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Add messages to check_integrity
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@@ -532,7 +532,7 @@ class Proposal:
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kwargs = self.update_kwargs(y, kwargs)
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kwargs = self.update_kwargs(y, kwargs)
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# Build the model
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# Build the model
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super(self.class_type, self.estimator).fit(self.Xd, y, **kwargs)
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super(self.class_type, self.estimator).fit(self.Xd, y, **kwargs)
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self.check_integrity("f", self.Xd)
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self.check_integrity("fit", self.Xd)
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# Local discretization based on the model
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# Local discretization based on the model
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features = kwargs["features"]
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features = kwargs["features"]
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# assign indices to feature names
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# assign indices to feature names
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@@ -543,7 +543,7 @@ class Proposal:
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super(self.class_type, self.estimator).fit(self.Xd, y, **kwargs)
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super(self.class_type, self.estimator).fit(self.Xd, y, **kwargs)
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def predict(self, X):
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def predict(self, X):
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self.check_integrity("p", self.discretizer.transform(X))
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self.check_integrity("predict", self.discretizer.transform(X))
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return super(self.class_type, self.estimator).predict(
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return super(self.class_type, self.estimator).predict(
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self.discretizer.transform(X)
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self.discretizer.transform(X)
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)
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)
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