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utils.py
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utils.py
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import os
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
def save_plot(stats, epoch_idx, path, prefix=""):
_, ax = plt.subplots()
for mode in ["train", "val"]:
for label, arr in stats[mode].items():
ax.plot(
np.arange(1, len(arr) + 1),
arr,
label="%s_%s" % (mode, label),
)
ax.legend()
plt.savefig(os.path.join(path, "%s_loss_%d.png" % (prefix, epoch_idx)), dpi=300)
plt.cla()
plt.close()
def calc_unranked_metrics(scores, labels):
pred_labels = (scores > 0).long()
labels = (labels != 0).long()
# Number of TP by ANDing ground truth and prediction
TP_one_hot = pred_labels * labels
TP = torch.sum(TP_one_hot, dim=1)
# TODO: Buradaki lojiği açıklamak
info_one_hot = pred_labels - labels
FP = torch.sum(info_one_hot > 0, dim=1)
FN = torch.sum(info_one_hot < 0, dim=1)
TN = 9 - (TP + FP + FN)
precision = TP / (TP + FP)
recall = TP / (TP + FN)
acc = (TP + TN) / (TP + TN + FP + FN)
f1 = 2 * precision * recall / (precision + recall)
metrics = {"precision": precision, "recall": recall, "acc": acc, "f1": f1}
for key in metrics:
metrics[key][metrics[key].isnan()] = 0
metrics[key] = torch.mean(metrics[key]).detach().cpu().item()
metrics["TP"] = TP.int().detach().cpu()
metrics["TN"] = TN.int().detach().cpu()
metrics["FP"] = FP.int().detach().cpu()
metrics["FN"] = FN.int().detach().cpu()
return metrics
def mAP(scores, labels):
N, K = labels.shape
pair_map = torch.tensor([(i, j) for i in range(K - 1) for j in range(i + 1, K)])
precisions = torch.zeros(N, K).to(labels.device)
partial_labels = labels.clone()
N_idxs = torch.arange(N)
for k_idx in range(K):
left_labels = partial_labels[:, pair_map[:, 0]]
right_labels = partial_labels[:, pair_map[:, 1]]
bigger_labels = (left_labels > right_labels).int()
smaller_labels = (left_labels < right_labels).int()
left_scores = scores[:, pair_map[:, 0]]
right_scores = scores[:, pair_map[:, 1]]
bigger_scores = (left_scores > right_scores).int()
TP = (bigger_labels * bigger_scores).sum(1).float()
# TN = (smaller_labels * (1 - bigger_scores)).sum(1).float()
FP = (smaller_labels * bigger_scores).sum(1).float()
# FN = (bigger_labels * (1 - bigger_scores)).sum(1).float()
precision = TP / (TP + FP)
precision[precision.isnan()] = 0
precisions[:, k_idx] = precision
partial_labels[partial_labels == 0] = K + 1
min_idxs = torch.argmin(partial_labels, dim=1)
partial_labels[partial_labels == (K + 1)] = 0
partial_labels[N_idxs, min_idxs] = 0
mAP = precisions.sum(1) / (labels != 0).sum(1)
return mAP
def calc_ranked_metrics(scores, labels):
N, K = labels.shape
pair_map = torch.tensor([(i, j) for i in range(K - 1) for j in range(i + 1, K)])
left_labels = labels[:, pair_map[:, 0]]
right_labels = labels[:, pair_map[:, 1]]
bigger_labels = (left_labels > right_labels).int()
smaller_labels = (left_labels < right_labels).int()
left_scores = scores[:, pair_map[:, 0]]
right_scores = scores[:, pair_map[:, 1]]
bigger_scores = (left_scores > right_scores).int()
# smaller_scores = (left_scores < right_scores).int()
TP = (bigger_labels * bigger_scores).sum(1).float()
TN = (smaller_labels * (1 - bigger_scores)).sum(1).float()
FP = (smaller_labels * bigger_scores).sum(1).float()
FN = (bigger_labels * (1 - bigger_scores)).sum(1).float()
# Prediction bigger demedi ise smaller demiştir
precision = TP / (TP + FP)
recall = TP / (TP + FN)
acc = (TP + TN) / (TP + TN + FP + FN)
f1 = 2 * precision * recall / (precision + recall)
metrics = {"precision": precision, "recall": recall, "acc": acc, "f1": f1}
for key in metrics:
metrics[key][metrics[key].isnan()] = 0
metrics[key] = torch.mean(metrics[key]).detach().cpu().item()
metrics["TP"] = TP.int().detach().cpu()
metrics["TN"] = TN.int().detach().cpu()
metrics["FP"] = FP.int().detach().cpu()
metrics["FN"] = FN.int().detach().cpu()
return metrics