def predict_fn()

in src/transform.py [0:0]

• 6 lines of code
• 3 McCabe index (conditional complexity)

def predict_fn(input_data, model):
    """Preprocess input data

    We implement this because the default predict_fn uses .predict(), but our model is a preprocessor
    so we want to use .transform().

    The output is returned in the following order:

        rest of features either one hot encoded or standardized
    """
    features = model.transform(input_data)

    if label_column in input_data:
        # Return the label (as the first column) and the set of features.
        return np.insert(features, 0, input_data[label_column], axis=1)
    else:
        # Return only the set of features
        return features