Graph/GraphMAE_MQ9/code/lib/metrics.py

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

import numpy as np
import torch
import torch.nn.functional as F


def masked_mape_np(y_true, y_pred, null_val=np.nan):
    with np.errstate(divide='ignore', invalid='ignore'):
        if np.isnan(null_val):
            mask = ~np.isnan(y_true)
        else:
            mask = np.not_equal(y_true, null_val)
        mask = mask.astype('float32')
        mask /= np.mean(mask)
        mape = np.abs(np.divide(np.subtract(y_pred, y_true).astype('float32'),
                                y_true))
        mape = np.nan_to_num(mask * mape)
        return np.mean(mape)


def masked_mse(preds, labels, null_val=np.nan):
    if np.isnan(null_val):
        mask = ~torch.isnan(labels)
    else:
        mask = (labels != null_val)
    mask = mask.float()
    # print(mask.sum())
    # print(mask.shape[0]*mask.shape[1]*mask.shape[2])
    mask /= torch.mean((mask))
    mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
    loss = (preds - labels) ** 2
    loss = loss * mask
    loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
    return torch.mean(loss)


def masked_rmse(preds, labels, null_val=np.nan):
    return torch.sqrt(masked_mse(preds=preds, labels=labels,
                                 null_val=null_val))


def masked_mae(preds, labels, null_val=np.nan):
    if np.isnan(null_val):
        mask = ~torch.isnan(labels)
    else:
        mask = (labels != null_val)
    mask = mask.float()
    mask /= torch.mean((mask))
    mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
    loss = torch.abs(preds - labels)
    loss = loss * mask
    loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
    return torch.mean(loss)


def masked_mae_test(y_true, y_pred, null_val=np.nan):
    with np.errstate(divide='ignore', invalid='ignore'):
        if np.isnan(null_val):
            mask = ~np.isnan(y_true)
        else:
            mask = np.not_equal(y_true, null_val)
        mask = mask.astype('float32')
        mask /= np.mean(mask)
        mae = np.abs(np.subtract(y_pred, y_true).astype('float32'),
                     )
        mae = np.nan_to_num(mask * mae)
        return np.mean(mae)


def masked_rmse_test(y_true, y_pred, null_val=np.nan):
    with np.errstate(divide='ignore', invalid='ignore'):
        if np.isnan(null_val):
            mask = ~np.isnan(y_true)
        else:
            # null_val=null_val
            mask = np.not_equal(y_true, null_val)
        mask = mask.astype('float32')
        mask /= np.mean(mask)
        mse = ((y_pred - y_true) ** 2)
        mse = np.nan_to_num(mask * mse)
        return np.sqrt(np.mean(mse))


def sce_loss(x, y, alpha=3):
    x = F.normalize(x, p=2, dim=-1)
    y = F.normalize(y, p=2, dim=-1)

    # loss =  - (x * y).sum(dim=-1)
    # loss = (x_h - y_h).norm(dim=1).pow(alpha)

    loss = (1 - (x * y).sum(dim=-1)).pow_(alpha)

    loss = loss.mean()
    return loss