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accuracy.py
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accuracy.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.
from typing import Any, Dict, Optional
from torch import Tensor, tensor
from torchmetrics.functional.classification.accuracy import (
_accuracy_compute,
_accuracy_update,
_check_subset_validity,
_mode,
_subset_accuracy_compute,
_subset_accuracy_update,
)
from torchmetrics.utilities.enums import DataType
from torchmetrics.classification.stat_scores import StatScores # isort:skip
class Accuracy(StatScores):
r"""
Computes Accuracy_:
.. math::
\text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)
Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a
tensor of predictions.
For multi-class and multi-dimensional multi-class data with probability or logits predictions, the
parameter ``top_k`` generalizes this metric to a Top-K accuracy metric: for each sample the
top-K highest probability or logit score items are considered to find the correct label.
For multi-label and multi-dimensional multi-class inputs, this metric computes the "global"
accuracy by default, which counts all labels or sub-samples separately. This can be
changed to subset accuracy (which requires all labels or sub-samples in the sample to
be correctly predicted) by setting ``subset_accuracy=True``.
Accepts all input types listed in :ref:`pages/classification:input types`.
Args:
num_classes:
Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods.
threshold:
Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case
of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.
average:
Defines the reduction that is applied. Should be one of the following:
- ``'micro'`` [default]: Calculate the metric globally, across all samples and classes.
- ``'macro'``: Calculate the metric for each class separately, and average the
metrics across classes (with equal weights for each class).
- ``'weighted'``: Calculate the metric for each class separately, and average the
metrics across classes, weighting each class by its support (``tp + fn``).
- ``'none'`` or ``None``: Calculate the metric for each class separately, and return
the metric for every class.
- ``'samples'``: Calculate the metric for each sample, and average the metrics
across samples (with equal weights for each sample).
.. note:: What is considered a sample in the multi-dimensional multi-class case
depends on the value of ``mdmc_average``.
.. note:: If ``'none'`` and a given class doesn't occur in the ``preds`` or ``target``,
the value for the class will be ``nan``.
mdmc_average:
Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
``average`` parameter). Should be one of the following:
- ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
multi-class.
- ``'samplewise'``: In this case, the statistics are computed separately for each
sample on the ``N`` axis, and then averaged over samples.
The computation for each sample is done by treating the flattened extra axes ``...``
(see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
and computing the metric for the sample based on that.
- ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
(see :ref:`pages/classification:input types`)
are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
ignore_index:
Integer specifying a target class to ignore. If given, this class index does not contribute
to the returned score, regardless of reduction method. If an index is ignored, and ``average=None``
or ``'none'``, the score for the ignored class will be returned as ``nan``.
top_k:
Number of the highest probability or logit score predictions considered finding the correct label,
relevant only for (multi-dimensional) multi-class inputs. The
default value (``None``) will be interpreted as 1 for these inputs.
Should be left at default (``None``) for all other types of inputs.
multiclass:
Used only in certain special cases, where you want to treat inputs as a different type
than what they appear to be. See the parameter's
:ref:`documentation section <pages/classification:using the multiclass parameter>`
for a more detailed explanation and examples.
subset_accuracy:
Whether to compute subset accuracy for multi-label and multi-dimensional
multi-class inputs (has no effect for other input types).
- For multi-label inputs, if the parameter is set to ``True``, then all labels for
each sample must be correctly predicted for the sample to count as correct. If it
is set to ``False``, then all labels are counted separately - this is equivalent to
flattening inputs beforehand (i.e. ``preds = preds.flatten()`` and same for ``target``).
- For multi-dimensional multi-class inputs, if the parameter is set to ``True``, then all
sub-sample (on the extra axis) must be correct for the sample to be counted as correct.
If it is set to ``False``, then all sub-samples are counter separately - this is equivalent,
in the case of label predictions, to flattening the inputs beforehand (i.e.
``preds = preds.flatten()`` and same for ``target``). Note that the ``top_k`` parameter
still applies in both cases, if set.
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False.
.. deprecated:: v0.8
Argument has no use anymore and will be removed v0.9.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ValueError:
If ``top_k`` is not an ``integer`` larger than ``0``.
ValueError:
If ``average`` is none of ``"micro"``, ``"macro"``, ``"weighted"``, ``"samples"``, ``"none"``, ``None``.
ValueError:
If two different input modes are provided, eg. using ``multi-label`` with ``multi-class``.
ValueError:
If ``top_k`` parameter is set for ``multi-label`` inputs.
Example:
>>> import torch
>>> from torchmetrics import Accuracy
>>> target = torch.tensor([0, 1, 2, 3])
>>> preds = torch.tensor([0, 2, 1, 3])
>>> accuracy = Accuracy()
>>> accuracy(preds, target)
tensor(0.5000)
>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]])
>>> accuracy = Accuracy(top_k=2)
>>> accuracy(preds, target)
tensor(0.6667)
"""
is_differentiable = False
higher_is_better = True
correct: Tensor
total: Tensor
def __init__(
self,
threshold: float = 0.5,
num_classes: Optional[int] = None,
average: str = "micro",
mdmc_average: Optional[str] = "global",
ignore_index: Optional[int] = None,
top_k: Optional[int] = None,
multiclass: Optional[bool] = None,
subset_accuracy: bool = False,
compute_on_step: Optional[bool] = None,
**kwargs: Dict[str, Any],
) -> None:
allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
if average not in allowed_average:
raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
super().__init__(
reduce="macro" if average in ["weighted", "none", None] else average,
mdmc_reduce=mdmc_average,
threshold=threshold,
top_k=top_k,
num_classes=num_classes,
multiclass=multiclass,
ignore_index=ignore_index,
compute_on_step=compute_on_step,
**kwargs,
)
if top_k is not None and (not isinstance(top_k, int) or top_k <= 0):
raise ValueError(f"The `top_k` should be an integer larger than 0, got {top_k}")
self.average = average
self.threshold = threshold
self.top_k = top_k
self.subset_accuracy = subset_accuracy
self.mode: DataType = None # type: ignore
self.multiclass = multiclass
self.ignore_index = ignore_index
if self.subset_accuracy:
self.add_state("correct", default=tensor(0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets. See
:ref:`pages/classification:input types` for more information on input
types.
Args:
preds: Predictions from model (logits, probabilities, or labels)
target: Ground truth labels
"""
""" returns the mode of the data (binary, multi label, multi class, multi-dim multi class) """
mode = _mode(preds, target, self.threshold, self.top_k, self.num_classes, self.multiclass, self.ignore_index)
if not self.mode:
self.mode = mode
elif self.mode != mode:
raise ValueError(f"You can not use {mode} inputs with {self.mode} inputs.")
if self.subset_accuracy and not _check_subset_validity(self.mode):
self.subset_accuracy = False
if self.subset_accuracy:
correct, total = _subset_accuracy_update(
preds, target, threshold=self.threshold, top_k=self.top_k, ignore_index=self.ignore_index
)
self.correct += correct
self.total += total
else:
if not self.mode:
raise RuntimeError("You have to have determined mode.")
tp, fp, tn, fn = _accuracy_update(
preds,
target,
reduce=self.reduce,
mdmc_reduce=self.mdmc_reduce,
threshold=self.threshold,
num_classes=self.num_classes,
top_k=self.top_k,
multiclass=self.multiclass,
ignore_index=self.ignore_index,
mode=self.mode,
)
# Update states
if self.reduce != "samples" and self.mdmc_reduce != "samplewise":
self.tp += tp
self.fp += fp
self.tn += tn
self.fn += fn
else:
self.tp.append(tp)
self.fp.append(fp)
self.tn.append(tn)
self.fn.append(fn)
def compute(self) -> Tensor:
"""Computes accuracy based on inputs passed in to ``update`` previously."""
if not self.mode:
raise RuntimeError("You have to have determined mode.")
if self.subset_accuracy:
return _subset_accuracy_compute(self.correct, self.total)
tp, fp, tn, fn = self._get_final_stats()
return _accuracy_compute(tp, fp, tn, fn, self.average, self.mdmc_reduce, self.mode)