def main():
emotionals, rationals = emotional_rational()
preprocessor = Preprocessor()
emotionals = preprocessor.parse_sentences(emotionals)
rationals = preprocessor.parse_sentences(rationals)
train_pos = emotionals[:len(emotionals) // 2]
train_neg = rationals[:len(rationals) // 2]
test_pos = emotionals[len(emotionals) // 2:]
test_neg = rationals[len(rationals) // 2:]
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(train_pos + train_neg)
y_train = np.array([1] * len(train_pos) + [0] * len(train_neg))
X_test = vectorizer.transform(test_pos + test_neg)
y_test = np.array([1] * len(test_pos) + [0] * len(test_neg))
print('Vocabulary size : {}'.format(len(vectorizer.vocabulary_)))
nbsvm = NBSVM()
nbsvm.fit(X_train, y_train)
print('Test accuracy : {}'.format(nbsvm.score(X_test, y_test)))
y_pred = nbsvm.predict(X_test)
print('F1 score : {}'.format(f1_score(y_test, y_pred, average='macro')))
fpr, tpr, thresholds = roc_curve(y_test, y_pred, pos_label=1)
roc_auc = auc(fpr, tpr)
print('AUC of emotionals : {}'.format(roc_auc))
plot_roc_curve(fpr, tpr, roc_auc, 'nbsvm_emotional_roc.png')
fpr, tpr, thresholds = roc_curve(y_test, y_pred, pos_label=0)
roc_auc = auc(fpr, tpr)
print('AUC of rationals : {}'.format(roc_auc))
plot_roc_curve(fpr, tpr, roc_auc, 'nbsvm_rational_roc.png')
def main():
emotionals, rationals = emotional_rational()
preprocessor = Preprocessor()
emotionals = preprocessor.parse_sentences(emotionals)
rationals = preprocessor.parse_sentences(rationals)
emotionals = emotionals[:len(emotionals)]
rationals = rationals[:len(emotionals)]
sentences = emotionals + rationals
Y = np.array([[0, 1]] * len(emotionals) + [[1, 0]] * len(rationals))
max_features = 200
tokenizer = Tokenizer(num_words=max_features, split=' ')
tokenizer.fit_on_texts(sentences)
X = tokenizer.texts_to_sequences(sentences)
X = pad_sequences(X, maxlen=MAX_LEN)
epochs = 15
# --- Add Features ---
dict_loader = EmotionalDict('dataset/nouns', 'dataset/verbs')
emotional_dict = dict_loader.load()
features_loader = AdditionalFeatures(emotionals+rationals, emotional_dict)
add_features = features_loader.emotional_features()
######################
x_aux_train = add_features[:848]
x_aux_test = add_features[848:]
model = build_model(x_aux_train.shape)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)
print(X_train.shape, Y_train.shape)
print(X_test.shape, Y_test.shape)
batch_size = 32
model.fit({'main_input': X_train, 'add_input': x_aux_train}, Y_train, epochs=epochs, batch_size=batch_size, verbose=2)
score, acc = model.evaluate({'main_input': X_test, 'add_input': x_aux_test}, Y_test, verbose=2, batch_size=batch_size)
print('score: {}'.format(score))
print('acc: {}'.format(acc))
Y_pred = model.predict({'main_input': X_test, 'add_input': x_aux_test}, batch_size=1, verbose=2)
print(classification_report(Y_test[:, 1], np.round(Y_pred[:, 1]), target_names=['rationals', 'emotionals']))
fpr, tpr, _ = roc_curve(Y_test[:, 1], Y_pred[:, 1])
roc_auc = auc(fpr, tpr)
plot_roc_curve(fpr, tpr, roc_auc, 'roc.png')
cnf_matrix = confusion_matrix(Y_test[:, 1], np.round(Y_pred[:, 1]))
plot_confusion_matrix(cnf_matrix, ['rationals', 'emotionals'], 'cnf.png')
attention_vector = np.mean(get_activations(model, X_test, True, 'attention_vec')[0], axis=2).squeeze()
attention_vector = np.mean(attention_vector, axis=0)
import matplotlib.pyplot as plt
import pandas as pd
pd.DataFrame(attention_vector, columns=['attention (%)']).plot(kind='bar', title='Attention')
plt.savefig('attention_vec.png')
attention_vector_indices = np.argsort(attention_vector)[::-1]
word_index = tokenizer.word_index
word_index_inv = {v: k for k, v in word_index.items()}
with open('attention_word.txt', 'w') as f:
for i, attention_index in enumerate(attention_vector_indices, start=1):
try:
print('No.{} : {}'.format(i, word_index_inv[attention_index]), file=f)
except KeyError:
continue