example/multivariate_time_series/src/metrics.py

# !/usr/bin/env python

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# -*- coding: utf-8 -*-

import numpy as np
import mxnet as mx

def rse(label, pred):
    """computes the root relative squared error (condensed using standard deviation formula)"""
    numerator = np.sqrt(np.mean(np.square(label - pred), axis = None))
    denominator = np.std(label, axis = None)
    return numerator / denominator

def rae(label, pred):
    """computes the relative absolute error (condensed using standard deviation formula)"""
    numerator = np.mean(np.abs(label - pred), axis=None)
    denominator = np.mean(np.abs(label - np.mean(label, axis=None)), axis=None)
    return numerator / denominator

def corr(label, pred):
    """computes the empirical correlation coefficient"""
    numerator1 = label - np.mean(label, axis=0)
    numerator2 = pred - np.mean(pred, axis = 0)
    numerator = np.mean(numerator1 * numerator2, axis=0)
    denominator = np.std(label, axis=0) * np.std(pred, axis=0)
    return np.mean(numerator / denominator)

def get_custom_metrics():
    """
    :return: mxnet metric object
    """
    _rse = mx.metric.create(rse)
    _rae = mx.metric.create(rae)
    _corr = mx.metric.create(corr)
    return mx.metric.create([_rae, _rse, _corr])

def evaluate(pred, label):
    return {"RAE":rae(label, pred), "RSE":rse(label,pred),"CORR": corr(label,pred)}