def train_separated_test(self, x_train, y_train, x_test, y_test):
"""
:param x_train:
:param y_train:
:param x_test:
:param y_test:
:return:
"""
print("Training")
model = self.get_model()
x_train = x_train.reshape(
(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1))
x_test = x_test.reshape(
(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1))
model.fit([x_train, x_train, x_train, x_train],
y_train,
batch_size=BATCH_SIZE,
epochs=25,
shuffle=True)
y_pred = model.predict_classes([x_test, x_test, x_test, x_test],
batch_size=BATCH_SIZE)
p, r, f1, _ = eh.calculate_f_measure(y_test, y_pred)
print("p={} r={} f1={}".format(p, r, f1))
return p, r, f1
def cross_validation(self, x_data, y_data):
"""
:param x_data:
:param y_data:
:return:
"""
kf = KFold(n_splits=10, shuffle=True)
ret = []
i = 0
for train_index, test_index in kf.split(y_data):
i += 1
print("10-Fold step {}".format(i))
model = self.get_model()
x_train, x_test = x_data[train_index], x_data[test_index]
y_train, y_test = y_data[train_index], y_data[test_index]
x_train = x_train.reshape(
(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1))
x_test = x_test.reshape(
(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1))
model.fit([x_train, x_train, x_train, x_train],
y_train,
batch_size=BATCH_SIZE,
epochs=25)
y_pred = model.predict_classes([x_test, x_test, x_test, x_test],
batch_size=BATCH_SIZE)
p, r, f1, _ = eh.calculate_f_measure(y_test, y_pred)
print("p={} r={} f1={}".format(p, r, f1))
ret.append((p, r, f1))
return ret
