Character-CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv1D, Dense, Dropout, Concatenate, GlobalAveragePooling1D, GlobalMaxPooling1D, Input, MaxPooling1D, Flatten\n",
    "from keras.optimizers import Adam\n",
    "from keras.losses import sparse_categorical_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mayoor/dev/kaggle/tc2/tc2/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (1,10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "amazon_review = pd.read_csv('1429_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_df = amazon_review[['reviews.text','reviews.rating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mayoor/dev/kaggle/tc2/tc2/lib/python3.6/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",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "ratings_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    23774\n",
       "4.0     8541\n",
       "3.0     1499\n",
       "1.0      410\n",
       "2.0      402\n",
       "Name: reviews.rating, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df['reviews.rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxlen = 1024\n",
    "alphabet_list = \"abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\\\"/\\|_@#$%ˆ&* ̃‘+-=<>()[]{} \"\n",
    "alphabet_index = {v:i for i,v in enumerate(alphabet_list)}\n",
    "matrix = np.eye(len(alphabet_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data_for_character_cnn(documents, maxlen,alphabet_list, alphabet_index, matrix):\n",
    "    doc_array = []\n",
    "    for doc in documents:\n",
    "        doc_char_list = []\n",
    "        if len(str(doc)) <= maxlen:\n",
    "            try:\n",
    "                doc = str(doc) + \"\".join(['`']*(maxlen-len(doc)+1))\n",
    "            except:\n",
    "                print (type(doc),doc)\n",
    "        for c in str(doc).lower()[:maxlen]:\n",
    "            doc_char_list.append(matrix[alphabet_index[c]].T if c in alphabet_index else np.zeros(len(alphabet_list)))\n",
    "        doc_array.append(np.array(doc_char_list).T)\n",
    "    return np.array(doc_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(ratings_df,test_size=0.2, random_state=42,stratify=ratings_df['reviews.rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "charcnn_dataset_train = prepare_data_for_character_cnn(train['reviews.text'].values.tolist(), maxlen, alphabet_list, alphabet_index, matrix)\n",
    "charcnn_dataset_test = prepare_data_for_character_cnn(test['reviews.text'].values.tolist(), maxlen, alphabet_list, alphabet_index, matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(71, 1024)\n",
      "(71, 1024)\n"
     ]
    }
   ],
   "source": [
    "print(charcnn_dataset_train[0].shape)\n",
    "print(charcnn_dataset_test[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_char_cnn_model(kernels, maxlen, char_len):\n",
    "    input = Input(shape=(char_len,maxlen))\n",
    "    cnns = []\n",
    "    cnn = input\n",
    "    for kernel in kernels:\n",
    "        cnn = Conv1D(kernel_size=kernel, filters=128, padding=\"SAME\")(cnn)\n",
    "        cnn = MaxPooling1D(2)(cnn)\n",
    "#         cnn_average = GlobalAveragePooling1D()(cnn)\n",
    "#         cnns.append(cnn_max)\n",
    "#         cnns.append(cnn_average)\n",
    "#     cnn_layer = Concatenate()(cnns)\n",
    "    cnn_flatten = Flatten()(cnn)\n",
    "    output = Dense(128, activation='relu')(cnn_flatten)\n",
    "    output = Dropout(0.2)(output)\n",
    "    output = Dense(64, activation='relu')(output)\n",
    "    output = Dropout(0.2)(output)\n",
    "    output = Dense(32, activation='relu')(output)\n",
    "    output = Dropout(0.2)(output)\n",
    "    output = Dense(5, activation='softmax')(output)\n",
    "    model = Model(input, output)\n",
    "    return model\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/mayoor/dev/kaggle/tc2/tc2/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /Users/mayoor/dev/kaggle/tc2/tc2/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "char_cnn_model = get_char_cnn_model([7,6,5,4,3,2],maxlen, len(alphabet_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 71, 1024)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 71, 128)           917632    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 35, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 35, 128)           98432     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 17, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 17, 128)           82048     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 8, 128)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 8, 128)            65664     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1 (None, 4, 128)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 4, 128)            49280     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1 (None, 2, 128)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, 2, 128)            32896     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1 (None, 1, 128)            0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 5)                 165       \n",
      "=================================================================\n",
      "Total params: 1,272,965\n",
      "Trainable params: 1,272,965\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "char_cnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(char_cnn_model, 'char_cnn.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image('char_cnn.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "char_cnn_model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = train['reviews.rating'].values-1\n",
    "test_y = test['reviews.rating'].values-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/mayoor/dev/kaggle/tc2/tc2/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 27700 samples, validate on 6926 samples\n",
      "Epoch 1/10\n",
      "27700/27700 [==============================] - 123s 4ms/step - loss: 0.9031 - acc: 0.6781 - val_loss: 0.8325 - val_acc: 0.6865\n",
      "Epoch 2/10\n",
      "27700/27700 [==============================] - 130s 5ms/step - loss: 0.8324 - acc: 0.6869 - val_loss: 0.8282 - val_acc: 0.6865\n",
      "Epoch 3/10\n",
      "27700/27700 [==============================] - 130s 5ms/step - loss: 0.7495 - acc: 0.6932 - val_loss: 0.8680 - val_acc: 0.6711\n",
      "Epoch 4/10\n",
      "27700/27700 [==============================] - 128s 5ms/step - loss: 0.6499 - acc: 0.7260 - val_loss: 0.9763 - val_acc: 0.6656\n",
      "Epoch 5/10\n",
      "27700/27700 [==============================] - 130s 5ms/step - loss: 0.5460 - acc: 0.7755 - val_loss: 1.2320 - val_acc: 0.6393\n",
      "Epoch 6/10\n",
      "27700/27700 [==============================] - 133s 5ms/step - loss: 0.4382 - acc: 0.8244 - val_loss: 1.2721 - val_acc: 0.5746\n",
      "Epoch 7/10\n",
      "27700/27700 [==============================] - 128s 5ms/step - loss: 0.3576 - acc: 0.8619 - val_loss: 1.5179 - val_acc: 0.6055\n",
      "Epoch 8/10\n",
      "27700/27700 [==============================] - 128s 5ms/step - loss: 0.2896 - acc: 0.8907 - val_loss: 1.6387 - val_acc: 0.5998\n",
      "Epoch 9/10\n",
      "27700/27700 [==============================] - 128s 5ms/step - loss: 0.2430 - acc: 0.9108 - val_loss: 1.8327 - val_acc: 0.5699\n",
      "Epoch 10/10\n",
      "27700/27700 [==============================] - 130s 5ms/step - loss: 0.2088 - acc: 0.9242 - val_loss: 2.2888 - val_acc: 0.5778\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x138aff8d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_cnn_model.fit(charcnn_dataset_train,train_y.tolist(),validation_data=(charcnn_dataset_test,test_y.tolist()),batch_size=128, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for item in charcnn_dataset:\n",
    "#     if item.shape[0] != 71 or item.shape[1] != 1024:\n",
    "#         print (\"PROBLEM!!!!!\",item.shape, item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = char_cnn_model.predict(charcnn_dataset_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = np.argmax(values,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.00      0.00      0.00        82\n",
      "         1.0       0.00      0.00      0.00        81\n",
      "         2.0       0.07      0.02      0.04       300\n",
      "         3.0       0.27      0.33      0.30      1708\n",
      "         4.0       0.72      0.72      0.72      4755\n",
      "\n",
      "   micro avg       0.58      0.58      0.58      6926\n",
      "   macro avg       0.21      0.21      0.21      6926\n",
      "weighted avg       0.56      0.58      0.57      6926\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_1 = prepare_data_for_character_cnn(['I hate this. worst ever.'], maxlen, alphabet_list, alphabet_index, matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.03607366e-04, 1.19001408e-04, 2.64787138e-03, 3.11964661e-01,\n",
       "        6.85164869e-01]], dtype=float32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_cnn_model.predict(test_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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