def predict(self, F, datainfo, timeinfo):
print('\nFile: {} Class: {} Function: {} State: {}'.format(
'architectures.py', 'OriginalEnsemble', 'predict', 'Start'))
info = extract(datainfo, timeinfo)
self._info.update(info)
print_time_info(self._info)
test_data = get_data(F, self._info)
print('test_data.shape: {}'.format(test_data.shape))
transformed_test_data = self._transform(test_data, DataType.TEST)
train_data = self._transform(self._train_data, DataType.TRAIN)
train_labels = self._train_labels
print('transformed_test_data.shape: {}'.format(
transformed_test_data.shape))
print('train_data.shape: {}'.format(train_data.shape))
size = len(train_data) if len(transformed_test_data) > len(
train_data) else len(transformed_test_data)
train_weights = correct_covariate_shift(
train_data, self._test_sampler.sample(transformed_test_data, size),
self._random_state, self._correction_threshold,
self._correction_n_splits) if self._should_correct else None
fixed_hyperparameters, search_space = Profile.parse_profile(
self._profile)
if self._best_hyperparameters is None:
tuner = HyperparametersTuner(fixed_hyperparameters, search_space,
self._max_evaluations)
self._best_hyperparameters = tuner.get_best_hyperparameters(
train_data, train_labels, self._validation_ratio,
self._random_state)
print('self._best_hyperparameters: {}'.format(
self._best_hyperparameters))
if has_sufficient_time(self._dataset_budget_threshold, self._info):
t_d, validation_data, t_l, validation_labels = train_test_split(
train_data,
train_labels,
test_size=self._validation_ratio,
random_state=self._random_state,
shuffle=True,
stratify=train_labels)
self._classifier = LGBMClassifier()
self._classifier.set_params(**self._best_hyperparameters)
self._classifier.fit(train_data,
train_labels,
sample_weight=train_weights)
else:
print('Time budget exceeded.')
predictions = self._classifier.predict_proba(transformed_test_data)[:,
1]
self._iteration += 1
print('predictions.shape: {}'.format(predictions.shape))
print('File: {} Class: {} Function: {} State: {} \n'.format(
'architectures.py', 'OriginalEnsemble', 'predict', 'End'))
return predictions
def predict(self, F, datainfo, timeinfo):
print('\nFile: {} Class: {} Function: {} State: {}'.format(
'architectures.py', 'OriginalEnsemble', 'predict', 'Start'))
info = extract(datainfo, timeinfo)
self._info.update(info)
print_time_info(self._info)
test_data = get_data(F, self._info)
print('test_data.shape: {}'.format(test_data.shape))
transformed_test_data = self._transform(test_data, DataType.TEST)
train_data = self._transform(self._train_data, DataType.TRAIN)
train_labels = self._train_labels
print('transformed_test_data.shape: {}'.format(
transformed_test_data.shape))
print('train_data.shape: {}'.format(train_data.shape))
size = len(train_data) if len(transformed_test_data) > len(
train_data) else len(transformed_test_data)
train_weights = correct_covariate_shift(
train_data, self._test_sampler.sample(transformed_test_data, size),
self._random_state, self._correction_threshold,
self._correction_n_splits) if self._should_correct else None
fixed_hyperparameters, search_space = Profile.parse_profile(
self._profile)
if self._best_hyperparameters is None:
tuner = HyperparametersTuner(fixed_hyperparameters, search_space,
self._max_evaluations)
self._best_hyperparameters = tuner.get_best_hyperparameters(
train_data, train_labels, self._validation_ratio,
self._random_state)
print('self._best_hyperparameters: {}'.format(
self._best_hyperparameters))
if has_sufficient_time(self._dataset_budget_threshold,
self._info) or len(self._classifiers) == 0:
t_d, validation_data, t_l, validation_labels = train_test_split(
train_data,
train_labels,
test_size=self._validation_ratio,
random_state=self._random_state,
shuffle=True,
stratify=train_labels)
new_classifier = LGBMClassifier()
new_classifier.set_params(**self._best_hyperparameters)
new_classifier.fit(train_data,
train_labels,
sample_weight=train_weights)
new_predictions = new_classifier.predict_proba(validation_data)[:,
1]
new_weight = compute_weight(new_predictions, validation_labels,
self._epsilon)
self._ensemble_weights = np.array([])
for i in range(len(self._classifiers)):
currrent_classifier = self._classifiers[i]
currrent_classifier_predictions = currrent_classifier.predict_proba(
validation_data)[:, 1]
currrent_classifier_weight = compute_weight(
currrent_classifier_predictions, validation_labels,
self._epsilon)
self._ensemble_weights = np.append(self._ensemble_weights,
currrent_classifier_weight)
self._classifiers = np.append(self._classifiers, new_classifier)
self._ensemble_weights = np.append(self._ensemble_weights,
new_weight)
print('self._ensemble_weights: {}'.format(self._ensemble_weights))
if len(self._classifiers) > self._ensemble_size:
i = remove_worst_classifier(self._classifiers, validation_data,
validation_labels)
print('Removed classifier: {}'.format(i))
self._classifiers = np.delete(self._classifiers, i)
self._ensemble_weights = np.delete(self._ensemble_weights, i)
else:
print('Time budget exceeded.')
if len(self._classifiers) == 1:
predictions = self._classifiers[0].predict_proba(
transformed_test_data)[:, 1]
else:
predictions = np.zeros(len(transformed_test_data))
for i in range(len(self._classifiers)):
predictions = np.add(
predictions, self._ensemble_weights[i] *
self._classifiers[i].predict_proba(
transformed_test_data)[:, 1])
predictions = np.divide(predictions,
np.sum(self._ensemble_weights))
self._iteration += 1
print('predictions.shape: {}'.format(predictions.shape))
print('File: {} Class: {} Function: {} State: {} \n'.format(
'architectures.py', 'OriginalEnsemble', 'predict', 'End'))
return predictions
