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logistic.py

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+from keras.models import Sequential, load_model
+from keras.layers import Dense
+import sys
+import utils
+import random
+import numpy as np
+
+# Performs classification using Logistic Regression.
+
+FREQ_DIST_FILE = '../train-processed-freqdist.pkl'
+BI_FREQ_DIST_FILE = '../train-processed-freqdist-bi.pkl'
+TRAIN_PROCESSED_FILE = '../train-processed.csv'
+TEST_PROCESSED_FILE = '../test-processed.csv'
+TRAIN = True
+UNIGRAM_SIZE = 15000
+VOCAB_SIZE = UNIGRAM_SIZE
+USE_BIGRAMS = True
+if USE_BIGRAMS:
+    BIGRAM_SIZE = 10000
+    VOCAB_SIZE = UNIGRAM_SIZE + BIGRAM_SIZE
+FEAT_TYPE = 'frequency'
+
+
+def get_feature_vector(tweet):
+    uni_feature_vector = []
+    bi_feature_vector = []
+    words = tweet.split()
+    for i in xrange(len(words) - 1):
+        word = words[i]
+        next_word = words[i + 1]
+        if unigrams.get(word):
+            uni_feature_vector.append(word)
+        if USE_BIGRAMS:
+            if bigrams.get((word, next_word)):
+                bi_feature_vector.append((word, next_word))
+    if len(words) >= 1:
+        if unigrams.get(words[-1]):
+            uni_feature_vector.append(words[-1])
+    return uni_feature_vector, bi_feature_vector
+
+
+def extract_features(tweets, batch_size=500, test_file=True, feat_type='presence'):
+    num_batches = int(np.ceil(len(tweets) / float(batch_size)))
+    for i in xrange(num_batches):
+        batch = tweets[i * batch_size: (i + 1) * batch_size]
+        features = np.zeros((batch_size, VOCAB_SIZE))
+        labels = np.zeros(batch_size)
+        for j, tweet in enumerate(batch):
+            if test_file:
+                tweet_words = tweet[1][0]
+                tweet_bigrams = tweet[1][1]
+            else:
+                tweet_words = tweet[2][0]
+                tweet_bigrams = tweet[2][1]
+                labels[j] = tweet[1]
+            if feat_type == 'presence':
+                tweet_words = set(tweet_words)
+                tweet_bigrams = set(tweet_bigrams)
+            for word in tweet_words:
+                idx = unigrams.get(word)
+                if idx:
+                    features[j, idx] += 1
+            if USE_BIGRAMS:
+                for bigram in tweet_bigrams:
+                    idx = bigrams.get(bigram)
+                    if idx:
+                        features[j, UNIGRAM_SIZE + idx] += 1
+        yield features, labels
+
+
+def process_tweets(csv_file, test_file=True):
+    tweets = []
+    print 'Generating feature vectors'
+    with open(csv_file, 'r') as csv:
+        lines = csv.readlines()
+        total = len(lines)
+        for i, line in enumerate(lines):
+            if test_file:
+                tweet_id, tweet = line.split(',')
+            else:
+                tweet_id, sentiment, tweet = line.split(',')
+            feature_vector = get_feature_vector(tweet)
+            if test_file:
+                tweets.append((tweet_id, feature_vector))
+            else:
+                tweets.append((tweet_id, int(sentiment), feature_vector))
+            utils.write_status(i + 1, total)
+    print '\n'
+    return tweets
+
+
+def build_model():
+    model = Sequential()
+    model.add(Dense(1, input_dim=VOCAB_SIZE, activation='sigmoid'))
+    model.compile(loss='binary_crossentropy',
+                  optimizer='adam', metrics=['accuracy'])
+    return model
+
+
+def evaluate_model(model, val_tweets):
+    correct, total = 0, len(val_tweets)
+    for val_set_X, val_set_y in extract_features(val_tweets, feat_type=FEAT_TYPE, test_file=False):
+        prediction = model.predict_on_batch(val_set_X)
+        prediction = np.round(prediction)
+        correct += np.sum(prediction == val_set_y[:, None])
+    return float(correct) / total
+
+
+if __name__ == '__main__':
+    np.random.seed(1337)
+    unigrams = utils.top_n_words(FREQ_DIST_FILE, UNIGRAM_SIZE)
+    if USE_BIGRAMS:
+        bigrams = utils.top_n_bigrams(BI_FREQ_DIST_FILE, BIGRAM_SIZE)
+    tweets = process_tweets(TRAIN_PROCESSED_FILE, test_file=False)
+    if TRAIN:
+        train_tweets, val_tweets = utils.split_data(tweets)
+    else:
+        random.shuffle(tweets)
+        train_tweets = tweets
+    del tweets
+    print 'Extracting features & training batches'
+    nb_epochs = 20
+    batch_size = 500
+    model = build_model()
+    n_train_batches = int(np.ceil(len(train_tweets) / float(batch_size)))
+    best_val_acc = 0.0
+    for j in xrange(nb_epochs):
+        i = 1
+        for training_set_X, training_set_y in extract_features(train_tweets, feat_type=FEAT_TYPE, batch_size=batch_size, test_file=False):
+            o = model.train_on_batch(training_set_X, training_set_y)
+            sys.stdout.write('\rIteration %d/%d, loss:%.4f, acc:%.4f' %
+                             (i, n_train_batches, o[0], o[1]))
+            sys.stdout.flush()
+            i += 1
+        val_acc = evaluate_model(model, val_tweets)
+        print '\nEpoch: %d, val_acc:%.4f' % (j + 1, val_acc)
+        random.shuffle(train_tweets)
+        if val_acc > best_val_acc:
+            print 'Accuracy improved from %.4f to %.4f, saving model' % (best_val_acc, val_acc)
+            best_val_acc = val_acc
+            model.save('best_model.h5')
+    print 'Testing'
+    del train_tweets
+    del model
+    model = load_model('best_model.h5')
+    test_tweets = process_tweets(TEST_PROCESSED_FILE, test_file=True)
+    n_test_batches = int(np.ceil(len(test_tweets) / float(batch_size)))
+    predictions = np.array([])
+    print 'Predicting batches'
+    i = 1
+    for test_set_X, _ in extract_features(test_tweets, feat_type=FEAT_TYPE, batch_size=batch_size, test_file=True):
+        prediction = np.round(model.predict_on_batch(test_set_X).flatten())
+        predictions = np.concatenate((predictions, prediction))
+        utils.write_status(i, n_test_batches)
+        i += 1
+    predictions = [(str(j), int(predictions[j]))
+                   for j in range(len(test_tweets))]
+    utils.save_results_to_csv(predictions, 'logistic.csv')
+    print '\nSaved to logistic.csv'