def choose_features(self, samples, retrain=False):
if self.feature_type in ['tf-idf', 'bow']:
return Vectorizer(self.name, self.feature_type).vectors(samples, retrain).todense()
elif self.feature_type == 'word-embeddings':
return Embeddings(self.name, 100).encode_samples(samples)
else:
return samples # no change/manipulation
def train_experimental_CNN(self, features, labels, trainable_embeddings):
embedding_size = 100
if self.feature_type == 'word-embeddings':
x_train = pad_sequences(features, maxlen=self.max_len, padding='post')
pretrained_embeddings = Embeddings(self.name, embedding_size).vectors()
vocab_size = pretrained_embeddings.shape[0]
embedding_layer = Embedding(input_dim=vocab_size, output_dim=embedding_size,
input_length=self.max_len, trainable=trainable_embeddings, weights=[pretrained_embeddings])
elif self.feature_type in ['tf-idf', 'bow']:
x_train = features
vocab_size = x_train.shape[1]
embedding_layer = Embedding(input_dim=vocab_size, output_dim=embedding_size,
input_length=vocab_size, trainable=trainable_embeddings)
else:
raise Exception('Please select a valid feature')
model = Sequential()
model.add(embedding_layer)
model.add(Conv1D(filters=256, kernel_size=5, activation='relu'))
model.add(MaxPool1D(pool_size=5)) # output size 20 (self.max_len/5) (MaxPool acts on the input dimension/size)
model.add(Dropout(rate=0.3))
model.add(Conv1D(filters=128, kernel_size=5, activation='relu'))
model.add(MaxPool1D(pool_size=15)) # filter of size 5 applied on the size 20 output of 1st MaxPool1D
model.add(Flatten())
model.add(Dense(units=64, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=32, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=self.num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
print(model.summary())
self.model = model
numeric_labels = SentenceLabelEncoder().encode_numerical(labels)
class_weights = class_weight.compute_class_weight('balanced', np.unique(numeric_labels), numeric_labels)
y_train = SentenceLabelEncoder().encode_categorical(labels)
self.model.fit(x_train, y_train, validation_split=0.2, epochs=20, batch_size=128,
verbose=2, shuffle=True, class_weight=class_weights)
print('##########################\n\n\t Cross Validation completed \n\n\t##########################')
self.model.fit(x_train, y_train, epochs=20, batch_size=128,
verbose=2, shuffle=True, class_weight=class_weights)
loss, accuracy = self.model.evaluate(x_train, y_train)
print('loss, accuracy:', loss, accuracy)
self.labels_pred = SentenceLabelEncoder().decode(self.model.predict_classes(x_train))
def train_common_baseline_CNN(self, features, labels, trainable_embeddings):
embedding_size = 100
x_train = pad_sequences(features, maxlen=self.max_len, padding='post')
pretrained_embeddings = Embeddings(self.name, embedding_size).vectors()
vocab_size = pretrained_embeddings.shape[0]
embedding_layer = Embedding(input_dim=vocab_size, output_dim=embedding_size,
input_length=self.max_len, trainable=trainable_embeddings, weights=[pretrained_embeddings])
main_input = Input(shape=(self.max_len, ))
x = embedding_layer(main_input)
x3 = Conv1D(filters=100, kernel_size=2, activation='relu')(x) # generalization of bigrams
x4 = Conv1D(filters=100, kernel_size=4, activation='relu')(x) # generalization of ngrams, n=4
x5 = Conv1D(filters=100, kernel_size=5, activation='relu')(x) # generalization of ngrams, n=5
x3 = MaxPool1D(pool_size=99)(x3)
x4 = MaxPool1D(pool_size=97)(x4)
x5 = MaxPool1D(pool_size=96)(x5)
out = concatenate([x3, x4, x5])
out = Dropout(rate=0.5)(out)
out = Flatten()(out) # todo - check should we use a Dense (fully connected layer)?
main_output = Dense(self.num_classes, activation='softmax')(out)
model = Model(inputs=main_input, outputs=main_output)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
print(model.summary())
self.model = model
numeric_labels = SentenceLabelEncoder().encode_numerical(labels)
class_weights = class_weight.compute_class_weight('balanced', np.unique(numeric_labels), numeric_labels)
y_train = SentenceLabelEncoder().encode_categorical(labels)
self.model.fit(x_train, y_train, validation_split=0.2, epochs=30, batch_size=128,
verbose=2, shuffle=True, class_weight=class_weights)
print('##########################\n\n\t Cross Validation completed \n\n\t##########################')
self.model.fit(x_train, y_train, epochs=30, batch_size=128,
verbose=2, shuffle=True, class_weight=class_weights)
loss, accuracy = self.model.evaluate(x_train, y_train)
print('loss, accuracy:', loss, accuracy)
self.labels_pred = SentenceLabelEncoder().decode(np.argmax(self.model.predict(x_train), axis=1))
def train_vanilla_NN(self, features, labels, trainable_embeddings):
model = Sequential()
embedding_size = 100
if self.feature_type == 'word-embeddings':
x_train = pad_sequences(features,
maxlen=self.max_len,
padding='post')
pretrained_embeddings = Embeddings(self.name,
embedding_size).vectors()
vocab_size = pretrained_embeddings.shape[0]
model.add(
Embedding(input_dim=vocab_size,
output_dim=embedding_size,
input_length=self.max_len,
trainable=trainable_embeddings,
weights=[pretrained_embeddings]))
model.add(Flatten())
model.add(Dense(units=4 * embedding_size, activation='relu'))
elif self.feature_type in ['tf-idf', 'bow']:
x_train = features
vocab_size = x_train.shape[1]
model.add(
Dense(units=4 * embedding_size,
activation='relu',
input_dim=vocab_size))
else:
raise Exception('Please select a valid feature')
model.add(Dropout(rate=0.5))
model.add(Dense(units=self.num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
self.model = model
numeric_labels = SentenceLabelEncoder().encode_numerical(labels)
class_weights = class_weight.compute_class_weight(
'balanced', np.unique(numeric_labels), numeric_labels)
y_train = SentenceLabelEncoder().encode_categorical(labels)
self.model.fit(x_train,
y_train,
validation_split=0.2,
epochs=10,
batch_size=32,
verbose=2,
shuffle=True,
class_weight=class_weights)
print(
'##########################\n\n\t Cross Validation completed \n\n\t##########################'
)
self.model.fit(x_train,
y_train,
epochs=10,
batch_size=32,
verbose=2,
shuffle=True,
class_weight=class_weights)
loss, accuracy = self.model.evaluate(x_train, y_train)
print('loss, accuracy:', loss, accuracy)
self.labels_pred = SentenceLabelEncoder().decode(
self.model.predict_classes(x_train))
def generate_embeddings_coordinates(self, model_name, dimension,
reduced_dimension):
embeddings = Embeddings(model_name, dimension)
embeddings.words_coordinates(reduced_dimension)
def train_embeddings(self, model_name, dimension, sents):
embeddings = Embeddings(model_name, dimension)
embeddings.train(sents)