33851dea0f6c75ca1e685037393ea7160506b53a,auto_ml/predictor.py,Predictor,_prepare_for_training,#Predictor#Any#,207

Before Change

                        y_floats.append(float_val)
                    else:
                        indices_to_delete.append(idx)
                        bad_vals.append(val)
                except:
                    indices_to_delete.append(idx)
                    bad_vals.append(val)

After Change

        // Drop all rows that have an empty value for our output column
        // User logging so they can adjust if they pass in a bunch of bad values:
        bad_rows = X_df[pd.isnull(X_df[self.output_column])]
        if bad_rows.shape[0] > 0:
            print("We encountered a number of missing values for this output column")
            print("Specifically, here is the output column:")
            print(self.output_column)
            print("And here is the number of missing (nan, None, etc.) values for this column:")
            print(bad_rows.shape[0])
            print("We will remove these values, and continue with training on the cleaned dataset")
        X_df = X_df.dropna(subset=[self.output_column])


        // Remove the output column from the dataset, and store it into the y varaible

Instances