def predict(self):
Y_optimization_pred = self.predict_function(self.X_optimization,
self.model, self.task_type)
if self.X_valid is not None:
Y_valid_pred = self.predict_function(self.X_valid, self.model,
self.task_type)
else:
Y_valid_pred = None
if self.X_test is not None:
Y_test_pred = self.predict_function(self.X_test, self.model,
self.task_type)
else:
Y_test_pred = None
score = calculate_score(
self.Y_optimization, Y_optimization_pred, self.task_type,
self.metric, self.D.info['target_num'],
all_scoring_functions=self.all_scoring_functions)
if hasattr(score, '__len__'):
err = {key: 1 - score[key] for key in score}
else:
err = 1 - score
if self.with_predictions:
return err, Y_optimization_pred, Y_valid_pred, Y_test_pred
return err
def predict(self):
Y_optimization_pred = [None] * self.cv_folds
Y_targets = [None] * self.cv_folds
Y_valid_pred = [None] * self.cv_folds
Y_test_pred = [None] * self.cv_folds
for i in range(self.cv_folds):
# To support prediction when only partial_fit was called
if self.models[i] is None:
continue
train_indices, test_indices = self.indices[i]
opt_pred = self.predict_function(self.X_train[test_indices],
self.models[i], self.task_type,
self.Y_train[train_indices])
Y_optimization_pred[i] = opt_pred
Y_targets[i] = self.Y_train[test_indices]
if self.X_valid is not None:
X_valid = self.X_valid.copy()
valid_pred = self.predict_function(X_valid, self.models[i],
self.task_type,
self.Y_train[train_indices])
Y_valid_pred[i] = valid_pred
if self.X_test is not None:
X_test = self.X_test.copy()
test_pred = self.predict_function(X_test, self.models[i],
self.task_type,
self.Y_train[train_indices])
Y_test_pred[i] = test_pred
Y_optimization_pred = np.concatenate(
[Y_optimization_pred[i] for i in range(self.cv_folds)
if Y_optimization_pred[i] is not None])
Y_targets = np.concatenate([Y_targets[i] for i in range(self.cv_folds)
if Y_targets[i] is not None])
if self.X_valid is not None:
Y_valid_pred = np.array([Y_valid_pred[i]
for i in range(self.cv_folds)
if Y_valid_pred[i] is not None])
# Average the predictions of several models
if len(Y_valid_pred.shape) == 3:
Y_valid_pred = np.nanmean(Y_valid_pred, axis=0)
if self.X_test is not None:
Y_test_pred = np.array([Y_test_pred[i]
for i in range(self.cv_folds)
if Y_test_pred[i] is not None])
# Average the predictions of several models
if len(Y_test_pred.shape) == 3:
Y_test_pred = np.nanmean(Y_test_pred, axis=0)
self.Y_optimization = Y_targets
score = calculate_score(
Y_targets, Y_optimization_pred, self.task_type, self.metric,
self.D.info['target_num'],
all_scoring_functions=self.all_scoring_functions)
if hasattr(score, '__len__'):
err = {key: 1 - score[key] for key in score}
else:
err = 1 - score
if self.with_predictions:
return err, Y_optimization_pred, Y_valid_pred, Y_test_pred
return err
