TP = np.sum(results * y)
precision = TP / np.sum(results)
recall = TP / np.sum(y)
print('Precision: %g' % precision)
print('Recall: %g' % recall)
print('F-measure: %g' % (2 * precision * recall / (precision + recall)))
config.log('Precision: %g' % precision)
config.log('Recall: %g' % recall)
config.log('F-measure: %g' % (2 * precision * recall /
(precision + recall)))
if __name__ == '__main__':
dataset = FLAGS.dataset
print('########### Start CNN on Dataset ' + dataset + ' ###########')
config.init('CNN_' + dataset)
train_dir = config.path + FLAGS.train_dir
if dataset == 'HDFS':
data_instances = config.HDFS_data
(x_train, y_train), (x_test,
y_test), (x_validate,
y_validate) = dataloader.load_HDFS(
data_instances,
train_ratio=0.3,
is_data_instance=True,
test_ratio=0.6,
CNN_option=True)
elif dataset == 'BGL':
data_instances = config.BGL_data
self.best_f1_score = 0.0
def on_epoch_end(self, epoch, logs=None):
if (epoch + 1) % FLAGS.checkpoint_frequency == 0:
f1_score = apply_model(self.x_val, self.y_val, self.model,
'validation')
if f1_score > self.best_f1_score:
print('Saving model to %s' % checkpoint_name)
model.save_weights(checkpoint_name)
self.best_f1_score = f1_score
if __name__ == '__main__':
dataset = FLAGS.dataset
print('########### Start LSTM on Dataset ' + dataset + ' ###########')
config.init('LSTM_' + dataset)
checkpoint_name = config.path + FLAGS.checkpoint_name
(x_train, y_train), (x_test, y_test), (x_validate,
y_validate) = (None,
None), (None,
None), (None,
None)
collector = None
if dataset == 'BGL':
data_instances = config.BGL_data
(x_train,
y_train), (x_test,
y_test), (x_validate,
y_validate) = load_BGL(data_instances, 0.35, 0.6)