utils.py

import numpy as np
from scipy.optimize import curve_fit
from scipy import stats
import torch
import torch.nn.functional as F
import torchsort


import torch.nn as nn
def logistic_func(X, bayta1, bayta2, bayta3, bayta4):
    logisticPart = 1 + np.exp(np.negative(np.divide(X - bayta3, np.abs(bayta4))))
    yhat = bayta2 + np.divide(bayta1 - bayta2, logisticPart)
    return yhat

def fit_function(y_label, y_output):
    beta = [np.max(y_label), np.min(y_label), np.mean(y_output), 0.5]
    popt, _ = curve_fit(logistic_func, y_output, \
        y_label, p0=beta, maxfev=100000000)
    y_output_logistic = logistic_func(y_output, *popt)
    
    return y_output_logistic


def performance_fit(y_label, y_output):
    y_output_logistic = fit_function(y_label, y_output)
    PLCC = stats.pearsonr(y_output_logistic, y_label)[0]
    SRCC = stats.spearmanr(y_output, y_label)[0]
    KRCC = stats.stats.kendalltau(y_output, y_label)[0]
    RMSE = np.sqrt(((y_output_logistic-y_label) ** 2).mean())

    return PLCC, SRCC, KRCC, RMSE


def performance_no_fit(y_label, y_output):
    PLCC = stats.pearsonr(y_output, y_label)[0]
    SRCC = stats.spearmanr(y_output, y_label)[0]
    KRCC = stats.stats.kendalltau(y_output, y_label)[0]
    RMSE = np.sqrt(((y_output-y_label) ** 2).mean())

    return PLCC, SRCC, KRCC, RMSE


def plcc_loss(y_pred, y):
    sigma_hat, m_hat = torch.std_mean(y_pred, unbiased=False)
    y_pred = (y_pred - m_hat) / (sigma_hat + 1e-8)
    sigma, m = torch.std_mean(y, unbiased=False)
    y = (y - m) / (sigma + 1e-8)
    loss0 = torch.nn.functional.mse_loss(y_pred, y) / 4
    rho = torch.mean(y_pred * y)
    loss1 = torch.nn.functional.mse_loss(rho * y_pred, y) / 4
    return ((loss0 + loss1) / 2).float()