLtaGDRu0bNkyvfjii3K7mRuRLU7lqEsn5roAQOoNtUnCWRdmQqGQtm3bplWrVmnhwoWSpE2bNmn+/PnavXu3Fi9enNkCh4hEgko4HNUvX6vhqAsAZJlEJwkvnj84JglnXZiprq6W3+/X3Llzu5YVFBRoypQpOnjwIGEmBVIZVDjqAgDZq7f36MF09MaybTurrjS2e/duXXfddfrf//1feb3eruX/9E//pPb2dj366KNJ7e8b3/iGIpGIiouLU11qv9m2rXA4LJfLJevPJzVtSb39n7D+/J9UjLEltQfDPY5zOi25c5xqa+/o8UJ0LqdDXo9rwMfYtq0clzMt38vUMU6HJZ83J2vqydYx/kCox+d8ttac7jGRSLTrPSkb6smGMV8dNxh71NvPA4fDktftVF9BwbL+/DNIJ9+3Ozo6lJOTE7dXiaqrq5PT6dSbb77Z59isOzITCJxMiF+dG+PxeNTU1JT0/jwej0Kh0Ck1NNU6LwYXs6zrP31tnJoxiaTsYbl9H1lhDGNMGVOQ58mqehhj1phsrClVY1J91MWyLHk8fb/e+uJyuRKeJ5t1YabzaEwoFIo5MhMMBuXz+ZLeX2VlZcpqAwAA2ceR6QK+avTo0ZKk+vr6mOX19fUaNWpUJkoCAABZLOvCzKRJk5Sfn68DBw50LWtubtbhw4dVVlaWwcoAAEA2yrrTTG63WxUVFdq4caNGjBihM844Qxs2bFBxcbEWLVqU6fIAAECWybowI0nXX3+9wuGw1q5dq/b2dpWVlenxxx9XTk52fzQMAACkX9Z9NBsAACAZWTdnBgAAIBmEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmBkA0GtVDDz2k+fPnq7S0VMuXL9fx48cT2vaFF17QxIkT9eGHHw5wlZmXbJ86e/PVr8Hcq2R71NHRofvvv79rfEVFhaqqqtJYcWYk06eHH3447vNo4sSJuvnmm9Ncefok+1z67LPPdNNNN2nOnDk6//zztXLlSp04cSKNFadfsj2qqanR1VdfrVmzZmnBggV66KGHFA6H01hx5j366KO68sorex3T2Niom266SWVlZZo9e7buuOMOBQKB1BZiI+Uefvhh+/zzz7dfffVVu6qqyv77v/97e9GiRXYwGOx1uw8//NCeOXOmPWHCBPv48eNpqjZzku3TfffdZ1dUVNj19fUxX+FwOM2Vp0+yPbrlllvsefPm2b/97W/to0eP2tddd539zW9+025ubk5z5emVTJ9aW1u7PYfuvfdeu7S01K6urs5A9emR7HOpoqLC/v73v28fPnzYfvvtt+3LLrvMXrp0aZqrTq9kevT555/b8+bNsysqKuy33nrLPnjwoH3xxRfbN998cwYqz4ynn37anjRpkl1RUdHruIqKCnvp0qX2W2+9Zb/22mv2hRdeaK9ZsyaltRBmUiwYDNrTp0+3n3nmma5lTU1N9tSpU+0XX3yxx+0ikYh9+eWX23/7t387JMJMf/q0bNky+6677kpXiRmXbI8++OADe+LEifarr74aM/7CCy+0X3vttXSUnBH9fc11evvtt+2/+Iu/sHft2jWQZWZUsj1qamqyJ0yYYO/Zs6dr2SuvvGJPmDDBbmxsTEfJaZdsj5544gm7tLTU/uyzz7qWVVZWDon377q6OnvFihV2aWmpffHFF/caZv74xz/aEyZMsI8ePdq17He/+509ceJEu66uLmU1cZopxaqrq+X3+zV37tyuZQUFBZoyZYoOHjzY43ZbtmxRR0eHVqxYkY4yM64/fXrnnXc0bty4dJWYccn26Pe//72GDRumBQsWxIz/n//5n5h9DDb9fc11uvPOOzVr1iwtWbJkIMvMqGR75PV6lZeXp+eff16tra1qbW3Vf/3Xf6mkpEQFBQXpLD1tku1RbW2tzj33XI0YMaJr2ZQpUyRJlZWVA19wBr399tvKycnRCy+8oGnTpvU6trKyUoWFhTHv3bNnz5ZlWTp06FDKasrKG02arK6uTpI0evTomOVFRUVd677qjTfe0LZt2/Tcc88N+nPSnZLtU1NTk06cOKHKykrt2LFDjY2Nmjp1qlavXq2SkpK01Jxuyfbo2LFjOuuss7R7925t3bpVJ06c0JQpU/STn/xkUIfA/rzmOr366qt6/fXX9fzzzw9UeVkh2R653W6tX79et912m2bNmiXLslRUVKSnn35aDsfg/B042R4VFRWpvr5ekUhETqdTkvTRRx9JOjnfaDArLy9XeXl5QmNPnDjRradut1tf+9rX9Mknn6SspsH5rMygzklNbrc7ZrnH41EwGOw2vq2tTatWrdKqVas0duzYdJSYFZLt05EjRyRJtm3rnnvu0YMPPqhgMKgrrrhCDQ0NA19wBiTbo9bWVtXW1mrz5s268cYb9W//9m9yuVy64oorBvWba7J9+rInnnhCF154oSZPnjxg9WWDZHtk27aqqqo0ffp0PfPMM9q+fbvGjBmjf/zHf1Rra2taak63ZHt0ySWX6PPPP9c999yjtrY2NTQ0aN26dXK5XOro6EhLzSYIBALdeiol9vpMBmEmxbxeryQpFArFLA8Gg/L5fN3Gr1u3TiUlJfr+97+flvqyRbJ9mjVrlvbt26f7779fX//61zVr1iw98sgjikaj2rVrV1pqTrdke+RyudTa2qpNmzbpggsu0NSpU7Vp0yZJ0i9+8YuBLzhDku1Tp48//lgHDhzQ5ZdfPqD1ZYNke/TLX/5STz/9tDZs2KCZM2dq9uzZ2rJliz766CM999xzaak53ZLt0dixY/Wv//qv+tWvfqWZM2fq29/+thYuXKjhw4dr2LBhaanZBF6vt1tPpZN9zc3NTdn3IcykWOfhtPr6+pjl9fX1GjVqVLfxO3fu1Guvvabp06dr+vTpWr58uSRp8eLF2rJly8AXnCHJ9kmSRowYIcuyuv7t8/l05plnDtpTc8n2qLi4WC6XK+aUktfr1VlnnTWoP77en+eSJL3yyisaMWKEvvnNbw5ofdkg2R5VVlaqpKRE+fn5XctOO+00lZSUqLa2dmCLzZD+PI/Ky8u1d+9e/eY3v9G+fft02WWXqaGhQWedddaA12uK4uLibj0NhUL6/PPPVVRUlLLvQ5hJsUmTJik/P18HDhzoWtbc3KzDhw+rrKys2/jdu3frv//7v/X888/r+eef17p16yRJW7duHdRHa5Lt07PPPqvzzz9fbW1tXctaW1tVU1Oj8ePHp6XmdEu2R2VlZQqHw3rzzTe7lrW3t+v48eM655xz0lJzJiTbp06VlZWaPXu2XK7BP3Uw2R4VFxertrY25jRAW1ubPvzww0F7OjzZHlVWVurKK69UOBxWUVGR3G63du/eLZ/PpxkzZqSz9KxWVlamurq6mBD8hz/8QZI0c+bMlH0fwkyKud1uVVRUaOPGjdqzZ4+qq6u1cuVKFRcXa9GiRYpEIvr000/V3t4uSTrnnHNivjp/AxgzZoy+9rWvZfCRDKxk+7RgwQJFo1GtWbNGR44c0ZtvvqnrrrtOI0aM0He/+90MP5qBkWyPZs2apXnz5unHP/6xKisrdfToUa1Zs0ZOp1N/8zd/k+FHM3CS7VOnw4cPa9KkSRmqOr2S7dF3vvMdSdINN9yg6upqVVdX68Ybb5TH4+H19ucenXvuuXrnnXd077336vjx43rllVe0bt06rVixIuaI1lDz1T5NmzZNM2bM0MqVK/XGG29o//79uu222/Sd73yn1yOnSUvZh7zRJRwO2/fdd589Z84cu7S01F6+fHnXdQeOHz9uT5gwwd65c2fcbffv3z8krlNg28n36a233rKvuuoqe+bMmfaMGTPs6667zv74448zVX5aJNujlpYW+/bbb7fPP/98e9q0afZVV11lHzlyJFPlp01/XnNTp061d+zYkYlyMyLZHh09etResWKFPXv2bHvOnDn2j370o0H/vpRsjw4dOmR/73vfs6dOnWpfdNFF9hNPPJGhyjPnxz/+ccx1ZuL1qaGhwb7uuuvs0tJS+/zzz7dvv/12u729PaV1WLZt26mLRgAAAOnFaSYAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGO3/Aw+Jq+EHixvqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "plt.figure()\n",
    "sm_all_df.drop_duplicates('revised_text')['max_3'].hist(alpha=.7, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare = score_agg_df.merge(sm_all_df, how = 'left', left_on='origin', right_on='origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare['max_1_eq'] = compare['max_1_col_x']==compare['max_1_col_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7065210846396243"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare['max_1_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6310929458068423"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare[compare['counts']>1]['max_1_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare['max_3_eq'] = compare.apply(lambda x: IoU([x['max_1_col_x'],x['max_2_col_x'],x['max_3_col_x']],\n",
    "                                                 [x['max_1_col_y'],x['max_2_col_y'],x['max_3_col_y']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8499367761877044"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare['max_3_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8113684529215864"
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare[compare['counts']>1]['max_3_eq'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68410"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(score_df[(score_df.max_3>0.8) & (score_df.max_1>1./3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_dict_1 = {'max_{}_col_x'.format(i):'max_{}_col_agg'.format(i) for i in range(1,4,1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_1_col_x': 'max_1_col_agg',\n",
       " 'max_2_col_x': 'max_2_col_agg',\n",
       " 'max_3_col_x': 'max_3_col_agg'}"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_dict_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>Criteria i_x</th>\n",
       "      <th>Criteria ii_x</th>\n",
       "      <th>Criteria iii_x</th>\n",
       "      <th>Criteria iv_x</th>\n",
       "      <th>Criteria v_x</th>\n",
       "      <th>Criteria vi_x</th>\n",
       "      <th>Criteria vii_x</th>\n",
       "      <th>Criteria viii_x</th>\n",
       "      <th>Criteria ix_x</th>\n",
       "      <th>...</th>\n",
       "      <th>max_1_col_y</th>\n",
       "      <th>max_2_val_y</th>\n",
       "      <th>max_2_col_y</th>\n",
       "      <th>max_3_val_y</th>\n",
       "      <th>max_3_col_y</th>\n",
       "      <th>max_1_y</th>\n",
       "      <th>max_3_y</th>\n",
       "      <th>valid</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>0.163718</td>\n",
       "      <td>0.085216</td>\n",
       "      <td>0.280832</td>\n",
       "      <td>0.310720</td>\n",
       "      <td>0.030539</td>\n",
       "      <td>0.108568</td>\n",
       "      <td>0.008998</td>\n",
       "      <td>0.004779</td>\n",
       "      <td>0.003014</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.300038</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.110439</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.391179</td>\n",
       "      <td>0.801656</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0.167033</td>\n",
       "      <td>0.084983</td>\n",
       "      <td>0.267453</td>\n",
       "      <td>0.304577</td>\n",
       "      <td>0.030154</td>\n",
       "      <td>0.122434</td>\n",
       "      <td>0.010389</td>\n",
       "      <td>0.005576</td>\n",
       "      <td>0.003196</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.300537</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.111514</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.387872</td>\n",
       "      <td>0.799923</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.145369</td>\n",
       "      <td>0.133964</td>\n",
       "      <td>0.213757</td>\n",
       "      <td>0.243041</td>\n",
       "      <td>0.029157</td>\n",
       "      <td>0.108096</td>\n",
       "      <td>0.052678</td>\n",
       "      <td>0.034805</td>\n",
       "      <td>0.018227</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.225481</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.183531</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.449886</td>\n",
       "      <td>0.858898</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>0.076119</td>\n",
       "      <td>0.089460</td>\n",
       "      <td>0.266471</td>\n",
       "      <td>0.234519</td>\n",
       "      <td>0.053818</td>\n",
       "      <td>0.085219</td>\n",
       "      <td>0.074331</td>\n",
       "      <td>0.058889</td>\n",
       "      <td>0.032048</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.205980</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.132468</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.378402</td>\n",
       "      <td>0.716850</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0.057712</td>\n",
       "      <td>0.137347</td>\n",
       "      <td>0.384181</td>\n",
       "      <td>0.192520</td>\n",
       "      <td>0.054644</td>\n",
       "      <td>0.106413</td>\n",
       "      <td>0.035951</td>\n",
       "      <td>0.008720</td>\n",
       "      <td>0.009331</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.247151</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.187583</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.351008</td>\n",
       "      <td>0.785742</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49818</th>\n",
       "      <td>116670</td>\n",
       "      <td>0.054110</td>\n",
       "      <td>0.117932</td>\n",
       "      <td>0.374972</td>\n",
       "      <td>0.214453</td>\n",
       "      <td>0.053853</td>\n",
       "      <td>0.143065</td>\n",
       "      <td>0.015148</td>\n",
       "      <td>0.010605</td>\n",
       "      <td>0.006706</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.236805</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.161494</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.416799</td>\n",
       "      <td>0.815098</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49819</th>\n",
       "      <td>116671</td>\n",
       "      <td>0.051298</td>\n",
       "      <td>0.201248</td>\n",
       "      <td>0.309790</td>\n",
       "      <td>0.134733</td>\n",
       "      <td>0.018713</td>\n",
       "      <td>0.177148</td>\n",
       "      <td>0.037058</td>\n",
       "      <td>0.037359</td>\n",
       "      <td>0.011223</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.297634</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.185791</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.334180</td>\n",
       "      <td>0.817606</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49820</th>\n",
       "      <td>116672</td>\n",
       "      <td>0.071557</td>\n",
       "      <td>0.186005</td>\n",
       "      <td>0.324673</td>\n",
       "      <td>0.162889</td>\n",
       "      <td>0.018604</td>\n",
       "      <td>0.175369</td>\n",
       "      <td>0.024129</td>\n",
       "      <td>0.018525</td>\n",
       "      <td>0.007105</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.222477</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.184820</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365697</td>\n",
       "      <td>0.772994</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49821</th>\n",
       "      <td>116673</td>\n",
       "      <td>0.063146</td>\n",
       "      <td>0.063445</td>\n",
       "      <td>0.359193</td>\n",
       "      <td>0.133555</td>\n",
       "      <td>0.022294</td>\n",
       "      <td>0.347638</td>\n",
       "      <td>0.003461</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>0.002171</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.322296</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.350125</td>\n",
       "      <td>0.836224</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49822</th>\n",
       "      <td>116674</td>\n",
       "      <td>0.038102</td>\n",
       "      <td>0.230652</td>\n",
       "      <td>0.318270</td>\n",
       "      <td>0.144274</td>\n",
       "      <td>0.018510</td>\n",
       "      <td>0.240692</td>\n",
       "      <td>0.002965</td>\n",
       "      <td>0.002306</td>\n",
       "      <td>0.001708</td>\n",
       "      <td>...</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.339121</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.147517</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.343892</td>\n",
       "      <td>0.830531</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49823 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       origin  Criteria i_x  Criteria ii_x  Criteria iii_x  Criteria iv_x  \\\n",
       "0           6      0.163718       0.085216        0.280832       0.310720   \n",
       "1           7      0.167033       0.084983        0.267453       0.304577   \n",
       "2          10      0.145369       0.133964        0.213757       0.243041   \n",
       "3          11      0.076119       0.089460        0.266471       0.234519   \n",
       "4          12      0.057712       0.137347        0.384181       0.192520   \n",
       "...       ...           ...            ...             ...            ...   \n",
       "49818  116670      0.054110       0.117932        0.374972       0.214453   \n",
       "49819  116671      0.051298       0.201248        0.309790       0.134733   \n",
       "49820  116672      0.071557       0.186005        0.324673       0.162889   \n",
       "49821  116673      0.063146       0.063445        0.359193       0.133555   \n",
       "49822  116674      0.038102       0.230652        0.318270       0.144274   \n",
       "\n",
       "       Criteria v_x  Criteria vi_x  Criteria vii_x  Criteria viii_x  \\\n",
       "0          0.030539       0.108568        0.008998         0.004779   \n",
       "1          0.030154       0.122434        0.010389         0.005576   \n",
       "2          0.029157       0.108096        0.052678         0.034805   \n",
       "3          0.053818       0.085219        0.074331         0.058889   \n",
       "4          0.054644       0.106413        0.035951         0.008720   \n",
       "...             ...            ...             ...              ...   \n",
       "49818      0.053853       0.143065        0.015148         0.010605   \n",
       "49819      0.018713       0.177148        0.037058         0.037359   \n",
       "49820      0.018604       0.175369        0.024129         0.018525   \n",
       "49821      0.022294       0.347638        0.003461         0.002547   \n",
       "49822      0.018510       0.240692        0.002965         0.002306   \n",
       "\n",
       "       Criteria ix_x  ...   max_1_col_y  max_2_val_y   max_2_col_y  \\\n",
       "0           0.003014  ...   Criteria iv     0.300038    Criteria i   \n",
       "1           0.003196  ...   Criteria iv     0.300537    Criteria i   \n",
       "2           0.018227  ...   Criteria iv     0.225481   Criteria ii   \n",
       "3           0.032048  ...   Criteria iv     0.205980  Criteria iii   \n",
       "4           0.009331  ...   Criteria iv     0.247151  Criteria iii   \n",
       "...              ...  ...           ...          ...           ...   \n",
       "49818       0.006706  ...  Criteria iii     0.236805   Criteria iv   \n",
       "49819       0.011223  ...  Criteria iii     0.297634   Criteria ii   \n",
       "49820       0.007105  ...  Criteria iii     0.222477   Criteria ii   \n",
       "49821       0.002171  ...  Criteria iii     0.322296   Criteria vi   \n",
       "49822       0.001708  ...  Criteria iii     0.339121   Criteria ii   \n",
       "\n",
       "      max_3_val_y   max_3_col_y   max_1_y   max_3_y valid  max_1_eq  max_3_eq  \n",
       "0        0.110439  Criteria iii  0.391179  0.801656  True      True       1.0  \n",
       "1        0.111514  Criteria iii  0.387872  0.799923  True      True       1.0  \n",
       "2        0.183531    Criteria i  0.449886  0.858898  True      True       0.5  \n",
       "3        0.132468   Criteria ii  0.378402  0.716850  True     False       1.0  \n",
       "4        0.187583   Criteria ii  0.351008  0.785742  True     False       1.0  \n",
       "...           ...           ...       ...       ...   ...       ...       ...  \n",
       "49818    0.161494   Criteria ii  0.416799  0.815098  True      True       0.5  \n",
       "49819    0.185791   Criteria vi  0.334180  0.817606  True      True       1.0  \n",
       "49820    0.184820   Criteria vi  0.365697  0.772994  True      True       1.0  \n",
       "49821    0.163804   Criteria iv  0.350125  0.836224  True      True       1.0  \n",
       "49822    0.147517   Criteria vi  0.343892  0.830531  True      True       1.0  \n",
       "\n",
       "[49823 rows x 74 columns]"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare.rename(columns=cp_dict_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_final = sm_all_df.merge(compare.rename(columns=cp_dict_1)[['origin','max_1_col_agg','max_2_col_agg','max_3_col_agg',\n",
    "                                                    'max_1_eq','max_3_eq']], how='left', left_on='origin',right_on='origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>index</th>\n",
       "      <th>index.1</th>\n",
       "      <th>owner</th>\n",
       "      <th>owner_loc</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>comments</th>\n",
       "      <th>taken</th>\n",
       "      <th>views</th>\n",
       "      <th>...</th>\n",
       "      <th>max_3_val</th>\n",
       "      <th>max_3_col</th>\n",
       "      <th>max_1</th>\n",
       "      <th>max_3</th>\n",
       "      <th>valid</th>\n",
       "      <th>max_1_col_agg</th>\n",
       "      <th>max_2_col_agg</th>\n",
       "      <th>max_3_col_agg</th>\n",
       "      <th>max_1_eq</th>\n",
       "      <th>max_3_eq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>50770113131</td>\n",
       "      <td>148125504@N04</td>\n",
       "      <td>Tarazona (Zaragoza), España</td>\n",
       "      <td>Venecia - Patio Palacio Minelli</td>\n",
       "      <td>El palacio Contarini del Bovolo (también llamado palacio Contarini dal Bovolo Minelli) es un pequeño palacio de Venecia, muy conocido por su escalera de caracol exterior, que tiene un gran número de arcos, y que es conocida como la Scala Contarini del Bovolo (del caracol).\\n\\nEl palacio data del siglo XV, construido por la familia Contarini y está, al parecer, en no muy buen estado de conservación. La escalera de caracol conduce a una galería, desde donde se ofrece una encantadora vista panorámica sobre algunos de los tejados de la ciudad. El palacio está situado cerca de la calle Campo Ma...</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-03-27 12:47:45</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110439</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.391179</td>\n",
       "      <td>0.801656</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>50770228312</td>\n",
       "      <td>148125504@N04</td>\n",
       "      <td>Tarazona (Zaragoza), España</td>\n",
       "      <td>Venecia - Palacio Minelli</td>\n",
       "      <td>El palacio Contarini del Bovolo (también llamado palacio Contarini dal Bovolo Minelli) es un pequeño palacio de Venecia, muy conocido por su escalera de caracol exterior, que tiene un gran número de arcos, y que es conocida como la Scala Contarini del Bovolo (del caracol).\\n\\nEl palacio data del siglo XV, construido por la familia Contarini y está, al parecer, en no muy buen estado de conservación. La escalera de caracol conduce a una galería, desde donde se ofrece una encantadora vista panorámica sobre algunos de los tejados de la ciudad. El palacio está situado cerca de la calle Campo Ma...</td>\n",
       "      <td>7</td>\n",
       "      <td>2010-03-27 12:45:28</td>\n",
       "      <td>238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111514</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.387872</td>\n",
       "      <td>0.799923</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50766039303</td>\n",
       "      <td>32980384@N05</td>\n",
       "      <td>Berlin, Germany</td>\n",
       "      <td>_DSC7807</td>\n",
       "      <td>Canal Grande on Venetsian pääliikenneväylä. Kuva on otettu Rialton sillalta, joka on yksi Italian tunnetuimmista nähtävyyksistä.</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-08-11 11:48:15</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.183531</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.449886</td>\n",
       "      <td>0.858898</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>50766893587</td>\n",
       "      <td>32980384@N05</td>\n",
       "      <td>Berlin, Germany</td>\n",
       "      <td>_DSC7805</td>\n",
       "      <td>Corona-rajoitusten aikaan oli hyvä tilaisuus käydä Venetsiassa, joka normaalisti on täynnä valtameriristeilijöiden tuomia turisteja.</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-08-11 11:44:31</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132468</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.378402</td>\n",
       "      <td>0.716850</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>50766892772</td>\n",
       "      <td>32980384@N05</td>\n",
       "      <td>Berlin, Germany</td>\n",
       "      <td>_DSC7802</td>\n",
       "      <td>Corona-rajoitusten aikaan oli hyvä tilaisuus käydä Venetsiassa, joka normaalisti on täynnä valtameriristeilijöiden tuomia turisteja.</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-08-11 11:43:19</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.187583</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.351008</td>\n",
       "      <td>0.785742</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80958</th>\n",
       "      <td>116670</td>\n",
       "      <td>123096</td>\n",
       "      <td>20879329608</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Hier werden die Pakete mit dem Lastkahn zugestellt.</td>\n",
       "      <td>&lt;a href=\"http://www.brusatotrasporti.it/index.php?pg=Ttrasl\" rel=\"noreferrer nofollow\"&gt;www.brusatotrasporti.it/index.php?pg=Ttrasl&lt;/a&gt;</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 11:34:58</td>\n",
       "      <td>244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161494</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.416799</td>\n",
       "      <td>0.815098</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80959</th>\n",
       "      <td>116671</td>\n",
       "      <td>123097</td>\n",
       "      <td>21040989736</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Tür No.342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 11:04:10</td>\n",
       "      <td>156</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185791</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.334180</td>\n",
       "      <td>0.817606</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80960</th>\n",
       "      <td>116672</td>\n",
       "      <td>123098</td>\n",
       "      <td>20880415099</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Ave Maria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 11:34:25</td>\n",
       "      <td>163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.184820</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.365697</td>\n",
       "      <td>0.772994</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80961</th>\n",
       "      <td>116673</td>\n",
       "      <td>123099</td>\n",
       "      <td>20879281658</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Kirchenportal der Chiesa di Santa Sofia.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 12:46:41</td>\n",
       "      <td>237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.350125</td>\n",
       "      <td>0.836224</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80962</th>\n",
       "      <td>116674</td>\n",
       "      <td>123100</td>\n",
       "      <td>21074834751</td>\n",
       "      <td>23097900@N02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Tür im jüdischer Bezirk von Venedig.</td>\n",
       "      <td>&lt;a href=\"http://www.venediginformationen.eu/ueberblick/sehenswertes/ghetto-vecchio/ghettovecchio.htm\" rel=\"noreferrer nofollow\"&gt;www.venediginformationen.eu/ueberblick/sehenswertes/ghett...&lt;/a&gt;</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-03 11:45:53</td>\n",
       "      <td>160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147517</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.343892</td>\n",
       "      <td>0.830531</td>\n",
       "      <td>True</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80963 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       origin   index      index.1          owner  \\\n",
       "0           6       6  50770113131  148125504@N04   \n",
       "1           7       7  50770228312  148125504@N04   \n",
       "2          10      10  50766039303   32980384@N05   \n",
       "3          11      11  50766893587   32980384@N05   \n",
       "4          12      12  50766892772   32980384@N05   \n",
       "...       ...     ...          ...            ...   \n",
       "80958  116670  123096  20879329608   23097900@N02   \n",
       "80959  116671  123097  21040989736   23097900@N02   \n",
       "80960  116672  123098  20880415099   23097900@N02   \n",
       "80961  116673  123099  20879281658   23097900@N02   \n",
       "80962  116674  123100  21074834751   23097900@N02   \n",
       "\n",
       "                         owner_loc  \\\n",
       "0      Tarazona (Zaragoza), España   \n",
       "1      Tarazona (Zaragoza), España   \n",
       "2                  Berlin, Germany   \n",
       "3                  Berlin, Germany   \n",
       "4                  Berlin, Germany   \n",
       "...                            ...   \n",
       "80958                      Germany   \n",
       "80959                      Germany   \n",
       "80960                      Germany   \n",
       "80961                      Germany   \n",
       "80962                      Germany   \n",
       "\n",
       "                                                     title  \\\n",
       "0                          Venecia - Patio Palacio Minelli   \n",
       "1                                Venecia - Palacio Minelli   \n",
       "2                                                 _DSC7807   \n",
       "3                                                 _DSC7805   \n",
       "4                                                 _DSC7802   \n",
       "...                                                    ...   \n",
       "80958  Hier werden die Pakete mit dem Lastkahn zugestellt.   \n",
       "80959                                           Tür No.342   \n",
       "80960                                            Ave Maria   \n",
       "80961             Kirchenportal der Chiesa di Santa Sofia.   \n",
       "80962                 Tür im jüdischer Bezirk von Venedig.   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   description  \\\n",
       "0      El palacio Contarini del Bovolo (también llamado palacio Contarini dal Bovolo Minelli) es un pequeño palacio de Venecia, muy conocido por su escalera de caracol exterior, que tiene un gran número de arcos, y que es conocida como la Scala Contarini del Bovolo (del caracol).\\n\\nEl palacio data del siglo XV, construido por la familia Contarini y está, al parecer, en no muy buen estado de conservación. La escalera de caracol conduce a una galería, desde donde se ofrece una encantadora vista panorámica sobre algunos de los tejados de la ciudad. El palacio está situado cerca de la calle Campo Ma...   \n",
       "1      El palacio Contarini del Bovolo (también llamado palacio Contarini dal Bovolo Minelli) es un pequeño palacio de Venecia, muy conocido por su escalera de caracol exterior, que tiene un gran número de arcos, y que es conocida como la Scala Contarini del Bovolo (del caracol).\\n\\nEl palacio data del siglo XV, construido por la familia Contarini y está, al parecer, en no muy buen estado de conservación. La escalera de caracol conduce a una galería, desde donde se ofrece una encantadora vista panorámica sobre algunos de los tejados de la ciudad. El palacio está situado cerca de la calle Campo Ma...   \n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Canal Grande on Venetsian pääliikenneväylä. Kuva on otettu Rialton sillalta, joka on yksi Italian tunnetuimmista nähtävyyksistä.   \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Corona-rajoitusten aikaan oli hyvä tilaisuus käydä Venetsiassa, joka normaalisti on täynnä valtameriristeilijöiden tuomia turisteja.   \n",
       "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Corona-rajoitusten aikaan oli hyvä tilaisuus käydä Venetsiassa, joka normaalisti on täynnä valtameriristeilijöiden tuomia turisteja.   \n",
       "...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        ...   \n",
       "80958                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   <a href=\"http://www.brusatotrasporti.it/index.php?pg=Ttrasl\" rel=\"noreferrer nofollow\">www.brusatotrasporti.it/index.php?pg=Ttrasl</a>   \n",
       "80959                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN   \n",
       "80960                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN   \n",
       "80961                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN   \n",
       "80962                                                                                                                                                                                                                                                                                                                                                                                                                         <a href=\"http://www.venediginformationen.eu/ueberblick/sehenswertes/ghetto-vecchio/ghettovecchio.htm\" rel=\"noreferrer nofollow\">www.venediginformationen.eu/ueberblick/sehenswertes/ghett...</a>   \n",
       "\n",
       "       comments                taken  views  ...  max_3_val     max_3_col  \\\n",
       "0             3  2010-03-27 12:47:45     90  ...   0.110439  Criteria iii   \n",
       "1             7  2010-03-27 12:45:28    238  ...   0.111514  Criteria iii   \n",
       "2             0  2020-08-11 11:48:15     25  ...   0.183531    Criteria i   \n",
       "3             0  2020-08-11 11:44:31     27  ...   0.132468   Criteria ii   \n",
       "4             0  2020-08-11 11:43:19     20  ...   0.187583   Criteria ii   \n",
       "...         ...                  ...    ...  ...        ...           ...   \n",
       "80958         0  2015-07-03 11:34:58    244  ...   0.161494   Criteria ii   \n",
       "80959         0  2015-07-03 11:04:10    156  ...   0.185791   Criteria vi   \n",
       "80960         0  2015-07-03 11:34:25    163  ...   0.184820   Criteria vi   \n",
       "80961         0  2015-07-03 12:46:41    237  ...   0.163804   Criteria iv   \n",
       "80962         0  2015-07-03 11:45:53    160  ...   0.147517   Criteria vi   \n",
       "\n",
       "          max_1     max_3 valid max_1_col_agg  max_2_col_agg  max_3_col_agg  \\\n",
       "0      0.391179  0.801656  True   Criteria iv   Criteria iii     Criteria i   \n",
       "1      0.387872  0.799923  True   Criteria iv   Criteria iii     Criteria i   \n",
       "2      0.449886  0.858898  True   Criteria iv   Criteria iii     Criteria i   \n",
       "3      0.378402  0.716850  True  Criteria iii    Criteria iv    Criteria ii   \n",
       "4      0.351008  0.785742  True  Criteria iii    Criteria iv    Criteria ii   \n",
       "...         ...       ...   ...           ...            ...            ...   \n",
       "80958  0.416799  0.815098  True  Criteria iii    Criteria iv    Criteria vi   \n",
       "80959  0.334180  0.817606  True  Criteria iii    Criteria ii    Criteria vi   \n",
       "80960  0.365697  0.772994  True  Criteria iii    Criteria ii    Criteria vi   \n",
       "80961  0.350125  0.836224  True  Criteria iii    Criteria vi    Criteria iv   \n",
       "80962  0.343892  0.830531  True  Criteria iii    Criteria vi    Criteria ii   \n",
       "\n",
       "      max_1_eq  max_3_eq  \n",
       "0         True       1.0  \n",
       "1         True       1.0  \n",
       "2         True       0.5  \n",
       "3        False       1.0  \n",
       "4        False       1.0  \n",
       "...        ...       ...  \n",
       "80958     True       0.5  \n",
       "80959     True       1.0  \n",
       "80960     True       1.0  \n",
       "80961     True       1.0  \n",
       "80962     True       1.0  \n",
       "\n",
       "[80963 rows x 57 columns]"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_df.to_csv('../../../Venice_Flickr/data_storage/images/sentences_ulmfit.csv',sep='\\t', encoding='utf-8-sig')\n",
    "score_final.to_csv('../../../Venice_Flickr/data_storage/images/metadata_ulmfit.csv',sep='\\t', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-540-d098a755bcf0>:2: DtypeWarning: Columns (5,824) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  score_final_bert = pd.read_csv('../../../Venice_Flickr/data_storage/images/metadata_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')\n"
     ]
    }
   ],
   "source": [
    "sm_df_bert = pd.read_csv('../../../Venice_Flickr/data_storage/images/sentences_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')\n",
    "score_final_bert = pd.read_csv('../../../Venice_Flickr/data_storage/images/metadata_bert.csv',sep='\\t', encoding='utf-8-sig', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent = sm_df.iloc[:,:4]\n",
    "compare_sent['max_1_col_ulmfit'] = sm_df['max_1_col']\n",
    "compare_sent['max_2_col_ulmfit'] = sm_df['max_2_col']\n",
    "compare_sent['max_3_col_ulmfit'] = sm_df['max_3_col']\n",
    "compare_sent['max_1_ulmfit'] = sm_df['max_1']\n",
    "compare_sent['max_3_ulmfit'] = sm_df['max_3']\n",
    "compare_sent['max_1_col_bert'] = sm_df_bert['max_1_col']\n",
    "compare_sent['max_2_col_bert'] = sm_df_bert['max_2_col']\n",
    "compare_sent['max_3_col_bert'] = sm_df_bert['max_3_col']\n",
    "compare_sent['max_1_bert'] = sm_df_bert['max_1']\n",
    "compare_sent['max_3_bert'] = sm_df_bert['max_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['same_1'] = compare_sent['max_1_col_ulmfit']==compare_sent['max_1_col_bert']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent['same_3'] = compare_sent.apply(lambda x: IoU(x[['max_1_col_ulmfit','max_2_col_ulmfit','max_3_col_ulmfit']],\n",
    "                                                          x[['max_1_col_bert','max_2_col_bert','max_3_col_bert']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.261290812985312"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_sent.drop_duplicates()['same_1'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5537059126204509"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(compare_sent.drop_duplicates()['same_3']>=0.5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>origin</th>\n",
       "      <th>lang</th>\n",
       "      <th>max_1_col_ulmfit</th>\n",
       "      <th>max_2_col_ulmfit</th>\n",
       "      <th>max_3_col_ulmfit</th>\n",
       "      <th>max_1_ulmfit</th>\n",
       "      <th>max_3_ulmfit</th>\n",
       "      <th>max_1_col_bert</th>\n",
       "      <th>max_2_col_bert</th>\n",
       "      <th>max_3_col_bert</th>\n",
       "      <th>max_1_bert</th>\n",
       "      <th>max_3_bert</th>\n",
       "      <th>same_1</th>\n",
       "      <th>same_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>It has title of Lonely window</td>\n",
       "      <td>20</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.250929</td>\n",
       "      <td>0.628690</td>\n",
       "      <td>Criteria x</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.180201</td>\n",
       "      <td>0.498648</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>It has title of Reflection and bridge - Venice</td>\n",
       "      <td>24</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.280426</td>\n",
       "      <td>0.692337</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.626143</td>\n",
       "      <td>0.820457</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>It has tags of nopeople, architecture, outdoors, reflection, buildingexterior, traveldestination, night, sunset, city, cityscape, illuminated, italianculture, water, bridge, sky</td>\n",
       "      <td>24</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.650610</td>\n",
       "      <td>0.894722</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria v</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.306494</td>\n",
       "      <td>0.688902</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>It has tags of venice, venezia, italia, panorama, italy, water, channels, boat, old, ancient, middleage, fish, market, sellers, fishermen</td>\n",
       "      <td>28</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.606865</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.343468</td>\n",
       "      <td>0.875990</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Panorama view from Fondaco dei Tedeschi's rooftop with view on Rialto bridge</td>\n",
       "      <td>29</td>\n",
       "      <td>en</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>0.290368</td>\n",
       "      <td>0.662512</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.365714</td>\n",
       "      <td>0.709372</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171017</th>\n",
       "      <td>91496</td>\n",
       "      <td>It has tags of venezia, italy, veneto, metropolitan city, venice, italy, italie, italia, outdoor, geb, architecture, transporti, parcel service, boat, boat, barge, sestierecannaregio, venice</td>\n",
       "      <td>116670</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>0.401659</td>\n",
       "      <td>0.834411</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>0.440942</td>\n",
       "      <td>0.916597</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171019</th>\n",
       "      <td>91498</td>\n",
       "      <td>It has tags of Venezia, Italy, Veneto, Metropolitan City, Venice, Italy, Italia, Italia, Outdoor, Geb, Architecture, Door, No, Venice</td>\n",
       "      <td>116671</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.357152</td>\n",
       "      <td>0.866123</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.466190</td>\n",
       "      <td>0.893082</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171021</th>\n",
       "      <td>91500</td>\n",
       "      <td>It has tags of Venezia, Italy, Veneto, Metropolitan City, Venice, Italy, Italia, Italia, Outdoor, Geb, Architecture, Avemaria, Window, Window, Venice</td>\n",
       "      <td>116672</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.365819</td>\n",
       "      <td>0.867949</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.411557</td>\n",
       "      <td>0.881536</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171022</th>\n",
       "      <td>91501</td>\n",
       "      <td>It has title of Door in the Jewish district of Venice.</td>\n",
       "      <td>116674</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria vi</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.407372</td>\n",
       "      <td>0.856523</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>0.366306</td>\n",
       "      <td>0.785971</td>\n",
       "      <td>False</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171023</th>\n",
       "      <td>91502</td>\n",
       "      <td>It has tags of venezia, italy, veneto, metropolitan city, venice, italy, italie, italia, outdoor, geb, architecture, venice</td>\n",
       "      <td>116674</td>\n",
       "      <td>other</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria iii</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.406759</td>\n",
       "      <td>0.882695</td>\n",
       "      <td>Criteria ii</td>\n",
       "      <td>Criteria i</td>\n",
       "      <td>Criteria iv</td>\n",
       "      <td>0.566851</td>\n",
       "      <td>0.922566</td>\n",
       "      <td>True</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94697 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  \\\n",
       "0           0   \n",
       "1           1   \n",
       "2           2   \n",
       "4           4   \n",
       "5           5   \n",
       "...       ...   \n",
       "171017  91496   \n",
       "171019  91498   \n",
       "171021  91500   \n",
       "171022  91501   \n",
       "171023  91502   \n",
       "\n",
       "                                                                                                                                                                                              sentence  \\\n",
       "0                                                                                                                                                                        It has title of Lonely window   \n",
       "1                                                                                                                                                       It has title of Reflection and bridge - Venice   \n",
       "2                    It has tags of nopeople, architecture, outdoors, reflection, buildingexterior, traveldestination, night, sunset, city, cityscape, illuminated, italianculture, water, bridge, sky   \n",
       "4                                                            It has tags of venice, venezia, italia, panorama, italy, water, channels, boat, old, ancient, middleage, fish, market, sellers, fishermen   \n",
       "5                                                                                                                         Panorama view from Fondaco dei Tedeschi's rooftop with view on Rialto bridge   \n",
       "...                                                                                                                                                                                                ...   \n",
       "171017  It has tags of venezia, italy, veneto, metropolitan city, venice, italy, italie, italia, outdoor, geb, architecture, transporti, parcel service, boat, boat, barge, sestierecannaregio, venice   \n",
       "171019                                                           It has tags of Venezia, Italy, Veneto, Metropolitan City, Venice, Italy, Italia, Italia, Outdoor, Geb, Architecture, Door, No, Venice   \n",
       "171021                                           It has tags of Venezia, Italy, Veneto, Metropolitan City, Venice, Italy, Italia, Italia, Outdoor, Geb, Architecture, Avemaria, Window, Window, Venice   \n",
       "171022                                                                                                                                          It has title of Door in the Jewish district of Venice.   \n",
       "171023                                                                     It has tags of venezia, italy, veneto, metropolitan city, venice, italy, italie, italia, outdoor, geb, architecture, venice   \n",
       "\n",
       "        origin   lang max_1_col_ulmfit max_2_col_ulmfit max_3_col_ulmfit  \\\n",
       "0           20     en     Criteria iii      Criteria vi       Criteria i   \n",
       "1           24     en     Criteria iii      Criteria vi       Criteria i   \n",
       "2           24     en     Criteria iii      Criteria vi      Criteria iv   \n",
       "4           28     en     Criteria iii      Criteria iv      Criteria ii   \n",
       "5           29     en     Criteria iii       Criteria i      Criteria vi   \n",
       "...        ...    ...              ...              ...              ...   \n",
       "171017  116670  other     Criteria iii      Criteria iv      Criteria ii   \n",
       "171019  116671  other      Criteria ii     Criteria iii      Criteria iv   \n",
       "171021  116672  other     Criteria iii      Criteria ii      Criteria iv   \n",
       "171022  116674  other      Criteria vi     Criteria iii      Criteria iv   \n",
       "171023  116674  other      Criteria ii     Criteria iii      Criteria iv   \n",
       "\n",
       "        max_1_ulmfit  max_3_ulmfit max_1_col_bert max_2_col_bert  \\\n",
       "0           0.250929      0.628690     Criteria x     Criteria i   \n",
       "1           0.280426      0.692337     Criteria i    Criteria ii   \n",
       "2           0.650610      0.894722    Criteria iv     Criteria v   \n",
       "4           0.606865      0.829000    Criteria iv   Criteria iii   \n",
       "5           0.290368      0.662512    Criteria vi     Criteria i   \n",
       "...              ...           ...            ...            ...   \n",
       "171017      0.401659      0.834411    Criteria iv    Criteria ii   \n",
       "171019      0.357152      0.866123    Criteria ii     Criteria i   \n",
       "171021      0.365819      0.867949    Criteria ii     Criteria i   \n",
       "171022      0.407372      0.856523     Criteria i    Criteria iv   \n",
       "171023      0.406759      0.882695    Criteria ii     Criteria i   \n",
       "\n",
       "       max_3_col_bert  max_1_bert  max_3_bert  same_1  same_3  \n",
       "0        Criteria iii    0.180201    0.498648   False     0.5  \n",
       "1         Criteria vi    0.626143    0.820457   False     0.5  \n",
       "2         Criteria vi    0.306494    0.688902   False     0.5  \n",
       "4         Criteria ii    0.343468    0.875990   False     1.0  \n",
       "5         Criteria iv    0.365714    0.709372   False     0.5  \n",
       "...               ...         ...         ...     ...     ...  \n",
       "171017     Criteria i    0.440942    0.916597   False     0.5  \n",
       "171019    Criteria iv    0.466190    0.893082    True     0.5  \n",
       "171021    Criteria iv    0.411557    0.881536   False     0.5  \n",
       "171022   Criteria iii    0.366306    0.785971   False     0.5  \n",
       "171023    Criteria iv    0.566851    0.922566    True     0.5  \n",
       "\n",
       "[94697 rows x 16 columns]"
      ]
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_sent[compare_sent['same_3']>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_sent.drop_duplicates('sentence')['same_3'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post = score_final.iloc[:,:2]\n",
    "compare_post['max_1_col_ulmfit'] = score_final['max_1_col']\n",
    "compare_post['max_2_col_ulmfit'] = score_final['max_2_col']\n",
    "compare_post['max_3_col_ulmfit'] = score_final['max_3_col']\n",
    "compare_post['max_1_ulmfit'] = score_final['max_1']\n",
    "compare_post['max_3_ulmfit'] = score_final['max_3']\n",
    "compare_post['max_1_col_bert'] = score_final_bert['max_1_col']\n",
    "compare_post['max_2_col_bert'] = score_final_bert['max_2_col']\n",
    "compare_post['max_3_col_bert'] = score_final_bert['max_3_col']\n",
    "compare_post['max_1_bert'] = score_final_bert['max_1']\n",
    "compare_post['max_3_bert'] = score_final_bert['max_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['same_1'] = compare_post['max_1_col_ulmfit']==compare_post['max_1_col_bert']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_post['same_3'] = compare_post.apply(lambda x: IoU(x[['max_1_col_ulmfit','max_2_col_ulmfit','max_3_col_ulmfit']],\n",
    "                                                          x[['max_1_col_bert','max_2_col_bert','max_3_col_bert']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21665452120104245"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post['same_1'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45277966478514886"
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_post['same_3'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5945802403567062"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(compare_post['same_3']>=0.5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_post['same_3'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_sent.to_csv('../../../Venice_Flickr/data_storage/images/sentences_compare.csv',sep='\\t', encoding='utf-8-sig')\n",
    "compare_post.to_csv('../../../Venice_Flickr/data_storage/images/metadata_compare.csv',sep='\\t', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}