eval.py

import numpy as np
import torch

def mask_np(array, null_val):
    if np.isnan(null_val):
        return (~np.isnan(null_val)).astype('float32')
    else:
        return np.not_equal(array, null_val).astype('float32')


def masked_mape_np(y_true, y_pred, null_val=np.nan):
    with np.errstate(divide='ignore', invalid='ignore'):
        mask = mask_np(y_true, null_val)
        mask /= mask.mean()
        mape = np.abs((y_pred - y_true) / y_true)
        mape = np.nan_to_num(mask * mape)
        return np.mean(mape) * 100

def masked_mae_np(y_true, y_pred, null_val=np.nan):
    mask = mask_np(y_true, null_val)
    mask /= mask.mean()
    mae = np.abs(y_true - y_pred)
    return np.mean(np.nan_to_num(mask * mae))

def masked_rmse_np(y_true, y_pred, null_val=np.nan):
    mask = mask_np(y_true, null_val)
    mask /= mask.mean()
    mse = (y_true - y_pred) ** 2
    return np.sqrt(np.mean(np.nan_to_num(mask * mse)))