45c9fcd8b1df849a0c3b19403d13da38b7cd68c3,tpot/operators/preprocessors/zero_count.py,ZeroCount,transform,#ZeroCount#Any#,34

Before Change

        modified_df["zero_col"] = feature_cols_only.\
            apply(lambda row: (num_features - np.count_nonzero(row)), axis=1).astype(np.float64)

        return modified_df.copy()


class TPOTZeroCount(Preprocessor):
    Uses TPOT"s ZeroCount to transform the feature set

After Change

        X_transformed: array-like, shape (n_samples, n_features)
            The transformed feature set
        
        X = check_array(X)
        n_samples, n_features = X.shape

        X_transformed = np.copy(X)

        non_zero = np.apply_along_axis(lambda row: np.count_nonzero(row), axis=1, arr=X)
        zero_col = np.apply_along_axis(lambda row: (n_features - np.count_nonzero(row)), axis=1, arr=X)

        X_transformed = np.insert(X_transformed, n_features, non_zero, axis=1)
        X_transformed = np.insert(X_transformed, n_features + 1, zero_col, axis=1)

        return X_transformed

Instances