NeuralNetKernel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import pickle\n",
    "\n",
    "train = pickle.load(open('train.p','rb')).sample(10000)\n",
    "test = pickle.load(open('test.p','rb')).sample(10000)\n",
    "\n",
    "train_text = train['comment_text']\n",
    "list_classes = [\"toxic\", \"severe_toxic\", \"obscene\", \"threat\", \"insult\", \"identity_hate\"]\n",
    "train_y = train[list_classes].values\n",
    "test_text = test\n",
    "\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "\n",
    "tokenizer = Tokenizer(num_words=10000)\n",
    "tokenizer.fit_on_texts(train_text)\n",
    "\n",
    "x_train = tokenizer.texts_to_matrix(train_text,mode='tfidf')\n",
    "\n",
    "y_train = np.asarray(train_y).astype('float32')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/8\n",
      "8000/8000 [==============================] - 206s 26ms/step - loss: 0.3396 - acc: 0.9335 - val_loss: 0.2229 - val_acc: 0.9637\n",
      "Epoch 2/8\n",
      "8000/8000 [==============================] - 216s 27ms/step - loss: 0.1797 - acc: 0.9664 - val_loss: 0.1719 - val_acc: 0.9688\n",
      "Epoch 3/8\n",
      "8000/8000 [==============================] - 212s 26ms/step - loss: 0.1281 - acc: 0.9732 - val_loss: 0.1451 - val_acc: 0.9716\n",
      "Epoch 4/8\n",
      "8000/8000 [==============================] - 224s 28ms/step - loss: 0.0935 - acc: 0.9793 - val_loss: 0.1280 - val_acc: 0.9759\n",
      "Epoch 5/8\n",
      "8000/8000 [==============================] - 216s 27ms/step - loss: 0.0701 - acc: 0.9852 - val_loss: 0.1249 - val_acc: 0.9757\n",
      "Epoch 6/8\n",
      "8000/8000 [==============================] - 227s 28ms/step - loss: 0.0537 - acc: 0.9872 - val_loss: 0.1235 - val_acc: 0.9760\n",
      "Epoch 7/8\n",
      "8000/8000 [==============================] - 212s 26ms/step - loss: 0.0419 - acc: 0.9896 - val_loss: 0.1242 - val_acc: 0.9761\n",
      "Epoch 8/8\n",
      "8000/8000 [==============================] - 206s 26ms/step - loss: 0.0337 - acc: 0.9916 - val_loss: 0.1318 - val_acc: 0.9759\n",
      "0.954080247699\n",
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from keras import models\n",
    "from keras import layers\n",
    "num_classes =6\n",
    "\n",
    "model = models.Sequential()\n",
    "\n",
    "model.add(layers.Dense(32,activation='relu',input_shape=(10000,)))\n",
    "model.add(layers.Dense(32,activation='relu'))\n",
    "model.add(layers.Dense(num_classes,activation='sigmoid'))\n",
    "model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['acc'])\n",
    "\n",
    "\n",
    "history = model.fit(x_train,y_train,epochs=8,batch_size=512, validation_split=0.2)\n",
    "preds = model.predict(x_train)\n",
    "print(roc_auc_score(y_train, preds))\n",
    "\n",
    "history_dict = history.history\n",
    "print(history_dict.keys())\n",
    "\n",
    "acc =history_dict['acc']\n",
    "val_acc =history_dict['val_acc']"
   ]
  },
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   "execution_count": 28,
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   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error when checking input: expected conv1d_24_input to have 3 dimensions, but got array with shape (10000, 10000)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-eb20386c475b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mhistory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m512\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[0mpreds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mroc_auc_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[0;32m    891\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    892\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 893\u001b[1;33m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[0;32m    894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    895\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1553\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1554\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1555\u001b[1;33m             batch_size=batch_size)\n\u001b[0m\u001b[0;32m   1556\u001b[0m         \u001b[1;31m# Prepare validation data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1557\u001b[0m         \u001b[0mdo_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)\u001b[0m\n\u001b[0;32m   1407\u001b[0m                                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_feed_input_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1408\u001b[0m                                     \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1409\u001b[1;33m                                     exception_prefix='input')\n\u001b[0m\u001b[0;32m   1410\u001b[0m         y = _standardize_input_data(y, self._feed_output_names,\n\u001b[0;32m   1411\u001b[0m                                     \u001b[0moutput_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    140\u001b[0m                                  \u001b[1;34m' to have '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshapes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m                                  \u001b[1;34m' dimensions, but got array with shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m                                  str(array.shape))\n\u001b[0m\u001b[0;32m    143\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mref_dim\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshapes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error when checking input: expected conv1d_24_input to have 3 dimensions, but got array with shape (10000, 10000)"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from keras import models\n",
    "from keras import layers\n",
    "from keras.layers import Dense, Embedding, Input,GRU, Flatten\n",
    "from keras.layers import Conv2D,Conv1D, MaxPooling1D, GlobalMaxPool1D, Dropout, concatenate\n",
    "num_classes =6\n",
    "\n",
    "model = models.Sequential()\n",
    "\n",
    "model.add(Conv1D(64, 4, activation='relu', padding='causal',input_shape=(10000,10000)))\n",
    "model.add(MaxPooling1D(2))\n",
    "model.add(Conv1D(64, 4, activation='relu', padding='causal'))\n",
    "model.add(GlobalMaxPool1D())\n",
    "#model.add(Dropout(0.5))\n",
    "#model.add(Flatten())\n",
    "model.add(layers.Dense(32,activation='relu'))\n",
    "model.add(layers.Dense(num_classes,activation='sigmoid'))\n",
    "model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['acc'])\n",
    "\n",
    "\n",
    "history = model.fit(x_train,y_train,epochs=8,batch_size=512, validation_split=0.2)\n",
    "preds = model.predict(x_train)\n",
    "print(roc_auc_score(y_train, preds))\n",
    "\n",
    "history_dict = history.history\n",
    "print(history_dict.keys())\n",
    "\n",
    "acc =history_dict['acc']\n",
    "val_acc =history_dict['val_acc']"
   ]
  },
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   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from keras import models\n",
    "from keras import layers\n",
    "num_classes =6\n",
    "\n",
    "model = models.Sequential()\n",
    "\n",
    "model.add(Conv1D(32, 4, activation='relu', padding='causal',input_shape=(10000,10000)))\n",
    "model.add(MaxPooling1D(2))\n",
    "model.add(Conv1D(32, 4, activation='relu', padding='causal'))\n",
    "model.add(MaxPooling1D(2))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(GRU(32))\n",
    "model.add(layers.Dense(16,activation='relu'))\n",
    "model.add(layers.Dense(num_classes,activation='sigmoid'))\n",
    "model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['acc'])\n",
    "\n",
    "\n",
    "history = model.fit(x_train,y_train,epochs=8,batch_size=512, validation_split=0.2)\n",
    "preds = model.predict(x_train)\n",
    "print(roc_auc_score(y_train, preds))\n",
    "\n",
    "history_dict = history.history\n",
    "print(history_dict.keys())\n",
    "\n",
    "acc =history_dict['acc']\n",
    "val_acc =history_dict['val_acc']"
   ]
  },
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    {
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1BYlIw6OEIZXKzoY77wyz5c6cGTrNNQWJSMOjhCGxNWsWVvl74w146y246KKw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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d7419634e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xnV2sXJm+JzF48K57XcysdWv1gRGR7h29Zs3Wb/6bN9e9Tdu2W7+Z77dfGqW1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\n5eLAMDOzXP4/Ts/0lfCkUYcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d703ebbcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyError",
     "evalue": "'id'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\kasra.manshaei\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   2433\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2434\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlibts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2435\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.get_value_box (pandas\\_libs\\tslib.c:18843)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.get_value_box (pandas\\_libs\\tslib.c:18477)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-8197fd754908>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;31m#creating dataframe of id's\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m \u001b[0mid_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\kasra.manshaei\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    599\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    603\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\kasra.manshaei\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   2440\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2441\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2442\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2443\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2444\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\kasra.manshaei\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   2426\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2427\u001b[0m             return self._engine.get_value(s, k,\n\u001b[1;32m-> 2428\u001b[1;33m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[0;32m   2429\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2430\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'integer'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'boolean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value (pandas\\_libs\\index.c:4363)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value (pandas\\_libs\\index.c:4046)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas\\_libs\\index.c:5085)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas\\_libs\\hashtable.c:20405)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas\\_libs\\hashtable.c:20359)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'id'"
     ]
    }
   ],
   "source": [
    "\n",
    "#Plot the training and val loss and accuracy\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "loss_values = history_dict['loss']\n",
    "val_loss_values = history_dict['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "\n",
    "plt.plot(epochs, loss_values, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss_values, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.clf()\n",
    "acc_values = history_dict['acc']\n",
    "val_acc_values = history_dict['val_acc']\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_data_test(test_data):\n",
    "    data_comments = []\n",
    "    for index, row in test_data.iterrows():\n",
    "        comment = row.comment_text\n",
    "        if comment is not np.nan:\n",
    "            data_comments.append(comment)\n",
    "    return data_comments\n",
    "\n",
    "#test = pd.read_csv('../input/test.csv',encoding='utf-8',error_bad_lines=False)\n",
    "\n",
    "#test_comments = read_data_test(test)\n",
    "\n",
    "tokenizer.fit_on_texts(test_text)\n",
    "x_test = tokenizer.texts_to_matrix(test_text,mode='tfidf')\n",
    "\n",
    "# values of probabilities for each id\n",
    "probabilities =model.predict(x_test)\n",
    "\n",
    "#creating dataframe\n",
    "prob_df= pd.DataFrame(probabilities)\n",
    "\n",
    "\n",
    "#creating dataframe of id's\n",
    "id_df = test['id']\n",
    "\n",
    "\n",
    "#final results will be\n",
    "results = pd.concat([id_df,prob_df],axis=1)\n",
    "\n",
    "print(results)\n",
    "\n",
    "# results.to_csv('final_results.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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