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main.py
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main.py
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import pandas
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from tensorflow.python.keras.utils.np_utils import to_categorical
from methodology.models.cnn import create_cnn_model
from methodology.models.nbc import create_nbc_model
from methodology.preprocessing.StopWordsFilter import StopWordsFilter
from methodology.preprocessing.TrainTestSplit import TrainTestSplit
from methodology.preprocessing.vectorization import TextVectorizer
from tensorflow.python.keras.utils.vis_utils import plot_model
if __name__ == '__main__':
df = pandas.read_csv('methodology/data/dailydialog/dialog-with-columnNames.csv')
# df = pandas.read_csv('methodology/data/dailydialog/dailydialog.csv')
# print(df[['sentence']])
print('Dataset', df.shape, df.columns.values, sep='\n',
end='\n========================================================\n')
swf = StopWordsFilter()
print(len(swf.get_list()))
swf.G_PATH = 'methodology/preprocessing/'
swf.load_filter()
swf.filter()
# tv = TextVectorizer()
tv = TextVectorizer(stop_words=swf.get_list())
tv.vectorization(df['sentence'].values.astype('U'))
print('Vectorized Dataset', type(tv.tfidf_x_train), tv.tfidf_x_train.shape, sep='\n',
end='\n========================================================\n')
print(len(tv.tfidf_vectorizer.get_feature_names()))
print(tv.tfidf_vectorizer.get_feature_names())
print(tv.tfidf_x_train.shape)
print(tv.tfidf_x_train)
"""
Vectorization Completed without StopWords.
"""
tts_train = TrainTestSplit(tv.tfidf_x_train, df['emotion_index'],
test_set_ratio=0.15)
x_train, x_valid_test, y_train, y_valid_test = tts_train.split()
x_valid, x_test, y_valid, y_test = TrainTestSplit(x_valid_test, y_valid_test, test_set_ratio=0.4).split()
print('Training', x_train.shape, y_train.shape, sep='\n',
end='\n========================================================\n')
print('Validation', x_valid.shape, y_valid.shape, sep='\n',
end='\n========================================================\n')
print('Testing', x_test.shape, y_test.shape, sep='\n',
end='\n========================================================\n')
print('\t\tNAIVE BAYES CLASSIFIER')
nbc = create_nbc_model(type_='multi')
nbc.fit(x_train, y_train)
nbc.fit(x_valid, y_valid)
y_predict = nbc.predict(x_test)
# conf_mat = confusion_matrix(y_predict, y_test)
conf_mat = confusion_matrix(y_test, y_predict)
print(conf_mat)
print('accuracy::', round(accuracy_score(y_test, y_predict) * 100, 2), '%')
# print(classification_report(y_test, y_predict, labels=list(set(df['emotion_label']))))
print(classification_report(y_test, y_predict, labels=[0, 1, 2, 3, 4, 5, 6]))
#
print('========================================================')
print('\t\tCONVOLUTION NEURAL NETWORK')
# cnn = create_nn_model(input_dim=x_train.shape[1])
cnn = create_cnn_model(input_shape=(x_train.shape[1], 1))
plot_model(cnn, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
x_train = x_train.todense()
x_valid = x_valid.todense()
x_test = x_test.todense()
x_train = np.array(x_train)
x_valid = np.array(x_valid)
x_test = np.array(x_test)
x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], 1))
x_valid = x_valid.reshape((x_valid.shape[0], x_valid.shape[1], 1))
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], 1))
y_train = to_categorical(y_train, 7)
y_valid = to_categorical(y_valid, 7)
y_test = to_categorical(y_test, 7)
print(x_train.shape, x_train.ndim)
print(x_valid.shape, x_valid.ndim)
print(x_test.shape, x_test.ndim)
history = cnn.fit(x_train, y_train, epochs=16, validation_data=(x_valid, y_valid))
for key in history.history.keys():
print(key, '->', history.history[key])
cnn.save('methodology/models/pretrained_neural_network/cnn_trained_model.h5')
# cnn.fit(x_train, y_train, epochs=20, verbose=True, batch_size=1,
# validation_data=(x_valid, y_valid))
y_predict_prob = cnn.predict(x_test)
test_eval = cnn.evaluate(x_test, y_test, verbose=0)
print('Evaluation Metrics ->', test_eval)
# Visualization of confusion-matrix with a heat-map
conf_mat = confusion_matrix(y_test.argmax(axis=1), y_predict_prob.argmax(axis=1))
print(conf_mat)
fig = plt.figure(figsize=(10, 6))
plt.title('Confusion Matrix')
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
sns.heatmap(conf_mat, annot=True,
xticklabels=['no_emotion', 'anger', 'disgust', 'fear', 'happiness', 'sadness', 'surprise'],
yticklabels=['no_emotion', 'anger', 'disgust', 'fear', 'happiness', 'sadness', 'surprise'])
plt.show()
print(classification_report(y_test.argmax(axis=1), y_predict_prob.argmax(axis=1),
labels=[0, 1, 2, 3, 4, 5, 6]))
print('\n\nPredicted Values\n', y_predict_prob)
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper left')
plt.show()
# summarize history for f1-score
plt.plot(history.history['f1_m'])
plt.plot(history.history['val_f1_m'])
plt.title('Model F1-Score')
plt.ylabel('f1_scores')
plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper left')
plt.show()
# summarize history for precision
plt.plot(history.history['precision_m'])
plt.plot(history.history['val_precision_m'])
plt.title('Model Precision')
plt.ylabel('precision')
plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper left')
plt.show()
# summarize history for recall
plt.plot(history.history['recall_m'])
plt.plot(history.history['val_recall_m'])
plt.title('Model Recall')
plt.ylabel('recall_m')
plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper left')
plt.show()