pytorch_twitter_sentiment_analysis.ipynb

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   "source": [
    "# Twitter Sentiment Analysis Pytorch"
   ]
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     "end_time": "2021-04-07T08:57:44.243767Z",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import re\n",
    "import imblearn\n",
    "import torch\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from gensim.parsing.porter import PorterStemmer\n",
    "from gensim.utils import tokenize\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import remove_stopwords\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "import torch\n",
    "from torchtext.legacy import data\n",
    "# from torchtext import data\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "from scipy import interp\n",
    "from itertools import cycle\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from bokeh.models import ColumnDataSource, LabelSet\n",
    "from bokeh.plotting import figure, show, output_file\n",
    "from collections import Counter\n",
    "from functools import reduce\n",
    "# ! pip install captum bokeh spacy emot parameter-sherpa\n",
    "import sherpa\n",
    "import captum\n",
    "import spacy\n",
    "from captum.attr import LayerIntegratedGradients, TokenReferenceBase, visualization, IntegratedGradients, LayerConductance\n",
    "from captum.attr import visualization as viz\n",
    "from captum.attr import configure_interpretable_embedding_layer, remove_interpretable_embedding_layer\n",
    "import emot\n",
    "from bokeh.io import output_notebook\n",
    "output_notebook()\n",
    "# ! python -m spacy download en_core_web_sm\n",
    "nlp = spacy.load('en')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6DETgMPgJrUS",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:07.567355Z",
     "start_time": "2021-04-07T08:57:44.247754Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "id": "QwFJPG55JrUT",
    "outputId": "2ea2af17-7ab6-4f49-b0c6-f5b328fdcba3",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  0     0    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0\n",
      "  0 19.9M    0 61464    0     0  27686      0  0:12:34  0:00:02  0:12:32 27698\n",
      "  4 19.9M    4  823k    0     0   257k      0  0:01:19  0:00:03  0:01:16  257k\n",
      "  9 19.9M    9 1895k    0     0   450k      0  0:00:45  0:00:04  0:00:41  450k\n",
      " 14 19.9M   14 2935k    0     0   560k      0  0:00:36  0:00:05  0:00:31  589k\n",
      " 19 19.9M   19 3991k    0     0   642k      0  0:00:31  0:00:06  0:00:25  851k\n",
      " 24 19.9M   24 5063k    0     0   703k      0  0:00:28  0:00:07  0:00:21 1006k\n",
      " 30 19.9M   30 6135k    0     0   747k      0  0:00:27  0:00:08  0:00:19 1060k\n",
      " 35 19.9M   35 7175k    0     0   779k      0  0:00:26  0:00:09  0:00:17 1056k\n",
      " 40 19.9M   40 8231k    0     0   807k      0  0:00:25  0:00:10  0:00:15 1067k\n",
      " 43 19.9M   43 8871k    0     0   790k      0  0:00:25  0:00:11  0:00:14  973k\n",
      " 48 19.9M   48 9911k    0     0   812k      0  0:00:25  0:00:12  0:00:13  967k\n",
      " 53 19.9M   53 10.7M    0     0   830k      0  0:00:24  0:00:13  0:00:11  967k\n",
      " 58 19.9M   58 11.7M    0     0   847k      0  0:00:24  0:00:14  0:00:10  970k\n",
      " 64 19.9M   64 12.7M    0     0   861k      0  0:00:23  0:00:15  0:00:08  971k\n",
      " 69 19.9M   69 13.8M    0     0   872k      0  0:00:23  0:00:16  0:00:07 1056k\n",
      " 74 19.9M   74 14.8M    0     0   883k      0  0:00:23  0:00:17  0:00:06 1056k\n",
      " 79 19.9M   79 15.8M    0     0   893k      0  0:00:22  0:00:18  0:00:04 1058k\n",
      " 84 19.9M   84 16.8M    0     0   899k      0  0:00:22  0:00:19  0:00:03 1045k\n",
      " 90 19.9M   90 17.9M    0     0   910k      0  0:00:22  0:00:20  0:00:02 1058k\n",
      " 94 19.9M   94 18.9M    0     0   913k      0  0:00:22  0:00:21  0:00:01 1045k\n",
      " 98 19.9M   98 19.5M    0     0   902k      0  0:00:22  0:00:22 --:--:--  967k\n",
      "100 19.9M  100 19.9M    0     0   905k      0  0:00:22  0:00:22 --:--:--  954k\n",
      "A subdirectory or file checkpoint already exists.\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clean_text</th>\n",
       "      <th>category</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>3</th>\n",
       "      <td>asking his supporters prefix chowkidar their n...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>answer who among these the most powerful world...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          clean_text  category\n",
       "0  when modi promised “minimum government maximum...      -1.0\n",
       "1  talk all the nonsense and continue all the dra...       0.0\n",
       "2  what did just say vote for modi  welcome bjp t...       1.0\n",
       "3  asking his supporters prefix chowkidar their n...       1.0\n",
       "4  answer who among these the most powerful world...       1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!curl https://raw.githubusercontent.com/realsarm/deepLearningRecipes/main/.pytorch/tweets/Twitter_Data.csv --create-dirs -o .pytorch/tweets/Twitter_Data.csv\n",
    "!mkdir checkpoint\n",
    "df = pd.read_csv('.pytorch/tweets/Twitter_Data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wnZDU5kUJrUT"
   },
   "source": [
    "### Exploring features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:07.615454Z",
     "start_time": "2021-04-07T08:58:07.571357Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "8iEEuqAuJrUT",
    "outputId": "6de99fbf-d9bb-447f-b781-390761b9c625",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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       "            category\n",
       "count  162973.000000\n",
       "mean        0.225436\n",
       "std         0.781279\n",
       "min        -1.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         1.000000\n",
       "max         1.000000"
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     "execution_count": 7,
     "metadata": {},
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    "df.describe()"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:07.930752Z",
     "start_time": "2021-04-07T08:58:07.618977Z"
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
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    "id": "HLQhe0zgJrUU",
    "outputId": "d47ef3ed-1c18-4dcc-cd15-ef51dbd567e8",
    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "         clean_text                                                            \\\n",
       "              count unique                                                top   \n",
       "category                                                                        \n",
       "-1.0          35509  35509  why this coward didnt join the dynasty the las...   \n",
       " 0.0          55211  55211  modi — man who came riding the plank fighting ...   \n",
       " 1.0          72249  72249  gets ticket contest for modis party the upcomi...   \n",
       "\n",
       "               \n",
       "         freq  \n",
       "category       \n",
       "-1.0        1  \n",
       " 0.0        1  \n",
       " 1.0        1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('category').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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     "end_time": "2021-04-07T08:58:07.977750Z",
     "start_time": "2021-04-07T08:58:07.937753Z"
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
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    "id": "mgpw8FCZJrUV",
    "outputId": "bd16add0-dea6-44c8-f6f9-baeb99987b00",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 162980 entries, 0 to 162979\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   clean_text  162976 non-null  object \n",
      " 1   category    162973 non-null  float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:08.009753Z",
     "start_time": "2021-04-07T08:58:07.983754Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "6oGU6704JrUV",
    "outputId": "831a46a8-d50e-44a1-a27a-f4a15e177566",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clean_text    4\n",
       "category      7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:08.136756Z",
     "start_time": "2021-04-07T08:58:08.011752Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "QFUejj4jJrUV",
    "outputId": "e5d0b1d4-edb6-4706-af65-cbfdd96d0952",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      clean_text  category\n",
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   "source": [
    "df[df['clean_text'].str.len() < 2]"
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   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:08.232375Z",
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    {
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       "       clean_text  category\n",
       "158694        NaN      -1.0\n",
       "159443        NaN       0.0\n",
       "160560        NaN       1.0"
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     "execution_count": 12,
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   "source": [
    "df[df.duplicated('clean_text')]"
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:08.390373Z",
     "start_time": "2021-04-07T08:58:08.235377Z"
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     "height": 49
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    "id": "HHfQVsljJrUW",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clean_text</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [clean_text, category]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates('clean_text', inplace=True)\n",
    "df[df.duplicated('clean_text')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:08.689988Z",
     "start_time": "2021-04-07T08:58:08.394383Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "id": "y_GcsS8iJrUX",
    "outputId": "68e4aa22-b89d-45e4-f970-ac19deb1562f",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c42baf3048>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['category', 'clean_text']].groupby('category').count().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qRNRGUr1JrUX"
   },
   "source": [
    "### Preprocessing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8_Sbn3-qJrUX"
   },
   "source": [
    "#### Steps\n",
    "1. encode lables\n",
    "2. Replace tags and metion with a unique symbol\n",
    "3. Replace `emoji` and `emoticons` with their meaning\n",
    "4. Remove stop-words\n",
    "5. Remove punctuation and tokenize sentences\n",
    "6. Lemmatizing each token\n",
    "7. Remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:08.721992Z",
     "start_time": "2021-04-07T08:58:08.691992Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "iy0yXqzYJrUY",
    "outputId": "06d3dd29-ac3c-4890-a7fd-4e8cdcceb145",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>when modi promised “minimum government maximum...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>talk all the nonsense and continue all the dra...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>what did just say vote for modi  welcome bjp t...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>asking his supporters prefix chowkidar their n...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>answer who among these the most powerful world...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  sentiment\n",
       "0  when modi promised “minimum government maximum...          2\n",
       "1  talk all the nonsense and continue all the dra...          0\n",
       "2  what did just say vote for modi  welcome bjp t...          1\n",
       "3  asking his supporters prefix chowkidar their n...          1\n",
       "4  answer who among these the most powerful world...          1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "condlist = [\n",
    "    df['category'] == 1, # pos\n",
    "    df['category'] == 0, # neu\n",
    "    df['category'] == -1 # neg\n",
    "]\n",
    "df['sentiment'] = np.select(condlist, [1,0,2])\n",
    "d = df[['clean_text', 'sentiment']]\n",
    "d.rename(columns={'clean_text': 'text'}, inplace=True)\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:52.131548Z",
     "start_time": "2021-04-07T08:58:08.724992Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "xHC-GTGIJrUY",
    "outputId": "482750e2-af8b-41ec-f5b5-f45428211405",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[modi, promised, minimum, government, maximum,...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[talk, nonsense, continue, drama, vote, modi]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[vote, modi, welcome, bjp, told, rahul, main, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[asking, supporter, prefix, chowkidar, name, m...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[answer, powerful, world, leader, today, trump...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162975</th>\n",
       "      <td>[crore, paid, neerav, modi, recovered, congres...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162976</th>\n",
       "      <td>[dear, r, terrorist, payal, gawar, modi, killi...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162977</th>\n",
       "      <td>[cover, interaction, forum, left]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162978</th>\n",
       "      <td>[big, project, came, india, modi, dream, proje...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162979</th>\n",
       "      <td>[listen, like, gurukul, discipline, maintained...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162977 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     text  sentiment\n",
       "0       [modi, promised, minimum, government, maximum,...          2\n",
       "1           [talk, nonsense, continue, drama, vote, modi]          0\n",
       "2       [vote, modi, welcome, bjp, told, rahul, main, ...          1\n",
       "3       [asking, supporter, prefix, chowkidar, name, m...          1\n",
       "4       [answer, powerful, world, leader, today, trump...          1\n",
       "...                                                   ...        ...\n",
       "162975  [crore, paid, neerav, modi, recovered, congres...          2\n",
       "162976  [dear, r, terrorist, payal, gawar, modi, killi...          2\n",
       "162977                  [cover, interaction, forum, left]          0\n",
       "162978  [big, project, came, india, modi, dream, proje...          0\n",
       "162979  [listen, like, gurukul, discipline, maintained...          1\n",
       "\n",
       "[162977 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def emoji_helper(text):\n",
    "    clean_mean = lambda x:  x.replace('-', '_').replace(':', ' ')\n",
    "    for emoti in emot.emo_unicode.EMOTICONS:\n",
    "        if emoti in text:\n",
    "            text = text.replace(emoti, clean_mean(emot.emo_unicode.EMOTICONS.get(emoti, '')))\n",
    "\n",
    "    for emoti in emot.emo_unicode.UNICODE_EMO:\n",
    "        if emoti in text:\n",
    "            text = text.replace(emoti, clean_mean(emot.emo_unicode.UNICODE_EMO.get(emoti, '')))\n",
    "\n",
    "    for emoti in emot.emo_unicode.EMOTICONS_EMO:\n",
    "        if emoti in text:\n",
    "            text = text.replace(emoti, clean_mean(emot.emo_unicode.EMOTICONS_EMO.get(emoti, '')).replace(' ','_'))\n",
    "    return text\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "def preprocess(x):\n",
    "    return [lemmatizer.lemmatize(word) for word in\n",
    "        simple_preprocess(remove_stopwords(emoji_helper(re.sub(r'\\s*([@#][\\w_-]+)', '', str(x)))), deacc=True)\n",
    "    ]\n",
    "\n",
    "d['text'] = d['text'].apply(func=lambda x:preprocess(x))\n",
    "\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V1lsg8zVJrUY"
   },
   "source": [
    "#### Analyze review length\n",
    "Here we remove the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:52.322118Z",
     "start_time": "2021-04-07T08:58:52.134551Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "nE-zLQLWJrUZ",
    "outputId": "0b428133-b8bd-42cc-a6cf-bb8385305c9c",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    162977.000000\n",
       "mean         12.894905\n",
       "std           6.987476\n",
       "min           0.000000\n",
       "25%           7.000000\n",
       "50%          12.000000\n",
       "75%          18.000000\n",
       "max          42.000000\n",
       "Name: text, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['text'].str.len().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:52.638411Z",
     "start_time": "2021-04-07T08:58:52.326120Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "241NoGKyJrUZ",
    "outputId": "9205ee56-8718-4efc-863f-dfabb6e80798",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c429e16808>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d['text'].str.len().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:53.143863Z",
     "start_time": "2021-04-07T08:58:52.641417Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "YKCZ7SYYJrUZ",
    "outputId": "45978ed8-f83f-4a90-f828-19cd6241d072",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c42bf1ef48>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = d[ (2 < d['text'].str.len()) & (d['text'].str.len() < 24) ]\n",
    "d['text'].str.len().hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8FTBryPnJrUa",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Creating vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:58:53.239644Z",
     "start_time": "2021-04-07T08:58:53.146858Z"
    },
    "id": "j6jBsOu6JrUa",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "max_document_length = d['text'].str.len().max()  # each sentence has until 100 words\n",
    "max_size = 5000\n",
    "Text = data.Field(batch_first=True, tokenize=lambda x: x, include_lengths=True, fix_length=max_document_length)\n",
    "Label = data.Field(sequential=False, use_vocab=False, pad_token=None, unk_token=None)\n",
    "fields = [('text', Text), ('labels', Label)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:09.865306Z",
     "start_time": "2021-04-07T08:58:53.241623Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "DOIRUDjzJrUa",
    "outputId": "4208296e-8ea5-4400-b3a6-33ca240af3c5",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': ['country',\n",
       "  'forward',\n",
       "  'economy',\n",
       "  'remove',\n",
       "  'poverty',\n",
       "  'protect',\n",
       "  'country',\n",
       "  'remove',\n",
       "  'modi',\n",
       "  'shri'],\n",
       " 'labels': 1}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class DataFrameDataset(data.Dataset):\n",
    "\n",
    "    def __init__(self, df, text_field, label_field, is_test=False, **kwargs):\n",
    "        fields = [('text', text_field), ('labels', label_field)]\n",
    "        examples = []\n",
    "        for i, row in df.iterrows():\n",
    "            label = row.sentiment\n",
    "            text = row.text\n",
    "            examples.append(data.Example.fromlist([text, label], fields))\n",
    "\n",
    "        super().__init__(examples, fields, **kwargs)\n",
    "\n",
    "    @staticmethod\n",
    "    def sort_key(ex):\n",
    "        return len(ex.text)\n",
    "\n",
    "    @classmethod\n",
    "    def splits(cls, text_field, label_field, train_df, test_df=None, **kwargs):\n",
    "        train_data, test_data = (None, None)\n",
    "\n",
    "        if train_df is not None:\n",
    "            train_data = cls(train_df.copy(), text_field, label_field, **kwargs)\n",
    "        if test_df is not None:\n",
    "            test_data = cls(test_df.copy(), text_field, label_field, True, **kwargs)\n",
    "\n",
    "        return tuple(d for d in (train_data, test_data) if d is not None)\n",
    "test_size = 0.2 # split percentage to train\\validation data\n",
    "X_train,X_test,y_train,y_test = train_test_split(d['text'].index,d['sentiment'], test_size=test_size, random_state=0, stratify=d['sentiment'])\n",
    "train_df = d.loc[X_train.values]\n",
    "test_df = d.loc[X_test.values]\n",
    "train_ds, test_ds = DataFrameDataset.splits(\n",
    "    text_field=Text, label_field=Label, train_df=train_df, test_df=test_df)\n",
    "vars(test_ds[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-rzlLLUsKDPJ"
   },
   "source": [
    "### Checking how balance is our testset in comparison to trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:09.895307Z",
     "start_time": "2021-04-07T08:59:09.870311Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "8fU_1ta2JuSZ",
    "outputId": "d17b0efb-4eb0-47c7-cf3c-a18f2aac91e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentiment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.421928</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.208927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.369144</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                text\n",
       "sentiment           \n",
       "0          34.421928\n",
       "1          44.208927\n",
       "2          21.369144"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.groupby('sentiment').count()/test_df['sentiment'].count()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:09.991304Z",
     "start_time": "2021-04-07T08:59:09.907305Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "W-8DkMpgJ8Nz",
    "outputId": "f121690e-6d06-41d5-db5d-f2027e6d0662"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentiment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.423089</td>\n",
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       "      <td>44.206720</td>\n",
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      "text/plain": [
       "                text\n",
       "sentiment           \n",
       "0          34.423089\n",
       "1          44.206720\n",
       "2          21.370192"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby('sentiment').count()/train_df['sentiment'].count()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:10.070301Z",
     "start_time": "2021-04-07T08:59:09.993303Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "SFHEes6ewWpa",
    "outputId": "33340cb6-d56a-445c-a706-9e307893beae",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.452513476244202 1.1310497647449287 2.339707604700941\n"
     ]
    }
   ],
   "source": [
    "items = train_df.groupby('sentiment').count().to_numpy()\n",
    "neu, pos, neg = items[0][0], items[1][0], items[2][0]\n",
    "total = train_df.count()[0]\n",
    "weight_for_0 = (1 / neu)*(total)/2.0\n",
    "weight_for_1 = (1 / pos)*(total)/2.0\n",
    "weight_for_2 = (1 / neg)*(total)/2.0\n",
    "print(weight_for_0, weight_for_1, weight_for_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:11.211867Z",
     "start_time": "2021-04-07T08:59:10.073305Z"
    },
    "id": "L4QWhGExJrUb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "Text.build_vocab(train_ds, test_ds, max_size=max_size)\n",
    "Label.build_vocab(train_ds)\n",
    "vocab_size = len(Text.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:11.227868Z",
     "start_time": "2021-04-07T08:59:11.219871Z"
    },
    "id": "Fm69dUmTJrUb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "PAD_IND = Text.vocab.stoi['pad']\n",
    "# captum\n",
    "token_reference = TokenReferenceBase(reference_token_idx=PAD_IND) # create a reference (aka baseline) for the sentences and its constituent parts, tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f5xCcMgXJrUb",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Defining model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:11.258869Z",
     "start_time": "2021-04-07T08:59:11.234869Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "jjpQpWbkJrUb",
    "outputId": "881b9715-4db1-47cd-9260-1c3111ad7eed",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:11.306865Z",
     "start_time": "2021-04-07T08:59:11.267870Z"
    },
    "id": "zFri3ivlJrUc",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
    "class LSTM(nn.Module):\n",
    "\n",
    "    # define all the layers used in model\n",
    "    def __init__(self, vocab_size, embedding_dim, hidden_dim1, hidden_dim2, output_dim, n_layers,\n",
    "                 bidirectional, dropout, pad_index):\n",
    "        # Constructor\n",
    "        super().__init__()\n",
    "        self.n_layers = n_layers\n",
    "        self.hidden_dim1 = hidden_dim1\n",
    "        self.bidirectional = bidirectional\n",
    "        # embedding layer\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_index)\n",
    "\n",
    "        # lstm layer\n",
    "        self.lstm = nn.LSTM(embedding_dim,\n",
    "                            hidden_dim1,\n",
    "                            num_layers=n_layers,\n",
    "                            bidirectional=bidirectional,\n",
    "                            batch_first=True)\n",
    "        self.fc1 = nn.Linear(hidden_dim1 * 2, hidden_dim2)\n",
    "        self.fc2 = nn.Linear(hidden_dim2, output_dim)\n",
    "        self.relu = nn.SELU()\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.dropout1 = nn.Dropout(dropout)\n",
    "        # activation function\n",
    "        self.act = nn.Softmax() #\\ F.log_softmax(outp)\n",
    "\n",
    "    def forward(self, text, text_lengths, hid=None):\n",
    "        # text = [batch size,sent_length]\n",
    "        if hid == None:\n",
    "            hid = self.init_hidden(text.shape[0])\n",
    "        embedded = self.embedding(text)\n",
    "        # embedded = [batch size, sent_len, emb dim]\n",
    "\n",
    "        # packed sequence\n",
    "        packed_embedded = pack_padded_sequence(embedded, text_lengths.cpu(), batch_first=True) # unpad\n",
    "\n",
    "        packed_output, (hidden, cell) = self.lstm(packed_embedded, hid)\n",
    "        # packed_output shape = (batch, seq_len, num_directions * hidden_size)\n",
    "        # hidden shape  = (num_layers * num_directions, batch, hidden_size)\n",
    "\n",
    "        # concat the final forward and backward hidden state\n",
    "        cat = torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)\n",
    "        # output, output_lengths = pad_packed_sequence(packed_output)  # pad the sequence to the max length in the batch\n",
    "        cat = self.dropout1(cat)\n",
    "        rel = self.relu(cat)\n",
    "        dense1 = self.fc1(rel)\n",
    "\n",
    "        drop = self.dropout(dense1)\n",
    "        preds = self.fc2(drop)\n",
    "\n",
    "        # Final activation function\n",
    "        # preds = self.act(preds)\n",
    "        # preds = preds.argmax(dim=1).unsqueeze(0)\n",
    "        return preds, (hidden, cell)\n",
    "    \n",
    "    def init_hidden(self, batch_size):\n",
    "        ''' Initializes hidden state '''\n",
    "        # Create two new tensors with sizes n_layers x batch_size x n_hidden,\n",
    "        # initialized to zero, for hidden state and cell state of LSTM\n",
    "        weight = next(self.parameters()).data\n",
    "\n",
    "        hidden = (torch.zeros(self.n_layers*(2 if self.bidirectional else 1), batch_size, self.hidden_dim1).to(device),\n",
    "                  torch.zeros(self.n_layers*(2 if self.bidirectional else 1), batch_size, self.hidden_dim1).to(device))\n",
    "\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:11.400866Z",
     "start_time": "2021-04-07T08:59:11.309869Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "2zLwnk5UJrUg",
    "outputId": "546c7eae-8e0a-40f7-c01c-55dc26ec6f52",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM(\n",
      "  (embedding): Embedding(5002, 361, padding_idx=1)\n",
      "  (lstm): LSTM(361, 117, num_layers=2, batch_first=True, bidirectional=True)\n",
      "  (fc1): Linear(in_features=234, out_features=85, bias=True)\n",
      "  (fc2): Linear(in_features=85, out_features=3, bias=True)\n",
      "  (relu): SELU()\n",
      "  (dropout): Dropout(p=0.48209457439897463, inplace=False)\n",
      "  (dropout1): Dropout(p=0.48209457439897463, inplace=False)\n",
      "  (act): Softmax(dim=None)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# hyper-parameters:\n",
    "lr = 0.0011204927661213097\n",
    "batch_size = 50\n",
    "dropout_keep_prob = 0.48209457439897463\n",
    "embedding_size = 361\n",
    "seed = 0\n",
    "clip=5\n",
    "num_classes = 3\n",
    "num_hidden_nodes = 117\n",
    "hidden_dim2 = 85\n",
    "num_layers = 2  # LSTM layers\n",
    "bi_directional = True\n",
    "num_epochs = 7\n",
    "\n",
    "parameters = [sherpa.Discrete('num_hidden_nodes', [64, 128]),\n",
    "              sherpa.Discrete('embedding_size', [300, 800]),\n",
    "              sherpa.Discrete('hidden_dim2', [64, 128]),\n",
    "              sherpa.Continuous('lr',[1e-6,1e-2]),\n",
    "              sherpa.Continuous('dropout_keep_prob',[0.4,0.8])]\n",
    "algorithm= sherpa.algorithms.Genetic(max_num_trials=50)\n",
    "pad_index = Text.vocab.stoi[Text.pad_token]\n",
    "\n",
    "# Build the model\n",
    "lstm_model = LSTM(vocab_size, embedding_size, num_hidden_nodes, hidden_dim2 , num_classes, num_layers,\n",
    "                  bi_directional, dropout_keep_prob, pad_index)\n",
    "lstm_model.to(device)\n",
    "print(lstm_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZLmmdfsyJrUk",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:11.416866Z",
     "start_time": "2021-04-07T08:59:11.403868Z"
    },
    "id": "tESutgn6JrUk",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_iterator, test_iterator = data.BucketIterator.splits((train_ds, test_ds),\n",
    "                                                           batch_size=batch_size,\n",
    "                                                           sort_key=lambda x: len(x.text),\n",
    "                                                           # Sort the batches by text length size\n",
    "                                                           sort_within_batch=True,\n",
    "                                                           device=device,\n",
    "                                                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:11.464870Z",
     "start_time": "2021-04-07T08:59:11.419867Z"
    },
    "id": "Ne_LiAoSJrUk",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "report_pred_test =[]\n",
    "report_label_test =[]\n",
    "\n",
    "report_pred_train =[]\n",
    "report_label_train =[]\n",
    "def accuracy(probs, target):\n",
    "    winners = probs.argmax(dim=1)\n",
    "    corrects = (winners == target)\n",
    "    accuracy = corrects.sum().float() / float(target.size(0))\n",
    "    return accuracy\n",
    "\n",
    "def plot_cm(y_true, y_pred, target_names):\n",
    "    cm = confusion_matrix(y_true, y_pred, labels=target_names)\n",
    "    plt.figure(figsize=(5,5))\n",
    "    sb.heatmap(cm, annot=True, fmt=\"d\")\n",
    "    plt.title('Confusion matrix')\n",
    "    plt.ylabel('Actual label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "def train(model, iterator, optimizer, criterion):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "\n",
    "    model.train()\n",
    "    h = model.init_hidden(batch_size)\n",
    "    for batch in iterator:\n",
    "        optimizer.zero_grad()\n",
    "        # zero accumulated gradients\n",
    "        model.zero_grad()\n",
    "        # retrieve text and no. of words\n",
    "        text, text_lengths = batch.text\n",
    "        if (text.shape[0], text.shape[1]) != (batch_size, max_document_length):\n",
    "            continue\n",
    "\n",
    "        # Creating new variables for the hidden state, otherwise\n",
    "        # we'd backprop through the entire training history\n",
    "        h = tuple([each.data for each in h])\n",
    "\n",
    "        predictions, h = model(text, text_lengths, h)\n",
    "        loss = criterion(predictions, batch.labels.squeeze())\n",
    "\n",
    "        acc = accuracy(predictions, batch.labels)\n",
    "\n",
    "        # perform backpropagation\n",
    "        loss.backward()\n",
    "        # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n",
    "        nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
    "        optimizer.step()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_acc += acc.item()\n",
    "\n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator)\n",
    "\n",
    "def evaluate(model, iterator, criterion, report=False):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    \n",
    "\n",
    "    model.eval()\n",
    "    val_h = model.init_hidden(batch_size)\n",
    "    with torch.no_grad():\n",
    "        for batch in iterator:\n",
    "            text, text_lengths = batch.text\n",
    "            if (text.shape[0], text.shape[1]) != (batch_size, max_document_length):\n",
    "                continue\n",
    "\n",
    "            # Creating new variables for the hidden state, otherwise\n",
    "            # we'd backprop through the entire training history\n",
    "            val_h = tuple([each.data for each in val_h])\n",
    "\n",
    "            predictions, val_h = model(text, text_lengths, val_h)\n",
    "\n",
    "            loss = criterion(predictions, batch.labels)\n",
    "\n",
    "            acc = accuracy(predictions, batch.labels)\n",
    "            if report:\n",
    "                report_pred_test.extend(predictions.argmax(dim=1).cpu())\n",
    "                report_label_test.extend(batch.labels.cpu())\n",
    "\n",
    "            epoch_loss += loss.item()\n",
    "            epoch_acc += acc.item()\n",
    "    if report:\n",
    "        print(classification_report(report_label_test, report_pred_test, target_names=['neutral', 'positive', 'negative']))\n",
    "        plot_cm(report_label_test, report_pred_test, target_names=[0, 1, 2])\n",
    "\n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator)\n",
    "\n",
    "\n",
    "def run_train(epochs, model, train_iterator, valid_iterator, optimizer, criterion, study=None, trial=None):\n",
    "    best_valid_loss = float('inf')\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "\n",
    "        # train the model\n",
    "        train_loss, train_acc = train(model, train_iterator, optimizer, criterion)\n",
    "\n",
    "        # evaluate the model\n",
    "        valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)\n",
    "        \n",
    "        # save the best model\n",
    "        if valid_loss < best_valid_loss:\n",
    "            best_valid_loss = valid_loss\n",
    "            torch.save(model.state_dict(), 'checkpoint/twitter.t7')\n",
    "\n",
    "        print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc * 100:.2f}%')\n",
    "        print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc * 100:.2f}%')\n",
    "        if study != None:\n",
    "          study.add_observation(trial=trial,\n",
    "                          iteration=epoch,\n",
    "                          objective=valid_loss)\n",
    "          if study.should_trial_stop(trial):\n",
    "            break "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T08:59:50.393162Z",
     "start_time": "2021-04-07T08:59:50.379164Z"
    },
    "id": "aPadIekjJrUl",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# loss function\n",
    "loss_func = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(lstm_model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Run this cell if you want to train the model with default parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 680
    },
    "id": "r9ILuYmE4PEQ",
    "outputId": "3d080712-f016-4fef-a9d9-e5d9b8d8ccd5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tTrain Loss: 0.648 | Train Acc: 74.37%\n",
      "\t Val. Loss: 0.528 |  Val. Acc: 80.83%\n",
      "\tTrain Loss: 0.520 | Train Acc: 81.65%\n",
      "\t Val. Loss: 0.505 |  Val. Acc: 82.19%\n",
      "\tTrain Loss: 0.487 | Train Acc: 82.75%\n",
      "\t Val. Loss: 0.508 |  Val. Acc: 82.72%\n",
      "\tTrain Loss: 0.459 | Train Acc: 83.70%\n",
      "\t Val. Loss: 0.506 |  Val. Acc: 82.51%\n",
      "\tTrain Loss: 0.428 | Train Acc: 84.79%\n",
      "\t Val. Loss: 0.523 |  Val. Acc: 81.96%\n",
      "\tTrain Loss: 0.392 | Train Acc: 85.94%\n",
      "\t Val. Loss: 0.552 |  Val. Acc: 81.39%\n",
      "\tTrain Loss: 0.356 | Train Acc: 87.17%\n",
      "\t Val. Loss: 0.606 |  Val. Acc: 80.55%\n",
      "\tTrain Loss: 0.320 | Train Acc: 88.43%\n",
      "\t Val. Loss: 0.630 |  Val. Acc: 80.52%\n",
      "\tTrain Loss: 0.283 | Train Acc: 89.79%\n",
      "\t Val. Loss: 0.656 |  Val. Acc: 80.15%\n",
      "\tTrain Loss: 0.253 | Train Acc: 91.01%\n",
      "\t Val. Loss: 0.747 |  Val. Acc: 80.09%\n",
      "\tTrain Loss: 0.228 | Train Acc: 91.97%\n",
      "\t Val. Loss: 0.758 |  Val. Acc: 79.64%\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-8395ce2faaf6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlstm_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_iterator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_iterator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-27-ebc1c9bdfa16>\u001b[0m in \u001b[0;36mrun_train\u001b[0;34m(epochs, model, train_iterator, valid_iterator, optimizer, criterion, study, trial)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;31m# train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_iterator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;31m# evaluate the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-27-ebc1c9bdfa16>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, iterator, optimizer, criterion)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0;31m# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip_grad_norm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/utils/clip_grad.py\u001b[0m in \u001b[0;36mclip_grad_norm_\u001b[0;34m(parameters, max_norm, norm_type)\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mtotal_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m         \u001b[0mtotal_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0mclip_coef\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_norm\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtotal_norm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1e-6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mclip_coef\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/utils/clip_grad.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mtotal_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m         \u001b[0mtotal_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0mclip_coef\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_norm\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtotal_norm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1e-6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mclip_coef\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/functional.py\u001b[0m in \u001b[0;36mnorm\u001b[0;34m(input, p, dim, keepdim, out, dtype)\u001b[0m\n\u001b[1;32m   1374\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m             \u001b[0m_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# noqa: C416 TODO: rewrite as list(range(m))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1376\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_VF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeepdim\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1378\u001b[0m     \u001b[0;31m# TODO: when https://github.com/pytorch/pytorch/issues/33782 is fixed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "run_train(num_epochs, lstm_model, train_iterator, test_iterator, optimizer, loss_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Run this cell if you want to fine tune the model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wlRLwH6Kav-o"
   },
   "outputs": [],
   "source": [
    "study = sherpa.Study(parameters=parameters,\n",
    "                     algorithm=algorithm,\n",
    "                     lower_is_better=True)\n",
    "for trial in study:\n",
    "    print(\"Trial {}:\\t{}\".format(trial.id, trial.parameters))\n",
    "    lstm_model = LSTM(vocab_size, trial.parameters['embedding_size'], trial.parameters['num_hidden_nodes'], trial.parameters['hidden_dim2'] , num_classes, num_layers,\n",
    "                  bi_directional, trial.parameters['dropout_keep_prob'], pad_index)\n",
    "    lstm_model.to(device)\n",
    "    print(lstm_model)\n",
    "    optimizer = torch.optim.Adam(lstm_model.parameters(), lr=trial.parameters['lr'])\n",
    "    run_train(num_epochs, lstm_model, train_iterator, test_iterator, optimizer, loss_func, study, trial)\n",
    "    study.finalize(trial=trial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> run this cell to set the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1M3eT4p7197o"
   },
   "outputs": [],
   "source": [
    "print(study.get_best_result())\n",
    "lr = study.get_best_result()['lr']\n",
    "dropout_keep_prob = study.get_best_result()['dropout_keep_prob']\n",
    "embedding_size = study.get_best_result()['embedding_size']\n",
    "num_hidden_nodes = study.get_best_result()['num_hidden_nodes']\n",
    "hidden_dim2 = study.get_best_result()['hidden_dim2']\n",
    "num_epochs = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> run this cell to train the model again with best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IXSVKXpkJrUl",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "run_train(num_epochs, lstm_model, train_iterator, test_iterator, optimizer, loss_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:00:17.219970Z",
     "start_time": "2021-04-07T08:59:57.778508Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 537
    },
    "id": "zNhQggYQJrUm",
    "outputId": "976cdbe1-fc04-4ed8-9d2b-1724a6346e10",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     neutral       0.78      0.91      0.84      9970\n",
      "    positive       0.91      0.79      0.84     12798\n",
      "    negative       0.78      0.78      0.78      6182\n",
      "\n",
      "    accuracy                           0.83     28950\n",
      "   macro avg       0.82      0.82      0.82     28950\n",
      "weighted avg       0.84      0.83      0.83     28950\n",
      "\n",
      "Test Loss: 0.495 | Test Acc: 82.58%\n"
     ]
    },
    {
     "data": {
      "image/png": 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B7t4KyATOBu4FHnb35sAqfn8rsQ+wyt33Bh4Oy2Fm+4XH7Q90A/5lZpmJ3KoCp4gkR/G+q54FVDKzLKAysAg4Fngj3P8i0Cv8vWe4Tri/s5lZuP01d9/k7j8C8wieNY9MgVNEksLzPKFlh+d1XwA8APxMEDDXAFOB1e6eExabT/CyDeHPX8Jjc8Lyu8duL+CYSBQ4RSQ5Emyqx07uEy59Y09rZjUJssW9CObAqAJ0L6AGW6NwQQ/UexHbI9OouogkR4IPwMdO7lOILsCP7r4MwMzeBA4HaphZVphVNgIWhuXnA42B+WHTvjqwMmb7VrHHRKKMU0SSI88TW3bsZ6CDmVUO+yo7A98BnwCnh2UuBLbOZTgyXCfc/7EH75aPBM4OR933ApoTzBccmTJOEUmOYnpzyN2/NLM3gGlADvAVQYb6LvCamd0VbhscHjIYeMnM5hFkmmeH55lpZq8TBN0c4KpEJx1S4BSR5CjGVy7dvR/Qb7vNP1DAqLi7/wacUch57gbu3tn6KHCKSHKUoTeHFDhFJDnK0CQfCpwikhzxDfTsEhQ4RSQ5NB+niEhEyjhFRKLxMtTHqQfgRUQiUsYpIsmhprqISEQaHBIRiUgZp4hIRGVocEiBU0SSQxmniEhE6uMUEYlIGaeISDRl6QF4BU4RSQ5lnCIiESlwiohEpMEhEZGIlHGKiETjCpwiIhEpcIqIRKTHkUREIlLGKSISURkKnJoBXkQkImWcIpIU7mUn41TgFJHkKENN9VIbOI8+7elUVyEtbFgwPtVVSBuN9z4h1VXYtSlwiohEowfgRUSiUuAUEYmo7Dz/rsApIsmhprqISFQKnCIiEampLiISjZrqIiJRKeMUEYlGGaeISFTKOEVEoilD39WmwCkiSaLAKSISTVnKODWRsYhIRMo4RSQ5ylDGqcApIklRlprqCpwikhQKnICZ1SrqQHdfmfzqiEi6UuAMTAUcsAL2OdC0WGokIunJCwoVu6ZCR9XdfS93bxr+3H5R0BSRbXheYks8zKyGmb1hZrPNbJaZHWZmtcxsjJnNDX/WDMuamT1mZvPMbLqZtY05z4Vh+blmdmGi97rDx5HCSvQ2szvC9T3M7JBELygiuybPs4SWOD0KfODu+wAHAbOAW4Cx7t4cGBuuA3QHmodLX+ApyO9+7AccChwC9NsabKOK5znOfwGHAeeG6+uAJxO5mIjsuoor4zSzasDRwGAAd9/s7quBnsCLYbEXgV7h7z2BoR74AqhhZvWB44Ex7r7S3VcBY4BuidxrPKPqh7p7WzP7Kqz0KjMrn8jFRGTX5cXXx9kUWAYMMbODCMZfrgWy3X1RcG1fZGZ1w/INgV9ijp8fbitse2TxZJxbzCyTYEAIM6tDmXrUVUTikWjGaWZ9zWxKzNJ3u1NnAW2Bp9y9DbCB35vlBSlsQLuw7ZHFk3E+BrwFZJvZ3cDpwO2JXExEdl0R+iu3Pc59EDCoiCLzgfnu/mW4/gZB4FxiZvXDbLM+sDSmfOOY4xsBC8PtnbbbPi6ROu8w43T3V4C/AveEF+/l7v9O5GIisutyT2zZ8Xl9MfCLmbUMN3UGvgNGAltHxi8ERoS/jwQuCAe2OwBrwib9h0BXM6sZDgp1DbdFFu+bQ5WBrc31SolcSER2bYlmnHG6GnglHF/5AbiYIPF73cz6AD8DZ4Rl3wN6APOAX8OyuPtKMxsATA7L/SPRF3l2GDjN7O9hhYYT9BEMMbN/u/tdiVxQRHZNxRk43f1r4OACdnUuoKwDVxVynueB53e2PvFknOcAbdz9NwAzGwhMAxQ4RSRfPM3uXUU8gfN/QEXgt3C9AvDf4qqQiKSnYm6qlypFTfLxOEGf5iZgppmNCdePAz4rmeqJiJQ+RWWcU8KfUwkeR9pqXLHVRkTSVjE+AF/qFBo43f3FwvaJiGxP08rFMLPmwD+B/Qj6OgHQDEkiEiuvDGWc8bxyOYRgdpEc4BhgKPBScVZKRNKPuyW0pKN4Amcldx8LmLv/5O79gWOLt1oikm6KeVq5UiWex5F+M7MMYK6Z/RlYANTdwTEiUsboOc5tXUfwyuU1wACCbDPhmZNFZNeUrtljInYYON1963ud6wnf+RQR2V5ZGhwq6gH4URQxV527n1wsNRKRtJSuAz2JKCrjfKDEapFCtz90M0d2OYxVy1dxzrFBQn330/3Ys1kwnV/ValVZv3Y9vY+7lPqN6jHs06H8/MPPAHw79TsG3vIQAFnlsrjp7utod1hr8jyPpwY+xyfvjU/NTSXJ7fc8xPiJ/6FWzRq8/fLTAKxZu44b7vgnCxcvoUG9bB4ccCvVq+2Gu/PPR55mwqTJVKxYgbv/dgP7tdwbgAOPOoHmTZsAUD+7Dk/c1x+Am/vfy8zZc8nKyqLVfi3o99drKJcV74Rd6eHSy8+n9wVnYGa8PPTfPPvUUPZr1ZL7HupPlSqV+eWXBVx52U2sX7cBgKuvv4xzzz+N3Nw8br/5bsZ9PDHFdxA/9XEC7v5pSVYkVd4d9j7/HvIm/R+9LX/b3y6/M//3a/9+Zf5faoAFPy2g93GX/uE8F197PquWr+L0o3pjZlSrWa14K14CevU4jnNPO5nbBvz+f+hzL71Oh4Nbc+n5Z/LcS68z+OXX+cuVfZgwaTI/z1/Ie8MGM33mbAY88ASvPvsIABUqlGf4i3/8mqoTuh7DwH5/BeCv/e9l+KgPOPuUE0vm5krAPvs2p/cFZ9C985ls3ryFV4c/y0cffspDjw3gzjvuZ9LEyZzT+1SuvKYP9939GC1aNqPXaT3o2OEk6tWvy+tvP8/h7bqTl5ceT5aXpaZ6PI8j7dK++nI6a1etK3R/l5OPYfTbH+3wPCef3YMXHn8FAHdnzco1Satjqhzc+gCqV9ttm22fTJhEz+5dAOjZvQsfj58UbP/sC07u1hkz46BW+7Ju3XqWLS96qsOjDz8EM8PMOGDflixZurx4biRFmrdoytQp37Bx42/k5uYyaeJkepzYhWZ778WkicHQwaeffM6JJx0HwPE9juXt4e+xefMWfv5pAT/+8DNt2h2YyluIRM9xFiMzS5sBpjaHHsjKZSv55ccF+dsa7FGfl0Y/x9PDH6X1IcFf6qrVqgJw+V/7MPTDZ/nnM3dSq3ZC3zpa6q1YtZo6tWsBUKd2LVauDv6DWLJsBfXq1s4vl123NkuWBYFw8+bNnHnJNZx72XWMHf/5H865JSeHUR+O5chDC5puMX3NnjWXDocfTM2aNahUqSKdjzuaBo3qMXvWXI7vETwKfVKv42nQsD4A9etns3DB4vzjFy1cQv366fPkX3HNAF8apSLjvHPHRUqHrr268OHbY/PXly9dwcntz+T8rpfySP8nGfCvO6hStTKZWZlkN6jLN5NncMHxlzFj6kyu+fuVKax5yfMC/gWYBdnEmOFDef35x7i3/83c++gz/Dx/4Tbl7nrgSdod1Ip2rVuVSF1Lytzvf+CJR59j2NuD+b/hzzLz29nk5ORy/Z//xsWXnsuH496gatUqbN6yBfj984rliX2XWErkuSW0pKNiGVU3s+mF7QKyiziuL8EXyLNn9ebUrVy/sKLFLjMzk049juLCbr9/4d6WzVtYszn4Sz57xvfM/98C9mjamFnT57Dx142Me38CAB+98wknn9MjJfUubrvXrMGy5SupU7sWy5avpFaN6gDUq1ubxTFN7SVLl1O39u4A1K0T/GzcsD7t2xzI7Ln/ZY9GDQD41/OvsGr1Gvrds2t+/9+rLw3n1ZeGA3DrHdexaOES5s39kbNPDfrJmzZrQpeuHQFYuHAxDRrWyz+2foNsFi9aVvKVTlC6NrsTUVTG+QDwYBFLUbKBC4CTClhWFHaQuw9y94Pd/eBUBk2A9ke146d5P7M05i9ujVrVycgIPrIGe9Sn8V6NWPBzkD1NGPM57Q5vHRx7ZDt+/P6nkq90Ceh0ZAdGvB/0+Y54/yOOOeqw/O0jPxiLu/PNt7OoWrUKdWrXYs3adWzevBmAVavX8NWM72jWZA8A3hj5ARO/nMp9d96c/7nuamqH3RoNG9Wnx0nH8dYb7+ZvMzOuv+lyhg4ZBsDo9z+h12k9KF++HHvs2ZCmzfbkq6mF5SCljzJOdnpU/R2gavg9Idsws3E7cd6kG/Cvv9PusNbUqFWdUVP+zbMPDmHkq+/RteexjI5ppgO06XAQf7rpEnJzcsnNy2PgLQ+xdnUwsPTEXc9w5+N/4/o7r2b1itX84y8DU3E7SXVTv4FM/mo6q1evpXOv3lzZ53wuPf9MbrjjHt5850PqZ9fhobv+BsDRh7VnwqTJdD/zEipVrMiA264H4IeffuEf9z2OZRie5/TpfSbN9toTgAEPPE797Lqc1/cvAHTpeDhXXHJeam62mDw39FFq1arBlpwcbr1xAGvWrOXSy8/n4kvPBeC9UWN49eU3AZgzex4j3/qA8V++Q05OLrfeOCBtRtTLGiuob2qbAimaVu6QBh3Tp3MnhSZOH5LqKqSNxnufkOoqpIXFq2cllAZ+0eDUhP7Ndlj4ZtqlnfE8bTwE6Ac8TDCt3MUEfZUiIvnStdmdCE0rJyJJUZae49S0ciKSFGWpNzaejDN2Wrl2wPloWjkR2Y5jCS3pSNPKiUhS5JWh4dx4vqztEwp4EN7d1c8pIvny0jR7TEQ8fZw3xvxeETiN4IvbRETypWuzOxHxNNWnbrdpopmViSnnRCR+ZWlwKJ6meq2Y1QyCAaJ6hRQXkTJKGee2phL0cRpBE/1HoE9xVkpE0o8yzm3t6+6/xW4wswrFVB8RSVNlKXDG8xznH2eehUnJroiIpDc9xwmYWT2gIVDJzNrw+/vp1QgeiBcRyVeGvla9yKb68cBFQCOC+Te3fixrgdsKOUZEyig9xwm4+4vAi2Z2mrsPL8E6iUgaKkMvDsXVx9nOzGpsXTGzmmZ2VzHWSUSkVIsncHZ399VbV9x9FbBrfqGOiCQsL8ElHcXzOFKmmVVw900AZlYJ0ONIIrKNvAK+pXNXFU/gfBkYa2ZDCLoxLgGGFmutRCTtlKU+znjeVb8v/LrfLgQj6wPc/cNir5mIpJV0bXYnIp6ME3f/APgAwMyOMLMn3f2qYq2ZiKQVPce5HTNrDZwDnEXwrvqbxVkpEUk/eo4TMLMWwNkEAXMFMIzgC9uOKaG6iUgaUR9nYDYwATjJ3ecBmNn1JVIrEUk7ZampXtRznKcBi4FPzOxZM+uMvk9dRApRlp7jLDRwuvtb7n4WsA8wDrgeyDazp8ysawnVT0TShCe4pKMdvjnk7hvc/RV3P5Fgwo+vgVuKvWYiklbyLLElHcXzymU+d1/p7s/oGy5FZHvF2VQ3s0wz+8rM3gnX9zKzL81srpkNM7Py4fYK4fq8cH+TmHPcGm6fY2bH78y9RgqcIiKFKeY+zmuBWTHr9wIPu3tzYBW/f51PH2CVu+8NPByWw8z2I3hKaH+gG/AvM8uMfpcBBU4RSQq3xJYdMbNGwAnAc+G6AccCb4RFXgR6hb/3DNcJ93cOy/cEXnP3Te7+IzAPOCTRe1XgFJGkSDTjNLO+ZjYlZum73akfAf7K7wnq7sBqd88J1+cTfFsF4c9fAML9a8Ly+dsLOCayuN4cEhHZkUQfLXL3QcCggvaZ2YnAUnefamadtm4u6DQ72FfUMZEpcIpIUhTTo0VHACebWQ+gIsF3nj0C1DCzrDCrbAQsDMvPBxoD880sC6gOrIzZvlXsMZGpqS4ipZa73+rujdy9CcHgzsfufh7wCXB6WOxCYET4+8hwnXD/x+7u4fazw1H3vYDmwH8SrZcyThFJihJ+JvNm4LXwa3y+AgaH2wcDL5nZPIJM82wAd59pZq8D3wE5wFXunpvoxRU4RSQpivv1SXcfR/AWI+7+AwWMirv7b8AZhRx/N3B3MuqiwCkiSZGu750nQoFTRJIiXd87T4QCp4gkRbq+d54IBU4RSQo11UVEIlJTvRT4Yf2iVFchLVRt1DHVVUgbo6ofkeoq7NLyylDoLLWBU0TSi5rqIiIRlZ18U4FTRJJEGaeISER6HElEJCINDomIRFR2wqYCp4gkifo4RUQiKktNdU1kLCISkTJOEUmKspNvKnCKSJKoj1NEJKKy1MepwCkiSVGRJYhXAAAOPElEQVR2wqYCp4gkiZrqIiIReRnKORU4RSQplHGKiESkwSERkYjKTthU4BSRJFHGKSISkfo4RUQi0qi6iEhEyjhFRCJSxikiEpEyThGRiPK87GScmshYRCQiZZwikhRlJ99U4BSRJNED8CIiEWlUXUQkIo2qi4hEpKa6iEhEaqqLiESkprqISERehh6AV+AUkaRQH6eISERqqouIRKTBIRGRiNRUFxGJSINDIiIRqY+zDLv8qovofcEZuDuzvvueq6+4hTdGvEDVqlUAqFOnFtOmzuCCc68E4J77bqdL145s/HUjV19xC9O/+S6V1S8xc+Z8zvp1G8jNzSUnJ5fDjziBU089gTtuv5599mnOEUeexLRp0wEoV64cTz45kHZtDyQvL48bbuzH+PFfpPgOilmG0WH0PWxavIqvet9H+xH9yaxaEYDytaux9qv/8vVFD5JVvQr7P/InKjfJJm/TFmZe9zTrZ88no0I52o/oR0b5clhmBkve+ZL/3v9Gim+qaOrjLKPq1c/msj+dzxGH9OC33zbx3AuPcMppJ3BSt3Pzywx56XHef28sAF26dqRpsyYc0vo42rU/iPsfvpPjjz0jVdUvcV2PP5MVK1blr383cw5nndWXJ54cuE25PpcEn1+7g4+jTp3dGTliKIcfceIu3bTb87LubJi7kKzdKgEwuWf//H0HDb6epR9MAaDptb1Y9+1PfHPxQ1TeuwH7DryEqaffRd6mLUw5dQC5v27CsjI5ZNSdLP/4a9ZMnZeK24lLcfVxmlljYChQjyCxHeTuj5pZLWAY0AT4H3Cmu68yMwMeBXoAvwIXufu08FwXAreHp77L3V9MpE7FNpGxme1jZp3NrOp227sV1zWTISsri4qVKpKZmUnlypVYvHhp/r6qVatw1NEdeO+dMQB079GZ1199C4Cpk7+hevXdyM6uk5J6lwaz58zj+7k//GH7vvs255NPPgNg2bIVrFmzlnbtDirp6pWYCvVrUfu4tix45eM/7MusUpFaR+7P0veDwFmlRUNWTvgWgF/nLaRS4zqUr1MdgNxfNwFg5TKxrMyyNeHltnKAG9x9X6ADcJWZ7QfcAox19+bA2HAdoDvQPFz6Ak8BhIG2H3AocAjQz8xqJlKhYgmcZnYNMAK4GvjWzHrG7L6nOK6ZDIsXLeHJxwfz9cxxzJw7kbVr1zHu44n5+3ucdBzjP53E+nUbAKjfIJsF8xfn71+4YAn1G2SXeL1Twp1333mFSZ+/S58+5xZZdPqM7zjpxK5kZmbSpElj2rQ5gEaN6pdQRUvePgMu5Pt/vILn/THS1e3RnpUTZpK7fiMA6777mbonHAJAtTbNqNioNhXq1woKZxgdxg6k08xBrPh0Bmumld5sE4LBoUSWOM67aGvG6O7rgFlAQ6AnsDVjfBHoFf7eExjqgS+AGmZWHzgeGOPuK919FTAGSCiRK66M8zKgnbv3AjoBd5jZteE+K6Zr7rTqNarRvUdn2h1wLK1aHEnlypU546yT8/efevqJvPnGO/nrQYtgW7ty8zNWp2NOpcNhPTi55wVc/qcLOfLIQwst+8ILw1iwYDGTPn+XB+7vzxdfTCU3J7cEa1tyah/Xls3L17Bu+o8F7q9/yhEseuv3/4x/fGwE5apXocPYgezRpxvrZvwP3/rZ5DlfdL6F8a2vpHrbZlTdp1FJ3ELC8vCElijMrAnQBvgSyHb3RRAEV6BuWKwh8EvMYfPDbYVtj6y4+jgz3X09gLv/z8w6AW+Y2Z4UETjNrC9Bak2VCnWpWL56MVWvYB07Hc5PP83P77d7Z9Ro2h/ahn8PG0nNWjVo2+4ALgwHhQAWLlhMw0b18tcbNMxm8aKlfzjvrmjRoiVA0PQeMfID2h/cms8++7LAsrm5udz01zvz18d98hZz5xUcWNJdjUNaUOf4dtTu3IaMiuXIqlqJVk9exbdXPUm5mlWp1qYZyy9+ML987vqNzLzu6fz1oyY/zsafl21zzpy1v7Jy4nfsfkxr1s+eX2L3ElWig0Ox/+5Dg9x9UAHlqgLDgevcfW1BicvWogVWr/DtkRVXxrnYzFpvXQmD6IlAbeCAwg5y90HufrC7H1zSQRNg/vyFHNy+NZUqBaOfR3c8jO/nBH12PXt1Y/QH49i0aXN++Q/e/5gzzzkFgHbtD2Lt2vUsWbLsjyfexVSuXCn/KYPKlSvRpfPRzJw5p9DylSpVpHLlYJCkc+ejyMnNZfbsuSVS15I27+7XGN/mKia0v5rpf3qMlRNn8u1VTwKQfVIHlo+ZRt6mLfnls6pVxsplAtCw97Gs+mIWues3Um733ciqVhmAjIrl2P3oA9gwb2HJ31AEee4JLbH/7sOloKBZjiBovuLub4abl4RNcMKfW7OW+UDjmMMbAQuL2B5ZcWWcFxB06OZz9xzgAjN7ppiuudOmTZnOqBEf8vGEt8nJyWHG9FkMHfIaAKecdgKPPrztn+eYD8fRpWtHJn/zERt/3cg1V96aimqXuOzsOrw+7FkAsrIyeW3YCEaPGcfJJ3fj4Yf+QZ06tXj7rReYPv07TjypN3Xr1uadUS+Tl5fHwoWLueSSa3dwhV1TvV6H8+PjI7bZVqVFQ1o9fiXk5rH++wXMvD7451EhuyatHrsCy8zAMjJYPGISy8dMS0W141ZcnVThKPlgYJa7PxSzayRwITAw/DkiZvufzew1goGgNe6+yMw+BO6JGRDqCiT0j9ZKa59c7WotSmfFSpl1mzemugppY1T1I1JdhbTQdclrCY1DHNHw2IT+zU5c8HGR1zOzI4EJwAx+f87+NoJ+zteBPYCfgTPcfWUYaJ8gGPj5FbjY3aeE57okPBbgbncfkkid9RyniCRFcT3H6e6fUfjYSOcCyjtwVSHneh54fmfrpMApIklRWluvxUGBU0SSQrMjiYhEpHfVRUQiUlNdRCQiNdVFRCJSxikiEpEyThGRiDQ4JCISUV4ZaqoX20TGIiK7KmWcIpIUaqqLiERUlprqCpwikhTKOEVEIlLGKSISkTJOEZGIlHGKiESkjFNEJCL3vB0X2kUocIpIUuhddRGRiDQ7kohIRMo4RUQiUsYpIhKRHkcSEYlIjyOJiESkprqISEQaHBIRiagsZZyaAV5EJCJlnCKSFBpVFxGJqCw11RU4RSQpNDgkIhKRMk4RkYjUxykiEpHeHBIRiUgZp4hIROrjFBGJSE11EZGIlHGKiESkwCkiElHZCZtgZel/iZ1lZn3dfVCq65EO9FnFR59TetLsSNH0TXUF0og+q/joc0pDCpwiIhEpcIqIRKTAGY36ouKnzyo++pzSkAaHREQiUsYpIhKRAmeczKybmc0xs3lmdkuq61NamdnzZrbUzL5NdV1KMzNrbGafmNksM5tpZtemuk4SPzXV42BmmcD3wHHAfGAycI67f5fSipVCZnY0sB4Y6u6tUl2f0srM6gP13X2ame0GTAV66e9UelDGGZ9DgHnu/oO7bwZeA3qmuE6lkruPB1amuh6lnbsvcvdp4e/rgFlAw9TWSuKlwBmfhsAvMevz0V9ySRIzawK0Ab5MbU0kXgqc8bECtqmPQ3aamVUFhgPXufvaVNdH4qPAGZ/5QOOY9UbAwhTVRXYRZlaOIGi+4u5vpro+Ej8FzvhMBpqb2V5mVh44GxiZ4jpJGjMzAwYDs9z9oVTXR6JR4IyDu+cAfwY+JOjEf93dZ6a2VqWTmb0KTAJamtl8M+uT6jqVUkcA5wPHmtnX4dIj1ZWS+OhxJBGRiJRxiohEpMApIhKRAqeISEQKnCIiESlwiohEpMC5CzCz3PBxlm/N7N9mVnknztXJzN4Jfz+5qJmgzKyGmV2ZwDX6m9mN8W7frswLZnZ6hGs10UxNkmwKnLuGje7eOpyNaDNweexOC0T+s3b3ke4+sIgiNYDIgVMk3Slw7nomAHuHmdYsM/sXMA1obGZdzWySmU0LM9OqkD/X6Gwz+ww4deuJzOwiM3si/D3bzN4ys2/C5XBgINAszHbvD8vdZGaTzWy6md0Zc66/hfOZfgS03NFNmNll4Xm+MbPh22XRXcxsgpl9b2YnhuUzzez+mGv/aWc/SJHCKHDuQswsC+gOzAg3tSSYF7MNsAG4Heji7m2BKcBfzKwi8CxwEnAUUK+Q0z8GfOruBwFtgZnALcB/w2z3JjPrCjQnmIavNdDOzI42s3YEr6m2IQjM7eO4nTfdvX14vVlA7BtITYCOwAnA0+E99AHWuHv78PyXmdlecVxHJLKsVFdAkqKSmX0d/j6B4B3oBsBP7v5FuL0DsB8wMXhNmvIEr0buA/zo7nMBzOxlCv6u72OBCwDcPRdYY2Y1tyvTNVy+CterEgTS3YC33P3X8BrxvOffyszuIugOqErwuutWr7t7HjDXzH4I76ErcGBM/2f18Nrfx3EtkUgUOHcNG929deyGMDhuiN0EjHH3c7Yr15rkTZFnwD/d/ZntrnFdAtd4gWBG9G/M7CKgU8y+7c/l4bWvdvfYALt1rkuRpFJTvez4AjjCzPYGMLPKZtYCmA3sZWbNwnLnFHL8WOCK8NhMM6sGrCPIJrf6ELgkpu+0oZnVBcYDp5hZpfBrIk6Ko767AYvCqdfO227fGWaWEda5KTAnvPYVYXnMrIWZVYnjOiKRKeMsI9x9WZi5vWpmFcLNt7v792bWF3jXzJYDnwEFfVfQtcCgcLajXOAKd59kZhPDx33eD/s59wUmhRnveqB3+L06w4CvgZ8IuhN25A6CGdF/IuizjQ3Qc4BPgWzgcnf/zcyeI+j7nBZO2bYM6BXfpyMSjWZHEhGJSE11EZGIFDhFRCJS4BQRiUiBU0QkIgVOEZGIFDhFRCJS4BQRiUiBU0Qkov8HTWfAP2CWAdAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lstm_model.load_state_dict(torch.load('checkpoint/twitter.t7', map_location=device))\n",
    "# predict\n",
    "test_loss, test_acc = evaluate(lstm_model, test_iterator, loss_func, report=True)\n",
    "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ocDF29euwWph"
   },
   "source": [
    "If the model is predicted perfectly confusion matrix should be diagonal which indicates values off the main diagonal representing incorrect prediction is zero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2C02kTb7RT7Y"
   },
   "source": [
    "### Ploting the ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:00:17.252004Z",
     "start_time": "2021-04-07T09:00:17.223972Z"
    },
    "id": "tEPjRV2mRaNC"
   },
   "outputs": [],
   "source": [
    "def plot_roc(name, labels, predictions, **kwargs):\n",
    "  fpr = dict()\n",
    "  tpr = dict()\n",
    "  roc_auc = dict()\n",
    "\n",
    "  for i in range(num_classes):\n",
    "      fpr[i], tpr[i], _ = roc_curve(np.array(np.eye(num_classes,num_classes)[labels])[:, i], np.array(np.eye(num_classes,num_classes)[predictions])[:, i])\n",
    "      roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "\n",
    "  all_fpr = np.unique(np.concatenate([fpr[i] for i in range(num_classes)]))\n",
    "\n",
    "  mean_tpr = np.zeros_like(all_fpr)\n",
    "  for i in range(num_classes):\n",
    "      mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "\n",
    "  mean_tpr /= num_classes\n",
    "\n",
    "  fpr[\"macro\"] = all_fpr\n",
    "  tpr[\"macro\"] = mean_tpr\n",
    "  roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
    "\n",
    "  lw=2\n",
    "  plt.figure(figsize=(10,7))\n",
    "  plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "          label='macro-average ROC curve (area = {0:0.2f})'\n",
    "                ''.format(roc_auc[\"macro\"]),\n",
    "          color='green', linestyle=':', linewidth=4)\n",
    "\n",
    "  colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n",
    "  for i, color in zip(range(num_classes), colors):\n",
    "      plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
    "              label='ROC curve of class {0} (area = {1:0.2f})'\n",
    "              ''.format(i, roc_auc[i]), **kwargs)\n",
    "\n",
    "  plt.plot([0, 1], [0, 1],color='red', lw=lw, **kwargs)\n",
    "  plt.xlim([0.0, 1.0])\n",
    "  plt.ylim([0.0, 1.05])\n",
    "  plt.annotate(name,(.5,.48),color='red')\n",
    "  plt.xlabel('False Positive Rate')\n",
    "  plt.ylabel('True Positive Rate')\n",
    "  plt.title('Receiver Operating Characteristic for Model')\n",
    "  plt.grid(True)\n",
    "  ax = plt.gca()\n",
    "  ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:00:18.106165Z",
     "start_time": "2021-04-07T09:00:17.254971Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "id": "vMdx-5SORfJk",
    "outputId": "88de8fb6-13ab-46d2-c66b-ecf97e92ca29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c44f5df6c8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Test Baseline\", report_label_test, report_pred_test, linestyle='--')\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z5poLy0ybCkw"
   },
   "source": [
    "The ideal case of `ROC` is when it has an `inverse L` meaning classes are greatly separated with an `AUC` of 1 under the curve.\n",
    "\n",
    "Here `AUC` of near 0.9 and curl is almost `inverse L` shaped which indicates it has a good measure of separability. This graph also conveys model's strength is on class-0 `neutral` tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QifVxbhqJrUm",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Visualizing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:01:44.365151Z",
     "start_time": "2021-04-07T09:01:44.342156Z"
    },
    "id": "gH1wMUQVJrUm",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "vis_data_records_ig = []\n",
    "def forward(input, text_length):\n",
    "    return F.softmax(lstm_model(input, text_length)[0])\n",
    "lig = LayerIntegratedGradients(forward, lstm_model.embedding)\n",
    "def interpret_sentence(model, sentence, min_len = max_document_length, label = 0):\n",
    "    model.train()\n",
    "    h = model.init_hidden(1)\n",
    "    h500 = model.init_hidden(500)\n",
    "    text = preprocess(sentence)\n",
    "    actual_len = len(text)\n",
    "    if len(text) < min_len:\n",
    "        text += ['<pad>'] * (min_len - len(text))\n",
    "    indexed = [Text.vocab.stoi[t] for t in text]\n",
    "\n",
    "    model.zero_grad()\n",
    "\n",
    "    input_indices = torch.tensor(indexed, device=device)\n",
    "    input_indices = input_indices.unsqueeze(0)\n",
    "    text_length = torch.tensor([actual_len], device=device)\n",
    "    # input_indices dim: [sequence_length]\n",
    "    seq_length = min_len\n",
    "\n",
    "    # predict\n",
    "    pred = F.softmax(model(input_indices, text_length, h)[0]).argmax(dim=1).item()\n",
    "    pred_ind = round(pred)\n",
    "\n",
    "    # generate reference indices for each sample\n",
    "    reference_indices = token_reference.generate_reference(seq_length, device=device).unsqueeze(0)\n",
    "\n",
    "    # compute attributions and approximation delta using layer integrated gradients\n",
    "    attributions_ig, delta = lig.attribute(input_indices, reference_indices,\n",
    "                                           n_steps=500,target=2-label, return_convergence_delta=True, additional_forward_args=text_length)\n",
    "\n",
    "    print(sentence, 'pred: ', {1:'positive', 0: 'netural', 2: 'negative'}[pred_ind], '(', '%.2f'%pred, ')', ', delta: ', abs(delta))\n",
    "\n",
    "    add_attributions_to_visualizer(attributions_ig, text, pred, pred_ind, label, delta, vis_data_records_ig)\n",
    "\n",
    "def add_attributions_to_visualizer(attributions, text, pred, pred_ind, label, delta, vis_data_records):\n",
    "    attributions = attributions.sum(dim=2).squeeze(0)\n",
    "    attributions = attributions / torch.norm(attributions)\n",
    "    attributions = attributions.cpu().detach().numpy()\n",
    "\n",
    "\n",
    "    # storing couple samples in an array for visualization purposes\n",
    "    vis_data_records.append(visualization.VisualizationDataRecord(\n",
    "        attributions,\n",
    "        pred,\n",
    "        {1:'positive', 0: 'netural', 2: 'negative'}[pred_ind],\n",
    "        {1:'positive', 0: 'netural', 2: 'negative'}[label],\n",
    "        {1:'positive', 0: 'netural', 2: 'negative'}[label],\n",
    "        attributions.sum(),\n",
    "        text,\n",
    "        delta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:01:49.331412Z",
     "start_time": "2021-04-07T09:01:47.197656Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "wUrqYoqLz9ks",
    "outputId": "8abb73a2-0ee6-484d-e416-b37e546f22a3",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It was a fantastic performance ! pred:  positive ( 1.00 ) , delta:  tensor([0.0376], dtype=torch.float64)\n",
      "Best film ever pred:  positive ( 1.00 ) , delta:  tensor([0.0537], dtype=torch.float64)\n",
      "Such a great show! pred:  positive ( 1.00 ) , delta:  tensor([0.0426], dtype=torch.float64)\n",
      "It was a horrible movie pred:  negative ( 2.00 ) , delta:  tensor([0.0218], dtype=torch.float64)\n",
      "I've never watched something as bad pred:  negative ( 2.00 ) , delta:  tensor([0.0536], dtype=torch.float64)\n",
      "It is a disgusting movie! pred:  negative ( 2.00 ) , delta:  tensor([0.0383], dtype=torch.float64)\n",
      "ok pred:  netural ( 0.00 ) , delta:  tensor([0.0444], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "interpret_sentence(lstm_model, 'It was a fantastic performance !', label=1)\n",
    "interpret_sentence(lstm_model, 'Best film ever', label=1)\n",
    "interpret_sentence(lstm_model, 'Such a great show!', label=1)\n",
    "interpret_sentence(lstm_model, 'It was a horrible movie', label=2)\n",
    "interpret_sentence(lstm_model, 'I\\'ve never watched something as bad', label=2)\n",
    "interpret_sentence(lstm_model, 'It is a disgusting movie!', label=2)\n",
    "interpret_sentence(lstm_model, 'ok', label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:01:56.797134Z",
     "start_time": "2021-04-07T09:01:56.785102Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 383
    },
    "id": "4pQF5kz7PN3f",
    "outputId": "4eebc70a-0ebc-4b12-eee5-c8876eb3f790"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>positive</b></text></td><td><text style=\"padding-right:2em\"><b>positive (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>positive</b></text></td><td><text style=\"padding-right:2em\"><b>1.05</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(120, 75%, 51%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> fantastic                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> performance                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>positive</b></text></td><td><text style=\"padding-right:2em\"><b>positive (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>positive</b></text></td><td><text style=\"padding-right:2em\"><b>1.03</b></text></td><td><mark style=\"background-color: hsl(120, 75%, 51%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> best                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> film                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>positive</b></text></td><td><text style=\"padding-right:2em\"><b>positive (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>positive</b></text></td><td><text style=\"padding-right:2em\"><b>1.01</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> such                    </font></mark><mark style=\"background-color: hsl(120, 75%, 51%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> great                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> show                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>negative</b></text></td><td><text style=\"padding-right:2em\"><b>negative (2.00)</b></text></td><td><text style=\"padding-right:2em\"><b>negative</b></text></td><td><text style=\"padding-right:2em\"><b>-1.05</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(0, 75%, 61%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> horrible                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> movie                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>negative</b></text></td><td><text style=\"padding-right:2em\"><b>negative (2.00)</b></text></td><td><text style=\"padding-right:2em\"><b>negative</b></text></td><td><text style=\"padding-right:2em\"><b>-1.01</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ve                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> watched                    </font></mark><mark style=\"background-color: hsl(0, 75%, 61%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> bad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>negative</b></text></td><td><text style=\"padding-right:2em\"><b>negative (2.00)</b></text></td><td><text style=\"padding-right:2em\"><b>negative</b></text></td><td><text style=\"padding-right:2em\"><b>-1.02</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(0, 75%, 61%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> disgusting                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> movie                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>netural</b></text></td><td><text style=\"padding-right:2em\"><b>netural (0.00)</b></text></td><td><text style=\"padding-right:2em\"><b>netural</b></text></td><td><text style=\"padding-right:2em\"><b>nan</b></text></td><td><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ok                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> #pad                    </font></mark></td><tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = visualization.visualize_text(vis_data_records_ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:00:20.482377Z",
     "start_time": "2021-04-07T09:00:20.470378Z"
    },
    "id": "3R8NdKcbz9kt",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "vis_word = []\n",
    "colorlist= []\n",
    "def interpret_word(model, word, min_len = max_document_length, label = 0):\n",
    "    model.train()\n",
    "    text = preprocess(word)\n",
    "    actual_len = 1\n",
    "    if len(text) < min_len:\n",
    "        text += ['<pad>'] * (min_len - len(text))\n",
    "    indexed = [Text.vocab.stoi[t] for t in text]\n",
    "\n",
    "    model.zero_grad()\n",
    "\n",
    "    input_indices = torch.tensor(indexed, device=device)\n",
    "    input_indices = input_indices.unsqueeze(0)\n",
    "    text_length = torch.tensor([actual_len], device=device)\n",
    "    # input_indices dim: [sequence_length]\n",
    "    seq_length = min_len\n",
    "\n",
    "    # predict\n",
    "    pred = F.softmax(model(input_indices, text_length)[0]).cpu()\n",
    "\n",
    "\n",
    "    vis_word.append(pred.detach().numpy().squeeze(0))\n",
    "    colorlist.append({0:'#00ff00', 1: '#00ffff', 2:'#000000' }[pred.argmax(dim=1).item()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:00:29.806542Z",
     "start_time": "2021-04-07T09:00:20.485380Z"
    },
    "id": "kMTrDK7sz9kt",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "for i in Text.vocab.itos:\n",
    "    interpret_word(lstm_model, i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedding Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:02:53.009780Z",
     "start_time": "2021-04-07T09:02:26.204974Z"
    },
    "id": "ndkX6219z9kt",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "words_top_ted_tsne = tsne.fit_transform(vis_word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:02:59.134060Z",
     "start_time": "2021-04-07T09:02:59.051085Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "Yd79T06dz9kt",
    "outputId": "6e6fd75c-940d-4966-82f7-897f3dfcdfd4",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"4ca50f08-051c-4906-b1ce-12ab3ab46204\" data-root-id=\"1166\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"8af7879a-ed1e-4d24-9a35-29162df5006f\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1177\"}],\"center\":[{\"id\":\"1180\"},{\"id\":\"1184\"}],\"left\":[{\"id\":\"1181\"}],\"renderers\":[{\"id\":\"1198\"}],\"title\":{\"id\":\"1167\"},\"toolbar\":{\"id\":\"1189\"},\"toolbar_location\":\"above\",\"x_range\":{\"id\":\"1169\"},\"x_scale\":{\"id\":\"1173\"},\"y_range\":{\"id\":\"1171\"},\"y_scale\":{\"id\":\"1175\"}},\"id\":\"1166\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"formatter\":{\"id\":\"1218\"},\"ticker\":{\"id\":\"1178\"}},\"id\":\"1177\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1169\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1186\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"formatter\":{\"id\":\"1220\"},\"ticker\":{\"id\":\"1182\"}},\"id\":\"1181\",\"type\":\"LinearAxis\"},{\"attributes\":{\"axis\":{\"id\":\"1177\"},\"ticker\":null},\"id\":\"1180\",\"type\":\"Grid\"},{\"attributes\":{\"text\":\"vector T-SNE 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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[5002]},\"x2\":{\"__ndarray__\":\"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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[5002]}},\"selected\":{\"id\":\"1222\"},\"selection_policy\":{\"id\":\"1221\"}},\"id\":\"1194\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1182\",\"type\":\"BasicTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1185\"},{\"id\":\"1186\"},{\"id\":\"1187\"},{\"id\":\"1188\"}]},\"id\":\"1189\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1218\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1187\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1221\",\"type\":\"UnionRenderers\"}],\"root_ids\":[\"1166\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.1\"}};\n",
       "  var render_items = [{\"docid\":\"8af7879a-ed1e-4d24-9a35-29162df5006f\",\"root_ids\":[\"1166\"],\"roots\":{\"1166\":\"4ca50f08-051c-4906-b1ce-12ab3ab46204\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1166"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n",
    "           toolbar_location=\"above\",\n",
    "           title=\"vector T-SNE for embeddings\")\n",
    "\n",
    "source = ColumnDataSource(data=dict(x1=words_top_ted_tsne[:,0],\n",
    "                                    x2=words_top_ted_tsne[:,1],\n",
    "                                    names=Text.vocab.itos,\n",
    "                                    color=colorlist))\n",
    "\n",
    "p.scatter(x=\"x1\", y=\"x2\", size=8, source=source, fill_color=\"color\")\n",
    "\n",
    "word_labels = LabelSet(x=\"x1\", y=\"x2\", text=\"names\", y_offset=6,\n",
    "                       text_font_size=\"8pt\", text_color=\"#555555\",\n",
    "                       source=source, text_align='center',render_mode='canvas')\n",
    "# p.add_layout(word_labels)\n",
    "\n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Showing labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-07T09:03:07.255845Z",
     "start_time": "2021-04-07T09:03:07.173848Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "wWbpD19Dz9ku",
    "outputId": "9cc27e08-0560-414c-b87d-4bcbd737272c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"4824a0d8-a79b-46d9-bc0b-be09285504e4\" data-root-id=\"1258\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[5002]},\"x2\":{\"__ndarray__\":\"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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[5002]}},\"selected\":{\"id\":\"1321\"},\"selection_policy\":{\"id\":\"1320\"}},\"id\":\"1286\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"text\":\"vector T-SNE for embeddings\"},\"id\":\"1259\",\"type\":\"Title\"},{\"attributes\":{\"source\":{\"id\":\"1286\"}},\"id\":\"1291\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1320\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1261\",\"type\":\"DataRange1d\"}],\"root_ids\":[\"1258\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.1\"}};\n",
       "  var render_items = [{\"docid\":\"d85b2db6-738f-4c40-a1f6-0d2be08fb6fb\",\"root_ids\":[\"1258\"],\"roots\":{\"1258\":\"4824a0d8-a79b-46d9-bc0b-be09285504e4\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1258"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n",
    "           toolbar_location=\"above\",\n",
    "           title=\"vector T-SNE for embeddings\")\n",
    "\n",
    "source = ColumnDataSource(data=dict(x1=words_top_ted_tsne[:,0],\n",
    "                                    x2=words_top_ted_tsne[:,1],\n",
    "                                    names=Text.vocab.itos,\n",
    "                                    color=colorlist))\n",
    "\n",
    "p.scatter(x=\"x1\", y=\"x2\", size=8, source=source, fill_color=\"color\")\n",
    "\n",
    "word_labels = LabelSet(x=\"x1\", y=\"x2\", text=\"names\", y_offset=6,\n",
    "                       text_font_size=\"8pt\", text_color=\"#555555\",\n",
    "                       source=source, text_align='center',render_mode='canvas')\n",
    "p.add_layout(word_labels)\n",
    "\n",
    "show(p)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "pytorch_twitter_sentiment_analysis.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
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   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
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    "builtin_function_or_method",
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 },
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}