File size: 6,214 Bytes
9e14838 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | # -*- coding: utf-8 -*-
import os
import numpy as np
import torch
from losses.losses import _avg_sigmoid, _sigmoid
from scipy.stats import beta, gaussian_kde
from sklearn import metrics as cal_metrics
from sklearn.metrics import (
average_precision_score,
balanced_accuracy_score,
f1_score,
precision_score,
recall_score,
)
def bin_calculate_acc(preds, labels, targets=None, threshold=0.5):
if torch.is_tensor(preds):
if preds.shape[-1] > 1:
preds = preds.softmax(dim=-1)
if preds.shape[-1] == 2:
preds = preds[:, -1]
else:
preds = preds.max(dim=-1, keepdim=True).values
labels = labels.max(dim=-1, keepdim=True).values
else:
preds = preds.sigmoid()
preds = preds.detach().cpu().numpy()
labels = labels.detach().cpu().numpy()
preds_ = (preds >= threshold).astype(int)
acc = np.mean((preds_ == labels).astype(int), axis=0)
if acc.ndim >= 1:
return acc[0]
else:
return acc
def hm_calculate_acc(preds, targets=None, labels=None, threshold=0.5):
cls_ = _avg_sigmoid(preds)
acc = bin_calculate_acc(cls_, labels, threshold=threshold)
return acc
def hm_bin_calculate_acc(
hm_preds, cls_preds, targets=None, labels=None, cls_lamda=0.05
):
# Select top hm_preds
hm_preds_ = _sigmoid(hm_preds.clone())
hm_preds_ = torch.reshape(hm_preds_, (hm_preds_.shape[0], hm_preds_.shape[1], -1))
top_k = torch.topk(hm_preds_, 10, -1).values
mean_hm_preds = torch.mean(top_k, -1)
cls_preds_ = cls_lamda * cls_preds + (1 - cls_lamda) * mean_hm_preds
acc = bin_calculate_acc(cls_preds_, labels)
return acc
def bin_calculate_auc_ap_ar(
cls_preds,
labels,
metrics_base="binary",
hm_preds=None,
cls_lamda=0.1,
threshold=0.5,
apr=True,
):
assert metrics_base in [
"binary",
"heatmap",
"combine",
], "Metric base is only one of these values [binary, heatmap, combine]"
if torch.is_tensor(cls_preds):
if cls_preds.shape[-1] > 1:
cls_preds = cls_preds.softmax(dim=-1)
if cls_preds.shape[-1] == 2:
cls_preds = cls_preds[:, -1]
else:
cls_preds = cls_preds.max(dim=-1, keepdim=True).values
labels = labels.max(dim=-1, keepdim=True).values
else:
cls_preds = cls_preds.sigmoid()
if metrics_base == "combine":
assert (
hm_preds is not None
), "Heatmap predict can not be None if metrics-base is combine"
hm_preds = _sigmoid(hm_preds)
hm_preds = torch.reshape(hm_preds, (hm_preds.shape[0], 1, -1))
top_k = torch.topk(hm_preds, 10, -1).values
mean_hm_preds = torch.mean(top_k, -1)
cls_preds = cls_lamda * cls_preds + (1 - cls_lamda) * mean_hm_preds
labels = labels.cpu().numpy()
cls_preds = cls_preds.cpu().numpy()
fpr, tpr, thresholds = cal_metrics.roc_curve(labels, cls_preds, pos_label=1)
metrics = {}
distances = np.sqrt((1 - tpr) ** 2 + fpr**2)
optimal_idx = np.argmin(distances)
optimal_threshold = thresholds[optimal_idx]
top_k_min_indices = np.argsort(distances)[:5]
top_k_thresholds = thresholds[top_k_min_indices]
metrics["best_thr"] = optimal_threshold
metrics["thr_var"] = np.var(top_k_thresholds, ddof=1)
# AUC
metrics["auc"] = cal_metrics.auc(fpr, tpr)
if apr:
# AP metric
ap = average_precision_score(labels, cls_preds)
metrics["ap"] = ap
# AR metric
ar = recall_score(labels, (cls_preds >= threshold).astype(int), average="macro")
metrics["ar"] = ar
# mF1 metric
metrics["mf1"] = (ap * ar * 2) / (ap + ar)
return metrics
else:
# False Negative Rate
fnr = 1 - tpr
eer_threshold = thresholds[np.nanargmin(np.absolute(fpr - fnr))]
eer = fpr[np.nanargmin(np.absolute(fpr - fnr))]
metrics["eer"] = eer
metrics["bacc"] = balanced_accuracy_score(
labels, (cls_preds >= threshold).astype(int)
)
metrics["p"] = precision_score(labels, (cls_preds >= threshold).astype(int))
metrics["r"] = recall_score(labels, (cls_preds >= threshold).astype(int))
metrics["s"] = recall_score(
labels, (cls_preds >= threshold).astype(int), pos_label=0
)
metrics["f1"] = f1_score(labels, (cls_preds >= threshold).astype(int))
return metrics
def get_acc_mesure_func(task="binary"):
if task == "binary":
return bin_calculate_acc
elif task == "heatmap":
return hm_calculate_acc
else:
return hm_bin_calculate_acc
# Compute Empirical CDF
def empirical_cdf(data):
sorted_data = np.sort(data)
return sorted_data, np.arange(1, len(sorted_data) + 1) / len(sorted_data)
# Transformations
def apply_cdf_transform(neg_data, pos_data, cdf_type="empirical"):
if cdf_type == "empirical":
return (
empirical_cdf(neg_data)[1],
empirical_cdf(pos_data)[1],
) # Get the ECDF values
elif cdf_type == "kde":
kde_data1 = gaussian_kde(neg_data)
kde_data2 = gaussian_kde(pos_data)
x_values = np.linspace(0, 1, 11) # 0.0; 0.1; 0.2; ...
cdf1 = np.cumsum(kde_data1(x_values))
cdf1 /= cdf1[-1]
cdf2 = np.cumsum(kde_data2(x_values))
cdf2 /= cdf2[-1]
return cdf1, cdf2
elif cdf_type == "para":
alp1, bta1, _, _ = beta.fit(neg_data, floc=0, fscale=1.0001)
alp2, bta2, _, _ = beta.fit(pos_data, floc=0, fscale=1.0001)
x_values = np.linspace(0, 1, 11) # 0.0; 0.1; 0.2; ...
cdf1 = beta.cdf(x_values, alp1, bta1)
cdf2 = beta.cdf(x_values, alp2, bta2)
return cdf1, cdf2
elif cdf_type == "quantile":
q = np.linspace(0, 1, min(len(neg_data), len(pos_data))) # Matching quantiles
F_inv_P0 = np.quantile(neg_data, q)
F_inv_P1 = np.quantile(pos_data, q)
return F_inv_P0, F_inv_P1
else:
raise ValueError("Unknown CDF type")
|