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average_precision.py
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average_precision.py
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# Copyright The PyTorch Lightning team.
#
# 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.
import warnings
from typing import List, Optional, Sequence, Tuple, Union
import torch
from torch import Tensor
from torchmetrics.functional.classification.precision_recall_curve import (
_precision_recall_curve_compute,
_precision_recall_curve_update,
)
from torchmetrics.utilities.data import _bincount
def _average_precision_update(
preds: Tensor,
target: Tensor,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
average: Optional[str] = "macro",
) -> Tuple[Tensor, Tensor, int, Optional[int]]:
"""Format the predictions and target based on the ``num_classes``, ``pos_label`` and ``average`` parameter.
Args:
preds: predictions from model (logits or probabilities)
target: ground truth values
num_classes: integer with number of classes.
pos_label: integer determining the positive class. Default is ``None`` which for binary problem is translated
to 1. For multiclass problems this argument should not be set as we iteratively change it in the
range ``[0, num_classes-1]``
average: reduction method for multi-class or multi-label problems
"""
preds, target, num_classes, pos_label = _precision_recall_curve_update(preds, target, num_classes, pos_label)
if average == "micro" and preds.ndim != target.ndim:
raise ValueError("Cannot use `micro` average with multi-class input")
return preds, target, num_classes, pos_label
def _average_precision_compute(
preds: Tensor,
target: Tensor,
num_classes: int,
pos_label: Optional[int] = None,
average: Optional[str] = "macro",
sample_weights: Optional[Sequence] = None,
) -> Union[List[Tensor], Tensor]:
"""Computes the average precision score.
Args:
preds: predictions from model (logits or probabilities)
target: ground truth values
num_classes: integer with number of classes.
pos_label: integer determining the positive class. Default is ``None`` which for binary problem is translated
to 1. For multiclass problems his argument should not be set as we iteratively change it in the
range ``[0, num_classes-1]``
average: reduction method for multi-class or multi-label problems
sample_weights: sample weights for each data point
Example:
>>> # binary case
>>> preds = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> pos_label = 1
>>> preds, target, num_classes, pos_label = _average_precision_update(preds, target, pos_label=pos_label)
>>> _average_precision_compute(preds, target, num_classes, pos_label)
tensor(1.)
>>> # multiclass case
>>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
... [0.05, 0.75, 0.05, 0.05, 0.05],
... [0.05, 0.05, 0.75, 0.05, 0.05],
... [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> num_classes = 5
>>> preds, target, num_classes, pos_label = _average_precision_update(preds, target, num_classes)
>>> _average_precision_compute(preds, target, num_classes, average=None)
[tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)]
"""
# todo: `sample_weights` is unused
if average == "micro" and preds.ndim == target.ndim:
preds = preds.flatten()
target = target.flatten()
num_classes = 1
precision, recall, _ = _precision_recall_curve_compute(preds, target, num_classes, pos_label)
if average == "weighted":
if preds.ndim == target.ndim and target.ndim > 1:
weights = target.sum(dim=0).float()
else:
weights = _bincount(target, minlength=num_classes).float()
weights = weights / torch.sum(weights)
else:
weights = None
return _average_precision_compute_with_precision_recall(precision, recall, num_classes, average, weights)
def _average_precision_compute_with_precision_recall(
precision: Tensor,
recall: Tensor,
num_classes: int,
average: Optional[str] = "macro",
weights: Optional[Tensor] = None,
) -> Union[List[Tensor], Tensor]:
"""Computes the average precision score from precision and recall.
Args:
precision: precision values
recall: recall values
num_classes: integer with number of classes. Not nessesary to provide
for binary problems.
average: reduction method for multi-class or multi-label problems
weights: weights to use when average='weighted'
Example:
>>> # binary case
>>> preds = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> pos_label = 1
>>> preds, target, num_classes, pos_label = _average_precision_update(preds, target, pos_label=pos_label)
>>> precision, recall, _ = _precision_recall_curve_compute(preds, target, num_classes, pos_label)
>>> _average_precision_compute_with_precision_recall(precision, recall, num_classes, average=None)
tensor(1.)
>>> # multiclass case
>>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
... [0.05, 0.75, 0.05, 0.05, 0.05],
... [0.05, 0.05, 0.75, 0.05, 0.05],
... [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> num_classes = 5
>>> preds, target, num_classes, pos_label = _average_precision_update(preds, target, num_classes)
>>> precision, recall, _ = _precision_recall_curve_compute(preds, target, num_classes)
>>> _average_precision_compute_with_precision_recall(precision, recall, num_classes, average=None)
[tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)]
"""
# Return the step function integral
# The following works because the last entry of precision is
# guaranteed to be 1, as returned by precision_recall_curve
if num_classes == 1:
return -torch.sum((recall[1:] - recall[:-1]) * precision[:-1])
res = []
for p, r in zip(precision, recall):
res.append(-torch.sum((r[1:] - r[:-1]) * p[:-1]))
# Reduce
if average in ("macro", "weighted"):
res = torch.stack(res)
if torch.isnan(res).any():
warnings.warn(
"Average precision score for one or more classes was `nan`. Ignoring these classes in average",
UserWarning,
)
if average == "macro":
return res[~torch.isnan(res)].mean()
weights = torch.ones_like(res) if weights is None else weights
return (res * weights)[~torch.isnan(res)].sum()
if average is None or average == "none":
return res
allowed_average = ("micro", "macro", "weighted", "none", None)
raise ValueError(f"Expected argument `average` to be one of {allowed_average}" f" but got {average}")
def average_precision(
preds: Tensor,
target: Tensor,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
average: Optional[str] = "macro",
sample_weights: Optional[Sequence] = None,
) -> Union[List[Tensor], Tensor]:
"""Computes the average precision score.
Args:
preds: predictions from model (logits or probabilities)
target: ground truth values
num_classes: integer with number of classes. Not nessesary to provide
for binary problems.
pos_label: integer determining the positive class. Default is ``None`` which for binary problem is translated
to 1. For multiclass problems his argument should not be set as we iteratively change it in the
range ``[0, num_classes-1]``
average:
defines the reduction that is applied in the case of multiclass and multilabel input.
Should be one of the following:
- ``'macro'`` [default]: Calculate the metric for each class separately, and average the
metrics across classes (with equal weights for each class).
- ``'micro'``: Calculate the metric globally, across all samples and classes. Cannot be
used with multiclass input.
- ``'weighted'``: Calculate the metric for each class separately, and average the
metrics across classes, weighting each class by its support.
- ``'none'`` or ``None``: Calculate the metric for each class separately, and return
the metric for every class.
sample_weights: sample weights for each data point
Returns:
tensor with average precision. If multiclass will return list
of such tensors, one for each class
Example (binary case):
>>> from torchmetrics.functional import average_precision
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> average_precision(pred, target, pos_label=1)
tensor(1.)
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
... [0.05, 0.75, 0.05, 0.05, 0.05],
... [0.05, 0.05, 0.75, 0.05, 0.05],
... [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> average_precision(pred, target, num_classes=5, average=None)
[tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)]
"""
# fixme: `sample_weights` is unused
preds, target, num_classes, pos_label = _average_precision_update(preds, target, num_classes, pos_label, average)
return _average_precision_compute(preds, target, num_classes, pos_label, average, sample_weights)