utils.py

import numpy as np
from scipy.optimize import curve_fit
from scipy import stats
import torch

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 fit_function_regression_values(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, popt


def performance_fit(y_label, y_output):
    y_output_logistic, popt = fit_function_regression_values(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())
    MAE = np.absolute((y_output_logistic-y_label)).mean()

    return PLCC, SRCC, KRCC, RMSE, MAE, popt

EPS = 1e-2
esp = 1e-8

class Fidelity_Loss(torch.nn.Module):

    def __init__(self):
        super(Fidelity_Loss, self).__init__()

    def forward(self, p, g):
        g = g.view(-1, 1)
        p = p.view(-1, 1)
        loss = 1 - (torch.sqrt(p * g + esp) + torch.sqrt((1 - p) * (1 - g) + esp))

        return torch.mean(loss)