def _loss(self, y_true, y_hat, all_scoring_functions=None):
all_scoring_functions = (self.all_scoring_functions
if all_scoring_functions is None else
all_scoring_functions)
if not isinstance(self.configuration, Configuration):
if all_scoring_functions:
return {self.metric: 1.0}
else:
return 1.0
score = calculate_score(y_true,
y_hat,
self.task_type,
self.metric,
all_scoring_functions=all_scoring_functions)
if hasattr(score, '__len__'):
# TODO: instead of using self.metric, it should use all metrics given by key.
# But now this throws error...
err = {
key: metric._optimum - score[key]
for key, metric in CLASSIFICATION_METRICS.items()
if key in score
}
else:
err = self.metric._optimum - score
return err
def predict_and_loss(self, train=False):
if train:
Y_pred = self.predict_function(self.X_train, self.model,
self.task_type, self.Y_train)
score = calculate_score(
solution=self.Y_train,
prediction=Y_pred,
task_type=self.task_type,
metric=self.metric,
scoring_functions=self.scoring_functions)
else:
Y_pred = self.predict_function(self.X_test, self.model,
self.task_type, self.Y_train)
score = calculate_score(
solution=self.Y_test,
prediction=Y_pred,
task_type=self.task_type,
metric=self.metric,
scoring_functions=self.scoring_functions)
if hasattr(score, '__len__'):
if self.task_type in CLASSIFICATION_TASKS:
err = {key: metric._optimum - score[key] for key, metric in
CLASSIFICATION_METRICS.items() if key in score}
else:
err = {key: metric._optimum - score[key] for key, metric in
REGRESSION_METRICS.items() if key in score}
else:
err = self.metric._optimum - score
return err, Y_pred, None, None
def _loss(self, y_true, y_hat, all_scoring_functions=None):
"""Auto-sklearn follows a minimization goal, so the make_scorer
sign is used as a guide to obtain the value to reduce.
On this regard, to optimize a metric:
1- score is calculared with calculate_score, with the caveat, that if
for the metric greater is not better, a negative score is returned.
2- the err (the optimization goal) is then:
optimum - (metric.sign * actual_score)
For accuracy for example: optimum(1) - (+1 * actual score)
For logloss for example: optimum(0) - (-1 * actual score)
"""
all_scoring_functions = (self.all_scoring_functions
if all_scoring_functions is None else
all_scoring_functions)
if not isinstance(self.configuration, Configuration):
if all_scoring_functions:
return {self.metric: 1.0}
else:
return 1.0
score = calculate_score(y_true,
y_hat,
self.task_type,
self.metric,
all_scoring_functions=all_scoring_functions)
if hasattr(score, '__len__'):
# TODO: instead of using self.metric, it should use all metrics given by key.
# But now this throws error...
if self.task_type in CLASSIFICATION_TASKS:
err = {
key: metric._optimum - score[key]
for key, metric in CLASSIFICATION_METRICS.items()
if key in score
}
else:
err = {
key: metric._optimum - score[key]
for key, metric in REGRESSION_METRICS.items()
if key in score
}
else:
err = self.metric._optimum - score
return err