created Task 04
This commit is contained in:
Jonas Weinz
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444ea7d876
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84
Jonas_Solutions/Task03_Instructions.py
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Jonas_Solutions/Task03_Instructions.py
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import numpy as np
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import pandas as pd
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from sklearn.feature_extraction.text import CountVectorizer
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from keras.preprocessing.text import Tokenizer
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from keras.preprocessing.sequence import pad_sequences
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from keras.models import Sequential
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from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
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from sklearn.model_selection import train_test_split
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from keras.utils.np_utils import to_categorical
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import re
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'''
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Task 3: playing with NN framwork/keras and basic sentiment analysis
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- use the following model as a baseline and improve it!
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- export your metadata (just basic hyperparameters and outcomes for test data!)
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- test data = 0.3 (not in this example, change it!)
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- random_state = 4222
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- no need to cross-validation!
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'''
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# parameters
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max_fatures = 500
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embed_dim = 128
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lstm_out = 196
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dropout = 0.1
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dropout_1d = 0.4
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recurrent_dropout = 0.1
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random_state = 1324
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validation_size = 1000
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batch_size = 16
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epochs=2
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verbose= 2
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df = pd.read_csv('dataset_sentiment.csv')
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df = df[['text','sentiment']]
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print(df[0:10])
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df = df[df.sentiment != "Neutral"]
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df['text'] = df['text'].apply(lambda x: x.lower())
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df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))
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df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\s]','',x)))
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tok = Tokenizer(num_words=max_fatures, split=' ')
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tok.fit_on_texts(df['text'].values)
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X = tok.texts_to_sequences(df['text'].values)
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X = pad_sequences(X)
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nn = Sequential()
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nn.add(Embedding(max_fatures, embed_dim, input_length = X.shape[1]))
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nn.add(SpatialDropout1D(dropout_1d))
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nn.add(LSTM(lstm_out, dropout=dropout, recurrent_dropout=recurrent_dropout))
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nn.add(Dense(2, activation='softmax'))
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nn.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics = ['accuracy'])
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print(nn.summary())
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Y = pd.get_dummies(df['sentiment']).values
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.30, random_state = random_state)
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nn.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, verbose=verbose)
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X_validate = X_test[-validation_size:]
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Y_validate = Y_test[-validation_size:]
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X_test = X_test[:-validation_size]
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Y_test = Y_test[:-validation_size]
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score, accuracy = nn.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)
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print("score: %.2f" % (score))
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print("acc: %.2f" % (accuracy))
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pos_cnt, neg_cnt, pos_ok, neg_ok = 0, 0, 0, 0
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for x in range(len(X_validate)):
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result = nn.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]
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if np.argmax(result) == np.argmax(Y_validate[x]):
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if np.argmax(Y_validate[x]) == 0: neg_ok += 1
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else: pos_ok += 1
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if np.argmax(Y_validate[x]) == 0: neg_cnt += 1
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else: pos_cnt += 1
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print("pos_acc", pos_ok/pos_cnt*100, "%")
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print("neg_acc", neg_ok/neg_cnt*100, "%")
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X2 = ['what are u going to say about that? the truth, wassock?!']
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X2 = tok.texts_to_sequences(X2)
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X2 = pad_sequences(X2, maxlen=26, dtype='int32', value=0)
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print(X2)
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print(nn.predict(X2, batch_size=1, verbose = 2)[0])
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Jonas_Solutions/Task_03.ipynb
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Jonas_Solutions/Task_03.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np \n",
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"import pandas as pd \n",
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"from sklearn.feature_extraction.text import CountVectorizer\n",
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"from keras.preprocessing.text import Tokenizer\n",
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"from keras.preprocessing.sequence import pad_sequences\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\n",
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"from sklearn.model_selection import train_test_split\n",
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"from keras.utils.np_utils import to_categorical\n",
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"import re\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"dataset already downloaded\n"
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]
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}
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],
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"source": [
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"%%bash\n",
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"\n",
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"if [ ! -e 'dataset_sentiment.csv' ]\n",
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"then\n",
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" echo \"downloading dataset\"\n",
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" wget https://raw.githubusercontent.com/SmartDataAnalytics/MA-INF-4222-NLP-Lab/master/2018_SoSe/exercises/dataset_sentiment.csv\n",
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"else\n",
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" echo \"dataset already downloaded\"\n",
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"fi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# parameters\n",
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"max_fatures = 500\n",
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"embed_dim = 128\n",
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"lstm_out = 196\n",
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"dropout = 0.1\n",
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"dropout_1d = 0.4\n",
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"recurrent_dropout = 0.1\n",
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"random_state = 1324\n",
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"validation_size = 1000\n",
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"batch_size = 16\n",
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"epochs=2\n",
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"verbose= 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" text sentiment\n",
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"0 RT @NancyLeeGrahn: How did everyone feel about... Neutral\n",
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"1 RT @ScottWalker: Didn't catch the full #GOPdeb... Positive\n",
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"2 RT @TJMShow: No mention of Tamir Rice and the ... Neutral\n",
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"3 RT @RobGeorge: That Carly Fiorina is trending ... Positive\n",
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"4 RT @DanScavino: #GOPDebate w/ @realDonaldTrump... Positive\n",
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"5 RT @GregAbbott_TX: @TedCruz: \"On my first day ... Positive\n",
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"6 RT @warriorwoman91: I liked her and was happy ... Negative\n",
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"7 Going on #MSNBC Live with @ThomasARoberts arou... Neutral\n",
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"8 Deer in the headlights RT @lizzwinstead: Ben C... Negative\n",
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"9 RT @NancyOsborne180: Last night's debate prove... Negative\n"
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]
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}
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],
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"source": [
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"df = pd.read_csv('dataset_sentiment.csv')\n",
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"df = df[['text','sentiment']]\n",
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"print(df[0:10])\n",
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"\n",
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"df = df[df.sentiment != \"Neutral\"]\n",
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"df['text'] = df['text'].apply(lambda x: x.lower())\n",
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"df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))\n",
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"df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\\s]','',x)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"tok = Tokenizer(num_words=max_fatures, split=' ')\n",
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"tok.fit_on_texts(df['text'].values)\n",
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"X = tok.texts_to_sequences(df['text'].values)\n",
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"X = pad_sequences(X)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"embedding_1 (Embedding) (None, 26, 128) 64000 \n",
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"_________________________________________________________________\n",
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"spatial_dropout1d_1 (Spatial (None, 26, 128) 0 \n",
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"_________________________________________________________________\n",
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"lstm_1 (LSTM) (None, 196) 254800 \n",
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"_________________________________________________________________\n",
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"dense_1 (Dense) (None, 2) 394 \n",
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"=================================================================\n",
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"Total params: 319,194\n",
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"Trainable params: 319,194\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n",
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"None\n"
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]
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}
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],
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"source": [
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"nn = Sequential()\n",
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"nn.add(Embedding(max_fatures, embed_dim, input_length = X.shape[1]))\n",
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"nn.add(SpatialDropout1D(dropout_1d))\n",
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"nn.add(LSTM(lstm_out, dropout=dropout, recurrent_dropout=recurrent_dropout))\n",
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"nn.add(Dense(2, activation='softmax'))\n",
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"nn.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics = ['accuracy'])\n",
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"print(nn.summary())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/2\n",
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" - 30s - loss: 0.4374 - acc: 0.8161\n",
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"Epoch 2/2\n",
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" - 30s - loss: 0.3614 - acc: 0.8487\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<keras.callbacks.History at 0x7fe38072e978>"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Y = pd.get_dummies(df['sentiment']).values\n",
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"X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.30, random_state = random_state)\n",
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"nn.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, verbose=verbose)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"score: 0.37\n",
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"acc: 0.85\n"
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]
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}
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],
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"source": [
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"X_validate = X_test[-validation_size:]\n",
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"Y_validate = Y_test[-validation_size:]\n",
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"X_test = X_test[:-validation_size]\n",
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"Y_test = Y_test[:-validation_size]\n",
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"score, accuracy = nn.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)\n",
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"print(\"score: %.2f\" % (score))\n",
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"print(\"acc: %.2f\" % (accuracy))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"pos_cnt, neg_cnt, pos_ok, neg_ok = 0, 0, 0, 0\n",
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"for x in range(len(X_validate)):\n",
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" result = nn.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]\n",
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" if np.argmax(result) == np.argmax(Y_validate[x]):\n",
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" if np.argmax(Y_validate[x]) == 0: neg_ok += 1\n",
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" else: pos_ok += 1\n",
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" if np.argmax(Y_validate[x]) == 0: neg_cnt += 1\n",
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" else: pos_cnt += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pos_acc 31.770833333333332 %\n",
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"neg_acc 97.27722772277228 %\n"
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]
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}
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],
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"source": [
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"print(\"pos_acc\", pos_ok/pos_cnt*100, \"%\")\n",
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"print(\"neg_acc\", neg_ok/neg_cnt*100, \"%\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 37\n",
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" 311 189 4 144 22 16 1 281]]\n",
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"[0.93431044 0.06568963]\n"
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]
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}
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],
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"source": [
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"X2 = ['what are u going to say about that? the truth, wassock?!']\n",
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"X2 = tok.texts_to_sequences(X2)\n",
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"X2 = pad_sequences(X2, maxlen=26, dtype='int32', value=0)\n",
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"print(X2)\n",
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"print(nn.predict(X2, batch_size=1, verbose = 2)[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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