aab2ef03ff533c1160742fafd2bf12133e227ddb,nonconformist/ensemble.py,AggregatedCp,predict,#AggregatedCp#Any#Any#,41

Before Change

		if self.cp_class.problem_type == "regression":
			intervals = None
			for predictor in self.predictors:
				if intervals is None:
					intervals = predictor.predict(x, significance)
				else:
					intervals = np.dstack([intervals,
					                       predictor.predict(x, significance)])

			p_values = self.p_agg_func(intervals)

			return p_values

class BootstrapCp(AggregatedCp):
	def __init__(self,
	             cp_class,

After Change

			predictions = np.dstack([predictor.predict(x, significance) for
			                         predictor in self.predictors])
		else:
			predictions = np.dstack([predictor.predict(x) for
			                         predictor in self.predictors])

		predictions = self.p_agg_func(predictions)

		if significance and not is_regression:
			return predictions >= significance
		else:
			return predictions

Instances