LSTM_NLP_SEM4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import re\n",
    "import shutil\n",
    "import string\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import regularizers\n",
    "\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import losses\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import sklearn\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from tensorflow.keras import preprocessing\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import pydot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "reviews=pd.read_csv('C:/Users/vishe/OneDrive/Desktop/Mini_project_final_review/review_clean_countvectorize.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>clean_review</th>\n",
       "      <th>emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>change look iphone barely protects it. one dro...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fits like glove. packaged great.</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>screen protector arrived within week. fits pho...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bought \"blinged\" version case, want ruin work,...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>think phone good. blu studio energy upgrading,...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34995</th>\n",
       "      <td>product excellent. went ease protects well. re...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34996</th>\n",
       "      <td>good case device. recommend buying one discont...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34997</th>\n",
       "      <td>worked brilliantly month stopped working sudde...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34998</th>\n",
       "      <td>finally easier way put screen protector on.</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34999</th>\n",
       "      <td>many similar adapter available amazon, read re...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            clean_review emotion\n",
       "0      change look iphone barely protects it. one dro...     neg\n",
       "1                       fits like glove. packaged great.     pos\n",
       "2      screen protector arrived within week. fits pho...     pos\n",
       "3      bought \"blinged\" version case, want ruin work,...     pos\n",
       "4      think phone good. blu studio energy upgrading,...     neg\n",
       "...                                                  ...     ...\n",
       "34995  product excellent. went ease protects well. re...     pos\n",
       "34996  good case device. recommend buying one discont...     pos\n",
       "34997  worked brilliantly month stopped working sudde...     pos\n",
       "34998        finally easier way put screen protector on.     pos\n",
       "34999  many similar adapter available amazon, read re...     pos\n",
       "\n",
       "[35000 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews_train=reviews[:35000]\n",
    "test_data=reviews[35000:]\n",
    "reviews_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data len:17500\n",
      "Class distributionCounter({'pos': 13324, 'neg': 4176})\n",
      "Valid data len:17500\n",
      "Class distributionCounter({'pos': 13323, 'neg': 4177})\n"
     ]
    }
   ],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(reviews_train['clean_review'].tolist(),\\\n",
    "                                                      reviews_train['emotion'].tolist(),\\\n",
    "                                                      test_size=0.5,\\\n",
    "                                                      stratify = reviews_train['emotion'].tolist(),\\\n",
    "                                                      random_state=0)\n",
    "\n",
    "\n",
    "print('Train data len:'+str(len(X_train)))\n",
    "print('Class distribution'+str(Counter(y_train)))\n",
    "print('Valid data len:'+str(len(X_valid)))\n",
    "print('Class distribution'+ str(Counter(y_valid)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 4855, 1432, 343]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "num_words = 50000\n",
    "\n",
    "tokenizer = Tokenizer(num_words=num_words,oov_token=\"unk\")\n",
    "tokenizer.fit_on_texts(X_train)\n",
    "\n",
    "\n",
    "print(str(tokenizer.texts_to_sequences(['xyz how are you'])))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:3: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:4: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  \"\"\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data len:17500\n",
      "Class distributionCounter({1: 13324, 0: 4176})\n",
      "Validation data len:17500\n",
      "Class distributionCounter({1: 13323, 0: 4177})\n",
      "Test data len:5000\n",
      "Class distributionCounter({1: 3724, 0: 1276})\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import LabelEncoder as le\n",
    "\n",
    "x_train = np.array( tokenizer.texts_to_sequences(X_train) )\n",
    "x_valid = np.array( tokenizer.texts_to_sequences(X_valid) )\n",
    "x_test  = np.array( tokenizer.texts_to_sequences(test_data['clean_review'].tolist()) )\n",
    "\n",
    "sequence_length=50\n",
    "\n",
    "x_train = pad_sequences(x_train, padding='post', maxlen=sequence_length)\n",
    "x_valid = pad_sequences(x_valid, padding='post', maxlen=sequence_length)\n",
    "x_test = pad_sequences(x_test, padding='post', maxlen=sequence_length)\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "train_labels =  np.asarray(le.fit_transform(y_train))\n",
    "#train_labels = np.asarray( tf.keras.utils.to_categorical(train_labels))\n",
    "valid_labels = np.asarray(le.fit_transform(y_valid))\n",
    "#valid_labels = np.asarray( tf.keras.utils.to_categorical(valid_labels))\n",
    "\n",
    "test_labels = np.asarray(test_data['emotion'].tolist())\n",
    "test_labels = np.asarray(le.fit_transform(test_labels))\n",
    "\n",
    "print('Train data len:'+str(len(x_train)))\n",
    "print('Class distribution'+str(Counter(train_labels)))\n",
    "\n",
    "print('Validation data len:'+str(len(x_valid)))\n",
    "print('Class distribution'+str(Counter(valid_labels)))\n",
    "\n",
    "print('Test data len:'+str(len(x_test)))\n",
    "print('Class distribution'+str(Counter(test_labels)))\n",
    "\n",
    "\n",
    "train_ds = tf.data.Dataset.from_tensor_slices((x_train,train_labels))\n",
    "valid_ds = tf.data.Dataset.from_tensor_slices((x_valid,valid_labels))\n",
    "test_ds = tf.data.Dataset.from_tensor_slices((x_test,test_labels))\n",
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======Train dataset ====\n",
      "tf.Tensor(\n",
      "[  70   12   90 1056 1075 1990  730 2971  279 2612  233  421   87  277\n",
      " 1091   70  253  205 6820  148   42  216 1169  244  907  583  385 1057\n",
      "  289 1076  205  244 1233   69   80 6821  279 1870 2971  202  320  180\n",
      "  593  706 1368    0    0    0    0    0], shape=(50,), dtype=int32) tf.Tensor(0, shape=(), dtype=int64)\n",
      "======Validation dataset ====\n",
      "tf.Tensor(\n",
      "[  12   19   27   19   91  182   12  139  352   44 3162   19   91  410\n",
      "  921   27   11    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0], shape=(50,), dtype=int32) tf.Tensor(1, shape=(), dtype=int64)\n",
      "======Test dataset ====\n",
      "tf.Tensor(\n",
      "[1071    2  508   66  257  136  145    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0], shape=(50,), dtype=int32) tf.Tensor(0, shape=(), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "count =0\n",
    "print('======Train dataset ====')\n",
    "for value,label in train_ds:\n",
    "    count += 1\n",
    "    print(value,label)\n",
    "    if count==1:\n",
    "        break\n",
    "count =0\n",
    "print('======Validation dataset ====')\n",
    "for value,label in valid_ds:\n",
    "    count += 1\n",
    "    print(value,label)\n",
    "    if count==1:\n",
    "        break\n",
    "count =0\n",
    "print('======Test dataset ====')\n",
    "for value,label in test_ds:\n",
    "    count += 1\n",
    "    print(value,label)\n",
    "    if count==1:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 50, 16)            800016    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 50, 16)            0         \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 50, 16)            2112      \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               410112    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 8)                 4104      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 1,216,353\n",
      "Trainable params: 1,216,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "max_features =50000\n",
    "embedding_dim =16\n",
    "sequence_length = 50\n",
    "\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Embedding(max_features +1, embedding_dim, input_length=sequence_length,\\\n",
    "                                    embeddings_regularizer = regularizers.l2(0.005))) \n",
    "model.add(tf.keras.layers.Dropout(0.4))\n",
    "\n",
    "model.add(tf.keras.layers.LSTM(embedding_dim,dropout=0.2, recurrent_dropout=0.2,return_sequences=True,\\\n",
    "                                                             kernel_regularizer=regularizers.l2(0.005),\\\n",
    "                                                             bias_regularizer=regularizers.l2(0.005)))\n",
    "\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "\n",
    "model.add(tf.keras.layers.Dense(512, activation='relu',\\\n",
    "                                kernel_regularizer=regularizers.l2(0.001),\\\n",
    "                                bias_regularizer=regularizers.l2(0.001),))\n",
    "model.add(tf.keras.layers.Dropout(0.4))\n",
    "\n",
    "model.add(tf.keras.layers.Dense(8, activation='relu',\\\n",
    "                                kernel_regularizer=regularizers.l2(0.001),\\\n",
    "                                bias_regularizer=regularizers.l2(0.001),))\n",
    "model.add(tf.keras.layers.Dropout(0.4))\n",
    "\n",
    "\n",
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
    "                               \n",
    "\n",
    "\n",
    "\n",
    "model.summary()\n",
    "model.compile(loss=tf.keras.losses.BinaryCrossentropy(),optimizer=tf.keras.optimizers.Adam(1e-3),metrics=[tf.keras.metrics.BinaryAccuracy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(model, \"multi_input_and_output_model.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 18 steps, validate for 18 steps\n",
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n",
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18/18 [==============================] - 15s 812ms/step - loss: 3.2947 - binary_accuracy: 0.7534 - val_loss: 1.9976 - val_binary_accuracy: 0.7613\n",
      "Epoch 2/15\n",
      "18/18 [==============================] - 4s 198ms/step - loss: 1.4525 - binary_accuracy: 0.7614 - val_loss: 1.0177 - val_binary_accuracy: 0.7613\n",
      "Epoch 3/15\n",
      "18/18 [==============================] - 5s 268ms/step - loss: 0.8809 - binary_accuracy: 0.7614 - val_loss: 0.7529 - val_binary_accuracy: 0.7613\n",
      "Epoch 4/15\n",
      "18/18 [==============================] - 5s 252ms/step - loss: 0.7026 - binary_accuracy: 0.7614 - val_loss: 0.6137 - val_binary_accuracy: 0.7613\n",
      "Epoch 5/15\n",
      "18/18 [==============================] - 5s 254ms/step - loss: 0.6100 - binary_accuracy: 0.7698 - val_loss: 0.5668 - val_binary_accuracy: 0.7910\n",
      "Epoch 6/15\n",
      "18/18 [==============================] - 4s 222ms/step - loss: 0.5648 - binary_accuracy: 0.8102 - val_loss: 0.5458 - val_binary_accuracy: 0.8349\n",
      "Epoch 7/15\n",
      "18/18 [==============================] - 4s 206ms/step - loss: 0.5338 - binary_accuracy: 0.8202 - val_loss: 0.5296 - val_binary_accuracy: 0.8410\n",
      "Epoch 8/15\n",
      "18/18 [==============================] - 3s 192ms/step - loss: 0.5124 - binary_accuracy: 0.8354 - val_loss: 0.5126 - val_binary_accuracy: 0.8438\n",
      "Epoch 9/15\n",
      "18/18 [==============================] - 4s 233ms/step - loss: 0.4905 - binary_accuracy: 0.8533 - val_loss: 0.5048 - val_binary_accuracy: 0.8449\n",
      "Epoch 10/15\n",
      "18/18 [==============================] - 4s 244ms/step - loss: 0.4731 - binary_accuracy: 0.8581 - val_loss: 0.5008 - val_binary_accuracy: 0.8462\n",
      "Epoch 11/15\n",
      "18/18 [==============================] - 5s 270ms/step - loss: 0.4684 - binary_accuracy: 0.8631 - val_loss: 0.5004 - val_binary_accuracy: 0.8450\n",
      "Epoch 12/15\n",
      "18/18 [==============================] - 14s 769ms/step - loss: 0.4642 - binary_accuracy: 0.8594 - val_loss: 0.5147 - val_binary_accuracy: 0.8413\n",
      "Epoch 13/15\n",
      "18/18 [==============================] - 11s 591ms/step - loss: 0.4566 - binary_accuracy: 0.8621 - val_loss: 0.5077 - val_binary_accuracy: 0.8431\n",
      "Epoch 14/15\n",
      "18/18 [==============================] - 12s 658ms/step - loss: 0.4391 - binary_accuracy: 0.8682 - val_loss: 0.4902 - val_binary_accuracy: 0.8462\n",
      "Epoch 15/15\n",
      "18/18 [==============================] - 13s 729ms/step - loss: 0.4301 - binary_accuracy: 0.8737 - val_loss: 0.4940 - val_binary_accuracy: 0.8475\n",
      "<tensorflow.python.keras.callbacks.History object at 0x000001BABF3082C8>\n",
      "1min 46s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1 \n",
    "with tf.device('/GPU:0'):\n",
    "    epochs = 15\n",
    "    print(model.fit(train_ds.batch(1000),epochs= epochs ,validation_data=valid_ds.batch(1000),verbose=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(model.history .history['loss'], label=' training data')\n",
    "plt.plot(model.history .history['val_loss'], label='validation data')\n",
    "plt.title('Loss for Text Classification')\n",
    "plt.ylabel('Loss value')\n",
    "plt.xlabel('No. epoch')\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(model.history.history['binary_accuracy'], label=' training data')\n",
    "plt.plot(model.history.history['val_binary_accuracy'], label='validation data')\n",
    "plt.title('Accuracy for Text Classification')\n",
    "plt.ylabel('Accuracy value')\n",
    "plt.xlabel('No. epoch')\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "model.save('C:\\\\Users\\\\vishe\\\\OneDrive\\\\Desktop\\\\Mini_project_final_review\\\\lstm_out\\\\tf_lstmmodel.h5') \n",
    "json_string = tokenizer.to_json()\n",
    "with open('C:\\\\Users\\\\vishe\\\\OneDrive\\\\Desktop\\\\Mini_project_final_review\\\\lstm_out\\\\tokenizer.json', 'w') as outfile:\n",
    "    json.dump(json_string, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.40411243]\n",
      " [0.9789153 ]\n",
      " [0.27797252]\n",
      " [0.99015665]\n",
      " [0.34559816]\n",
      " [0.58976847]\n",
      " [0.9637389 ]\n",
      " [0.99928814]\n",
      " [0.92841816]\n",
      " [0.29957852]]\n"
     ]
    }
   ],
   "source": [
    "valid_predict= model.predict(x_valid)\n",
    "print(valid_predict[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc(name, labels, predictions, **kwargs):\n",
    "    fp, tp, thresholds = sklearn.metrics.roc_curve(labels, predictions)\n",
    "    plt.plot(fp, tp, label=name, linewidth=2, **kwargs)\n",
    "    plt.xlabel('False positives Rate')\n",
    "    plt.ylabel('True positives Rate')\n",
    "    plt.xlim([-0.03, 1.0])\n",
    "    plt.ylim([0.0, 1.03])\n",
    "    plt.grid(True)\n",
    "    thresholdsLength = len(thresholds)\n",
    "    thresholds_every = 1000\n",
    "    colorMap = plt.get_cmap('jet', thresholdsLength)\n",
    "    for i in range(0, thresholdsLength, thresholds_every):\n",
    "        threshold_value_with_max_four_decimals = str(thresholds[i])[:5]\n",
    "        plt.text(fp[i] - 0.03, tp[i] + 0.001, threshold_value_with_max_four_decimals, fontdict={'size': 15}, color=colorMap(i/thresholdsLength));\n",
    "\n",
    "    ax = plt.gca()\n",
    "    ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.figsize'] = (16, 16)\n",
    "\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bb83ae81c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Valid Baseline\", valid_labels, valid_predict, color=colors[0], linestyle='--')\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 50, 16)            800016    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 50, 16)            0         \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 50, 16)            2112      \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               410112    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 8)                 4104      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 1,216,353\n",
      "Trainable params: 1,216,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "new_model = tf.keras.models.load_model('C:\\\\Users\\\\vishe\\\\OneDrive\\\\Desktop\\\\Mini_project_final_review\\\\lstm_out\\\\tf_lstmmodel.h5')\n",
    "new_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('C:\\\\Users\\\\vishe\\\\OneDrive\\\\Desktop\\\\Mini_project_final_review\\\\lstm_out\\\\tokenizer.json') as json_file:\n",
    "    json_string = json.load(json_file)\n",
    "tokenizer1 = tf.keras.preprocessing.text.tokenizer_from_json(json_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "x_test  = np.array( tokenizer.texts_to_sequences(test_data['clean_review'].tolist()) )\n",
    "x_test = pad_sequences(x_test, padding='post', maxlen=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generate predictions for all samples\n"
     ]
    }
   ],
   "source": [
    "# Generate predictions (probabilities -- the output of the last layer)\n",
    "# on test  data using `predict`\n",
    "print(\"Generate predictions for all samples\")\n",
    "predictions = new_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "test_data['pred_sentiment']= predictions\n",
    "test_data['pred_sentiment'] = np.where((test_data.pred_sentiment >= 0.730),int(1),test_data.pred_sentiment)\n",
    "test_data['pred_sentiment'] = np.where((test_data.pred_sentiment < 0.730),int(0),test_data.pred_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishe\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>clean_review</th>\n",
       "      <th>emotion</th>\n",
       "      <th>pred_sentiment</th>\n",
       "      <th>emo_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35000</th>\n",
       "      <td>keeps phone safe. pretty heavy bulky though.</td>\n",
       "      <td>neg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35001</th>\n",
       "      <td>liked phone working well.</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35002</th>\n",
       "      <td>phone problem hotspot, tried hotspot share net...</td>\n",
       "      <td>neg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35003</th>\n",
       "      <td>got part gift promotion bought 9000ah external...</td>\n",
       "      <td>pos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35004</th>\n",
       "      <td>purchased cable google nexus 6p. although phon...</td>\n",
       "      <td>pos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39995</th>\n",
       "      <td>far working fine, hopefully last time order ch...</td>\n",
       "      <td>pos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39996</th>\n",
       "      <td>great case money. fits look great.</td>\n",
       "      <td>pos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39997</th>\n",
       "      <td>bad product. worked well 1 month stopped worki...</td>\n",
       "      <td>neg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39998</th>\n",
       "      <td>gets job done. quite thought ordering though.</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39999</th>\n",
       "      <td>great low price nice case</td>\n",
       "      <td>pos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            clean_review emotion  \\\n",
       "35000       keeps phone safe. pretty heavy bulky though.     neg   \n",
       "35001                          liked phone working well.     pos   \n",
       "35002  phone problem hotspot, tried hotspot share net...     neg   \n",
       "35003  got part gift promotion bought 9000ah external...     pos   \n",
       "35004  purchased cable google nexus 6p. although phon...     pos   \n",
       "...                                                  ...     ...   \n",
       "39995  far working fine, hopefully last time order ch...     pos   \n",
       "39996                 great case money. fits look great.     pos   \n",
       "39997  bad product. worked well 1 month stopped worki...     neg   \n",
       "39998      gets job done. quite thought ordering though.     pos   \n",
       "39999                          great low price nice case     pos   \n",
       "\n",
       "       pred_sentiment  emo_key  \n",
       "35000             1.0        0  \n",
       "35001             0.0        1  \n",
       "35002             0.0        0  \n",
       "35003             1.0        1  \n",
       "35004             1.0        1  \n",
       "...               ...      ...  \n",
       "39995             1.0        1  \n",
       "39996             1.0        1  \n",
       "39997             0.0        0  \n",
       "39998             0.0        1  \n",
       "39999             1.0        1  \n",
       "\n",
       "[5000 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['emo_key']=test_labels\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.75      0.71      1276\n",
      "           1       0.91      0.88      0.89      3724\n",
      "\n",
      "    accuracy                           0.84      5000\n",
      "   macro avg       0.79      0.81      0.80      5000\n",
      "weighted avg       0.85      0.84      0.84      5000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = [0, 1]\n",
    "print(classification_report(test_data['emo_key'].tolist(),test_data['pred_sentiment'].tolist(),labels=labels))"
   ]
  }
 ],
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