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Source code for pytorch_lightning.metrics.functional.precision_recall

# 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 Optional

import torch

from pytorch_lightning.metrics.classification.helpers import _reduce_stat_scores
from pytorch_lightning.metrics.functional.stat_scores import _stat_scores_update
from pytorch_lightning.utilities import rank_zero_warn


def _precision_compute(
    tp: torch.Tensor,
    fp: torch.Tensor,
    tn: torch.Tensor,
    fn: torch.Tensor,
    average: str,
    mdmc_average: Optional[str],
) -> torch.Tensor:
    return _reduce_stat_scores(
        numerator=tp,
        denominator=tp + fp,
        weights=None if average != "weighted" else tp + fn,
        average=average,
        mdmc_average=mdmc_average,
    )


[docs]def precision( preds: torch.Tensor, target: torch.Tensor, average: str = "micro", mdmc_average: Optional[str] = None, ignore_index: Optional[int] = None, num_classes: Optional[int] = None, threshold: float = 0.5, top_k: Optional[int] = None, is_multiclass: Optional[bool] = None, class_reduction: Optional[str] = None, ) -> torch.Tensor: r""" Computes `Precision <https://en.wikipedia.org/wiki/Precision_and_recall>`_: .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. With the use of ``top_k`` parameter, this metric can generalize to Precision@K. The reduction method (how the precision scores are aggregated) is controlled by the ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the multi-dimensional multi-class case. Accepts all inputs listed in :ref:`extensions/metrics:input types`. Args: preds: Predictions from model (probabilities or labels) target: Ground truth values average: Defines the reduction that is applied. Should be one of the following: - ``'micro'`` [default]: Calculate the metric globally, accross all samples and classes. - ``'macro'``: Calculate the metric for each class separately, and average the metrics accross classes (with equal weights for each class). - ``'weighted'``: Calculate the metric for each class separately, and average the metrics accross 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 that what is considered a sample in the multi-dimensional multi-class case depends on the value of ``mdmc_average``. class_reduction: .. warning :: This parameter is deprecated, use ``average``. Will be removed in v1.4.0. 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:`extensions/metrics: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:`extensions/metrics: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``. num_classes: Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods. threshold: Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs. top_k: Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over ``threshold``. For (multi-dim) multi-class inputs, this parameter defaults to 1. Should be left unset (``None``) for inputs with label predictions. is_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 <extensions/metrics:using the is_multiclass parameter>` for a more detailed explanation and examples. class_reduction: .. warning :: This parameter is deprecated, use ``average``. Will be removed in v1.4.0. Return: The shape of the returned tensor depends on the ``average`` parameter - If ``average in ['micro', 'macro', 'weighted', 'samples']``, a one-element tensor will be returned - If ``average in ['none', None]``, the shape will be ``(C,)``, where ``C`` stands for the number of classes Example: >>> from pytorch_lightning.metrics.functional import precision >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> precision(preds, target, average='macro', num_classes=3) tensor(0.1667) >>> precision(preds, target, average='micro') tensor(0.2500) """ if class_reduction: rank_zero_warn( "This `class_reduction` parameter was deprecated in v1.2.0 in favor of" " `reduce`. It will be removed in v1.4.0", DeprecationWarning, ) average = class_reduction 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}.") allowed_mdmc_average = [None, "samplewise", "global"] if mdmc_average not in allowed_mdmc_average: raise ValueError(f"The `mdmc_average` has to be one of {allowed_mdmc_average}, got {mdmc_average}.") if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1): raise ValueError(f"When you set `average` as {average}, you have to provide the number of classes.") if num_classes and ignore_index is not None and (not 0 <= ignore_index < num_classes or num_classes == 1): raise ValueError(f"The `ignore_index` {ignore_index} is not valid for inputs with {num_classes} classes") reduce = "macro" if average in ["weighted", "none", None] else average tp, fp, tn, fn = _stat_scores_update( preds, target, reduce=reduce, mdmc_reduce=mdmc_average, threshold=threshold, num_classes=num_classes, top_k=top_k, is_multiclass=is_multiclass, ignore_index=ignore_index, ) return _precision_compute(tp, fp, tn, fn, average, mdmc_average)
def _recall_compute( tp: torch.Tensor, fp: torch.Tensor, tn: torch.Tensor, fn: torch.Tensor, average: str, mdmc_average: Optional[str], ) -> torch.Tensor: return _reduce_stat_scores( numerator=tp, denominator=tp + fn, weights=None if average != "weighted" else tp + fn, average=average, mdmc_average=mdmc_average, )
[docs]def recall( preds: torch.Tensor, target: torch.Tensor, average: str = "micro", mdmc_average: Optional[str] = None, ignore_index: Optional[int] = None, num_classes: Optional[int] = None, threshold: float = 0.5, top_k: Optional[int] = None, is_multiclass: Optional[bool] = None, class_reduction: Optional[str] = None, ) -> torch.Tensor: r""" Computes `Recall <https://en.wikipedia.org/wiki/Precision_and_recall>`_: .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. With the use of ``top_k`` parameter, this metric can generalize to Recall@K. The reduction method (how the recall scores are aggregated) is controlled by the ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the multi-dimensional multi-class case. Accepts all inputs listed in :ref:`extensions/metrics:input types`. Args: preds: Predictions from model (probabilities, or labels) target: Ground truth values average: Defines the reduction that is applied. Should be one of the following: - ``'micro'`` [default]: Calculate the metric globally, accross all samples and classes. - ``'macro'``: Calculate the metric for each class separately, and average the metrics accross classes (with equal weights for each class). - ``'weighted'``: Calculate the metric for each class separately, and average the metrics accross 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 that what is considered a sample in the multi-dimensional multi-class case depends on the value of ``mdmc_average``. 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:`extensions/metrics: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:`extensions/metrics: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``. num_classes: Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods. threshold: Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs top_k: Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over ``threshold``. For (multi-dim) multi-class inputs, this parameter defaults to 1. Should be left unset (``None``) for inputs with label predictions. is_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 <extensions/metrics:using the is_multiclass parameter>` for a more detailed explanation and examples. class_reduction: .. warning :: This parameter is deprecated, use ``average``. Will be removed in v1.4.0. Return: The shape of the returned tensor depends on the ``average`` parameter - If ``average in ['micro', 'macro', 'weighted', 'samples']``, a one-element tensor will be returned - If ``average in ['none', None]``, the shape will be ``(C,)``, where ``C`` stands for the number of classes Example: >>> from pytorch_lightning.metrics.functional import recall >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> recall(preds, target, average='macro', num_classes=3) tensor(0.3333) >>> recall(preds, target, average='micro') tensor(0.2500) """ if class_reduction: rank_zero_warn( "This `class_reduction` parameter was deprecated in v1.2.0 in favor of" " `reduce`. It will be removed in v1.4.0", DeprecationWarning, ) average = class_reduction 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}.") allowed_mdmc_average = [None, "samplewise", "global"] if mdmc_average not in allowed_mdmc_average: raise ValueError("The `mdmc_average` has to be one of {allowed_mdmc_average}, got {mdmc_average}.") if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1): raise ValueError(f"When you set `average` as {average}, you have to provide the number of classes.") if num_classes and ignore_index is not None and (not 0 <= ignore_index < num_classes or num_classes == 1): raise ValueError(f"The `ignore_index` {ignore_index} is not valid for inputs with {num_classes} classes") reduce = "macro" if average in ["weighted", "none", None] else average tp, fp, tn, fn = _stat_scores_update( preds, target, reduce=reduce, mdmc_reduce=mdmc_average, threshold=threshold, num_classes=num_classes, top_k=top_k, is_multiclass=is_multiclass, ignore_index=ignore_index, ) return _recall_compute(tp, fp, tn, fn, average, mdmc_average)
[docs]def precision_recall( preds: torch.Tensor, target: torch.Tensor, average: str = "micro", mdmc_average: Optional[str] = None, ignore_index: Optional[int] = None, num_classes: Optional[int] = None, threshold: float = 0.5, top_k: Optional[int] = None, is_multiclass: Optional[bool] = None, class_reduction: Optional[str] = None, ) -> torch.Tensor: r""" Computes `Precision and Recall <https://en.wikipedia.org/wiki/Precision_and_recall>`_: .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}`m :math:`\text{FN}` and :math:`\text{FP}` represent the number of true positives, false negatives and false positives respecitively. With the use of ``top_k`` parameter, this metric can generalize to Recall@K and Precision@K. The reduction method (how the recall scores are aggregated) is controlled by the ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the multi-dimensional multi-class case. Accepts all inputs listed in :ref:`extensions/metrics:input types`. Args: preds: Predictions from model (probabilities, or labels) target: Ground truth values average: Defines the reduction that is applied. Should be one of the following: - ``'micro'`` [default]: Calculate the metric globally, accross all samples and classes. - ``'macro'``: Calculate the metric for each class separately, and average the metrics accross classes (with equal weights for each class). - ``'weighted'``: Calculate the metric for each class separately, and average the metrics accross 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 that what is considered a sample in the multi-dimensional multi-class case depends on the value of ``mdmc_average``. 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:`extensions/metrics: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:`extensions/metrics: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``. num_classes: Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods. threshold: Threshold probability value for transforming probability predictions to binary (0,1) predictions, in the case of binary or multi-label inputs top_k: Number of highest probability entries for each sample to convert to 1s - relevant only for inputs with probability predictions. If this parameter is set for multi-label inputs, it will take precedence over ``threshold``. For (multi-dim) multi-class inputs, this parameter defaults to 1. Should be left unset (``None``) for inputs with label predictions. is_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 <extensions/metrics:using the is_multiclass parameter>` for a more detailed explanation and examples. class_reduction: .. warning :: This parameter is deprecated, use ``average``. Will be removed in v1.4.0. Return: The function returns a tuple with two elements: precision and recall. Their shape depends on the ``average`` parameter - If ``average in ['micro', 'macro', 'weighted', 'samples']``, they are a single element tensor - If ``average in ['none', None]``, they are a tensor of shape ``(C, )``, where ``C`` stands for the number of classes Example: >>> from pytorch_lightning.metrics.functional import precision_recall >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> precision_recall(preds, target, average='macro', num_classes=3) (tensor(0.1667), tensor(0.3333)) >>> precision_recall(preds, target, average='micro') (tensor(0.2500), tensor(0.2500)) """ if class_reduction: rank_zero_warn( "This `class_reduction` parameter was deprecated in v1.2.0 in favor of" " `reduce`. It will be removed in v1.4.0", DeprecationWarning, ) average = class_reduction 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}.") allowed_mdmc_average = [None, "samplewise", "global"] if mdmc_average not in allowed_mdmc_average: raise ValueError("The `mdmc_average` has to be one of {allowed_mdmc_average}, got {mdmc_average}.") if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1): raise ValueError(f"When you set `average` as {average}, you have to provide the number of classes.") if num_classes and ignore_index is not None and (not 0 <= ignore_index < num_classes or num_classes == 1): raise ValueError(f"The `ignore_index` {ignore_index} is not valid for inputs with {num_classes} classes") reduce = "macro" if average in ["weighted", "none", None] else average tp, fp, tn, fn = _stat_scores_update( preds, target, reduce=reduce, mdmc_reduce=mdmc_average, threshold=threshold, num_classes=num_classes, top_k=top_k, is_multiclass=is_multiclass, ignore_index=ignore_index, ) precision = _precision_compute(tp, fp, tn, fn, average, mdmc_average) recall = _recall_compute(tp, fp, tn, fn, average, mdmc_average) return precision, recall

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