def classify(self):
self.tokenize()
self.X_train, self.X_test, self.Y_train, self.Y_test = train_test_split(
self.X, self.Y, test_size=0.20, random_state=42)
if self.avoid_skewness:
Y_train = np.argmax(self.Y_train, axis=1)
Y_train = [self.labels_dict_rev[int(i)] for i in list(Y_train)]
self.X_train, self.Y_train = BasicFunctions.getUnskewedSubset(
self.X_train, self.Y_train, Y_train)
self.X_train = np.array(self.X_train)
self.Y_train = np.array(self.Y_train)
self.printDataInformation()
self.model = Sequential()
# Single 500-neuron hidden layer with sigmoid activation
self.model.add(
Dense(input_dim=self.X.shape[1], units=500, activation='relu'))
# Output layer with softmax activation
self.model.add(Dense(units=self.Y.shape[1], activation='softmax'))
# Specify optimizer, loss and validation metric
self.model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Train the model
self.model.fit(self.X_train,
self.Y_train,
epochs=4,
batch_size=10,
validation_split=0.2)
def fit(self, X, y):
label_distribution = BasicFunctions.keyCounter(y)
highest_amount = 0
for label in label_distribution:
if label_distribution[
label] > highest_amount or highest_amount == 0:
highest_amount = label_distribution[label]
self.most_frequent_class = label
def evaluate(self):
self.Y_predicted = self.model.predict(self.X_test)
self.Y_predicted = np.argmax(self.Y_predicted, axis=1)
self.Y_predicted = [
self.labels_dict_rev[int(i)] for i in list(self.Y_predicted)
]
self.Y_test = np.argmax(self.Y_test, axis=1)
self.Y_test = [self.labels_dict_rev[int(i)] for i in list(self.Y_test)]
self.accuracy, self.precision, self.recall, self.f1score = BasicFunctions.getMetrics(
self.Y_test, self.Y_predicted, self.labels)
def printClassEvaluation(self):
BasicFunctions.printClassEvaluation(self.Y_test, self.Y_predicted,
self.labels)
def printBasicEvaluation(self):
BasicFunctions.printEvaluation(self.accuracy, self.precision,
self.recall, self.f1score,
"Basic Evaluation")
def evaluate(self):
self.Y_predicted = self.classifier.predict(self.X_test)
self.accuracy, self.precision, self.recall, self.f1score = BasicFunctions.getMetrics(
self.Y_test, self.Y_predicted, self.labels)
default='Word Sense Disambiguation',
help='word sense')
parser.add_argument(
'--avoid_skewness',
type=bool,
default=False,
help=
'how to train the dataset, without skewness in the data or with skewness')
parser.add_argument(
'--kfold',
type=int,
default=1,
help='Amount of Ks for cross validation, if cross validation.')
args = parser.parse_args()
predict_languages = BasicFunctions.getLanguages(args.predict_languages)
data = data('./train/', './test_00/')
data.collectXY(data_method=args.data_method)
BasicFunctions.printStandardText(args.method, predict_languages,
args.predict_label)
labels = list(set(data.Y))
#BasicFunctions.printLabelDistribution(data.Y_train)
words = list(set(data.words))
#print(data.X_train)
#BasicFunctions.printLabelDistribution(data.words_train)
if len(labels) > 1: #otherwise, there is nothing to train