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81 lines
2.3 KiB
81 lines
2.3 KiB
""" Eval metrics and related
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import torch
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from sklearn.metrics import precision_score,accuracy_score ,recall_score ,log_loss,f1_score,confusion_matrix
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import torch.nn.functional as F
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import warnings
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warnings.filterwarnings('always') # "error", "ignore", "always", "default", "module" or "once"
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class AverageMeter:
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"""Computes and stores the average and current value"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def accuracy(output, target, topk=(1,)):
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"""Computes the accuracy over the k top predictions for the specified values of k"""
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maxk = min(max(topk), output.size()[1])
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t()
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correct = pred.eq(target.reshape(1, -1).expand_as(pred))
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return [correct[:min(k, maxk)].reshape(-1).float().sum(0) * 100. / batch_size for k in topk]
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def precision(output, target):
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_, y_pred = output.topk(1, 1, True, True)
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y_true = target
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y_true=y_true.tolist()
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y_pred=y_pred.tolist()
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y_pred=sum(y_pred, [])
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TP=0+0.00000000000000009
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FP=0
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for i in range(len(y_true)):
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if y_true[i]==1 and y_pred[i]==1:
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TP=TP+1
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if y_true[i]==0 and y_pred[i]==1:
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FP=FP+1
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precision=precision_score(y_true,y_pred)
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return precision*100
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def recall(output, target):
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y_pred = torch.ge(output, 0.0)
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_, y_pred = output.topk(1, 1, True, True)
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y_true = target
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true_positive = len((y_true.flatten() == y_pred.flatten()).nonzero().flatten())
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y_true=y_true.tolist()
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y_pred=y_pred.tolist()
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y_pred=sum(y_pred, [])
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TP=0+0.00000000000000009
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FN=0
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for i in range(len(y_true)):
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if y_true[i]==1 and y_pred[i]==1:
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TP=TP+1
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if y_true[i]==1 and y_pred[i]==0:
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FN=FN+1
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recall =recall_score(y_true,y_pred)
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return recall*100
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def f1_scor(output, target):
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y_pred = torch.ge(output, 0.5)
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_, y_pred = output.topk(1, 1, True, True)
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y_true = target
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y_true=y_true.tolist()
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y_pred=y_pred.tolist()
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return f1_score(y_true,y_pred)*100
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