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from typing import Any, Callable, Optional
import torch
from pytorch_lightning.metrics.classification.stat_scores import StatScores
from pytorch_lightning.metrics.functional.precision_recall import _precision_compute, _recall_compute
[docs]class Precision(StatScores):
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:
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.
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``.
multilabel:
.. warning :: This parameter is deprecated and has no effect. 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``.
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.
compute_on_step:
Forward only calls ``update()`` and return ``None`` if this is set to ``False``.
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step
process_group:
Specify the process group on which synchronization is called.
default: ``None`` (which selects the entire world)
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When ``None``, DDP
will be used to perform the allgather.
Example:
>>> from pytorch_lightning.metrics import Precision
>>> preds = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> precision = Precision(average='macro', num_classes=3)
>>> precision(preds, target)
tensor(0.1667)
>>> precision = Precision(average='micro')
>>> precision(preds, target)
tensor(0.2500)
"""
def __init__(
self,
num_classes: Optional[int] = None,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False,
mdmc_average: Optional[str] = None,
ignore_index: Optional[int] = None,
top_k: Optional[int] = None,
is_multiclass: Optional[bool] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = 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,
is_multiclass=is_multiclass,
ignore_index=ignore_index,
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
self.average = average
[docs] def compute(self) -> torch.Tensor:
"""
Computes the precision score based on inputs passed in to ``update`` previously.
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
"""
tp, fp, tn, fn = self._get_final_stats()
return _precision_compute(tp, fp, tn, fn, self.average, self.mdmc_reduce)
[docs]class Recall(StatScores):
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:
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.
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``.
multilabel:
.. warning :: This parameter is deprecated and has no effect. 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``.
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.
compute_on_step:
Forward only calls ``update()`` and return ``None`` if this is set to ``False``.
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step
process_group:
Specify the process group on which synchronization is called.
default: ``None`` (which selects the entire world)
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When ``None``, DDP
will be used to perform the allgather.
Example:
>>> from pytorch_lightning.metrics import Recall
>>> preds = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> recall = Recall(average='macro', num_classes=3)
>>> recall(preds, target)
tensor(0.3333)
>>> recall = Recall(average='micro')
>>> recall(preds, target)
tensor(0.2500)
"""
def __init__(
self,
num_classes: Optional[int] = None,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False,
mdmc_average: Optional[str] = None,
ignore_index: Optional[int] = None,
top_k: Optional[int] = None,
is_multiclass: Optional[bool] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = 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,
is_multiclass=is_multiclass,
ignore_index=ignore_index,
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
self.average = average
[docs] def compute(self) -> torch.Tensor:
"""
Computes the recall score based on inputs passed in to ``update`` previously.
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
"""
tp, fp, tn, fn = self._get_final_stats()
return _recall_compute(tp, fp, tn, fn, self.average, self.mdmc_reduce)
