Python check_non_zero_sample_size 예제들

예제 #1

 def compute(self):
     check_non_zero_sample_size(self._n)
     val = self._squared_error / self._n
     try:
         return val.item()
     except:
         return val

예제 #2

    def compute(self):
        check_non_zero_sample_size(len(self._target))
        target = torch.cat(self._target, dim=0)
        pred = torch.cat(self._pred, dim=0)

        # if pred.shape[1]!=1:
        #     import pdb
        #     pdb.set_trace()
        target_rank_idx = target.argsort(dim=0, descending=True)
        pred_rank_idx = pred.argsort(dim=0, descending=True)

        # TODO: find better way, very inefficient
        for col in range(target.shape[1]):
            for count, idx in enumerate(target_rank_idx[:, col]):
                target[idx, col] = count + 1
            for count, idx in enumerate(pred_rank_idx[:, col]):
                pred[idx, col] = count + 1

        # TODO: take care of tied ranks

        target_mean = target.mean(dim=0, keepdim=True)
        pred_mean = pred.mean(dim=0, keepdim=True)

        val1 = ((target - target_mean) * (pred - pred_mean)).sum(dim=0)
        val2 = torch.pow(target - target_mean, 2.0).sum(dim=0).sqrt()
        val3 = torch.pow(pred - pred_mean, 2.0).sum(dim=0).sqrt()
        val = val1 / (val2 * val3)

        try:
            return val.item()  # scalar
        except:
            return val  # tensor

예제 #3

 def compute(self):
     check_non_zero_sample_size(self._n)
     val = self._num_correct / self._n
     try:
         return val.item()
     except:
         return val

예제 #4

파일: meansquaredlogarithmicerror.py 프로젝트: ucrscholar/pytorch-metrics

 def compute(self):
     check_non_zero_sample_size(self._n)
     val = self._logarithmic_error / self._n
     try:
         return val.item()
     except:
         return val

예제 #5

파일: cosinesimilarity.py 프로젝트: ucrscholar/pytorch-metrics

 def compute(self):
     check_non_zero_sample_size(self._n)
     val = self._dot_xy / (self._norm_x.sqrt() * self._norm_y.sqrt())
     try:
         return val.item()
     except:
         return val

예제 #6

 def compute(self):
     check_non_zero_sample_size(len(self._target))
     target = torch.cat(self._target, dim=0)
     pred = torch.cat(self._pred, dim=0)
     ss_res = torch.pow(target - pred, 2.0).sum(dim=0)
     ss_tot = torch.pow(target - target.mean(dim=0), 2.0).sum(dim=0)
     return 1 - ss_res / ss_tot

예제 #7

파일: explainedvariance.py 프로젝트: ucrscholar/pytorch-metrics

 def compute(self):
     check_non_zero_sample_size(len(self._target))
     target = torch.cat(self._target, dim=0)
     pred = torch.cat(self._pred, dim=0)
     val = 1 - (target - pred).var(dim=0) / target.var(dim=0)
     try:
         return val.item()
     except:
         return val

예제 #8

파일: pearsoncorrelation.py 프로젝트: ucrscholar/pytorch-metrics

    def compute(self):
        check_non_zero_sample_size(len(self._target))
        target = torch.cat(self._target, dim=0)
        pred = torch.cat(self._pred, dim=0)

        target_mean = target.mean(dim=0, keepdim=True)
        pred_mean = pred.mean(dim=0, keepdim=True)

        val1 = ((target - target_mean) * (pred - pred_mean)).sum(dim=0)
        val2 = torch.pow(target - target_mean, 2.0).sum(dim=0).sqrt()
        val3 = torch.pow(pred - pred_mean, 2.0).sum(dim=0).sqrt()
        val = val1 / (val2 * val3)

        try:
            return val.item()  # scalar
        except:
            return val  # tensor