def cross_validate(self, X, y):
binary = self.num_classes == 2
df, cv_scores = cross_validate_pipeline(pipeline=self.pipeline,
X=X,
y=y,
binary=binary,
n_folds=self.N_FOLDS)
self.cv_judgment_metric = np.mean(df[self.judgment_metric])
self.cv_judgment_metric_stdev = np.std(df[self.judgment_metric])
return cv_scores
def cross_validate(self, X, y):
# TODO: this is hacky. See https://github.com/HDI-Project/ATM/issues/48
binary = self.num_classes == 2
kwargs = {}
if self.verbose_metrics:
kwargs['include_curves'] = True
if not binary:
kwargs['include_per_class'] = True
df, cv_scores = cross_validate_pipeline(pipeline=self.pipeline,
X=X,
y=y,
binary=binary,
n_folds=self.N_FOLDS,
**kwargs)
self.cv_judgment_metric = np.mean(df[self.judgment_metric])
self.cv_judgment_metric_stdev = np.std(df[self.judgment_metric])
self.mu_sigma_judgment_metric = (self.cv_judgment_metric -
2 * self.cv_judgment_metric_stdev)
return cv_scores