019f0c822cd8f2833d3b25855f2df99cd5a465a4,auto_ml/utils_model_training.py,FinalModelATC,fit,#FinalModelATC#Any#Any#,57

Before Change

                self.model.fit(X_fit, y)

        except TypeError as e:
            if scipy.sparse.issparse(X_fit):
                X_fit = X_fit.todense()
            self.model.fit(X_fit, y)

        except KeyboardInterrupt as e:
            print("Stopping training at this point because we heard a KeyboardInterrupt")

After Change

            cat_feature_indices = self.get_categorical_feature_indices()
            if cat_feature_indices is None:
                self.model.fit(X_fit, y, eval_set=[(X_test, y_test)], early_stopping_rounds=50, eval_metric=eval_metric, eval_names=["random_holdout_set_from_training_data"])
            else:
                self.model.fit(X_fit, y, eval_set=[(X_test, y_test)], early_stopping_rounds=50, eval_metric=eval_metric, eval_names=["random_holdout_set_from_training_data"], categorical_feature=cat_feature_indices)


        elif self.model_name[:8] == "CatBoost":
            X_fit = X_fit.toarray()

            if self.type_of_estimator == "classifier" and len(pd.Series(y).unique()) > 2:

Instances