nlp-lab/Jonas_Solutions/Task_03.ipynb

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2018-05-17 13:47:30 +02:00
{
"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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