# Copyright 2019 The Texar Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Various metrics.
"""
from typing import Optional
import torch
__all__ = [
"accuracy",
"binary_clas_accuracy",
]
[docs]def accuracy(labels: torch.Tensor,
preds: torch.Tensor) -> torch.Tensor:
r"""Calculates the accuracy of predictions.
Args:
labels: The ground truth values. A Tensor of the same shape of
:attr:`preds`.
preds: A Tensor of any shape containing the predicted values.
Returns:
A float scalar Tensor containing the accuracy.
"""
labels = labels.type(preds.dtype).reshape(preds.shape)
return (labels == preds).float().mean()
[docs]def binary_clas_accuracy(pos_preds: Optional[torch.Tensor] = None,
neg_preds: Optional[torch.Tensor] = None) -> \
Optional[torch.Tensor]:
r"""Calculates the accuracy of binary predictions.
Args:
pos_preds (optional): A Tensor of any shape containing the
predicted values on positive data (i.e., ground truth labels are 1).
neg_preds (optional): A Tensor of any shape containing the
predicted values on negative data (i.e., ground truth labels are 0).
Returns:
A float scalar Tensor containing the accuracy.
"""
if pos_preds is None and neg_preds is None:
return None
if pos_preds is not None:
pos_accu = accuracy(torch.ones_like(pos_preds), pos_preds)
psize = float(torch.numel(pos_preds))
else:
pos_accu = torch.tensor(0.0)
psize = torch.tensor(0.0)
if neg_preds is not None:
neg_accu = accuracy(torch.zeros_like(neg_preds), neg_preds)
nsize = float(torch.numel(neg_preds))
else:
neg_accu = torch.tensor(0.0)
nsize = torch.tensor(0.0)
accu = (pos_accu * psize + neg_accu * nsize) / (psize + nsize)
return accu