nlp-lab/Jonas_Solutions/Task_03.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils.np_utils import to_categorical\n",
    "import re\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset already downloaded\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "if [ ! -e 'dataset_sentiment.csv' ]\n",
    "then\n",
    "    echo \"downloading dataset\"\n",
    "    wget https://raw.githubusercontent.com/SmartDataAnalytics/MA-INF-4222-NLP-Lab/master/2018_SoSe/exercises/dataset_sentiment.csv\n",
    "else\n",
    "    echo \"dataset already downloaded\"\n",
    "fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "max_fatures = 500\n",
    "embed_dim = 128\n",
    "lstm_out = 196\n",
    "dropout = 0.1\n",
    "dropout_1d = 0.4\n",
    "recurrent_dropout = 0.1\n",
    "random_state = 1324\n",
    "validation_size = 1000\n",
    "batch_size = 16\n",
    "epochs=2\n",
    "verbose= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                text sentiment\n",
      "0  RT @NancyLeeGrahn: How did everyone feel about...   Neutral\n",
      "1  RT @ScottWalker: Didn't catch the full #GOPdeb...  Positive\n",
      "2  RT @TJMShow: No mention of Tamir Rice and the ...   Neutral\n",
      "3  RT @RobGeorge: That Carly Fiorina is trending ...  Positive\n",
      "4  RT @DanScavino: #GOPDebate w/ @realDonaldTrump...  Positive\n",
      "5  RT @GregAbbott_TX: @TedCruz: \"On my first day ...  Positive\n",
      "6  RT @warriorwoman91: I liked her and was happy ...  Negative\n",
      "7  Going on #MSNBC Live with @ThomasARoberts arou...   Neutral\n",
      "8  Deer in the headlights RT @lizzwinstead: Ben C...  Negative\n",
      "9  RT @NancyOsborne180: Last night's debate prove...  Negative\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('dataset_sentiment.csv')\n",
    "df = df[['text','sentiment']]\n",
    "print(df[0:10])\n",
    "\n",
    "df = df[df.sentiment != \"Neutral\"]\n",
    "df['text'] = df['text'].apply(lambda x: x.lower())\n",
    "df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))\n",
    "df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\\s]','',x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tok = Tokenizer(num_words=max_fatures, split=' ')\n",
    "tok.fit_on_texts(df['text'].values)\n",
    "X = tok.texts_to_sequences(df['text'].values)\n",
    "X = pad_sequences(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 26, 128)           64000     \n",
      "_________________________________________________________________\n",
      "spatial_dropout1d_1 (Spatial (None, 26, 128)           0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 196)               254800    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 394       \n",
      "=================================================================\n",
      "Total params: 319,194\n",
      "Trainable params: 319,194\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "nn = Sequential()\n",
    "nn.add(Embedding(max_fatures, embed_dim, input_length = X.shape[1]))\n",
    "nn.add(SpatialDropout1D(dropout_1d))\n",
    "nn.add(LSTM(lstm_out, dropout=dropout, recurrent_dropout=recurrent_dropout))\n",
    "nn.add(Dense(2, activation='softmax'))\n",
    "nn.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics = ['accuracy'])\n",
    "print(nn.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      " - 30s - loss: 0.4374 - acc: 0.8161\n",
      "Epoch 2/2\n",
      " - 30s - loss: 0.3614 - acc: 0.8487\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fe38072e978>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = pd.get_dummies(df['sentiment']).values\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.30, random_state = random_state)\n",
    "nn.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, verbose=verbose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.37\n",
      "acc: 0.85\n"
     ]
    }
   ],
   "source": [
    "X_validate = X_test[-validation_size:]\n",
    "Y_validate = Y_test[-validation_size:]\n",
    "X_test = X_test[:-validation_size]\n",
    "Y_test = Y_test[:-validation_size]\n",
    "score, accuracy = nn.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)\n",
    "print(\"score: %.2f\" % (score))\n",
    "print(\"acc: %.2f\" % (accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_cnt, neg_cnt, pos_ok, neg_ok = 0, 0, 0, 0\n",
    "for x in range(len(X_validate)):\n",
    "    result = nn.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]\n",
    "    if np.argmax(result) == np.argmax(Y_validate[x]):\n",
    "        if np.argmax(Y_validate[x]) == 0: neg_ok += 1\n",
    "        else: pos_ok += 1\n",
    "    if np.argmax(Y_validate[x]) == 0: neg_cnt += 1\n",
    "    else: pos_cnt += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pos_acc 31.770833333333332 %\n",
      "neg_acc 97.27722772277228 %\n"
     ]
    }
   ],
   "source": [
    "print(\"pos_acc\", pos_ok/pos_cnt*100, \"%\")\n",
    "print(\"neg_acc\", neg_ok/neg_cnt*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  48  37\n",
      "  311 189   4 144  22  16   1 281]]\n",
      "[0.93431044 0.06568963]\n"
     ]
    }
   ],
   "source": [
    "X2 = ['what are u going to say about that? the truth, wassock?!']\n",
    "X2 = tok.texts_to_sequences(X2)\n",
    "X2 = pad_sequences(X2, maxlen=26, dtype='int32', value=0)\n",
    "print(X2)\n",
    "print(nn.predict(X2, batch_size=1, verbose = 2)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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