mape.py

# below code from
# https://alex.miller.im/posts/linear-model-custom-loss-function-regularization-python/

def mean_absolute_percentage_error(y_pred, y_true, sample_weights=None):
    """Mean absolute percentage error regression loss"""
    y_true = np.array(y_true)
    y_pred = np.array(y_pred)
    assert len(y_true) == len(y_pred)
    
    if np.any(y_true==0):
        print("Found zeroes in y_true. MAPE undefined. Removing from set...")
        idx = np.where(y_true==0)
        y_true = np.delete(y_true, idx)
        y_pred = np.delete(y_pred, idx)
        if type(sample_weights) != type(None):
            sample_weights = np.array(sample_weights)
            sample_weights = np.delete(sample_weights, idx)
        
    if type(sample_weights) == type(None):
        return(np.mean(np.abs((y_true - y_pred) / y_true)) * 100)
    else:
        sample_weights = np.array(sample_weights)
        assert len(sample_weights) == len(y_true)
        return(100/sum(sample_weights)*np.dot(
                sample_weights, (np.abs((y_true - y_pred) / y_true))
        ))