def fit(self, x_train, y_train, x_test, y_test, y_true):
y_pred = np.zeros(shape=y_test.shape)
l = 0
for dataset in datasets_names:
if dataset == self.dataset_name:
continue
curr_dir = self.transfer_directory + dataset + '/' + self.dataset_name + '/'
predictions_file_name = curr_dir + 'y_pred.npy'
if check_if_file_exits(predictions_file_name):
# then load only the predictions from the file
curr_y_pred = np.load(predictions_file_name)
else:
# predict from models saved
model = keras.models.load_model(curr_dir + 'best_model.hdf5')
curr_y_pred = model.predict(x_test)
keras.backend.clear_session()
np.save(predictions_file_name, curr_y_pred)
y_pred = y_pred + curr_y_pred
l += 1
keras.backend.clear_session()
y_pred = y_pred / l
# save predictions
np.save(self.output_directory + 'y_pred.npy', y_pred)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
df_metrics = calculate_metrics(y_true, y_pred, 0.0)
df_metrics.to_csv(self.output_directory + 'df_metrics.csv',
index=False)
print(self.dataset_name, df_metrics['accuracy'][0])
gc.collect()
def convert_to_float(s):
s = s.split('_')[0]
return float(s)
subprocess.call(['./receptive_field_remove_non_completed.sh'],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
if len(sys.argv) == 1:
matplotlib.use('agg')
out_df = root_output_directory_df + 'df_res_sub_0.csv'
idx_out = 1
while check_if_file_exits(out_df):
out_df = root_output_directory_df + 'df_res_sub_' + str(
idx_out) + '.csv'
idx_out += 1
columns = [
'pattern_len', 'pattern_pos', 'ts_len', 'ts_n', 'nb_classes',
'filters', 'kernel_size', 'depth', 'use_residual', 'use_bottleneck',
'accuracy'
]
df_results = pd.DataFrame(index=[], columns=columns)
df_results.to_csv(out_df)
curr_idx = 0
def fit(self, x_train, y_train, x_test, y_test, y_true):
# no training since models are pre-trained
start_time = time.time()
y_pred = np.zeros(shape=y_test.shape)
ll = 0
# loop through all classifiers
for model_name in self.classifiers:
# loop through different initialization of classifiers
for itr in self.iterations_to_take:
if itr == 0:
itr_str = ''
else:
itr_str = '_itr_' + str(itr)
curr_archive_name = self.archive_name + itr_str
curr_dir = self.models_dir.replace('classifier',
model_name).replace(
self.archive_name,
curr_archive_name)
model = self.create_classifier(model_name,
None,
None,
curr_dir,
build=False)
predictions_file_name = curr_dir + 'y_pred.npy'
# check if predictions already made
if check_if_file_exits(predictions_file_name):
# then load only the predictions from the file
curr_y_pred = np.load(predictions_file_name)
else:
# then compute the predictions
curr_y_pred = model.predict(x_test,
y_true,
return_df_metrics=False)
keras.backend.clear_session()
np.save(predictions_file_name, curr_y_pred)
y_pred = y_pred + curr_y_pred
ll += 1
# average predictions
y_pred = y_pred / ll
# save predictions
np.save(self.output_directory + 'y_pred.npy', y_pred)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
duration = time.time() - start_time
df_metrics = calculate_metrics(y_true, y_pred, duration)
df_metrics.to_csv(self.output_directory + 'df_metrics.csv',
index=False)
gc.collect()