Offensive_Tweet_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gaXaXHOM3vzu"
   },
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (0.0)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn) (0.24.2)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.19.5)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.0.1)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.5.4)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (3.0.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_federated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "qx6xGg5nQt_O"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn.metrics as metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tfK0M8FNacGi",
    "outputId": "8b91498c-ccd7-4309-90e1-68fde69a0b99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow_federated in /usr/local/lib/python3.6/dist-packages (0.19.0)\n",
      "Requirement already satisfied: retrying~=1.3.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow_federated) (1.3.3)\n",
      "Requirement already satisfied: attrs~=19.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow_federated) (19.3.0)\n",
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      "Requirement already satisfied: tqdm~=4.28.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow_federated) (4.28.1)\n",
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      "Requirement already satisfied: cached-property; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from h5py~=3.1.0->tensorflow~=2.5.0->tensorflow_federated) (1.5.2)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (3.3.4)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (1.8.0)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (2.0.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (0.6.1)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (1.30.0)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (2.25.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (0.4.4)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (56.2.0)\n",
      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (4.0.1)\n",
      "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from werkzeug>=0.11.15->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (0.8)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (4.7.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (1.26.4)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (4.0.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/lib/python3/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (2.6)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (2020.12.5)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (1.3.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (3.4.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow~=2.5.0->tensorflow_federated) (3.1.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 21.3.1 is available.\r\n",
      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow_federated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Hp3iX7WRxN_",
    "outputId": "aa298a02-327d-4448-af0c-25ad0a2f306a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in /usr/local/lib/python3.6/dist-packages (4.8.2)\n",
      "Requirement already satisfied: sacremoses in /usr/local/lib/python3.6/dist-packages (from transformers) (0.0.45)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (1.19.5)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2021.7.6)\n",
      "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.6/dist-packages (from transformers) (0.10.3)\n",
      "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.28.1)\n",
      "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8)\n",
      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from transformers) (4.0.1)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.9)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.25.1)\n",
      "Requirement already satisfied: huggingface-hub==0.0.12 in /usr/local/lib/python3.6/dist-packages (from transformers) (0.0.12)\n",
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      "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.0.1)\n",
      "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (8.0.1)\n",
      "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/lib/python3/dist-packages (from requests->transformers) (2.6)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.12.5)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (4.0.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.26.4)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "SApcBgwMLgZt"
   },
   "outputs": [],
   "source": [
    "import tensorflow_federated as tff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "AyGaybwE9TCi"
   },
   "outputs": [],
   "source": [
    "def create_model():\n",
    "    # TODO: Add a tensorflow seed here and check if the performance improves.\n",
    "    # This would mean that each model gets the same initialized parameters.\n",
    "    # Which could be important to convergence.\n",
    "    input = keras.Input(shape=(max_len,embed_dim))\n",
    "    layer1 = layers.Bidirectional(layers.LSTM(64,return_sequences=True))(input)\n",
    "    layer5 = layers.Dropout(0.5)(layer1)\n",
    "    layer2 = layers.Bidirectional(layers.LSTM(32))(layer5)\n",
    "    layer3 = layers.Dropout(0.5)(layer2)\n",
    "\n",
    "    # 1 class in the model after merging classes\n",
    "    output = layers.Dense(1, activation=\"sigmoid\")(layer3)\n",
    "    model = Model(inputs=input,outputs=output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training on the Entire Model- Vanilla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#infile = open('dhot.pickle','rb')\n",
    "#df_dhot = pickle.load(infile)\n",
    "#infile.close()\n",
    "\n",
    "#dataset1 = []\n",
    "#df_dhot['Final'] = None\n",
    "#print(df_dhot.shape)\n",
    "#for i in range(len(df_dhot)):\n",
    " #   dataset1.append(df_dhot['Embeddings'][i][0]) \n",
    "  #  if i == 0:\n",
    "        #print(i, df_dhot['Embeddings'][i][0].device, df_dhot['Embeddings'][i][0].shape) # GC\n",
    "#df_dhot['Final'] = dataset1\n",
    "\n",
    "#idx = math.floor(df_dhot.shape[0]*0.8)\n",
    "#print(df_dhot.shape[0])\n",
    "#print(idx)\n",
    "\n",
    "#dhot_train_tf_x = df_dhot['Final'].to_list()[:idx]\n",
    "#dhot_train_tf_y = df_dhot['Label'].to_list()[:idx]\n",
    "\n",
    "#df_dhot=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(dhot_train_tf_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#infile = open('df_hasoc.pickle','rb')\n",
    "#df_hasoc = pickle.load(infile)\n",
    "#infile.close()\n",
    "\n",
    "#dataset1 = []\n",
    "#df_hasoc['Final'] = None\n",
    "#for i in range(len(df_hasoc)):\n",
    "#    dataset1.append(df_hasoc['Embeddings'][i][0])\n",
    "#df_hasoc['Final'] = dataset1\n",
    "\n",
    "#idx = math.floor(df_hasoc.shape[0]*0.8)\n",
    "#print(df_hasoc.shape[0])\n",
    "#print(idx)\n",
    "\n",
    "#hasoc_train_tf_x = df_hasoc['Final'].to_list()[:idx]\n",
    "#hasoc_train_tf_y = df_hasoc['Label'].to_list()[:idx]\n",
    "\n",
    "#df_hasoc=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#infile = open('df_traceng_train.pickle','rb')\n",
    "#df_traceng = pickle.load(infile)\n",
    "#infile.close()\n",
    "\n",
    "#dataset1 = []\n",
    "#df_traceng['Final'] = None\n",
    "#for i in range(len(df_traceng)):\n",
    "#    dataset1.append(df_traceng['Embeddings'][i][0])\n",
    "#df_traceng['Final'] = dataset1\n",
    "\n",
    "#traceng20_train_tf_x = df_traceng['Final'].to_list()\n",
    "#traceng20_train_tf_y = df_traceng['Label'].to_list()\n",
    "\n",
    "#df_traceng = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#infile = open('df_trachin_train.pickle','rb')\n",
    "#df_trachin = pickle.load(infile)\n",
    "#infile.close()\n",
    "\n",
    "#dataset1 = []\n",
    "#df_trachin['Final'] = None\n",
    "#for i in range(len(df_trachin)):\n",
    " # dataset1.append(df_trachin['Embeddings'][i][0])\n",
    "#df_trachin['Final'] = dataset1\n",
    "\n",
    "#trachin20_train_tf_x = df_trachin['Final'].to_list()\n",
    "#trachin20_train_tf_y = df_trachin['Label'].to_list()\n",
    "\n",
    "#y_pred = centralized_model.predict(trachin20_test_x)\n",
    "#fy_pred = centralized_model.predict(trachin20_test_x)\n",
    "#fdf_trachin = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#infile = open('Trac-2019/english/df_trac_eng_2019.pickle','rb')\n",
    "#df_traceng19 = pickle.load(infile)\n",
    "#infile.close()\n",
    "\n",
    "#dataset1 = []\n",
    "#df_traceng19['Final'] = None\n",
    "#for i in range(len(df_traceng19)):\n",
    " # dataset1.append(df_traceng19['Embeddings'][i][0])\n",
    "#df_traceng19['Final'] = dataset1\n",
    "\n",
    "#idx = math.floor(df_traceng19.shape[0]*0.8)\n",
    "#print(df_traceng19.shape[0])\n",
    "#print(idx)\n",
    "\n",
    "#traceng19_train_tf_x = df_traceng19['Final'].to_list()[:idx]\n",
    "#traceng19_train_tf_y = df_traceng19['Label'].to_list()[:idx]\n",
    "\n",
    "#df_traceng19=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#infile = open('Trac-2019/hindi/df_trac_hin_2019.pickle','rb')\n",
    "#df_trachin19 = pickle.load(infile)\n",
    "#infile.close()\n",
    "\n",
    "#dataset1 = []\n",
    "#df_trachin19['Final'] = None\n",
    "#for i in range(len(df_trachin19)):\n",
    " # dataset1.append(df_trachin19['Embeddings'][i][0])\n",
    "#df_trachin19['Final'] = dataset1\n",
    "\n",
    "#idx = math.floor(df_trachin19.shape[0]*0.8)\n",
    "#print(df_trachin19.shape[0])\n",
    "#print(idx)\n",
    "\n",
    "#trachin19_train_tf_x = df_trachin19['Final'].to_list()[:idx]\n",
    "#trachin19_train_tf_y = df_trachin19['Label'].to_list()[:idx]\n",
    "\n",
    "#df_trachin19=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_train=[]\n",
    "\n",
    "#x_train = x_train + hasoc_train_tf_x\n",
    "#hasoc_train_tf_x=0\n",
    "#x_train = x_train + traceng20_train_tf_x\n",
    "#traceng20_train_tf_x=0\n",
    "#x_train = x_train + trachin20_train_tf_x\n",
    "#trachin20_train_tf_x=0\n",
    "#x_train = x_train + traceng19_train_tf_x\n",
    "#traceng19_train_tf_x=0\n",
    "#x_train = x_train + trachin19_train_tf_x\n",
    "#trachin19_train_tf_x=0\n",
    "#x_train = x_train + dhot_train_tf_x\n",
    "#dhot_train_tf_x=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_train=[]\n",
    "\n",
    "#y_train = y_train + hasoc_train_tf_y\n",
    "#hasoc_train_tf_y=0\n",
    "#y_train = y_train + traceng20_train_tf_y\n",
    "#traceng20_train_tf_y=0\n",
    "#y_train = y_train + trachin20_train_tf_y\n",
    "#trachin20_train_tf_y=0\n",
    "#y_train = y_train + traceng19_train_tf_y\n",
    "#traceng19_train_tf_y=0\n",
    "#y_train = y_train + trachin19_train_tf_y\n",
    "#trachin19_train_tf_y=0\n",
    "#y_train = y_train + dhot_train_tf_y\n",
    "#dhot_train_tf_y=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_len = 256\n",
    "embed_dim = 768\n",
    "centralized_model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_train=np.array(x_train)\n",
    "#y_train=np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#centralized_model.compile(loss='binary_crossentropy', optimizer='adam',metrics='acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#centralized_model.fit(x_train,y_train,epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OWljk854BZgE"
   },
   "source": [
    "# Federated Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'),\n",
       " PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DHOT - run this cell to load DHOT train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1603, 5)\n",
      "0 /job:localhost/replica:0/task:0/device:GPU:0 (256, 768)\n",
      "1603\n",
      "1282\n"
     ]
    }
   ],
   "source": [
    "infile = open('dhot.pickle','rb')\n",
    "df_dhot = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_dhot['Final'] = None\n",
    "print(df_dhot.shape)\n",
    "for i in range(len(df_dhot)):\n",
    "    dataset1.append(df_dhot['Embeddings'][i][0]) \n",
    "    if i == 0:\n",
    "        print(i, df_dhot['Embeddings'][i][0].device, df_dhot['Embeddings'][i][0].shape) # GC\n",
    "df_dhot['Final'] = dataset1\n",
    "\n",
    "idx = math.floor(df_dhot.shape[0]*0.8)\n",
    "print(df_dhot.shape[0])\n",
    "print(idx)\n",
    "\n",
    "dhot_train_tf = tf.data.Dataset.from_tensor_slices((df_dhot['Final'].to_list()[:idx], df_dhot['Label'].to_list()[:idx]))\n",
    "\n",
    "df_dhot=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HASOC - run this cell to load HASOC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4665\n",
      "3732\n",
      "<class 'numpy.int64'>\n",
      "0       0\n",
      "1       1\n",
      "2       1\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "4660    0\n",
      "4661    1\n",
      "4662    0\n",
      "4663    1\n",
      "4664    1\n",
      "Name: Label, Length: 4665, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "infile = open('df_hasoc.pickle','rb')\n",
    "df_hasoc = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_hasoc['Final'] = None\n",
    "for i in range(len(df_hasoc)):\n",
    "    # 1 x 265 x 768\n",
    "    dataset1.append(df_hasoc['Embeddings'][i][0])\n",
    "df_hasoc['Final'] = dataset1\n",
    "\n",
    "idx = math.floor(df_hasoc.shape[0]*0.8)\n",
    "print(df_hasoc.shape[0])\n",
    "print(idx)\n",
    "\n",
    "hasoc_train_tf = tf.data.Dataset.from_tensor_slices((df_hasoc['Final'].to_list()[:idx], df_hasoc['Label'].to_list()[:idx]))\n",
    "\n",
    "print(type(df_hasoc['Label'][0]))\n",
    "print(df_hasoc['Label'])\n",
    "df_hasoc = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#next(iter(hasoc_train_tf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAC 20 Eng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = open('df_traceng_train.pickle','rb')\n",
    "df_traceng = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_traceng['Final'] = None\n",
    "for i in range(len(df_traceng)):\n",
    "    dataset1.append(df_traceng['Embeddings'][i][0])\n",
    "df_traceng['Final'] = dataset1\n",
    "#y_pred = centralized_model.predict(trachin20_test_x)\n",
    "\n",
    "traceng20_train_tf = tf.data.Dataset.from_tensor_slices((df_traceng['Final'].to_list(), df_traceng['Label'].to_list()))\n",
    "\n",
    "df_traceng = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAC 20 Hindi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = open('df_trachin_train.pickle','rb')\n",
    "df_trachin = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_trachin['Final'] = None\n",
    "for i in range(len(df_trachin)):\n",
    "  dataset1.append(df_trachin['Embeddings'][i][0])\n",
    "df_trachin['Final'] = dataset1\n",
    "\n",
    "trachin20_train_tf = tf.data.Dataset.from_tensor_slices((df_trachin['Final'].to_list(), df_trachin['Label'].to_list()))\n",
    "\n",
    "df_trachin = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAC 19 Eng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "4000\n"
     ]
    }
   ],
   "source": [
    "infile = open('Trac-2019/english/df_trac_eng_2019.pickle','rb')\n",
    "df_traceng19 = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_traceng19['Final'] = None\n",
    "for i in range(len(df_traceng19)):\n",
    "  dataset1.append(df_traceng19['Embeddings'][i][0])\n",
    "df_traceng19['Final'] = dataset1\n",
    "\n",
    "idx = math.floor(df_traceng19.shape[0]*0.8)\n",
    "print(df_traceng19.shape[0])\n",
    "print(idx)\n",
    "\n",
    "traceng19_train_tf = tf.data.Dataset.from_tensor_slices((df_traceng19['Final'].to_list()[:idx], df_traceng19['Label'].to_list()[:idx]))\n",
    "df_traceng19 = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAC 19 Hin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "4000\n"
     ]
    }
   ],
   "source": [
    "infile = open('Trac-2019/hindi/df_trac_hin_2019.pickle','rb')\n",
    "df_trachin19 = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_trachin19['Final'] = None\n",
    "for i in range(len(df_trachin19)):\n",
    "  dataset1.append(df_trachin19['Embeddings'][i][0])\n",
    "df_trachin19['Final'] = dataset1\n",
    "\n",
    "idx = math.floor(df_trachin19.shape[0]*0.8)\n",
    "print(df_trachin19.shape[0])\n",
    "print(idx)\n",
    "\n",
    "trachin19_train_tf = tf.data.Dataset.from_tensor_slices((df_trachin19['Final'].to_list()[:idx], df_trachin19['Label'].to_list()[:idx]))\n",
    "df_trachin19 = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IndoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "4000\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "infile = open('indoML.pickle','rb')\n",
    "\n",
    "import pickle\n",
    "import math\n",
    "df_indoML = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "\n",
    "dataset1=[]\n",
    "for i in range(len(df_indoML)):\n",
    "    dataset1.append(df_indoML[i][0])\n",
    "    \n",
    "idx = math.floor(df_indoML.shape[0]*0.8)\n",
    "print(df_indoML.shape[0])\n",
    "print(idx)\n",
    "\n",
    "indoml_train_x = dataset1[:idx]\n",
    "df_indoML=0\n",
    "\n",
    "infile = open('indoML_labels.pickle','rb')\n",
    "\n",
    "import pickle\n",
    "df_indoML_labels = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "indoml_train_y = df_indoML_labels[:idx].to_list()\n",
    "\n",
    "for i in range(len(indoml_train_y)):\n",
    "    indoml_train_y[i] = int(indoml_train_y[i])\n",
    "    \n",
    "df_indoML_labels=0\n",
    "\n",
    "indoml_train_tf = tf.data.Dataset.from_tensor_slices((indoml_train_x, indoml_train_y))\n",
    "\n",
    "print(type(indoml_train_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "print(type(indoml_train_y[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "indoml_train_x=0\n",
    "indoml_train_y=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Generation For Federated Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "# Function allows mapping each dataset to a map\n",
    "def g(dataset):\n",
    "    def batch_format_fn(x_val, y_val):\n",
    "        return collections.OrderedDict(\n",
    "            x = tf.reshape(x_val,[-1,max_len,768]),\n",
    "            y = tf.reshape(y_val,[-1,1])\n",
    "        )\n",
    "    return dataset.batch(BATCH_SIZE).map(batch_format_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "gFrAP9hMhUII"
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "BATCH_SIZE = 32\n",
    "max_len=256\n",
    "def g(dataset):\n",
    "    def batch_format_fn(x_val, y_val):\n",
    "        return collections.OrderedDict(\n",
    "            x = tf.reshape(x_val,[-1,max_len,768]),\n",
    "            y = tf.reshape(y_val,[-1,1])\n",
    "        )\n",
    "    return dataset.batch(BATCH_SIZE).map(batch_format_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_dim=768\n",
    "max_len = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "\n",
    "# Comment the datasets you do not want to include in the training clients\n",
    "\n",
    "#Hindi\n",
    "results.append(g(hasoc_train_tf))\n",
    "hasoc_train_tf=0\n",
    "\n",
    "# English\n",
    "results.append(g(traceng20_train_tf))\n",
    "traceng20_train_tf=0\n",
    "\n",
    "#Hindi\n",
    "results.append(g(trachin20_train_tf))\n",
    "trachin20_train_tf=0\n",
    "\n",
    "#English\n",
    "results.append(g(traceng19_train_tf))\n",
    "traceng19_train_tf=0\n",
    "\n",
    "#Hindi\n",
    "results.append(g(trachin19_train_tf))\n",
    "trachin19_train_tf=0\n",
    "\n",
    "#Hindi\n",
    "results.append(g(dhot_train_tf))\n",
    "dhot_train_tf=0\n",
    "\n",
    "# indoML\n",
    "results.append(g(indoml_train_tf))\n",
    "indoml_train_tf=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  2\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "aZUum8yLOH9L"
   },
   "outputs": [],
   "source": [
    "MARK_FOR_BATCH = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "A_BDW4d9Hpbj"
   },
   "outputs": [],
   "source": [
    "federated_learning = results\n",
    "federated_learning\n",
    "results=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lstm= create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<MapDataset shapes: OrderedDict([(x, (None, 256, 768)), (y, (None, 1))]), types: OrderedDict([(x, tf.float32), (y, tf.int32)])>,\n",
       " <MapDataset shapes: OrderedDict([(x, (None, 256, 768)), (y, (None, 1))]), types: OrderedDict([(x, tf.float32), (y, tf.int32)])>,\n",
       " <MapDataset shapes: OrderedDict([(x, (None, 256, 768)), (y, (None, 1))]), types: OrderedDict([(x, tf.float32), (y, tf.int32)])>,\n",
       " <MapDataset shapes: OrderedDict([(x, (None, 256, 768)), (y, (None, 1))]), types: OrderedDict([(x, tf.float32), (y, tf.int32)])>,\n",
       " <MapDataset shapes: OrderedDict([(x, (None, 256, 768)), (y, (None, 1))]), types: OrderedDict([(x, tf.float32), (y, tf.int32)])>,\n",
       " <MapDataset shapes: OrderedDict([(x, (None, 256, 768)), (y, (None, 1))]), types: OrderedDict([(x, tf.float32), (y, tf.int32)])>,\n",
       " <MapDataset shapes: OrderedDict([(x, (None, 256, 768)), (y, (None, 1))]), types: OrderedDict([(x, tf.float32), (y, tf.int32)])>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "federated_learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Model.summary of <tensorflow.python.keras.engine.functional.Functional object at 0x7fb6bc07f898>>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lstm.summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "5R6YFbg2H3Xj"
   },
   "outputs": [],
   "source": [
    "def create_tff_model():\n",
    "    input_spec = federated_learning[0].element_spec\n",
    "    model_lstm= create_model() # This is the only changet\n",
    "    keras_model_clone = tf.keras.models.clone_model(model_lstm)\n",
    "    return tff.learning.from_keras_model(\n",
    "        keras_model_clone,\n",
    "        input_spec = input_spec,\n",
    "        loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fed_avg\n",
    "\n",
    "# step1: weights sent to each client\n",
    "# loss is computed independently at each client\n",
    "# gradients for each weight\n",
    "# sent back to the server\n",
    "\n",
    "# 7 updates\n",
    "# sum(updates)/7\n",
    "# w = w + update\n",
    "\n",
    "# go back to step 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7Ba0BffnLNcr",
    "outputId": "4311ddac-1a90-4475-b260-2d522a8109c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    }
   ],
   "source": [
    "fed_avg = tff.learning.build_federated_averaging_process(\n",
    "    model_fn = create_tff_model,\n",
    "    client_optimizer_fn = lambda: tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MapDataset shapes: OrderedDict([(x, (None, 256, 768)), (y, (None, 1))]), types: OrderedDict([(x, tf.float32), (y, tf.int32)])>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "federated_learning[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lnqNH6i1Mzo9",
    "outputId": "f43a4c75-7eaa-4280-c333-9a45180908e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nest_asyncio in /usr/local/lib/python3.6/dist-packages (1.5.1)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install nest_asyncio\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "WlA5sIKyLcQo"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_federated/python/core/impl/compiler/tensorflow_computation_transformations.py:60: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_federated/python/core/impl/compiler/tensorflow_computation_transformations.py:60: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n"
     ]
    }
   ],
   "source": [
    "state = fed_avg.initialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "4fhsAo1_L5nO"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 1/10 [08:48<1:19:12, 528.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.582138)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 2/10 [17:21<1:09:48, 523.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.56518734)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 3/10 [25:56<1:00:48, 521.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.5566687)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 4/10 [34:33<51:58, 519.73s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.5476993)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 5/10 [43:09<43:12, 518.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.5405695)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 6/10 [51:43<34:29, 517.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.5349485)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 7/10 [1:00:17<25:49, 516.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.5305593)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 8/10 [1:08:52<17:11, 515.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.52592325)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 9/10 [1:17:29<08:36, 516.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.52089626)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [1:26:05<00:00, 516.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('loss', 0.51897067)])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(10)):\n",
    "  state,metrics = fed_avg.next(state,federated_learning)\n",
    "  print(metrics['train'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xIK5AzvnNL35",
    "outputId": "4b030e57-a003-45b3-d327-7d1857a7e6d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('loss', 0.51897067)])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save and Clone Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "uYlaGumAZ2Py"
   },
   "outputs": [],
   "source": [
    "fed_model = tf.keras.models.clone_model(model_lstm)\n",
    "state.model.assign_weights_to(fed_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "CRtOUPITfSL3"
   },
   "outputs": [],
   "source": [
    "fed_model.compile(loss=tf.keras.losses.BinaryCrossentropy(), optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "K_3D7-oP_fZr"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Federated Model Performance: \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Federated Model Performance: \")\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as lstm_cell_13_layer_call_fn, lstm_cell_13_layer_call_and_return_conditional_losses, lstm_cell_14_layer_call_fn, lstm_cell_14_layer_call_and_return_conditional_losses, lstm_cell_16_layer_call_fn while saving (showing 5 of 20). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tf/shrey-vishwa/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tf/shrey-vishwa/assets\n"
     ]
    }
   ],
   "source": [
    "fed_model.save('/tf/shrey-vishwa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "fed_model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "fed_model = keras.models.load_model('/tf/shrey-vishwa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 256, 768)]        0         \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, 256, 128)          426496    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256, 128)          0         \n",
      "_________________________________________________________________\n",
      "bidirectional_3 (Bidirection (None, 64)                41216     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 467,777\n",
      "Trainable params: 467,777\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "fed_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indo ML Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "4000\n"
     ]
    }
   ],
   "source": [
    "infile = open('indoML.pickle','rb')\n",
    "\n",
    "import pickle\n",
    "import math\n",
    "df_indoML = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "\n",
    "dataset1=[]\n",
    "for i in range(len(df_indoML)):\n",
    "    dataset1.append(df_indoML[i][0])\n",
    "    \n",
    "idx = math.floor(df_indoML.shape[0]*0.8)\n",
    "print(df_indoML.shape[0])\n",
    "print(idx)\n",
    "\n",
    "indoml_test_x = dataset1[idx:]\n",
    "df_indoML=0\n",
    "\n",
    "infile = open('indoML_labels.pickle','rb')\n",
    "\n",
    "import pickle\n",
    "df_indoML_labels = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "indoml_test_y = df_indoML_labels[idx:].to_list()\n",
    "\n",
    "for i in indoml_test_y:\n",
    "    i = int(i)\n",
    "    \n",
    "df_indoML_labels=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "indoml_test_x=np.array(indoml_test_x)\n",
    "indoml_test_y=np.array(indoml_test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "0.582\n",
      "prec-recall-f1-weighted\n",
      "(0.5293776193111974, 0.582, 0.5316095444905066, None)\n",
      "prec-recall-f1-label-wise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.6242497 , 0.37125749]),\n",
       " array([0.832     , 0.16533333]),\n",
       " array([0.7133059 , 0.22878229]),\n",
       " array([625, 375]))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "y_pred = fed_model.predict(indoml_test_x)\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] < 0.5:\n",
    "        y_pred[i]=0\n",
    "    else:\n",
    "        y_pred[i]=1\n",
    "\n",
    "print(\"accuracy\")\n",
    "print(accuracy_score(indoml_test_y, y_pred))\n",
    "print(\"prec-recall-f1-weighted\")\n",
    "print(precision_recall_fscore_support(indoml_test_y, y_pred, average='weighted'))\n",
    "print(\"prec-recall-f1-label-wise\")\n",
    "precision_recall_fscore_support(indoml_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HASOC Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4665\n",
      "3732\n"
     ]
    }
   ],
   "source": [
    "infile = open('df_hasoc.pickle','rb')\n",
    "df_hasoc = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_hasoc['Final'] = None\n",
    "for i in range(len(df_hasoc)):\n",
    "  dataset1.append(df_hasoc['Embeddings'][i][0])\n",
    "df_hasoc['Final'] = dataset1\n",
    "\n",
    "idx = math.floor(df_hasoc.shape[0]*0.8)\n",
    "print(df_hasoc.shape[0])\n",
    "print(idx)\n",
    "\n",
    "hasoc_test_x = df_hasoc['Final'].to_list()[idx:]\n",
    "hasoc_test_y = df_hasoc['Label'].to_list()[idx:]\n",
    "\n",
    "df_hasoc = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "hasoc_test_x = np.array(hasoc_test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "hasoc_test_y = np.array(hasoc_test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "0.6259378349410504\n",
      "prec-recall-f1-weighted\n",
      "(0.7237770784668718, 0.6259378349410504, 0.6568858772349011, None)\n",
      "prec-recall-f1-label-wise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.3      , 0.8401421]),\n",
       " array([0.55223881, 0.64617486]),\n",
       " array([0.38879159, 0.73050193]),\n",
       " array([201, 732]))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "y_pred = fed_model.predict(hasoc_test_x)\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] < 0.5:\n",
    "        y_pred[i]=0\n",
    "    else:\n",
    "        y_pred[i]=1\n",
    "\n",
    "print(\"accuracy\")\n",
    "print(accuracy_score(hasoc_test_y, y_pred))\n",
    "print(\"prec-recall-f1-weighted\")\n",
    "print(precision_recall_fscore_support(hasoc_test_y, y_pred, average='weighted'))\n",
    "print(\"prec-recall-f1-label-wise\")\n",
    "precision_recall_fscore_support(hasoc_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "fpr, tpr, threshold = metrics.roc_curve(hasoc_test_y, y_pred)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "#plt.title('Receiver Operating Characteristic')\n",
    "#plt.plot(fpr, tpr, 'b', label = 'hasoc AUC = %0.2f' % roc_auc)\n",
    "#plt.legend(loc = 'lower right')\n",
    "#plt.plot([0, 1], [0, 1],'r--')\n",
    "#plt.xlim([0, 1])\n",
    "#plt.ylim([0, 1])\n",
    "#plt.ylabel('True Positive Rate')\n",
    "#plt.xlabel('False Positive Rate')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.metrics import accuracy_score\n",
    "#from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "#y_pred = centralized_model.predict(hasoc_test_x)\n",
    "#for i in range(len(y_pred)):\n",
    "#    if y_pred[i] < 0.5:\n",
    " #       y_pred[i]=0\n",
    "  #  else:\n",
    "   #     y_pred[i]=1\n",
    "\n",
    "#print(\"accuracy\")\n",
    "#print(accuracy_score(hasoc_test_y, y_pred))\n",
    "#print(\"prec-recall-f1-macro\")\n",
    "#print(precision_recall_fscore_support(hasoc_test_y, y_pred, average='macro'))\n",
    "#print(\"prec-recall-f1-micro\")\n",
    "#print(precision_recall_fscore_support(hasoc_test_y, y_pred, average='micro'))\n",
    "#print(\"prec-recall-f1-weighted\")\n",
    "#print(precision_recall_fscore_support(hasoc_test_y, y_pred, average='weighted'))\n",
    "#print(\"prec-recall-f1-label-wise\")\n",
    "#precision_recall_fscore_support(hasoc_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "hasoc_test_x=0\n",
    "hasoc_test_y=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DHOT Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = open('dhot.pickle','rb')\n",
    "df_dhot = pickle.load(infile)\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "dhot_train_tf=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1603\n",
      "1282\n"
     ]
    }
   ],
   "source": [
    "dataset1 = []\n",
    "df_dhot['Final'] = None\n",
    "for i in range(len(df_dhot)):\n",
    "  dataset1.append(df_dhot['Embeddings'][i][0])\n",
    "df_dhot['Final'] = dataset1\n",
    "\n",
    "idx = math.floor(df_dhot.shape[0]*0.8)\n",
    "print(df_dhot.shape[0])\n",
    "print(idx)\n",
    "\n",
    "#dhot_train_tf = tf.data.Dataset.from_tensor_slices((df_dhot['Final'].to_list()[:idx], df_dhot['Label'].to_list()[:idx]))\n",
    "dhot_test_x = df_dhot['Final'].to_list()[idx:]\n",
    "dhot_test_y = df_dhot['Label'].to_list()[idx:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "dhot_test_x = np.array(dhot_test_x)\n",
    "dhot_test_y = np.array(dhot_test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 1s 26ms/step - loss: 0.5317\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.531728208065033"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fed_model.evaluate(dhot_test_x,dhot_test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss\n",
      "11/11 [==============================] - 0s 25ms/step - loss: 0.5317\n",
      "accuracy\n",
      "0.7570093457943925\n",
      "prec-recall-f1-weighted\n",
      "(0.7486146853883546, 0.7570093457943925, 0.7527261590802543, None)\n",
      "prec-recall-f1-label-wise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.17777778, 0.85144928]),\n",
       " array([0.16326531, 0.86397059]),\n",
       " array([0.17021277, 0.85766423]),\n",
       " array([ 49, 272]))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "print(\"loss\")\n",
    "fed_model.evaluate(dhot_test_x,dhot_test_y)\n",
    "\n",
    "y_pred = fed_model.predict(dhot_test_x)\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] < 0.5:\n",
    "        y_pred[i]=0\n",
    "    else:\n",
    "        y_pred[i]=1\n",
    "\n",
    "print(\"accuracy\")\n",
    "print(accuracy_score(dhot_test_y, y_pred))\n",
    "print(\"prec-recall-f1-weighted\")\n",
    "print(precision_recall_fscore_support(dhot_test_y, y_pred, average='weighted'))\n",
    "print(\"prec-recall-f1-label-wise\")\n",
    "precision_recall_fscore_support(dhot_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr2, tpr2, threshold = metrics.roc_curve(dhot_test_y, y_pred)\n",
    "roc_auc2 = metrics.auc(fpr2, tpr2)\n",
    "# import matplotlib.pyplot as plt\n",
    "#\n",
    "#plt.plot(fpr, tpr, 'b', label = 'hasoc AUC = %0.2f' % roc_auc)\n",
    "#plt.plot(fpr2, tpr2, 'g', label = 'DHOT AUC = %0.2f' % roc_auc2)\n",
    "\n",
    "#plt.title('Receiver Operating Characteristic')\n",
    "#plt.legend(loc = 'lower right')\n",
    "#plt.plot([0, 1], [0, 1],'r--')\n",
    "#plt.xlim([0, 1])\n",
    "#plt.ylim([0, 1])\n",
    "#plt.ylabel('True Positive Rate')\n",
    "#plt.xlabel('False Positive Rate')\n",
    "\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.metrics import accuracy_score\n",
    "#from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "#print(\"loss\")\n",
    "#centralized_model.evaluate(dhot_test_x,dhot_test_y)\n",
    "\n",
    "#y_pred = centralized_model.predict(dhot_test_x)\n",
    "#for i in range(len(y_pred)):\n",
    " #   if y_pred[i] < 0.5:\n",
    "  #      y_pred[i]=0\n",
    "   # else:\n",
    "    #    y_pred[i]=1\n",
    "\n",
    "#print(\"accuracy\")\n",
    "#print(accuracy_score(dhot_test_y, y_pred))\n",
    "#print(\"prec-recall-f1-macro\")\n",
    "#print(precision_recall_fscore_support(dhot_test_y, y_pred, average='macro'))\n",
    "#print(\"prec-recall-f1-micro\")\n",
    "#print(precision_recall_fscore_support(dhot_test_y, y_pred, average='micro'))\n",
    "#print(\"prec-recall-f1-weighted\")\n",
    "#print(precision_recall_fscore_support(dhot_test_y, y_pred, average='weighted'))\n",
    "#print(\"prec-recall-f1-label-wise\")\n",
    "#precision_recall_fscore_support(dhot_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "dhot_test_x=0\n",
    "dhot_test_y=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAC Eng 19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "4000\n"
     ]
    }
   ],
   "source": [
    "infile = open('Trac-2019/english/df_trac_eng_2019.pickle','rb')\n",
    "df_traceng19 = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_traceng19['Final'] = None\n",
    "for i in range(len(df_traceng19)):\n",
    "  dataset1.append(df_traceng19['Embeddings'][i][0])\n",
    "df_traceng19['Final'] = dataset1\n",
    "\n",
    "idx = math.floor(df_traceng19.shape[0]*0.8)\n",
    "print(df_traceng19.shape[0])\n",
    "print(idx)\n",
    "\n",
    "#traceng19_train_tf = tf.data.Dataset.from_tensor_slices((df_traceng19['Final'].to_list()[:idx], df_traceng19['Label'].to_list()[:idx]))\n",
    "traceng19_test_x = df_traceng19['Final'].to_list()[idx:]\n",
    "traceng19_test_y = df_traceng19['Label'].to_list()[idx:]\n",
    "df_traceng19 = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "traceng19_test_x = np.array(traceng19_test_x)\n",
    "traceng19_test_y = np.array(traceng19_test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.7059\n",
      "accuracy\n",
      "0.577\n",
      "prec-recall-f1-weighted\n",
      "(0.6632280312290709, 0.577, 0.5655318408918739, None)\n",
      "prec-recall-f1-label-wise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.48480463, 0.78317152]),\n",
       " array([0.83333333, 0.40468227]),\n",
       " array([0.61299177, 0.53362734]),\n",
       " array([402, 598]))"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "print(\"loss\")\n",
    "fed_model.evaluate(traceng19_test_x,traceng19_test_y)\n",
    "\n",
    "y_pred = fed_model.predict(traceng19_test_x)\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] < 0.5:\n",
    "        y_pred[i]=0\n",
    "    else:\n",
    "        y_pred[i]=1\n",
    "\n",
    "print(\"accuracy\")\n",
    "print(accuracy_score(traceng19_test_y, y_pred))\n",
    "print(\"prec-recall-f1-weighted\")\n",
    "print(precision_recall_fscore_support(traceng19_test_y, y_pred, average='weighted'))\n",
    "print(\"prec-recall-f1-label-wise\")\n",
    "precision_recall_fscore_support(traceng19_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr3, tpr3, threshold3 = metrics.roc_curve(traceng19_test_y, y_pred)\n",
    "roc_auc3 = metrics.auc(fpr3, tpr3)\n",
    "#import matplotlib.pyplot as plt\n",
    "#plt.title('Receiver Operating Characteristic')\n",
    "#plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "#plt.legend(loc = 'lower right')\n",
    "#plt.plot([0, 1], [0, 1],'r--')\n",
    "#plt.xlim([0, 1])\n",
    "#plt.ylim([0, 1])\n",
    "#plt.ylabel('True Positive Rate')\n",
    "#plt.xlabel('False Positive Rate')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "traceng19_test_x=0\n",
    "traceng19_test_y=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAC Hin 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "4000\n"
     ]
    }
   ],
   "source": [
    "infile = open('Trac-2019/hindi/df_trac_hin_2019.pickle','rb')\n",
    "df_trachin19 = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_trachin19['Final'] = None\n",
    "for i in range(len(df_trachin19)):\n",
    "  dataset1.append(df_trachin19['Embeddings'][i][0])\n",
    "df_trachin19['Final'] = dataset1\n",
    "\n",
    "idx = math.floor(df_trachin19.shape[0]*0.8)\n",
    "print(df_trachin19.shape[0])\n",
    "print(idx)\n",
    "\n",
    "#trachin19_train_tf = tf.data.Dataset.from_tensor_slices((df_trachin19['Final'].to_list()[:idx], df_trachin19['Label'].to_list()[:idx]))\n",
    "trachin19_test_x = df_trachin19['Final'].to_list()[idx:]\n",
    "trachin19_test_y = df_trachin19['Label'].to_list()[idx:]\n",
    "df_trachin19 = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "trachin19_test_x = np.array(trachin19_test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "trachin19_test_y = np.array(trachin19_test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.6409\n",
      "accuracy\n",
      "0.644\n",
      "prec-recall-f1-weighted\n",
      "(0.7947090909090909, 0.644, 0.6827328740413363, None)\n",
      "prec-recall-f1-label-wise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.30909091, 0.90714286]),\n",
       " array([0.72340426, 0.62561576]),\n",
       " array([0.43312102, 0.74052478]),\n",
       " array([188, 812]))"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "print(\"loss\")\n",
    "fed_model.evaluate(trachin19_test_x,trachin19_test_y)\n",
    "\n",
    "y_pred = fed_model.predict(trachin19_test_x)\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] < 0.5:\n",
    "        y_pred[i]=0\n",
    "    else:\n",
    "        y_pred[i]=1\n",
    "\n",
    "print(\"accuracy\")\n",
    "print(accuracy_score(trachin19_test_y, y_pred))\n",
    "print(\"prec-recall-f1-weighted\")\n",
    "print(precision_recall_fscore_support(trachin19_test_y, y_pred, average='weighted'))\n",
    "print(\"prec-recall-f1-label-wise\")\n",
    "precision_recall_fscore_support(trachin19_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr4, tpr4, threshold4 = metrics.roc_curve(trachin19_test_y, y_pred)\n",
    "roc_auc4 = metrics.auc(fpr4, tpr4)\n",
    "#import matplotlib.pyplot as plt\n",
    "#plt.title('Receiver Operating Characteristic')\n",
    "#plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "#plt.legend(loc = 'lower right')\n",
    "#plt.plot([0, 1], [0, 1],'r--')\n",
    "#plt.xlim([0, 1])\n",
    "#plt.ylim([0, 1])\n",
    "#plt.ylabel('True Positive Rate')\n",
    "#plt.xlabel('False Positive Rate')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.metrics import accuracy_score\n",
    "#from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "#print(\"loss\")\n",
    "#centralized_model.evaluate(trachin19_test_x,trachin19_test_y)\n",
    "\n",
    "#y_pred = centralized_model.predict(trachin19_test_x)\n",
    "#for i in range(len(y_pred)):\n",
    " #   if y_pred[i] < 0.5:\n",
    "  #      y_pred[i]=0\n",
    "   # else:\n",
    "    #    y_pred[i]=1\n",
    "\n",
    "#print(\"accuracy\")\n",
    "#print(accuracy_score(trachin19_test_y, y_pred))\n",
    "#print(\"prec-recall-f1-macro\")\n",
    "#print(precision_recall_fscore_support(trachin19_test_y, y_pred, average='macro'))\n",
    "#print(\"prec-recall-f1-micro\")\n",
    "#print(precision_recall_fscore_support(trachin19_test_y, y_pred, average='micro'))\n",
    "#print(\"prec-recall-f1-weighted\")\n",
    "#print(precision_recall_fscore_support(trachin19_test_y, y_pred, average='weighted'))\n",
    "#print(\"prec-recall-f1-label-wise\")\n",
    "#precision_recall_fscore_support(trachin19_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "trachin19_test_x=0\n",
    "trachin19_test_y=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAC 20 Eng test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = open('df_traceng_dev.pickle','rb')\n",
    "df_traceng_test = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_traceng_test['Final'] = None\n",
    "for i in range(len(df_traceng_test)):\n",
    "  dataset1.append(df_traceng_test['Embeddings'][i][0])\n",
    "df_traceng_test['Final'] = dataset1\n",
    "\n",
    "traceng20_test_x = df_traceng_test['Final'].to_list()\n",
    "traceng20_test_y = df_traceng_test['Label'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "traceng20_test_x = np.array(traceng20_test_x)\n",
    "traceng20_test_y = np.array(traceng20_test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss\n",
      "34/34 [==============================] - 1s 27ms/step - loss: 0.4776\n",
      "accuracy\n",
      "0.7607879924953096\n",
      "prec-recall-f1-weighted\n",
      "(0.7766162335063119, 0.7607879924953096, 0.7675694585684927, None)\n",
      "prec-recall-f1-label-wise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.86540881, 0.45387454]),\n",
       " array([0.82296651, 0.53478261]),\n",
       " array([0.8436542 , 0.49101796]),\n",
       " array([836, 230]))"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "print(\"loss\")\n",
    "fed_model.evaluate(traceng20_test_x,traceng20_test_y)\n",
    "\n",
    "y_pred = fed_model.predict(traceng20_test_x)\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] < 0.5:\n",
    "        y_pred[i]=0\n",
    "    else:\n",
    "        y_pred[i]=1\n",
    "\n",
    "print(\"accuracy\")\n",
    "print(accuracy_score(traceng20_test_y, y_pred))\n",
    "print(\"prec-recall-f1-weighted\")\n",
    "print(precision_recall_fscore_support(traceng20_test_y, y_pred, average='weighted'))\n",
    "print(\"prec-recall-f1-label-wise\")\n",
    "precision_recall_fscore_support(traceng20_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr5, tpr5, threshold = metrics.roc_curve(traceng20_test_y, y_pred)\n",
    "roc_auc5 = metrics.auc(fpr5, tpr5)\n",
    "#import matplotlib.pyplot as plt\n",
    "#plt.title('Receiver Operating Characteristic')\n",
    "#plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "#plt.legend(loc = 'lower right')\n",
    "#plt.plot([0, 1], [0, 1],'r--')\n",
    "#plt.xlim([0, 1])\n",
    "#plt.ylim([0, 1])\n",
    "#plt.ylabel('True Positive Rate')\n",
    "#plt.xlabel('False Positive Rate')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.metrics import accuracy_score\n",
    "#from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "#print(\"loss\")\n",
    "#centralized_model.evaluate(traceng20_test_x,traceng20_test_y)\n",
    "\n",
    "#y_pred = centralized_model.predict(traceng20_test_x)\n",
    "#for i in range(len(y_pred)):\n",
    " #   if y_pred[i] < 0.5:\n",
    "  #      y_pred[i]=0\n",
    "   # else:\n",
    "    #    y_pred[i]=1\n",
    "\n",
    "#print(\"accuracy\")\n",
    "#print(accuracy_score(traceng20_test_y, y_pred))\n",
    "#print(\"prec-recall-f1-macro\")\n",
    "#print(precision_recall_fscore_support(traceng20_test_y, y_pred, average='macro'))\n",
    "#print(\"prec-recall-f1-micro\")\n",
    "#print(precision_recall_fscore_support(traceng20_test_y, y_pred, average='micro'))\n",
    "#print(\"prec-recall-f1-weighted\")\n",
    "#print(precision_recall_fscore_support(traceng20_test_y, y_pred, average='weighted'))\n",
    "#print(\"prec-recall-f1-label-wise\")\n",
    "#precision_recall_fscore_support(traceng20_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "traceng20_test_x =0\n",
    "traceng20_test_y =0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAC Hin 20 Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = open('df_trachin_dev.pickle','rb')\n",
    "df_trachin_test = pickle.load(infile)\n",
    "infile.close()\n",
    "\n",
    "dataset1 = []\n",
    "df_trachin_test['Final'] = None\n",
    "for i in range(len(df_trachin_test)):\n",
    "  dataset1.append(df_trachin_test['Embeddings'][i][0])\n",
    "df_trachin_test['Final'] = dataset1\n",
    "\n",
    "trachin20_test_x = df_trachin_test['Final'].to_list()\n",
    "trachin20_test_y = df_trachin_test['Label'].to_list()\n",
    "df_trachin_test=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "trachin20_test_x=np.array(trachin20_test_x)\n",
    "trachin20_test_y=np.array(trachin20_test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.5790\n",
      "accuracy\n",
      "0.695085255767302\n",
      "prec-recall-f1-weighted\n",
      "(0.6922616920374544, 0.695085255767302, 0.6879816899788325, None)\n",
      "prec-recall-f1-label-wise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.70570571, 0.67371601]),\n",
       " array([0.81314879, 0.53221957]),\n",
       " array([0.75562701, 0.59466667]),\n",
       " array([578, 419]))"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "print(\"loss\")\n",
    "fed_model.evaluate(trachin20_test_x,trachin20_test_y)\n",
    "\n",
    "y_pred = fed_model.predict(trachin20_test_x)\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] < 0.5:\n",
    "        y_pred[i]=0\n",
    "    else:\n",
    "        y_pred[i]=1\n",
    "\n",
    "print(\"accuracy\")\n",
    "print(accuracy_score(trachin20_test_y, y_pred))\n",
    "print(\"prec-recall-f1-weighted\")\n",
    "print(precision_recall_fscore_support(trachin20_test_y, y_pred, average='weighted'))\n",
    "print(\"prec-recall-f1-label-wise\")\n",
    "precision_recall_fscore_support(trachin20_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr6, tpr6, threshold = metrics.roc_curve(trachin20_test_y, y_pred)\n",
    "roc_auc6 = metrics.auc(fpr6, tpr6)\n",
    "#import matplotlib.pyplot as plt\n",
    "#plt.title('Receiver Operating Characteristic')\n",
    "#plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "#plt.legend(loc = 'lower right')\n",
    "#plt.plot([0, 1], [0, 1],'r--')\n",
    "#plt.xlim([0, 1])\n",
    "#plt.ylim([0, 1])\n",
    "#plt.ylabel('True Positive Rate')\n",
    "#plt.xlabel('False Positive Rate')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.metrics import accuracy_score\n",
    "#from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "#print(\"loss\")\n",
    "#centralized_model.evaluate(trachin20_test_x,trachin20_test_y)\n",
    "\n",
    "#y_pred = centralized_model.predict(trachin20_test_x)\n",
    "#for i in range(len(y_pred)):\n",
    " #   if y_pred[i] < 0.5:\n",
    "  #      y_pred[i]=0\n",
    "   # else:\n",
    "    #    y_pred[i]=1\n",
    "\n",
    "#print(\"accuracy\")\n",
    "#print(accuracy_score(trachin20_test_y, y_pred))\n",
    "#print(\"prec-recall-f1-macro\")\n",
    "#print(precision_recall_fscore_support(trachin20_test_y, y_pred, average='macro'))\n",
    "#print(\"prec-recall-f1-micro\")\n",
    "#print(precision_recall_fscore_support(trachin20_test_y, y_pred, average='micro'))\n",
    "#print(\"prec-recall-f1-weighted\")\n",
    "#print(precision_recall_fscore_support(trachin20_test_y, y_pred, average='weighted'))\n",
    "#print(\"prec-recall-f1-label-wise\")\n",
    "#precision_recall_fscore_support(trachin20_test_y, y_pred, average=None,labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "trachin20_test_x=0\n",
    "trachin20_test_y=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot ROC-AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(fpr, tpr, 'b', label = 'hasoc AUC = %0.2f' % roc_auc)\n",
    "plt.plot(fpr2, tpr2, 'g', label = 'DHOT AUC = %0.2f' % roc_auc2)\n",
    "plt.plot(fpr3, tpr3, 'b--', label = 'Trac eng 19 AUC = %0.2f' % roc_auc3)\n",
    "plt.plot(fpr4, tpr4, 'g--', label = 'Trac hin 19 AUC = %0.2f' % roc_auc4)\n",
    "plt.plot(fpr5, tpr5, 'r', label = 'Trac eng 20 AUC = %0.2f' % roc_auc5)\n",
    "plt.plot(fpr6, tpr6, 'y', label = 'Trac hin 20 AUC = %0.2f' % roc_auc6)\n",
    "\n",
    "plt.title('Redata:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAACVRUlEQVR4nOydd3hO5xvHPyeDRIi9Y4QEEXvvVdSqqk1rFrWp1vqp0ZZq7dp7lSL23mLVKGrvFTJJQgbZ73v//ngiQiKDRILzua5z5X3Pec557uckOfd5xv29NRFBR0dHR0fnTZiktAE6Ojo6Oqkb3VHo6Ojo6MSJ7ih0dHR0dOJEdxQ6Ojo6OnGiOwodHR0dnTjRHYWOjo6OTpzojkInUWiadlXTtDopbUdqQdO0/2matjiF6l6uadr4lKg7qdE07WtN0/a95bn632QyozuKDxhN01w0TQvWNO2ZpmlekQ+O9MlZp4g4isjh5KzjBZqmpdU0baKmaQ8j23lb07ShmqZp76P+WOypo2maW/R9IvKbiPRIpvo0TdMGapp2RdO055qmuWmatl7TtJLJUd/bomnaOE3TVr3LNURktYg0TEBdMZzj+/yb/FTRHcWHzxcikh4oA5QFRqasOYlH0zSzNxxaD3wGNAEyAJ2AXsCfyWCDpmlaavt/+BMYBAwEsgBFgC1A06SuKI7fQbKTknXrJBAR0bcPdANcgPrRvk8Cdkb7XgU4AfgBF4E60Y5lAZYBHsBTYEu0Y82AC5HnnQBKvV4nkAcIBrJEO1YW8AHMI793B65HXn8vUCBaWQH6AbeB+7G07TMgBMj32v7KgAGwi/x+GJgI/AsEAFtfsymue3AYmAD8E9kWO6BbpM2BwD3gu8iyVpFljMCzyC0PMA5YFVmmYGS7ugAPI+/FqGj1WQIrIu/HdWAY4PaG3619ZDsrxfH7Xw7MAXZG2nsaKBzt+J+Aa+R9OQfUjHZsHLABWBV5vAdQCTgZea88gdlAmmjnOAL7gSfAI+B/QCMgDAiPvCcXI8tmBJZEXscdGA+YRh7rGnnPpwO+kce6Ascjj2uRxx5H2nYZKIF6SQiPrO8ZsP31/wPANNKuu5H35Byv/Q3p21s8a1LaAH17h1/eq/8gNpH/UH9Gfs8b+U/YBNVzbBD5PXvk8Z3AOiAzYA7UjtxfNvIftHLkP12XyHrSxlLnIaBnNHsmA/MjP38J3AEcADPgJ+BEtLIS+dDJAljG0rbfgSNvaPcDXj7AD0c+iEqgHuYbefngju8eHEY90B0jbTRHva0XjnxY1QaCgHKR5evw2oOd2B3FIpRTKA2EAg7R2xR5z22AS69fL9p1ewMP4vn9L49sT6VI+1cDa6Md/wbIGnnsB8ALsIhmdzjQIvLeWALlUY7VLLIt14HBkeUzoB76PwAWkd8rv34PotW9GVgQ+TvJgXLkL35nXYEIYEBkXZa86ig+Rz3gM0X+HhyA3NHaPD6O/4OhqP+DopHnlgaypvT/6oe+pbgB+vYOvzz1D/IM9eYkwEEgU+Sx4cBfr5Xfi3rw50a9GWeO5ZrzgF9f23eTl44k+j9lD+BQ5GcN9fZaK/L7buDbaNcwQT10C0R+F6BeHG1bHP2h99qxU0S+qaMe9r9HO1Yc9cZpGtc9iHbuL/Hc4y3AoMjPdUiYo7CJdvxfoH3k53vA59GO9Xj9etGOjQJOxWPbcmBxtO9NgBtxlH8KlI5m99F4rj8Y2Bz5uQNw/g3lou5B5PecKAdpGW1fB8A58nNX4OFr1+jKS0dRD7iFclomsbQ5LkdxE/jyXf+39O3VLbWNyeoknhYikgH1ECsGZIvcXwBoo2ma34sNqIFyEvmAJyLyNJbrFQB+eO28fKhhltfZCFTVNC03UAvlfI5Fu86f0a7xBOVM8kY73zWOdvlE2hobuSOPx3adB6ieQTbivgex2qBpWmNN005pmvYksnwTXt7ThOIV7XMQ8GKBQZ7X6our/b68uf0JqQtN037UNO26pmn+kW3JyKtteb3tRTRN2xG5MCIA+C1a+Xyo4ZyEUAD1O/CMdt8XoHoWsdYdHRE5hBr2mgM81jRtoaZp1gmsOzF26iQQ3VF8JIjIEdTb1pTIXa6ot+lM0TYrEfk98lgWTdMyxXIpV2DCa+elE5E1sdT5FNgHtAM6onoAEu063712HUsRORH9EnE06QBQWdO0fNF3appWGfUwOBRtd/Qy+VFDKj7x3IMYNmialhbl/KYAOUUkE7AL5eDiszcheKKGnGKz+3UOAjaaplV4m4o0TauJmgNpi+o5ZgL8edkWiNmeecANwF5ErFFj/S/KuwKF3lDd69dxRfUoskW779Yi4hjHOa9eUGSmiJRH9RCLoIaU4j0vsu7C8ZTRSSS6o/i4mAE00DStNGqS8gtN0z7XNM1U0zSLyOWdNiLiiRoamqtpWmZN08w1TasVeY1FQG9N0ypHrgSy0jStqaZpGd5Q599AZ6B15OcXzAdGaprmCKBpWkZN09oktCEicgD1sNyoaZpjZBuqRLZrnojcjlb8G03Timualg74BdggIoa47sEbqk0DpAW8gQhN0xoD0ZdsPgKyapqWMaHteA0n1D3JrGlaXqD/mwpGtm8usCbS5jSR9rfXNG1EAurKgJoH8AbMNE0bA8T3Vp4BNXn8TNO0YkCfaMd2ALk1TRscuWw5Q6TTBnVfCr5YNRb597UPmKppmrWmaSaaphXWNK12AuxG07SKkX9/5sBz1KIGY7S63uSwQA1Z/qppmn3k328pTdOyJqRenTejO4qPCBHxBlYCY0TEFTWh/D/Uw8IV9Vb24nfeCfXmfQM1eT048hpngZ6orv9T1IR01ziq3YZaoeMlIhej2bIZ+ANYGzmMcQVonMgmtQKcgT2ouZhVqJU0A14r9xeqN+WFmmgdGGlDfPfgFUQkMPJcJ1TbO0a278XxG8Aa4F7kkEpsw3Fx8QvgBtxH9Zg2oN6838RAXg7B+KGGVL4Ctiegrr2o+3YLNRwXQtxDXQA/otociHphWPfiQOS9aQB8gbrPt4G6kYfXR/701TTtv8jPnVGO9xrqXm4gYUNpoBzaosjzHqCG4SZHHlsCFI+8/1tiOXca6ve3D+X0lqAmy3XeAe3lSIGOzoeHpmmHUROpKRId/S5omtYHNdGdoDdtHZ2UQu9R6Oi8JzRNy61pWvXIoZiiqKWmm1PaLh2d+Eg2R6Fp2lJN0x5rmnblDcc1TdNmapp2R9O0S5qmlUsuW3R0UglpUKt/AlGT8VtR8xA6OqmaZBt6ipwcfQasFJESsRxvghprboIK7vpTRCq/Xk5HR0dHJ2VJth6FiBxFrZ1/E1+inIiIyCkgU+R6fB0dHR2dVERKinHl5dVVGG6R+zxfL6hpWi+UzgtWVlblixUr9l4M1NHR0fkgESHksR9hQZ5YhgdjFgT/GfARkexvc7kPQrVRRBYCCwEqVKggZ8+eTWGLdHR0dFIZ4eH4bjiEy8LFWNpv5klTAxEZIfviNDzzykKVg14P3vbSKbnqyZ1XI1NtIvfp6Ojo6CSEiAhCdx7gTr1eeJbOxuPLjQj9bgM5rhgI25aGLWd/wmaRD5UPxBioSRQp2aPYBvTXNG0tajLbPzKiU0dHR0fnTRgMyNFjeP25jnT7NhBe1ge/NhpupYXsW6FsNw0TgwkV2k2g3qAfk6TKZHMUmqatQQnVZdNUVrCxKKEwRGQ+SkOnCSryNwiVB0BHR0dH53WMRjhxAv9F6zDZtAHLCC+CG5lzdZE5Znnh+R0zCvQNx/EGBNesQtplf5G2cNJJXiWboxCRDvEcF1TiGh0dHR2d1xGBU6cIXeVExJr1WD11R7Kl5fjXeTBpbkbadOHcemTF+r86Up1bNHL/DxZOw7JHD0jibMEfxGS2jo6OzieBCJw9i3GtE6GrnLB8/BBIw6EiVfAbmoM8lS+QRrvPMW+N85tsKRZgzug/OlDPth5M8oWsyaN/qDsKHR0dnZREBC5cgHXrCFvtRBq3+0RgzkGTBpz/rAWFOp8hb/6jpBVzNrsLW6/kYMhZYfORh2i5cqPlrqauk0xOAnRHoaOjo/P+EYErV2DdOiLWOGF27zYRmHKI+myx+hGbQQGUrDaLmla78PLNzsLlP7M1+wJqhgRxcZcpWe55wjffwPTpYGGR7ObqjkJHR0fnfXHtGjg5IWvXod28gQETDmv1WMdQvGtVpGO/VbTOPgwz7TlX/GH6+fQcu2ZPOrsZ/ObQg4EdZqDlTA87dkDTpu/NbN1R6Ojo6CQnt24p57BuHdqVKxjR+MesNn8zkGPZW9F+oAudak3DaOyDUYwc8RY238vMVdf8aDmu8Wtle3p22EJ2q+ywrip89hlYJzQzbNKgOwodHR2dpObePVi3Dpyc1PwD8J9lDZYxi51pW1G1ZQ66dNlCZ+uvCA09QWCgNYecB/Osmgurbu8mKOIpvao2YPJ+B6x/XglFukOt7PDVVynSHN1R6Ojo6CQFDx4ox+DkBJEyQ9cyVmER01lPa2zL29C1awADay7l0aM/MRhcuP/EjE2ecO2fYTzJ8g+PLu+mkV0j5kR8TqHhk8HLC4YOhYoVU7RpuqPQ0dHReVvc3GD9etV7OH0agHvZKrIkzWRWhbXBJHMBOg+Cgx0fYGr6A56ei/HwCOBagDlrXeHKw5JYWJrgWfAnKuapyJr6h6g7YTUs+R5KloStW6FChRRupO4odHR0dBKHpyds2KCcwz//AOCRqyx/ZZrIAr+2+IQWos038FcXKFXqFO7u0/Dw3IgYNTw827KNK7gEWRAYbM7TdCewz2LP+npOtHJohWZiAhVuQoECMHw4pEmTwo1V6I5CR0dHJz4ePYKNG9Ww0tGjIIJv3pJszPcrU1zbcvdxEerXh/Fd4MsvI3j+fDOurtO4cOEUz8PSss01Lc6z/qFFy9wE2fXgjMdOcqXPxfym8+merQHm/QZA+xDo1Al6907p1sZAdxQ6Ojo6seHjA5s2Kefg7AxGI4H5HNhTfCzjb7XlkrsDxYtDjz/g668hZ05/PD2XcOnSTEJDH+AdZM3frqbseRROUdM2lB42nal3VmP1wIrxdcczuNJArJatguFlwGBIsYnqhKA7Ch0dHZ0XPHkCW7aoYaWDB8FgIDS/PUcr/o8Jd9pxxNWRrFk1OvaGJZ2hfHkICbmPu/tMTpxYgtEYCCZVGHvNlX99Q6ho2Y1vSpmx6upyrt8zMrDSQEbVGkU296fweTPVO6lfHxYuBFvblG79G9EdhY6OzqeNv7+aNF63Dvbvh/BwIgoU4r/aQ5nq1g6nW6Ux99Ro1gw2d4YmTcDcXAgIOMnVq9Pw8dmM0ahx5Fop1s5aRNu25eneagVtwgIZe3gsxy8+5ZtS3/BL3V8omKmgqvPaP3DpEixdCl27JrmIX1KjKRHXDwc9w52Ojs47ExgI27apYaU9eyAsDMlfgJtl2jLfty2zT5bHYNSoUAG6dIH27SFbNjAaI/Dx2Yir6zQCA/8lNDwDm25kYdOzB/g/z8qmOncoVymYPrt6s+3mNqrYVGF+0/mUzlUaLl5UMRVduigbnj6FzJnfW5M1TTsnIm+1hErvUejo6HwaPH+upC/WrYNduyA0FMmbF8+v+rEqrC0TD1XGb5tGnjzw41Do3BmKF1enhof78fDhYtzdZxIa6oqJeX62PC7IgpsuEGpF79JT+PmLnmy+sRnHeYMJiQhhasOpDKo8CNPwCBg9Gn7/HXLnhnbtlD7Te3QS74ruKHR0dD5egoOVU1i3TjmJ4GDIlYuADr3YbN6OiYercnOdCZaW0LKletmvVw9MTV+cfhc3t5l4ei7BaHyOq2dVFsyZw8CRFTngU5/pjefTtUwXfIN86bCpA7tu76J6vuos/XIpRbIWgZMn4dtv4fp15XmmTXsvIn5Jje4odHR0Pi5CQmDvXuUctm1TPYns2Qn/uisHs7VjyqkaHFyuPEHt2jB8JLRuDRkyqNNFBD+/47i5TcfHZwuCGWfulmfRA1fuecHgKs2oUVbjcpPLACy7sIwhe4cQZghjxucz6F+pP6YmpuDurirIlUs5q8aNU+qOvDO6o9DR0fnwCQuDffvUnMPWrRAQAFmzIh2/5rx9W2ZerM36v80ICoLCheGXX5RKd/SFRkZjON7eG3Bzm0Zg4FlMzbLgafIZg/ZfxtvsFHlNK7C85zC+qaDmnh/6u9Jrey/23t1LrQK1WNJ8CXZZ7FTvwcEB8uZV9nz22Usv9IGiOwodHZ0Pk/BwtYTVyQk2bwY/P8iUCVq3xrVaOxbcqsvKNea4LoKMGZVj6NwZqlV7dZFRePhTPD0X4e4+i9BQNyIiinDo0DzyfBHCgIPfUyFnHZbWW0VTh8/QNA0RYdG5Rfyw7weMYmR249n0qdgHEz9/6N4dli1Ty15r1oQWLVLo5iQtuqPQ0dH5cIiIgMOH1bDSpk0q7sHaGlq0ILBJO1Y/qs/yv9NweimYmECjRjB5MjRvDpaWr14qKOgO7u5/4um5DKPxOf7PajDTuQrOB5vhEN4Fp65B/NO9EtXyVYs6x8XPhZ7be3Lg3gHqFqzLkuZLsM1sqxxV377g7Q0jR6a4iF9SozsKHR2d1I3BAMeOKeewcaN6GKdPD82bE9GqHXuMDVm2xoIdndUIVMmSMGWKipbOlevVS4kI/v5HcXWdjq/vNjTNDNI0p99yM65l3woZwmjxnS0bvwMTk3SAchJGMbLg7AKGHRgGwLym8+hVvhcmmsnLXkSZMrBzJ5Qr937vz3tAdxQ6OjqpD6NRCe45OSkBPi8vSJcOmjVD2rbjQu7GrHCy5O/eym9kz65e6Lt0gdKlY8avGY1hPH7shJvbdJ49+w9T06xERIziP/NwRh2YijG3RtPcXZjWahhFstm/cu69p/fosa0Hzi7O1C9Un8VfLKZAxvwvC1SpAvb28OOPYG7+Hm7O+0d3FDo6OqkDETh1SjmH9evVqiELC5Xys21bvMo3ZdVmK1aMU+mm06RRQ0pdusDnn8f+jA4Pf4KHxwLc3WcTFuaBmVkxDp8fxbSJfTGLyMPCI9voVzGEH6r+QL6M+V451yhG5vw7hxEHR2CqmbLoi0V8W/ZbtIcPoX1j6NhRTXr06vV+7k8KojsKHR2dlENEJflZt045h4cPlQdo3BgmTyb4s2ZsPZSBFctgXwfV0ahSBebNg7ZtIUuW2C8bFHQLN7cZeHmtwGgMIm3aBqw9NJiF9w8gdhMo/40Fa/r8hL19c1qXbB7j/DtP7vDttm85+uAojewasbDZQvJlyAtz58KIEcruNm2S+eakHnRHoaOj834RUVIWL1KF3r+vugMNG8L48cgXzfnnSkZWrACn3mqla758ao64UycoWvRNlxX8/A7j5jYNX98daFoasmX7BjeTCny//28umA7DslB2BlWcyIh6fcgYS9ybwWhg1r+z+N/B/5HGNA3LvlxGl9Jd0G7dgma14fhxZeeCBVCwYHLepVSF7ih0dHSSHxG4fFk5hnXr4M4dMDNTyqmjR0OLFtz3y8zKlbCyvEo5bWWlAuE6d4Y6ddQqpthQ8w9rI+cfLmBunp2wsDFMm9aX3Llz4t2gGb6GB0yuO4u+VbuTzjxdrNe56XOT7tu6c8L1BE3tm7Kg2QLyWueNPHgTrl6F5cuVQalcxC+p0R2Fjo5O8nHt2kvncOOGetrXqwfDhkHLlgSYZ2X9eljZQoUeaBrUrQtjxypJjfTp33zp8HBfPDzm4+4+h7AwT9KlK47v03n8uFLDJc9Mcrt24csvoWXzJWS2zEwa09izxRmMBmacmsFPzj9hYWbByhYr+abUN2gXLsCFfdCtm5oMuXdPxWl8guiOQkdHJ2m5devlsNKVK+rpX7s2DBoELVtiyJqDAwdg5UAVfhAcDEWKwIQJKiguf/64L//8+Q3c3Gbw6NFKjMZgMmf+nIJ28+g++wF7n/0GZVzJn6YMa3f5UrVAISDnG6913fs63bd155TbKZoXbc78pvPJbZ4ZRo2CSZNUdHWHDmpS/RN1EqA7Ch0dnaTg7l3lGJyc1PwDQI0aMGuWGj/KlYurV2HlVFi1Cjw8lHhq165q1VKlSnGP5qj5h0O4uk7jyZNdaFpasmTphLPzYEqVs6PZITvcTN0olrs6U75cQBP7RmhxXDDCGMHUE1MZe3gsVmmsWN1yNR1KdEA7cUKJ+N28qXoSU6d+kCJ+SY3uKHR0dN4OFxe1UmndOjh3Tu2rWhWmT1crgvLmxccH1qyBFStUEVNTlfhn5kxo1gzSpo27CqMxlEeP1uDmNp3nzy9hbp6DbNl+ZsX61sw+cIKQE4789BMMazaM0rlKU6tArXjNvvr4Kt22duOMxxlaOrRkbpO55EyfUy3HrVtX9SL27lWT1jqA7ih0dHQSg6urCoBbtw5On1b7KlZUOhlt2kCBAoSFqQDlFSvUz4gIKFtW+Y+OHSFHjvirCQvzjpp/CA9/hJVVCYoWXcrkBTWZeXY2hjIVoEEImyfWpkUte2BAvNcMN4Qz6Z9J/HL0F6zTWrOu9TraFG+Ddv06FM+pHMTGjcpZxDU58gmiOwodHZ248fBQzsHJSUVLg3ryT5yoghkKFYoKh1gxWfUgnjxR8hmDB6tFQiVLJqyq58+vRc4//IXRGEKWLI0xGIaQ07YUY46OYpn2HcYKRr4q9A2/NRlBsdeiqN/EpUeX6La1G/95/kdbx7bMbjyb7KGmanhpxQo4cgRq1YIvvni7e/SRozsKHR2dmDx6pN6u161TOksi6mk/frzqORQpAoCbG6z6HVauVOraFhZKMLVzZ2jQQK2AjQ8R4enTA7i5TePJkz2YmFiQM2dnvL0HM3x0fvZss2Lp6kB2eu6kd8Ve/Fjtx5e5p+Mh3BDOxOMTGX90PJktM7OhzQZaFW+l2tavH/j6qonrSpXe/l59AuiOQkdHR+HjoxRZ161TCq1Go8qrMHas6jk4OAAqD9CW1epF/MAB5UOqV4eFC5UPSejiIIMhhMeP/46cf7iCuXlOChb8lWvXvmPApDtcsh6GaYH7/PzLJb5slIGO1vdJaxbPpEY0LnhdoOuWrlx8dJEOJTows/FMsqXLpmbQV6xQ4n179igxP5040R2Fjs6nzJMnao2qk5PK7WAwKIG7//1P5XZ2dARNw2iEY0fU83X9enj2TAUmjx6teg+FCye8yrCwx3h4zMPdfS7h4Y+xsipFsWLLyZ69HQfuHaf19nYEV3TGSsvKkGqDGV4nPNJBJMxJhBnCGH90PBOPTySrZVY2t9tMi6JfvixQrZpyej/8kLAuj47q9iXXBjQCbgJ3gBGxHM8POAPngUtAk/iuWb58edHR0XkH/PxEli8XadJExMxMBEQKFRIZOVLk/HkRozGq6O3bIqNHixQsqIplyCDSvbvIkSMiBkPiqg0MvCzXr38rhw+nFWdn5OLFpuLldVDmzjVKxYoi6y5sE8YhOf/II1OOT5dnoc8S3bSz7mel5NySwjik06ZO4hvkK3Lvnkj9+qrNnzDAWXnbZ/nbnhjvhcEUuAsUAtIAF4Hir5VZCPSJ/FwccInvurqj0NF5CwICRFatEvniC5E0adS/foECIkOHipw584pzePpUZMECkWrVVDFNE2nYUGT1apHnzxNXrdFoFB+f3XLhQkNxdkaOHLGUmzd7i5fXdRn/W7hY1/hLKL1CKlUSuXYjXFZcWCEh4SGJbl5IeIj878D/xPRnU8kzNY9sv7ldJCJC5M8/RdKlE0mfXmTZskRf92PiXRxFcva7KgF3ROQegKZpa4EvgWvRyghgHfk5I+CRjPbo6HxaPH8OO3aoOYdduyA0VC0B7ddPDStFi3KLiFApp1esUCmnQ0PV6Mzvv6to6bx5E1e1wRDMo0ercHObQVDQNdKkyY2t7QTy5PmOhx5WFGy9nJDyTaD+fcpnqs+pgZ3RNDMc6JzoZp5xP0PXrV255n2NrmW6Mq3hNDK7eKlUpCdPKiXa+fPjD/nWeSPJ6SjyAq7RvrsBlV8rMw7Yp2naAMAKqB/bhTRN6wX0Asiv/7J1dN5MUBDs3q2cw44dSh8jVy6VM6FdOxUQF01d79Il5RxWr1YLnbJmhZ49VbR0+fKJ174LC3uEu/tcPDzmEh7uQ/r0ZShWbCVBQe04cSINaUpvZMDuAYTU98QxUyV+azSDZkWavZXGXkhECOMOj2PyicnkTp+bXR130di+sTp457iKrv7rL5Xq7hMT8Uty3rYrEt8GtAYWR/veCZj9WpkhwA+Rn6uiehsmcV1XH3rS0XmN4GCRzZtFOnQQsbJS40U5coj07Sty+LAagomGl5fItGkiZcqooubmIi1aqEuEhr6dCYGBl+T69W5y+HAacXbW5NKlL+TJE2e5eNEorTv5ikl6b8mQQWTH1YNSb0U9OXjvoBijDXcllhMPT0ix2cWEcUiPrT3EL9hP5OxZkSVLXhby93/r63+MkErnKKoCe6N9HwmMfK3MVSBftO/3gBxxXVd3FDo6op7o27eLdOokYm2t/pWzZhXp1Uvk4EGR8PBXigcHi6xfL9KsmYipqSpeoYLIrFki3t5vZ4LRaBAfn51y4UL9yPmHdHLzZl95/vymXL8u8lkLD6HBj8L/0ku5/w0SN7d3b3ZQWJD8sPcH0cZpkm9aPtl7Z69IUJDI8OGqYQULqsbqxOBdHEVyDj2dAew1TbMF3IH2QMfXyjwEPgOWa5rmAFgA3slok47Oh0t4uFrCum4dbNkCfn4qaKF1azWsVLfuK/lARZTKxooVsHatKp4nj0rt3LkzFC/+dmYYDEE8evRX5PzDDdKkyYOt7URy5+5FcHAWHoXe55dzfThYYhmaWTitirRnTL1vyftmEdcE8c/Df+i+rTu3fG/xXfnvmNRgEtanL0Dj0nD7thLzmzJFF/FLDt7WwyRkA5oAt1Crn0ZF7vsFaB75uTjwD2pF1AWgYXzX1HsUOp8U4eEi+/eL9OghkiWL6gpYW4t07iyyc2esY0UPHoiMHy9SpIgqbmkp8vXXIvv2xRiFShQhIR5y9+4oOXYsqzg7I2fOlBMvr1USGhoqa9aIlC4t0ry5SOfNnSXNr2mkx5Zecsf3zttXGMmz0GcyaPcg0cZpUnBGQTlw94A64Oamlvfa2oocOPDO9XzskBqHnpJr0x2FzkdPRITIoUMivXuLZM+u/k3Tp1dP+61bRUJiLh8NDFRhAnXrquWsIFK7thqyf9eh+sDAC3LtWhc5fNhcnJ01uXy5hTx9ekSCgowyb55Inor/Cu2+Etuq52XFCpGHfg/FzT8JxplE5IjLESn8Z2FhHNJvZz8JDA0UuXTpZYHt20WeJT7e4lPkXRyFHpaoo5MaMBqV4N66dUqA79EjSJdOidS1bauWeFpaxjjF2VnpLG3cqFbDFi4M48ap3NK2tm9vjogRX99duLlNx8/vECYmVuTJ8x158w4iXTo7RISeE46w5NZv0HQ/VqaZmPhTB9qVKAPke5c7AcCzsGeMPDCS2WdmUyhzIZy7OFMnfQn4to9KaPFCxK9Zs3euSyd+dEeho5NSGI1qEmHdOqWL4eGhxtebNlVzDk2aqMTRr3Hzppp3WLVKqX5bWyv57i5dlDrFu6wENRie4+W1Eje3GQQH3yJtWhsKFfqD3Ll78uRJZsaPh2rVhNl+jdlr2EvmYjkZUXsSvSt8h3Va6/grSACH7h+ix7YeuPi5MLDSQH6rNwGrrbugf3F4+lRpT1V+faW9TnKiOwodnfeJROpxv0gV6uoKadKoHkO7dqoHEUsuhCdP1IT0ypXKt5iYwOefqzQQzZvH6GwkmtBQD9zdZ+PhsYCIiCdkyFABB4c1ZM/eigcPzBk42MCSIzsIv9aU0T9pNGzckOZFm9OtTDcszd+x8kgCQwMZfmA4887Owy6LHUe6HqFmgZpq5v2vv6BCBTWZn1DNcp0kQ3cUOjrJjYhKD/rCOdy/r1Ynff65ShTdvDlkzBjjtPBwJW66YgVs3w5hYeoZOWWK6kHkzv3upgUG/oeb23QeP16HSATZsn2Fjc33ZMxYHU3TGDYyjCn7lyDVf4c2d1hWdz9da9VHhUAlHQfuHaDHth489H/IkCpD+LXuL6QzT6cO1q4NpUqp5Ba6iF+KoN91HZ3kQAQuX1aOYd06uHNHPeTq11eSqy1aqKTRsZx24YJyDn//Dd7ekD079O2rXqzLlHn3IGM1/7ADV9dp+PsfwdQ0PXny9MXGZiCWloU4cQKKlwxlxdX5LLaagnzhRsms5RhbbwNfOdR7t8pfwz/En6H7h7Lov0UUzVqU492PUy08FzRurrRDunVTy151UhTdUejoJCXXrr3sOdy4ocaI6tWD4cPhq6+URkYseHoqGY0VK+DKFTUa1by5mnf4/PNXwiPeGjX/sDxy/uEOadPmp3DhKeTK9S1mZpnYvRsm/m7k+DETZs2DyUGTKZG3EKNqLqZh4YZoSSyDsefOHnpu74lHoAdDqw3l55pjsJy/WCUSMjVVnlEnVaA7Ch2dd+XWrZfO4coV9cpfuzYMGgQtW74xSXRwsBLgW7kS9u5Vc9tVqsDcuWq6IkuWpDEvJMQNd/fZeHouICLCjwwZKlO8+ASyZWuJppmxdi2Mn/6Ya9YzMCu7jSkt/6Nbp7S05T9yWCUgwXUi8QvxY8jeISy7sAyHbA6c6H6CygEZoE59NQHTtKkS8bOxSfK6dd4O3VHo6LwNd+++HFa6eFE5hxo1YNYsFSmdK1esp4moVbArV6rT/f0hXz4YMUK9QBctmnQmBgScxc1tOt7eTogYyZ69JTY2Q8iYsSpGo+rsPPB7yPf7pvD480VoZqG0cGhD56b+WFllx4qkdxI7b+2k145ePHr2iJE1RjKm9hgszCxg5051T//+G9q310X8Uhm6o9DRSSguLmoZ67p1cO6c2le1KkyfrnKAxqHFff++WrizcqV6HlpZQatWamipTp1XBF3fCREDPj7bcHObjr//MUxNM5A37wDy5h2ApaUtAQEwaRIsWABLdlyiwYbyUAi6lOzEiBrDKZotCT1VNJ4GP2Xw3sGsvLiSEjlKsLX9Viq4Cyz7S8nVNm0K9+5BhgzJUr/OO/K2kXoptemR2TrvlYcPRaZOFalcWYU7g0jFiiJTpoi4uMR5qr+/ioyuVUuiEgDVqyeyYoWKpE5KwsMDxdV1ppw8WUicnZETJwrIw4fTJDxchWU/eiTyv/+JpLc7L5T4Wxo0ELl50ygTjk6QB34PktaY19h6Y6vkmpJLTH82lZ8O/iQh/k9EfvhBxMREyW/oIn7vBd4hMltT5384VKhQQc6ePZvSZuh8zHh4qOjodevgxAm1r2xZNXHQpg0UKvTGUw0GtdR/xQqVijo4GIoUUT2Hb75J+tw5ISGuuLvPwsNjIQaDP9bWVbGxGUK2bC0wMVEDBt7ekK/6P4RW/A2K7CKnhQ1uQ+9jZpK8Awq+Qb4M2jOI1ZdXUypnKZZ9uYxyNwOgRw/VrfruO/jjj1iXBuskPZqmnRORCm9zrj70pKMDSjJj40blHI4dU32HUqVg/HgloWFvH+fp1669jJb28FArX7t2VfMOlSsn/ZB7QMC/kfEP6wHInr1VZPxDFUDNqR85AlVb/sf3e78n9OujZE6bjR+rT6Bvxb7J7iQ2Xd9E35198Q32ZVztcYysOZI0no+hQWUoUAAOHVJqtzofBLqj0Pl08fFRzsHJCQ4fVsuOHByURETbtupzPKevWaMcxLlzakVnkybw558qwDpt2qQ1V80/bMHVdToBAf9gamqNjc1gbGwGYGFRAFAdoIm/G9mx7xnWaa3Z3kDj3tN7/NnoT3qU6/EyiC2Z8H7uzYDdA1h3dR1lc5Vl7zd7Kf0IME2jVjFt3aomZdIlrx06Sczbjlml1KbPUei8E76+IosXizRs+DKDT5EiIqNHi1y+HO/poaEimzapjHDm5ur0smVFpk9X8wDJQXi4vzx8OF1OnrQVZ2fk5ElbcXWdIeHhAVFlrl4VqVE7TCi9QkwHFpPS4zqLr2/k+YbwN1w5aXG64iTZJ2UX81/M5dcjv0qYp7vKugcq055OioKuHqujEwf+/irRj5MT7NsHERFqnmHYMDXvUKpUnGNDEinPtHKl6kH4+qrVrwMHqrmH5JIeCgl5gJvbLDw9F2EwBGBtXZ3ChaeQLduXaJopEREqUC9z9hC2eS7lVMVJkP4BDtlLMap2s6g4jOQeZnr8/DH9dvVjw7UNlM9dnoOdDlDS+Sq0Kq3u/c8/q9VhOh8ub+thUmrTexQ6CcLfX2TVKpEvvhBJk0a91RYoIDJ0qMqtnIB8zW5uIr//LuLgoE5Pm1akXTuRXbtiZBpNUvz8TsqVK23F2dlUnJ1N5erV9uLvfzrqeHCwyLx5IoUKidSsKTJi/whhHFJ1cVXZcXPHO+WiTgxGo1H+vvS3ZP0jq6T5NY1MPDZR9V6+/lrdsMqVRa5ceS+26MQP76NHoWlaOhEJSkafpaPzbjx/Djt2qAnpXbsgNFSNi/fvr+YcKlVK0KxyeDj06qXmHkSgenVYuFAteMqUKXlMNxoj8PHZjJvbdAICTmJqmpF8+YaQN+8ALCxUfoeAABWwPHWeD49tZ1LMvj7ff1eLihX708iuEbUK1EpymY034fXMiz47+7DlxhYq5a3Esi+WUDyHo7q/detC+fKqy2Vq+l7s0Ule4nUUmqZVAxYD6YH8mqaVBr4Tkb7JbZyOTrwEBSmn4OSknERwsJJV/e475RyqVk1UNFtoqBqN2roVvv9eifHZ2SWf+RER/nh6LsHNbSahoQ+wsCiMnd1McuXqhpnZq3LjM5a6M/bQVEw6LQDTIDrUMeer2rWAvNhkfHOwX1IiIqy+vJqBuwcSFB7EpPqT+D77F5i176OyJXXvrov4fYQkpEcxHfgc2AYgIhc1TauVrFbp6MRFSIjS33Zygm3bVE8iRw6lNNquneoCvMWbbEiIipbetQtmz4Z+/ZLB9kiCg+/j7j4TT88lGAyBZMxYCzu7GWTL9gWapmx3cVGS4pUrw408o5jybAqm1Qx0KNmBEdVH4JjDMfkMjAWPQA++2/EdO27toKpNVZY2XUixVXtgdFm1xEt3EB8tCRp6EhHX17q0huQxR0fnDYSGwv79alhp61YIDFRKrF9/rZxDrVrvlKsgKEgpfx84oIaZevZMOtNfICIEBJzE1XUaPj6b0TQTsmdvR75835MhQ/mocleuwO+/w5qDVzF5UpTs2c3Ibp+d7mW6M7T6UAplfnPAX3IgIqy8uJLBewcTEhHCtIbTGGhVD9MvuqlZ/i+/VEqGefK8V7t03h8J+c9yjRx+Ek3TzIFBwPXkNUtHBzVZcPCgcg6bN6sVNJkzq8mCdu3UWHgS6G8/e6biHo4ehWXL1EqmpETNP2zE1XU6gYGnMTPLRP78w8iTpx8WFq8qpA4dClPWnMa0zkSMvbcyo84qBtT+GhictEYlELcAN3pt78XuO7upkb8GS5svxT6rvep2PXig0u61bauL+H3kJMRR9Ab+BPIC7sA+QJ+f0EkeIiLA2VkNK23apHKAWlurXA5t26rEP2nSJFl1AQEqSO7UKRVV3aFDkl2a8HA/PD0X4+4+i9DQh1ha2mFvP5ucObtEzT+IKInxSpWEC/7O7Mv1G/Q8SIa0mRlcZRxfV2qcdAYlAhFh6fmlDNk3hAhjBH82+pP+UhGTjc7Qy17dtHv3Yk3bqvMREt+yKKB6Qva9r01fHvsREhEhcuiQyHffiWTLppZWpk+vlllu3SoSEpIs1T59qlZwmpmJbNiQdNcNCrort24NlKNH04uzM3L+fB3x9t4qRqMhqkxEhMjatSJlyqjmTppsEIfZDpJrSi6Z8s8UCQgJiKOG5OWB3wNp+FdDYRxSe1ltufvwksj33ytVw8KFk+33oZO88A7LYxPiKP5LyL73temO4iPBYBA5elSkXz+RnDnVn2K6dCpQYdMmkaCgZK3e11ekXDkVXb1167tfz2g0ytOnx+Ty5a/E2VmTw4fN5Nq1ThIQ8N9r5UQWLBApZBculFwtln1rydwlARIaKnLT56YEh6eckqrRaJT5Z+ZLht8yiNUEK5nz7xwxHNivAjZApE8fFZ+i80HyLo7ijUNPmqZVBaoB2TVNi55J3RrQF0frJB6jUWUwW7dO5XXw8AALC5WLoF079fM9aAB5e6sRrJs31bx443cY3TEaw/H23oCb2zQCA89iZpaF/PlHkjdvP9KmfTm5GxamRszCDKHM/Gcl7l/9AVZ3sc1WnFrNHpImjSNFshZJgta9HS5+LvTY1oOD9w9Sz7Yei79YjO1zc6hmC7a2SmGwlr7Y8VMlrjmKNKjYCTMgejaRAKB1chql8xEhAmfOqDkHJydwdVVLKRs3VnMOX3zxXse5vbzgs89UIqEdO5TDeBvCw5/i6bkocv7BDUvLItjbzyNXrs6Ymr50do8fK5HAhQthzzFvmu8qg0chDyrkqcComptpXrQ5JloSZS16C4xiZP7Z+QzbPwxN05jfdD69TCqiZbaFzMD27Sqtq6VlitmokwqIr8sBFHjb7kpybPrQ0weA0Shy7pzI8OEqMQ2oMZ5mzUT++ivFhi/c3JT+n5XV22vUPX9+W27d6i9HjlhFzj/UE2/v7a/MP4iI3L+vRtXSZnwiFNkurVqJ3L4t8uPeH2X/3f3vTWYjLu743pE6y+sI45AGKxvIw5tnRNq2FV3E7+OEZJbwCNI0bTLgCFhEczD1ksd16XyQiMDly2pYyckJ7txRcQ3168OYMWqtfebMKWbew4dQr556w9+7V8XkJRQRwd//GK6u0/D13YammZEjR0fy5fue9OlLxyjv7w8OFb0IKzcdk0FzSWMWzsIfPchimYXJdpOTsFVvh1GMzPl3DiMOjsDMxIzFzRbR/VpatKqfq7XC48dDtWopbaZOKiIhjmI1sA5ohloq2wXwTk6jdD4grl176Rxu3FAR0fXqwfDhaklr1qwpbSH37imT/PxUQF2lSgk7z2gMw9t7Pa6u03j27D/MzLJSoMAo8uTpS9q0uV8pe/KkChbv/aMn44+PxzBgCRBOa8e2jKg+giyWWZK8XW/Dbd/bfLvtW449PEZju8YsaLaAfL2HqXiIqlVhyZJ483DofILE1+UAzkX+vBRt35m37cK866YPPaUCbtwQ+eUXEUdHiUoGXbeukjR9/DilrXuFW7dEbGxEsmRRo2EJISzMV1xcJso//+QRZ2fk9Oli4u6+QCIinr9SzmgU2b07Mie2SbhkzSpy+tYdsRxvKT229pBbPreSoUVvR4QhQqadmCaW4y0l48SMsuzcEjEaIofLli4V+fNPtWZX56OFZB56Co/86alpWlPAA0gdr0c674+7d1/2HC5eVJG4NWrArFnQurVK0JDKuH5dTVy/iOErVSru8kFBt3Bz+xMvr+UYjUFkzlyfokUXkyXL52ivTThfuwYdO8LFx/9h2WAiJRqGcWrwVqysCuPxgweZLDIlX8MSyU2fm3Tb2o2TbidpVqQZi4sOI+f3P0FnUfpM3bqltIk6qZyEOIrxmqZlBH4AZqGWxw5OTqN0UgkuLi9XK507p/ZVrQozZijnkPf9KJa+DVeuKCdhYqKynBYvHns5EcHP7zBubtPx9d2BppmTM+fX2NgMJn36Vz1LSAi4uSk12QccxaXGb5B9L2nSZqRFpQFYpjMCJqnGSRiMBqadnMaYw2OwNLNk1RfL6bjfC617Q7UsWV/JpJNQ3qYbgh6Z/fHy8KHI1KkilSqpYSUQqVhRZMoUkQcPUtq6BPHffyJZs4rkzSty82bsZQyGUPH0XClnzpQRZ2fk+PFscu/eGAkN9YpR1t9fZNIkkVy5REqUEJl/ZqEwDsk+KbtMPDZR/IL9krlFiefq46tSeVFlYRzSYm0LeXzigEj58ur3+dVXIh4eKW2iznuGZAq4MwXaojSe9ojIFU3TmgH/AyyBsu/Bj+m8Dzw8YMMGNbR04oTaV66ckjBt21YFXH0gnDkDDRsqeahDh6Bw4VePh4f74uExH3f3OYSFeZIuXXGKFFlEzpxfY2r66hv248cwcybMnmvAP/dmylXPwuS+9ShdvBXhxlC6l+1OOvPkDxBMDBHGCKacmMLYw2PJkCYDa1qtoZ1jO7Tdu1UMy/r1SktdF/HTSQRxDT0tAfIB/wIzNU3zACoAI0RkS0IurmlaI5SgoCmwWER+j6VMW2AcIMBFEemYmAbovCWPHsHGjco5HDum+g6lSqmlkW3bgr19SluYaE6ehEaN1EIrZ2coUODlsefPb+DmNoNHj1ZiNAaTOXNDihVbRubMDd+YFW7H7nAm7FxN+u9+B4ubFC7ehnr16gFZ6F+p//tpVCK48vgK3bZ246zHWVo5tGJhlq5kOe4GJbSXIn5WViltps4HSFyOogJQSkSMmqZZAF5AYRHxTciFI3skc4AGgBtwRtO0bSJyLVoZe2AkaijrqaZpOd62IToJwMdHOQcnJzVwbzSqwftx45RzKFYspS18a44eVQoguXOrnoSNzYv5h0O4uk7nyZOdaFpacub8JnL+oUSMa1y5An/8obJ45vxsDT8/GQEtHmKXqwz/q+FES4eWKdCy+Ak3hPPHP3/wy5FfyGiRkY1NVtJy1VmY1Vx1qbp1U9HwupPQeUvichRhImIEEJEQTdPuJdRJRFIJuCMi9wA0TVsLfAlci1amJzBHRJ5G1vM4UdbrxM+TJyqXg5OTyu1gMECRIjBqlNJXcny/WdKSg4MHoXlz1YM4eBBy5gzF03MNbm7Tef78EubmOShYcBx58vQhTZqY7yInTqhRtu37/EmXNi0ODhYEhgWSL2M+5jebTyO7Ru8tF3ViufToEl23dOW813naObZjfpqWZGozTEUY9usHv/2mnISOzjsQl6MopmnapcjPGlA48rsGiIjEs9iQvIBrtO9uQOXXyhQB0DTtH9Tw1DgR2fP6hTRN6wX0AsifP3881erg56fU7tatU1nhIiKgUCEYNkw5h1KlPpox6r17VWY6e3vYs8eH0ND5nDw5m/DwR1hZlaBo0SXkyNERU1OLWM//4QeYNt8bizp/knbkbMbUGs/wuv0xSg96le/1fhuTCMIMYUw8NpHxx8aTxTILG9tupGX6iur3XLiw6mLVqJHSZup8JMTlKN5HeKYZYA/UAWyAo5qmlRQRv+iFRGQhsBCgQoUK8h7s+vAICFACbuvWqadnWJh6xR4yRA0rlSv30TiHF2zfrlbp1q17jT/+mMGdO39hNIaQJUsjbGyGkDlz/Rg9AYNBzdvXqAGSwY2HxadgPmwhoYTQ0qElnzuoh2tKCvXFx3nP83Td2pVLjy7RsWRH5uTsTiaHz9TBXbugZk21/FVHJ4l4o6MQkQfveG131GT4C2wi90XHDTgtIuHAfU3TbqEcx5l3rPvT4NkzJYHq5KQeEKGhanC+f3/lHCpV+uicwws2bRL++OMAf/45jWLF9uDvb0HOnJ2xsRmElVXMoImQEFi5EiZNUrGD48fDvrxf84/7P3xT+huGVx+OQ/bULV0RGhHK+KPjmXh8ItmtsrO73jIazdwJG+qrOafataFBg5Q2U+cj5O2z0cfPGcBe0zRblINoD7y+omkL0AFYpmlaNtRQ1L1ktOnDJyhIOYV162DnTggOVjO4332nhpWqVFFRZh8pBkMIO3b8jb//dP744wpmZjmxsfmVPHm+I02a7LGeM326chBecomsX0xh6W9T6NwqB828Z5LRIiMFMxV8v414C856nKXb1m5ceXyFzqU6McenMum/HKL+Hn77TRfx00lWks1RiEiEpmn9gb2o+YelInJV07RfUIEf2yKPNdQ07RpgAIYmcsL80yAkRCnOrVunxlueP4ccOdRqlnbt1DjKR+wcAMLCHuPhMY+7d+eSMeNjgoNLUaDAMgoU6ICJSczJ2oAAFUsBsPvKSSLa/AZZdxCWJgO5y3XE1LQRpXPFVH5NbYRGhPLzkZ+Z9M8kcqbPyY4OO2g6eiU49VcSuIsXf9Cr1XQ+DDQVsBdPIU2zBPKLyM3kNyluKlSoIGfPnk1pM94fv/+u3hgDA1WAQKtWyjnUrq2UWj9ynj+/iqvrdB49WoVIKCdPNuXu3e+ZMaMe6dPHHFZzcYGpU2HpUjj6TzjDLjXi0P1DZLHMwuDKg+lfqT+ZLVNO7jwx/Ov+L922duOa9zW6l+rK1EbTyGSZGVasUH8Pfft+9C8IOkmHpmnnRKTC25wbb49C07QvgCmojHe2mqaVAX4RkeZvU6FOIrh9Wy1jrV9fLc+pWxfMzVPaqmRHRHj6dB+urtN4+nQfJiaWPHnSjUGDBuHoWIxNm2LKFF29qmIgVv9tRLM5Q5f2lcmW2RyHbA40s29Gz/I9SZ/m/WXSexeCw4MZe3gsU09OJU+GPBytspCav64A343Qowd06ZLSJup8YiRk6GkcKibiMICIXIicd9BJbiZOVImWV6xIleqsSY3BEMyjR6txc5tOUNA10qTJja3tBLZv/44BA7LyxRdKgeL1sIDnz6FKtQjCi64l0/8m8tT0OsP736BA1iLMLjA7ZRrzlpx0PUm3rd246XuT3qW+ZfrlPFg076+C5d5jylgdnegkpN8aLiL+r+3Tl6gmNy4u8Ndf0KvXR+8kwsIecf/+WE6dys+tWz0xMUlDsWIrqVLFBSen/zFgQFZatVLLWtOmVWoje/dCnz4QEh7KqusLSD+yCKFNO5EntwmrWq6iUOZCKd2sRBEUHsQPe3+g+tLqBEcEc6LcPOb9cg6Lsb+qaMJr16B9+5Q2U+cTJSE9iquapnUETCMlNwYCJ5LXLB1+/12NPw8bltKWJBvPnl3GzW06jx6tRiSMrFm/wMZmCJky1UbTNMaPh9Gj1fPxr7/USl8nJ3Vrzp9XK4F7DfFj8N7BlMpZigU1Z9CsSLNUHQMRG8ceHKP7tu7ceXKH3uV7M6nBJDI4/wNeXkpypWXqlA7R+XRIiKMYAIwCQoG/USuVxienUZ88bm6wbBl0756qcz68DSJGnjzZi5vbNJ4+PYCJSTpy5+6Bjc0g0qUrEllGpdn+9Vfo1Enditu34Ysv4I77E7I2mUWZ9uc4NWgradPm5FLvS9hlsUu1Mhtv4nnYc/538H/M+ncWBTIV4KzDDMo/NIe0GZS64d27kC51qdPqfJokxFEUE5FRKGeh8z6YNEkJ9o0YkdKWJBlq/uGvyPmHG6RJkwdb24nkydMLc/OXCRNFVLMnTVJOYsAAtbjLIrsnxvrTsMgzH1/jM2rk/5IILYi0WGGf9cNTuj3icoTu27pz7+k9fnDsxW8HIc3gwUqL5Ntv1Rib7iR0UgkJcRRTNU3LBWwA1onIlWS26dPGywsWLYLOnV/Vyf5ACQ31wsNjDu7u84iI8CV9+nI4OKwie/Y2mJikeaWsiFIcmTFDKY5s3w5HjsCSg840W9uY8NzhtHdsz4jqIyiZs2TKNOgdeRb2jBEHRjDnzBwKZy7MxYKTKPXDbJUrYtAgFTKui/jppDLidRQiUjfSUbQFFmiaZo1yGPrwU3IwZYrSaRo5MqUteSeePbuIq+t0Hj/+G5EIsmZtTr58Q8iYsWasQ0RGI3TtquYizMzgP9dr1PziMVMH1MGxQGV6V+jNgEoDKJylcMzKPhAO3T/Et9u+5YHfAwZVHsRv9n1IV6yEyq16/LgeXa2TaklQwF1UYU0rCQwD2olImvjKJwcfdcCdj4/qRXz1FaxaldLWJBoRI76+u3Bzm46f3yFMTKzInbsbefMOIl06uzeeZzBA794qyFizOUO+DhN5aLWZ0jlLc/678x/c3MPrBIQGMGz/MBacW4B9ZjucCg6lTPNIZdoDB1RkvS7ip5PMJHfAnQPQDmgF+ALrgB/epjKdeJg+XWk3jfqwpoMMhiC8vFbg5jaD4OBbpE1rQ6FCf5A7d0/Mzd8cBX3qlAo6d3GBy0/+xXbMT9w32U+ARSZGVxrNwMoDP3gnse/uPnpu74mrvyvjinzHqHWemG39Dg4XVdH19euntIk6OvGSkDmKpSjn8LmIeCSzPZ8uT5/CrFlKN9shdauYviA01AN39zl4eMwnIuIJGTJUwMHhb7Jnb42JSewR5CKwb5+KJTxyRDC3CCc8JA3tfnnIYctL/FH1D3pX6I11Wuv33JqkxT/Enx/3/cji84spmqUIt9KPxK7vXKXb9ccfSqdJR+cDISFzFFXfhyGfPDNnKv2en35KaUviJTDwPG5u03n8eC0iEWTL1gIbmyFkzFg93h7AsGEwZaqBLDU3YDH4N0LOt2JS0zEM+eErwgxNsTS3jPP8D4Hdt3fTa0cvPAI9GFZtGL/Nv43ppt9UnojFi1WGQR2dD4g3OgpN05xEpK2maZd5NRI7oRnudBJKQIBa6vPllyr7XCpEzT/siJx/OIypaXry5OmDjc1ALC3fPMEcGqryQNSuDQULh2FV4y9yZPyDx4bb4F2Mrk0dGDoUwBRLkw/bSfiF+DFk7xCWXVhGiawObOz2D5XyVQHPlVC/gZKC10X8dD5A4upRDIr82ex9GPJJM2eOSl86enRKWxIDg+E5Xl7LcXP7k+Dg26RNm49ChSaTO3cPzM0zvfG8wEBYsACmTQNPTxg7Fu6X6cnKiyuxflYedm5kdv8W9Ov7cTw4d9zawXc7vuPRs0dMz9eLgYsuYZL2MvSsopY66+h8wMSV4c4z8mNfERke/ZimaX8Aw2OepZNonj9XT9PGjaF8+ZS2JoqQEDfc3Wfj6bmQiIinZMhQieLF15ItWytMTOIesfztN5g8Wb1hF2o/h5WtOvBN00KccvmeS6s6cmFTQxYv0vj22/fUmGTkSfATBu8ZzF+X/qJMVkf+fdacvH2WKgG/jBlT2jwdnSQhIZPZDYjpFBrHsk/nbZg/Xy2LTSW9iYCAs7i5Tcfb2wkRI9mzt8TG5nusravGOf/g4aES7Wka3Hv0mJwdZxCeZw73IgLwy27Ns2cDGNG1DJeOw8oVKur6Q2frja303tkbnyAf5uTuQe85pzG5NF/lC5k5UyWX0tH5CIhrjqIP0BcopGnapWiHMgD/JLdhnwTBwSrArl49qJpyawZEDPj4bMfNbRr+/scwNc1A3rz9yZt3IJaWcSvKv8gD8fffcOgQbAv5kdU55hAaEUrrIq0ZWWMkhdKV5fPP4d9/Vbl27d5Tw5IJnyAfBu4eyJorayidszS7Ou6i7MVH4LMLtmxRc006Oh8RcfUo/gZ2AxOB6KJDgSLyJFmt+lRYskRJdqxZkyLVR0Q8w8trGW5ufxIScpe0aQtQuPBUcuf+FjOzuIdNTp5UKq7btoFFzocMGJCfwoXh+fnndCjRgeHVh1M0W1GePoUGDeDCBaX8+qELoW68tpG+u/ryJPgJyzJ345vw0pjlLgu5gTt3YmZU0tH5CHhjZLamadYiEqBpWpbYjqeUs/hoIrNDQ5V0Q8GCcPSoGrN5T4SEuOLuPgsPj4UYDP5YW1fFxmYI2bK1iHf+AZTp+fJBSObz5OswkevaBk5+e5LKNpURkaghKh8faNhQ9To2bFDqrx8q3s+96b+7P05XnaiZqTSbzhcl2wontdT10iVdn0kn1ZNckdl/o1Y8nUMtj43+JBPgw8oMk9pYsULJiS9Z8t6cREDAGdzcpvH48XpAyJ69NTY235MxY5U4zzMYVFqEdetUr+Dso3+wGzuBkz67cUtrzYiKI6ISBb1wEo8fq6Dj27dh61almv0hIiKsv7aefrv64R/izxrLTrSb4ozmsUEpGP7yi+4kdD564lr11Czyp572NKkJD1ehyZUqqXGZZETNP2zF1XUaAQH/YGpqjY3NYGxsBmBhEbc67YsYiEmT1KhK0aJw895zmm1qhpmJGePrjqdfpX5kssj0ynmenvDZZ0qaY8cO9flD5NGzR/Td1ZdN1zdRIU8FVlWYSNFKjdWN2LABKldOaRN1dN4LCdF6qg5cEJHnmqZ9A5QDZojIw2S37mNl9Wr1FJ01K9l6ExERgXh5LY2cf7iPhYUtdnYzyJWrG2Zm8ctj3LkDtWqBp5eRwk23UKPfBg71X4W5mRW7Ou6iVM5SWKWxinGem5uam/f0hD171DU+NESEtVfW0n93f56FBrIyV186fPsnZiZmSn+kenWVy1xH5xMhIctj5wGlNU0rjRIDXAz8BdROTsM+WgwGFWhQpgw0bZrklw8JeYCb2yw8PRdhMARgbV2dwoWnkC3bl2iaaZznenvDlStQty7Y5A/HrtUazPP9zt3g62imdng9dydfxnxUzRf7Ci0XF+UkfH3V8zQFF3K9NZ6BnvTZ2YetN7fS1Kosa05kIsOeuVCsrQovr1s3pU3U0XnvJMRRRIiIaJr2JTBbRJZomvYRhEqlEOvWqYH7DRuStDcREHAaV9dpeHtvBCBHjjaR8Q+V4j33wQOYOlXJEKVPD86XbtN0bQMeZHtAKetSTGqyltbFW2Nq8mZHc/euchIBAUo5u2LFJGvae0FEWHVpFYP2DCI4PIg9wa1pOH0/WmioWsKsi/jpfMIkxFEEapo2EugE1NQ0zQSIXRpUJ26MRpgwARwdVc6Jd75cBD4+m3Fzm05AwElMTTOSL98Q8ubtj4VF/njPv3NH5aX++28Q0xAad73E5MGVsMthS6W8lZjdZDZN7ZvGK/R386aahwgJUbEUZcu+c9PeK+4B7ny34zt23t5JtXzV2LPJigw7N6gexOLFanWajs4nTEIcRTugI9BdRLw0TcsPTE5esz5SNm+Ga9fUk/kdxOEiIgLw9FyCm9ufhIY+wMKiEHZ2M8mVqytmZhniPT88HMzN4dEj1bHp2z+CayXbcubJvxSyf4iZSRqc2jglyJZr11RPQgScnaHkB5ShVERYfmE53+/9nojwUKY3mMqAKoMwNf8bvmgFPXvqIn46OiRMZtxL07TVQEVN05oB/4rIyuQ37SNDROVDLlIE2rZ9q0sEB7vg7j4TT8/FGAyBZMxYEzu7GWTL9kW88w8v8kD8/jsUKwbz5qnRFDc3YdSJgRw4u51ZjWeRxjThk7SXLqklsGZmcPDgB5NGAwBXf1d67ejFnjt76GxSjgVbDVjkygDVTD8OfREdnSQkIaue2qJ6EIdRsRSzNE0bKiIbktm2j4sdO1R48vLlYBr3Q/11/P1P4uY2DW/vTWiaCdmzt42cf4g/duZFDMTvv8P585A3r8qN9IKFVycx7+w8hlUbRv9K/RNs03//qZW96dKp4SZ7+0Q1KcUQEZacX8KQvUMwCY/glFtjKq04gJYxI2R+cza+pCA8PBw3NzdCQkKStR6dTxsLCwtsbGwwN0/CGQIRiXMDLgI5on3PDlyM77zk2sqXLy8fHEajSMWKIra2ImFhCTrFYAiXR4/WytmzlcXZGTl2LJPcuTNcgoNdE1X1iBEiIFKkiMiSJSIhIS+P7bm9RxiHtN/QXgxGQ4Kvefq0SKZMIgUKiNy7lyhzUhSXpy7SYGUDYRzSa1x5CXUoom5Ox44ijx8ne/337t0Tb29vMRqNyV6XzqeJ0WgUb29vuRfLPyZwVt7yuZuQOQoTEXkc7bsvoA/cJoZ9++DMGVi4UE0OxEF4uB+enotxd59FaOhDLC3tsLefTc6cXTAzSx9vVS/yQNSpAxUqQI8eSr38q69idmTqFKzD+Lrj+bHaj5hoCfuV/vOPUkTPnl31JArEHbOXKhARFp5byI/7f0REmNtkLt89scVkcU/Yvh2avZ+UKyEhIRQsWPCDzwOuk3rRNI2sWbPi7e2dpNdNiKPYo2naXuCFcl07YFeSWvExI6KWFtnYQJcubywWHHwPN7eZeHktwWB4RqZMdbC3n0XWrM3QEvAQ9/ZWytazZ6scSGPGKEdRuLDaonPL9xbZ0mUji2UWRtUaleCmHD6snql58yonkTdvgk9NMe4/vU+P7T04dP8QQ0LK8r9MX5C1Yh918PZtsLB4r/boTkInuUmOv7GETGYP1TStJVAjctdCEdmc5JZ8rBw+rF7DZ82KEc0rIvj7/4Ob23R8fLagaSbkyNEeG5vvyZChXIKrGDNGLfUPDlY9hxEjlDpIbHgEetDgrwYUzlyYQ10OJbiOAwegeXOwtVUT17lyJfjUFMEoRuadmcfwA8PJFKpx7XJNHDYfg2LBMPh/Sp/pPTsJHZ0PlTe+qmqaZq9p2lZN064AbYCpIjJEdxKJZPx49VSNls7NaAzn0aM1/PdfZS5cqImfnzP58w+nShUXHBz+SpCTuHlThWWAGs1q104tVd206c1OIjA0kKZ/N8U3yJepDacmuAm7d6uehL298nup3UncfXKXeivq0X93f4b42OOyyAqHrf/Ajz/CuXOfrIifi4sLJUqUSGkziIiIIHv27IwYMeKV/QULFsTHxyfq++HDh2kWbVhw9+7dVKhQgeLFi1O2bFl++OGHd7bl3LlzlCxZEjs7OwYOHPhiHjYGhw8fpkyZMjg6OlK7thKlcHV1pW7duhQvXhxHR0f+/PPPd7Yn1fKmyQvgGNATKAr8CGx624mQpNw+qMnsf/5Rk6VTp4qISFjYE3nw4A85ccJGnJ2RU6eKiJvbXImIeJbgS546JfLll+qymzcn3JSwiDD5/K/PxfRnU9l9e3eCz9u6VSRNGpFy5UR8fBJeX0pgMBpkxskZkm5COrGeaC1rd00Wo7m5SMmSIv/+m9LmybVr11K0/vv374ujo2OK2iAismvXLqlWrZoUKlTolYn9AgUKiLe3d9R3Z2dnadq0qYiIXL58WQoVKiTXr18XEZGIiAiZO3fuO9tSsWJFOXnypBiNRmnUqJHs2rUrRpmnT5+Kg4ODPHjwQEREHj16JCIiHh4ecu7cORERCQgIEHt7e7l69eo725QUxPa3RjJNZmcQkUWRn29qmvZfsnqsj5Fff4Vs2QjqUh/32wPw9FyG0ficTJnqYW8/j6xZmyRo/kEE9u9XgrOHD6tVnGPHQo0a8Z4axRjnMey9u5dFXyyikV3CNL83bIAOHaBcOdi7FzJlSnh975vbvrfpvq07xx8c5weqMniIEzbWNrCvAlSrlupE/AYPVqulk5IyZWDGjLjLGAwGevbsyYkTJ8ibNy9bt27F0tKSRYsWsXDhQsLCwrCzs+Ovv/4iXbp0rF+/np9//hlTU1MyZszI0aNHCQkJoU+fPpw9exYzMzOmTZtG3bp1MRgMDB8+nD179mBiYkLPnj0ZMGBADBvWrFnDoEGDmDdvHidPnqRatWrxtm3SpEmMGjWKYsWKAWBqakqfPn3e4i69xNPTk4CAAKpUUTL7nTt3ZsuWLTRu3PiVcn///TctW7Ykf36ldpAjMsVt7ty5yZ07NwAZMmTAwcEBd3d3ihcv/k52pUbichQWmqaV5WUeCsvo30UkXsehaVoj4E/AFFgsIr+/oVwrYANQUUQ+gqxEwJkzGPfv4cba0jy+XAZNMyNHjo7Y2AwmQ4YyibqUwQC9e0NYGEybpgKG08e/AOoVBlUZRMFMBelRrkeCyv/9N3TuDFWqwK5dYB2/4GyKYDAa+PP0n4w6NIpCz9Pw8J8y5Dt6Eurehdo2avmXThS3b99mzZo1LFq0iLZt27Jx40a++eYbWrZsSc+ePQH46aefWLJkCQMGDOCXX35h79695M2bFz8/PwDmzJmDpmlcvnyZGzdu0LBhQ27dusWyZctwcXHhwoULmJmZ8eRJzNxmISEhHDhwgAULFuDn58eaNWsS5CiuXLmSoKEmZ2dnvv/++xj706VLx4kTJ17Z5+7ujo2NTdR3Gxsb3N3dY5x769YtwsPDqVOnDoGBgQwaNIjOnTu/UsbFxYXz589T+SOVno/LUXgC06J994r2XYB6cV1YU6HCc4AGgBtwRtO0bSJy7bVyGYBBwOnEmZ7KGT+eR1+l43G2i9jYfE++fENJmzZ3gk59kQdi5Uq1stbSUj2sbW0TP7x+wvUElfJWIlf6XHxX4bsEnbNiBXTrpqSOtm9PvFN6X9zwuUH3rd059fAks9xL0sfpPiYRN5U3TUx3KwWI780/ubC1taVMmTIAlC9fHhcXF0A9iH/66Sf8/Px49uwZn3/+OQDVq1ena9eutG3blpaReWyPHz8e1VMoVqwYBQoU4NatWxw4cIDevXtjZqYeK1myxEyOuWPHDurWrYulpSWtWrXi119/ZcaMGZiamsa6WiexK3jq1q3LhSTuqkVERHDu3DkOHjxIcHAwVatWpUqVKhQpUgSAZ8+e0apVK2bMmIF1an2jekfiSlz0rnrKlYA7InIPQNO0tcCXwLXXyv0K/AEMfcf6Ug8XL2LctY0H2zORIYMjhQtPTdAffGCgCrWYNg08PFT8g4eHWt4a2eNOFPvv7qfJ300YVXMU4+qMS9A5ixbBd98paY4tW1TkdWojwhjBtJPTGOM8Bqs0Vjw4Vp58zueU6NSiRVBIT774JtJGe9MwNTUlODgYgK5du7JlyxZKly7N8uXLOXz4MADz58/n9OnT7Ny5k/Lly3Pu3Ll3qn/NmjUcP36cggULAuDr68uhQ4do0KABWbNm5enTp2TLlg2AJ0+eRH12dHTk3LlzlC5dOs7rJ6ZHkTdvXtzc3KK+u7m5kTeWNd82NjZkzZoVKysrrKysqFWrFhcvXqRIkSKEh4fTqlUrvv766yhH+lHytpMb8W1Aa9Rw04vvnVAy5dHLlAM2Rn4+DFR4w7V6AWeBs/nz53+nSZ73QuvW4t7KQpydER+fnQk6xcVFJHNmNUldr57I/v0qoPttueB5QTL8lkFKzi0pfsF+CTpn9mxVf5MmIsHBb193cnL18VWptKiSmI5Gvvr7S/EM9BT56y+RRYve7Ya9B1LbZPbkyZNl7NixIiKSNWtWefTokYSFhUn9+vWlS5cuIiJy586dqPIVKlSQ8+fPy9SpU6V79+4iInLz5k3Jnz+/hISEyLx586RVq1YSHh4uIiK+vr6v1O/v7y/Zs2eXkGjyAEuXLpVu3bqJiMgPP/wgo0ePFhE1Wf3VV1/JihUrRETk4sWLUrhwYbl586aIiBgMBpk3b94735PXJ7N37oz5/3rt2jWpV6+ehIeHy/Pnz8XR0VEuX74sRqNROnXqJIMGDXpnO5KapJ7MTjFHgVqaexgoKPE4iuhbql/1dPWqGMyRE7syytmzleKUa3jwQGTjRvXZaBQZNkzJY7wrD/0eSp6peSTv1Lzi6p8wyY9p09Rfw5dfvirzkVoIN4TLb0d/kzS/ppGagzOKj2MhMSbBg+J9kpodxdy5c6VgwYJSsWJF6d+/f5Sj+Oqrr6REiRLi6OgoAwcOFKPRKMHBwdK1a1cpUaKElClTRg4dOiQiIuHh4fL999+Lg4ODlCpVSmbNmvVK/cuXL5d27dq9ss/X11eyZcsmISEh4ufnJx06dJBSpUpJyZIlZejQoWIwvJSW2b59u5QrV06KFSsmDg4OMnTo0He+J2fOnBFHR0cpVKiQ9OvXL+r/dd68ea84okmTJomDg4M4OjrK9OnTRUTk2LFjAkjJkiWldOnSUrp06VgdTUrwITmKqsDeaN9HAiOjfc8I+AAukVsI4BGfs0j1juLrr8W9ZZrI3kTMpXYiIlevinTuLGJmJpIxo0hQUNJVbzQapcriKmI90VoueV1K0DkTJ6q/hNatEyxF9V655HVJyi8oL2l+QpxaO4jRzEwke/aXXvYDIaUdhc6nw3t3FKhVTt8AYyK/5wcqJeA8M+AeYAukQYkLOsZR/sPvUdy6JYY0mpzYmUHOnq0cozdx/frLGIh06UQGDVK9iqTmjPsZOXjvYLzljEaRn3+WKF28yBGDVENYRJj8cvgXMf/FXBoMyCR+hW2UsZ06pf6gjljQHYXO++J9xlG8YC5gRK1y+gUIBDYCcSa7FJEITdP6A3tRy2OXishVTdN+iTR4WwLq/rCYOBGvpqaEpgukqO3PMSawDQY4dkxJbgwYAJHzdEmCiHDw/kHqF6pPhTzxy4+LwOjRKuFely6wZEmi1c+TlYteF+m2tRvnvc7TvkR75pl/RcatQ9Xyr9fWuevo6CQz8XkS4L/In+ej7dNlxl/n/n0xWJrKiZ3p5dy5Kq/0Jq5fF3kx1Jpc4/+jDo4SxpHgnsSPP6qX8549X9qWGgiNCJWxzmPF7BczafldZrk4svvLg6lx8iQR6D0KnfdFUvcoEqItHR4ZE6HGoTQtO6qHoROd33/HsxGEpntGwYIvexPe3ipobWjk4t/kkBlaeG4hE45NoEfZHtQtGPeqZhEVFTxlCvTrB/Pnp55sn/95/kfFRRWZsedn9h0twMYFTym15aQKLIFPVqNJRyelScgjYiawGcihadoE4DjwW7Ja9aHh5oZx1VIe9rDA2roamTM3iDo0diw8e/aKJmCSsuv2Lvru7Etju8bMazYvzngNoxH69FFy5EOGKEHb1OAkQiNC+enQT1RaVIlyp13xWpqZuofuw7Bhn7SIn45OaiEhMuOrNU07B3yGmthuISLXk92yD4lJk/BsaCA03XOKFhwX9bC+ckUlEerbF5JD/uXx88e029CO0rlK49TGCTOTN/86DQYl/bFsGYwcqeYmUkNqhLMeZ+m6pStXva/yfZ5WTB2/Dc3BAXbtUwk1dHR0Up74xqZQq5xibG871vWuW6qbo/D0FEOGtPLPjnRy7lz1qLkJo1GkQQOVMjQ5F+isv7pePAI84iwTHi7y9ddqTmLs2NQRlxYcHiwj9o8Q03Em8lW/bLLj5g514MgRkdDQlDUumUgNcxQmJiZSunRpKV68uJQqVUqmTJkSFasQXa31BV26dJH169eLiEhoaKgMGjRIChcuLHZ2dtK8eXNxdXUVHx+fqDiCnDlzSp48eaK+h8byuzx//rwAsnv3SxXj2JRtx44dK5MnT476PnnyZClatKiULl1aKlSoEBWM9y4sX75c7OzsxM7OTpYvXx5rmbFjx77SphexEj4+PlKnTh2xsrKSfv36vbMtSUlKrHraiZqf0AAL1HLXm4Bj0rutD5CpU/GsH0aYleBg++rcxJ07MG4cZM2atFU+CX7Cde/rVM9fndbFW8dZNjwcvvkGnJxUL+J//0taW96GU26n6L61O8/uXOe/ozaU+s8N2qSHIkCtWilt3keNpaVllBbS48eP6dixIwEBAfz888/xnvu///2PwMBAbt68iampKcuWLaNly5acPn066prjxo0jffr0/Pjjj2+8zpo1a6hRowZr1qyhUaOEKRnPnz+f/fv38++//2JtbU1AQACbN79bapwnT57w888/c/bsWTRNo3z58jRv3pzMmTPHKPv999/HaJOFhQW//vorV65c4cqVK+9kS2onIUNPJaN/1zStHNA32Sz6kPDxwbB4Lg9WWZAxY3kyZXqpk5gjh0oklNRLTkMiQvhy7Zdc9LqIy2AXsljGFF57QVgYtG8PmzeryeskyPPyTgSHBzPGeQzTT0xl+JWM/LzHEjOeqkmTVC7il9QM3jOYC14XkvSaZXKVYUajGQkunyNHDhYuXEjFihUZN25cnGWDgoJYtmwZ9+/fxzTyj7pbt24sXbqUQ4cO8dlnnyWoThFh/fr17N+/n5o1axISEoJFAjIN/vbbbxw+fDhKdM/a2poucaQWTgh79+6lQYMGUeKFDRo0YM+ePXTo0CFB51tZWVGjRg3u3LnzTnZ8CCR6KlOUvPjHqaWbWKZPx7NuEGFWwa+sdPr3X5WW1MJCZZ9LKoxipMuWLhx/eJxFXyyK00mEhEDLlspJzJyZ8k7ihOsJyiwow5STUzi7Jz8TNvphVq2GmsgZMCB1BXF8QhQqVAiDwcDjx48BOHbsGGXKlInatm1T4U537twhf/78MdRRK1SowNWrVxNc34kTJ7C1taVw4cLUqVOHnTt3xntOQEAAgYGBFEqA2OPkyZNfsf/FNnDgwBhl3d3dyZcvX9T3N8mMA8yePZtSpUrRvXt3nj59Gq8dHxvx9ig0TRsS7asJSsjPI9ks+lB4+hTD/Jk8XGFBxoyVyJRJLUv19YXPP4cvvlAy4UnJ8P3DcbrqxOQGk2lXot0bywUFqdzZ+/apyfRevZLWjsQQFB7ET4d+YtY/07HJlJ/9nfZTpoi38mRdu6aOGfUUIDFv/u+TmjVrsmPHjqjvXbt2TdLrr1mzhvbt2wPQvn17Vq5cSatWrd64Wi+xMuNDhw5l6NCkFaLu06cPo0ePRtM0Ro8ezQ8//MDSpUuTtI7UTkLmKDJE+xyBmrPYmDzmfEDMnIlnnWeEpQeHaCudxo2DgAAYPjxpq9tzZw9TTk6hf8X+/FD1zd2D58+Vkzp8GJYuVXklUopjD47RfVt30l27w9392ckx+HssCtUHXQU81XDv3j1MTU3JkSMH16+/eTFj4cKFefjwIYGBgWTI8PKRcO7cuVfyWseFwWBg48aNbN26lQkTJiAi+Pr6EhgYGCUxHp0nT55ga2uLtbU16dOn5969e/H2KiZPnszq1atj7K9VqxYzZ858ZV/evHmj5NRByYzXiSXRVc6cOaM+9+zZM8Ht/ZiIc+gpMtAug4j8HLlNEJHVIhLynuxLnQQEYJg7nYfd0pIxY20yZ1a9iWvXYN48lY3OMYmn+hsWbsiKFiuY0WjGG9+yAgOVusWRI6o3k1JO4nnYcwbuHkjDRbUYtN2b84tNyR+oYWFTMGUM0okVb29vevfuTf/+/eN9c7eysqJLly4MGTIEg8EAwMqVKwkKCqJevThzmEVx8OBBSpUqhaurKy4uLjx48IBWrVqxefNm0qdPT+7cuTl06BCgnMSePXuoETl3NXLkSPr160dAQACgkgWtjKXLPnToUC5cuBBje91JAHz++efs27ePp0+f8vTpU/bt2xeVsCk6np6eUZ83b95MiRIlEtTej4k39ig0TTMTpddU/X0a9EEwZw6eNf2jehMv+OEHlQ0uAQtIEsxZj7NktcyKbWZbOpfu/MZyfn7KSZw5A2vWQNu2SWdDYjjscphvt31L1sv3uL83M7lcnyoxqWnTIJaMZzrvl+DgYMqUKUN4eDhmZmZ06tSJIUOGxH8iMHHiRH788UeKFCmCiYkJxYoVY/PmzQkeHlqzZg1fffXVK/tatWrFvHnz6Ny5MytXrqRfv35R9owdO5bChQsDavjn2bNnVKxYEXNzc8zNzROUGjUusmTJwujRo6lYUcnWjRkzJmpiu0ePHvTu3ZsKFSowbNgwLly4gKZpFCxYkAULFkRdo2DBggQEBBAWFsaWLVvYt2/fR5kzOyEaT/OAbah8Ei1fbG+7HvddtxSPo3j2TCLyZJV/tqeV8+frRO0OCBCpXl3ldUgqbvvelmyTskn1JdXjzGvh6ytSvryIubnI5s1JV39iCAwNlL47+grjkMJ/FpaLq6aKFCggsmdPyhiUCkkNcRQ6nwYpEUdhAfii1GNfxFMIsCnJvdaHwIIFeFT1JSw9FC/4suuQIYNShjUmkQqW93NvGq9WKqnLvlz2xrc2b29o0ABu3FArnJo2TZr6E8PBewf5dtu3FD33gM3patHwf7tJZ54OWvfT5Td0dD4C4nIUOSJXPF3hpYN4gSSrVamV4GAMf07i4Zw0ZMpUg0yZVHDYwYNqTiJXrqRZ5RkUHkTztc1xC3DDuYsz9lntYy3n5aVyW9+9C9u2QcOG7153YggIDWDovqE4/bOQxYcz0Oo04OgLxsiboDsJHZ2PgrgchSmQnlcdxAs+TUexZAkelR8Rnh4KRs5NPHkCbdpAzZqwdWvSVPPLkV847XaajW03UsWmSqxlPDygXj1wdYWdO9Xn98m+u/vosa0HlU678WC/FRn8g5SI1JgxuoPQ0fnIiMtReIrIL+/NktROaCiG6RN5+Kc5mTLVIlOmmoCauPb3h/Hjk66qn2r9RLV81WhetHmsx11dlWPw8oI9e5STel/4h/jzw74fWHJ+CXVNCuO0yRSTEkVU5qOyZd+fITo6Ou+NuJbHfpqRUG9ixQo8ynsQnj48qjdx4wbMmaMC2kqWjPv0hLD5+maehT0jfZr0b3QS9+8rOSRvb9i///06iV23d+E4pzh3tyxlePXh7Bp5BZNDznD6tO4kdHQ+YuJyFAkTb/kUCA/HMHUCDzubkTlTfTJlUmu7f/wRrKzglyTodzlddaKlU0t+P/77G8vcuQO1a6sezMGDKiHS++Bp8FO6bulKn7lNWbvEH+dlwu/mjbEws1AaTUmpU6Kjo5PqeKOjEJEn79OQVM3q1biXeUh4+ggK2qqVTmFhKixg7FjInv3dLn/swTE6be5Ejfw1+KnWT7GWuXFD9SSCg8HZGcqXf7c6E8r2m9spMbs41otWcmu+OdUfojIevc+ujE6S4OvrG6V9lCtXLvLmzRv1PSwsLKXNeyPr16/H0dERExMTzp49G7U/LCyMbt26UbJkSUqXLv1KlPXr+Pj4YG5uzvz581/Znz59+le+L1++nP79+0d9X7lyJSVKlKBkyZKULVuWKVOmvHN79uzZQ9GiRbGzs+P339/8Yujk5ETx4sVxdHSkY8eOAFy4cIGqVavi6OhIqVKlWLdu3TvbkyDedl1tSm3vPY4iIkIiHAvL8W1mcuFCwxiH3zW3w3Xv65L598xSdFZR8Q3yjbXM5csiOXKI5MypPr8PfIN85ZtN3wjjEOeSGVQyi88/F3FxeT8GfISkpjiK13M9iIiEh4enkDVxc+3aNblx44bUrl1bzpw5E7V/9uzZ0rVrVxERefTokZQrVy4qt8brzJ07V2rUqCG1atV6Zb+VldUr35ctWxaVW2LXrl1StmxZcXd3FxGRkJAQWbhw4Tu1JSIiQgoVKiR3796V0NBQKVWqlFy9ejVGuVu3bkmZMmXkyZMnUe0TEbl586bcunVLRETc3d0lV65c8vTp0xjnp0QcxaeNkxPuJe8SnuHlSqdt26BAAShd+t007USEblu7kcY0Dbu/3h2rGuzFi2oJrLk5HDoExYq9fX0JZcuNLfTf+h2PQ3wZU2sMNezswCDQqdMnK+KXHMQiK0TbtiojYlAQNGkS83jXrmrz8YHWr6UiieOF+o107doVCwsLzp8/T/Xq1Wnfvj2DBg0iJCQES0tLli1bRtGiRTEYDAwfPpw9e/ZgYmJCz549GTBgwCvXunv3Lv369cPb25t06dKxaNEiihUrRteuXbG2tubs2bN4eXkxadIkWrdujdFopH///hw6dIh8+fJhbm5O9+7daf1awxwcHGK1/dq1a1HyITly5CBTpkycPXuWSpUqxSi7Zs0apk6dSseOHXFzc8PGxibeezNx4kSmTJlCnjx5AEibNi09e/ZM0H19E//++y92dnZRmlXt27dn69atMaK5Fy1aRL9+/aJyY+TIkQOAIkWKRJXJkycPOXLkwNvbm0yZMr2TXfGhO4q4MBqJmPwLrj+bkjnzZ2TMWJWnT6F7dyhTBg4ceLfLa5rG3y3/xi/ED9vMtjGOnz2rYiPSp1dOws7u3eqLD58gHwbsHsDNfWs5sNsSq77DyFc3CfVIdFIlbm5unDhxAlNTUwICAjh27BhmZmYcOHCA//3vf2zcuJGFCxfi4uLChQsXMDMz48mTmCPTvXr1Yv78+djb23P69Gn69u0bpd3k6enJ8ePHuXHjBs2bN6d169Zs2rQJFxcXrl27xuPHj3FwcKB79+4Jtrt06dJs27aNDh064Orqyrlz53B1dY3hKFxdXfH09KRSpUq0bduWdevWJUj+48qVK5RPwBjv6tWrmTx5coz9dnZ2bNiw4ZV9sUmbnz59Osa5t27dAqB69eoYDAbGjRsXI8nTv//+S1hYWJTMSXKiO4q42LwZj6I3InsT6oH5668qdmLq1Le/bIQxglWXVtG5dOdYHQTAqVNKrjxLFjUnUbDg29eXEDZc28CQLX3ov+cJq/8xQcuREc2xavJW+okTVw8gXbq4j2fL9nY9iNho06ZNVDIif39/unTpwu3bt9E0jfDwcAAOHDhA7969MTNTj4wsr+l2PXv2jBMnTtCmTZuofaGhoVGfW7RogYmJCcWLF+fRo0cAHD9+nDZt2mBiYkKuXLmoW7duouzu3r07169fp0KFChQoUIBq1apFtSM669ato22k+Fn79u3p3r17nI4isdLmX3/9NV9//XWizomPiIgIbt++zeHDh3Fzc6NWrVpcvnw5qufg6elJp06dWLFiBSYmiU4rlGh0R/EmRIiY/DMPfzIhS+YGZMxYhVu31Dxujx5q2OntLiv029mPhf8txMbahvqF6scoc+yYGnbImVM5iWgvIEnO4+eP6b+rPw/3rufojrQUfGRUXaYpUyCWlJA6Hx9WVlZRn0ePHk3dunXZvHkzLi4uscpux4bRaCRTpkxRKVFfJ220IEw1XP7umJmZMX369Kjv1apVe2Vo5gVr1qzBy8srSn7cw8OD27dvY29vj6WlJWFhYaRJkwZQqrXZsmUDwNHRkXPnzsWrjpuYHkXevHlxdXWN+u7m5kbevHljnGtjY0PlypUxNzfH1taWIkWKcPv2bSpWrEhAQABNmzZlwoQJVHlPSx+T3xV9qOzYgXuhy0SkN1LQVq1//fFHsLRUvYq3ZeLxiSz8byEja4yM1Uk4O0OjRpA3Lxw9mnxOQkRYd2UdjnMd2XpzKwNLdKdAujwqOGPJEt1JfKL4+/tHPbiWL18etb9BgwYsWLCAiIgIgBhDT9bW1tja2rJ+/XpA/X1dvHgxzrqqV6/Oxo0bMRqNPHr0KM5VS7ERFBTE8+fPAdi/fz9mZmYxxvpv3brFs2fPcHd3x8XFBRcXF0aOHMmaNWsAqF27NqtWrQKUsq6Tk1NUz2bkyJEMHToULy8vQK2yWrx4cQw7vv7661ilzV93EgAVK1bk9u3b3L9/n7CwMNauXUvz5jFjplq0aBF1P3x8fLh16xaFChUiLCyMr776is6dO8eYy0lOdEcRGyJETBqHa0cTsmRujLV1JYxGFVP266/qTf9tWHVpFaMOjaJjyY5MqDchxvF9+1RPwtZW5ZSInENLcryeedHKqRXLfm/PqH8t+a/Xf3QcvATtxg01c67zyTJs2DBGjhxJ2bJlo5wCKNnt/PnzU6pUKUqXLs3ff/8d49zVq1ezZMkSSpcujaOjI1vj0bRp1aoVNjY2FC9enG+++YZy5cqRMWPGGOU2b96MjY0NJ0+epGnTplE5Ix4/fky5cuVwcHDgjz/+4K+//opx7pukzV84ij///JNNmzZRpkwZqlSpQps2bahVS2m4NWnShP79+1O/fn0cHR0pV65cVD6Mt8XMzIzZs2fz+eef4+DgQNu2bXGMTF4zZsyYqNSzn3/+OVmzZqV48eLUrVuXyZMnkzVrVpycnDh69CjLly+PWtr8pl5ckvK2y6VSansvy2P37BGXrxFnZ8Tf/3SSXNL7ubdYTbCSOsvrSEh4SIzjO3aIpEkjUrq0yOPHSVJlDIxGo6y6uEoKj8kkf5UxEQExliwhEhqaPBXqvEJqWh6bWggMDBQRER8fHylUqJB4enqmsEUfB/ry2ORGhIhJY3EdbEKWTJ9jbV0JJyewsFApRt92dWi2dNnY880eSuQoQVqzV0XzNm+Gdu2gVCnVq0iO/D6egZ703vEd5pu38+8eczIHm8BP/0P76SeIHJ/V0XnfNGvWDD8/P8LCwhg9ejS5cuVKaZN0YkF3FK9z5AjuuU8TkQEKFvoFf3/o10/JiH/xReIv5x7gzim3U7Qq3ooa+WvEOO7kBB07QsWKsHs3JPVyaBHhr0t/MWjPIHJ4B3NtsykmpUuhLVny9jPyOjpJRGLnJXRSBt1RvEbEpLG49tXImqkR1tYVGDoUfH1h+vTE9yYCQgNo8ncTXPxcqF2wNtnSZXvl+KpVKktotWqwa5dKfpSUuAe402t7T4L37caxdnWW9liKaUsfqFQJzPRfvY6OTsLQnxbROXECt6xHiUgPBQv/yp078OefarVoYsVRww3htHZqzTXva+zsuDOGk1i2DL79VkXnbt+uxAWTChFh2YVl/LlmMNM2Peezu2Do+gumWYtAtZjLB3V0dHTiQncU0Yj4YwxuPTWyZmpChgzl6dRJ5eBJbK4JEaHn9p7sv7efZV8uo2HhV1PPLVgAvXurqOvNm1VwVVLh6u/Kd1t7YL9mH6cOmZA2jSXMm4Jp7TpJV4mOjs4nhe4oXnDmDG6ZDkb1JkSgRQsV05DY+bUD9w6w4uIKxtUeR9cyXV85NmsWDByocltv2KAmyZMCEWHxf4v5Yd8PrFsRROObIE0+R5u/IHkj9nR0dD569DiKSMInj8WtjUbWjE3JkKEsmqbE13r3Tvy1GhRuwP5O+xlTe8wr+6dMUU6iRQvYtCnpnMQDvwc0Xl6f77b1okKeCpQbOg1WrULbsVN3EjpRJLfMeJ06dV6RAX/B2bNnGThwYKKuNWrUKPLlyxdDBvzBgwd89tlnlCpVijp16uDm5vbGa2zZsgVN07hx40bUvsOHD9OsWbNXynXt2jUqOC48PJwRI0Zgb29PuXLlqFq1Krt3706U7bExceJE7OzsKFq0KHv37o21jIgwatQoihQpgoODAzNnzozaP3DgQOzs7ChVqhT//fffO9uTaN52XW1CNqARcBO4A4yI5fgQ4BpwCTgIFIjvmskSR3HhgtzvrOImAgLOy19/icyYIRIRkbjL7L+7X866n4312IQJSqm7bVuRsLAksFlEDEaDzP13rtTsaymXcpnI0aHtxGCMXWZZJ+VJTXEUySEz/roM+Ltw8uRJ8fDwiCED3rp1a1m+fLmIiBw8eFC++eabN16jbdu2UqNGDRkzZkzUPmdnZ2natOkr5bp06SLr168XEZHhw4dL586dJSRExTp5eXnJunXr3qktV69elVKlSklISIjcu3dPChUqJBGxPFyWLl0qnTp1ipJKfyEtvnPnTmnUqJEYjUY5efKkVKpUKd46P5g4Ck3TTIE5QAPADTijado2EbkWrdh5oIKIBGma1geYBLRLLpveRPikMbh2gGwZm2E0lmHIECXnnZiXoPOe5/lq3VeUzlmaY92ORQmLiai82j//DN98oyaxk2LB0b2n9+i7oRt1VxzF+QRIzhyY1foGNL2T+KFQZ3mdGPvaOralb8W+BIUH0WR1TJ3xrmW60rVMV3yCfGjt9KqEw+GuhxNtQ1LKjINKMtS3b1/8/PxYsmQJNWvW5PDhw0yZMoUdO3Ywbtw4Hj58yL1793j48CGDBw+OtbfxJg2ja9euMW3aNADq1q1LixYtYi337Nkzjh8/jrOzM1988QU//xy/CnJQUBCLFi3i/v37UdpUOXPmjBIUfFu2bt1K+/btSZs2Lba2ttjZ2fHvv/9Steqropvz5s3j77//jhL5eyEtvnXrVjp37oymaVSpUgU/Pz88PT3JnTv3O9mVGJJzjqIScEdE7gFomrYW+BLVgwBARJyjlT8FfJOM9sTOtWu4pdmGIT0UtB/Pr78qrf/ELId96P+Qpn83JbNFZpzaOL3iJP73P/j9d+jWDRYtgljELROFUYzMPTOXjUt/ZNGGMOx8Qb79Fm3KlKQPwtD5JEgqmXFQqqf//vsvu3bt4ueff+ZALFr8N27cwNnZmcDAQIoWLUqfPn0wT2A63dKlS7Np0yYGDRrE5s2bCQwMxNfXl6xZs75SbuvWrTRq1IgiRYqQNWtWzp07F69k+J07d8ifPz/W1tbx2vH999/j7OwcY3/79u0ZMWLEK/vc3d1fcXw2Nja4u7vHOPfu3busW7eOzZs3kz17dmbOnIm9vX2s0uTu7u4fjaPIC7hG++4GVI6j/LdArIOBmqb1AnoB5M+fP6nsAyB8yljcWkM266Y8elSaGTNUbENCU40+DX5K49WNCQoP4p/u/5AngxJoEoEfflAO57vvYO5ceFc14DtP7vDttm85+uAowzJXoIC1N6xbgvaZnt78QySuHkA683RxHs+WLttb9SBiIylkxl/QsmVLAMqXL4+Li0usZZo2bUratGlJmzYtOXLk4NGjRwlKJAQwZcoU+vfvz/Lly6lVqxZ58+aNVVp8zZo1DBo0CFAP7zVr1lC+fPk3SognVlo8umptUhEaGoqFhQVnz55l06ZNdO/enWPHjiV5PW9Dqlj1pGnaN0AFoHZsx0VkIbAQoEKFCkmjUQxw+zZu2gYMVlCwyG906aIyyU2Iqdf3RqadnMZt39vs/WYvjjmUuJfRqIat5syBAQNULMa7JIYzGA3M+ncWR+YOp5aPCV0nLKVrma5ov0Qog3V03oGkkBl/wYshG1NT01dEBWMrE1+52MiTJw+bNm0C1PDSxo0bY2R3e/LkCYcOHeLy5ctomobBYEDTtChhvadPn8Yony1bNuzs7Hj48CEBAQHx9ioS06NIjLT4C0f71Vdf0a1bt0Sdn5wk54C2OxB9yY1N5L5X0DStPjAKaC4ioa8fT07Cp47DrRVky9CU9OlLMWAAzJ6dONXWsXXGcqTrEeraKmlio1GtlJozR8mSv6uTuOV7i+azqpGt1/ds/iuM0W6F6Ob4tXoD0p2EThLztjLj7wsfHx+MRiOgVhLFlhFvw4YNdOrUiQcPHuDi4oKrqyu2trYcO3YMe3t7PDw8uH79OqBWUV28eJEyZcqQLl06vv32WwYNGhS1Cszb2ztKOj0606dPj1Va/HUnAdC8eXPWrl1LaGgo9+/f5/bt27Gma23RokWU8zly5EhUbo3mzZuzcuVKRIRTp06RMWPG9zrsBMnrKM4A9pqm2WqalgZoD2yLXkDTtLLAApSTeJyMtsTExQW3iDUY0kHBohMBFSXdtWvCTp93Zh5ez7wwMzGjaj41KWUwqCjuRYtg1CiYNOntnYTBaGDqP1MY37cEK0adocN1U2TMGNKcPa+L+OkkG+8iM57UdtjY2BAUFISNjQ3jxo0D1PLWokWLUqRIER49esSoUaNinBuXtHjatGlZtWoV3bp1o0yZMrRu3ZrFixdHyZuPHz+e7NmzU7x4cUqUKEGzZs0SNGcRF46OjrRt25bixYvTqFEj5syZEzVc1qRJEzw8PAAYMWIEGzdupGTJkowcOTIq90WTJk0oVKgQdnZ29OzZk7lz576TPW/F2y6XSsgGNAFuAXeBUZH7fkE5BoADwCPgQuS2Lb5rJtXy2LABXeToDuTKmWayfLnIoEEiITHVv2Nl7r9zhXHITwd/itoXHi7SsaNaAvvLL+9m27XH16TK4iqSfzASZmYioeXKiFy69G4X1UlxUtPyWJ2Pmw9meWykE9oF7Hpt35hon1MmS46bG66hf2FIB9nyTWR4MyhcOGEv6ttubqP/7v40K9KMsXXGAhAerhRgN2yAiRMhlt5ngogwRjD1nykcWTqGWw4ZmNV9NWZtbdEqVXr35VI6Ojo6b0mqmMx+34TP+Bn3L41kt2rKjBklePRICfPFN0z0r/u/tN/QnnK5y7G21VrMTMwIDYW2bWHbNpg2Db7//u1suvr4Kj8t6sCAxZcZ7gK+uxeRtWTLt7uYjo6OThLy6TkKLy9cny3DYAlmGf9g2jTo3Fnlg4gLEeHHfT+SK30udnTYgVUaK0JCoGVLlUdi9myVtyKxhBvCmXz0d/wm/czqgwbM0loi86eTtWGLt2qejo6OTlLzyTmKsJm/4t7cQPZ0TRk71hEzM/jtt/jP0zSNze028zTkKTnT5yQoCL78Eg4ehIULoWfPxNty6dElum3txq+T/6PJHQht3IA0C5dCAteU6+jo6LwPPi29Bx8f3PwXYbCEgiUm8euvsHw5xLUkOTg8mPFHxxMaEUrWdFmxy2LHs2dK/fXQISXJkVgnEW4IZ/yBMVScXx5Xf1ey9Pke/v6btDv36k5CR0cn1fFJ9SjCZo/HrVk4OSyaYmVVHHt7sLd/c3mD0UCnzZ3YdH0TVW2q8lmhzwgIgCZN4NQp+OsvNYmdGC54XWDKjLYMX3Ybmy/K02zmnhhJjXR0dHRSE59Oj+LpU1x952G0gPP3JvPllxAQEPcpQ/cPZeP1jUxpOIXPCn3G06fQoAGcPg1r1ybOSYQZwhi/+384tyrHikm3sdOy0rXVr7qT0HlvJLfMeHIxdOhQihUrRqlSpfjqq6/w8/OLOpYQ+W6ACxcuoGkae/bsidrn4uJCiRIlXik3btw4pkyZEvV9ypQpFCtWjDJlylCxYkVWrlz5zu1ZsWIF9vb22Nvbs2LFiljLtGvXLup3U7BgQcqUKRN1LKFtTlLedl1tSm1vG0cROmGoHNmFXDrSRHLlEqlSRcRofHP5GSdnCOOQgbsGitFoFB8fkbJlRczNRbZsSVzdZ93PSuchheROZkRAgr/tKuLn91bt0PlwSU1xFMkhM55c7N27N8q2YcOGybBhw0Qk4fLdL86rUaOGdO7cOWrf/fv3xdHR8ZVy0e/LvHnzpGHDhuLv7y8iIv7+/lES52+Lr6+v2Nraiq+vrzx58kRsbW3lyZMncZ4zZMgQ+fnnn0Uk4W3+oOIoUg0BAbg+mokxLWw/PhUvL9iy5c3LYZ8EP+HnIz/zVbGvmPb5NLy9NerXh1u3YOtWaNw4YdWGRoTyy5Ff+OOfP2gRlJGc1nlg02osEqmfo/MRMngwXLiQtNcsUwZmzEjUKUkpM3737l369euHt7c36dKlY9GiRRQrVoyuXbtibW3N2bNn8fLyYtKkSbRu3Rqj0Uj//v05dOgQ+fLlw9zcnO7du9O69avy6Q0bvkwlXKVKlagkQwmV7xYR1q9fz/79+6lZsyYhISFYJCBr2G+//cbhw4ejIrOtra3p0qVLou7v6+zdu5cGDRpEiSo2aNCAPXv20KFDh1jLiwhOTk4cOnQISHibk5pPwlGELfgD90ahZIhoxC+/FOPrr6FyHDq2WSyzcPLbk+TPmJ/Hj0ypXx/u34cdO6B+AkMEz7ifYfn41ljdeUin/l2ZNnQa6WdlSJpkFDo6SUhSyYz36tWL+fPnY29vz+nTp+nbt2/UA87T05Pjx49z48YNmjdvTuvWrdm0aRMuLi5cu3aNx48f4+DgEKt2U3SWLl1Ku3YqZU1C5btPnDiBra0thQsXpk6dOuzcuZNWrVrFWU9AQACBgYEUKlQo3vs3efJkVq9eHWN/rVq1orLUveBNkuFv4tixY+TMmRP7yMnUhLY5qfn4n1rPn+PqOR1jefhr25+YmKjo6di45XuL3bd3M7DyQIpmK4q7O9SrB+7uKlaidqzatq8SEhHC5C3DsPtlFnMuQ4BDIawbL9D1mXReJZFv/slJUsiMP3v2jBMnTtCmTZuofaGhLzU+W7RogYmJCcWLF+fRo0cAHD9+nDZt2mBiYkKuXLmoW7dunHZOmDABMzMzvv7660S1b82aNbRv3x5Q6q4rV66kVatWSSY5PnToUIYOHZqocxLKmjVr3tjbeJ989I4ibMkU3BsGk9OsEWPHFqFRo9jTSD9+/pjGqxsTGBpIx5IdCfLJTr164O0Ne/dC9erx13XK9SQbRrdihJMnmcJNCBkzAutRY3UnoZOqSQqZcaPRSKZMmbjwhuG06NLiarg8cSxfvpwdO3Zw8ODBqAd5QuS3DQYDGzduZOvWrUyYMAERwdfXl8DAwDdKjtva2mJtbU369Om5d+9evL2KxPQo8ubNy+HDh1+x+U33OCIigk2bNnHu3LlXzk8RyfG3ndxIqS1Rk9lBQXL7x3TifBAJCLj5xmLPQp9JxYUVxXK8pZxyPSV374rkzy+SMaPIqVMJqCYsSH7Y+4MU/F6TEFPkaeliIleuJNxOnU+C1DiZHT1ftIhIixYtZMOGDVFlChQoICJqYrdVq1ZRk8q+vr4xrlm1alVxcnISERGj0SgXLlwQEYlRx4s82E5OTtK0aVMxGAzi5eUlmTNnfqXcC3bv3i0ODg7y+PHjV/ZfuXLllYldW1vbGBO7e/fulYYNG76yr3PnzrJixQoRESlfvrwcPHgwqk329vZy584dERGZM2eONGrUKGoyOzAwMOq8t8XX11cKFiwoT548kSdPnkjBggVjvZcv2l2rVq1Et1kk6SezP+rlsaErpuPxWRAGz4bUrFmEyB7vKxiMBjps7MA5z3OsabWGLMGVqVULnj1TAXVxzWUA/ONyjP6D7Zl6ciqf1+tF+OFDZDp3BRwdk6dROjrJyLvIjK9evZolS5ZQunRpHB0d2bp1a5x1tWrVChsbG4oXL84333xDuXLlouS+o9O/f38CAwNp0KABZcqUoXfv3kDc8t0viEtyHGDlypX8+uuvlClThnr16jF27FgKFy4MQJ8+fahbty4VK1akRIkS1KxZMyqf9duSJUsWRo8eTcWKFalYsSJjxoyJGsbr0aMHZ8+ejSq7du3aGMNOCWlzsvC2HialtgT3KEJC5Paw9OJ8AClb5pZUqiRiMMQsdvj+YTH52URmn54t166J5Molki2byMWLcV/+edhzGb+4izgXUEtez66ZljC7dD5ZUlOPIrUQGBgoIiI+Pj5SqFAh8fT0TGGLPg705bEJJHTVTDzqPePZ3Qacv2DPiROx56yuXbA2l/tcxuBVnNq1VZnDh+PuEBy9e4gTQ9owZNcTJG0agudPp3y7PsnWFh2dj5VmzZrh5+dHWFgYo0ePJleuXCltkk4sfJyOIjwc1zvjMRaEgRPn0qEDvL7M2OmqExnSZKCxfWNC3YrToAGkTauGm4oWjf2yz8KeMfLASJoOnM2Iu+DToDrZljslLneqjo5OFNEndnVSLx+lowhdOwePOgEE3PgMTy87fv/91eNHXI7QaXMnauSvQZYnjWjUSMPaWjmJyOHJGBy+sZdvd/fmXoALZds1JsSxPdk6dHq3hNg6Ojo6HwAfn6MwGHh462eMeaBOp/kcKgf58788fM37Gi3WtaBw5sIMLbCBBg00smUDZ2coUCDm5QJDA5k3uwtN/9hMl1rZqDv1KDUL1Hx/7dHR0dFJYT46RxG6YQEeNf3IHlIPa2u7V4acPAI9aLy6MRZmFoyz303rppnJk0f1JGJT9z50ZTv3+33Nj0cD8c+egWFdF2GhOwkdHZ1PjI9reazRyMProxET+HrkAu7cefXwigsr8A3yZazdTrq2KEC+fHDkSEwnERAawKTfv6BgzeZ8ezSQR51bkvmOGxbNWry3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Operating Characteristic')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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