# Source code for pytorch_lightning.metrics.classification.stat_scores

```
# Copyright The PyTorch Lightning team.
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any, Callable, Optional, Tuple
import torch
from pytorch_lightning.metrics.functional.stat_scores import _stat_scores_compute, _stat_scores_update
from pytorch_lightning.metrics.metric import Metric
[docs]class StatScores(Metric):
"""Computes the number of true positives, false positives, true negatives, false negatives.
Related to `Type I and Type II errors <https://en.wikipedia.org/wiki/Type_I_and_type_II_errors>`__
and the `confusion matrix <https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion>`__.
The reduction method (how the statistics are aggregated) is controlled by the
``reduce`` parameter, and additionally by the ``mdmc_reduce`` parameter in the
multi-dimensional multi-class case.
Accepts all inputs listed in :ref:`extensions/metrics:input types`.
Args:
threshold:
Threshold probability value for transforming probability predictions to binary
(0 or 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.
reduce:
Defines the reduction that is applied. Should be one of the following:
- ``'micro'`` [default]: Counts the statistics by summing over all [sample, class]
combinations (globally). Each statistic is represented by a single integer.
- ``'macro'``: Counts the statistics for each class separately (over all samples).
Each statistic is represented by a ``(C,)`` tensor. Requires ``num_classes``
to be set.
- ``'samples'``: Counts the statistics for each sample separately (over all classes).
Each statistic is represented by a ``(N, )`` 1d tensor.
Note that what is considered a sample in the multi-dimensional multi-class case
depends on the value of ``mdmc_reduce``.
num_classes:
Number of classes. Necessary for (multi-dimensional) multi-class or multi-label data.
ignore_index:
Specify a class (label) to ignore. If given, this class index does not contribute
to the returned score, regardless of reduction method. If an index is ignored, and
``reduce='macro'``, the class statistics for the ignored class will all be returned
as ``-1``.
mdmc_reduce:
Defines how the multi-dimensional multi-class inputs are handeled. Should be
one of the following:
- ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
multi-class (see :ref:`extensions/metrics:input types` for the definition of input types).
- ``'samplewise'``: In this case, the statistics are computed separately for each
sample on the ``N`` axis, and then the outputs are concatenated together. In each
sample the extra axes ``...`` are flattened to become the sub-sample axis, and
statistics for each sample are computed by treating the sub-sample axis as the
``N`` axis for that sample.
- ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs 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 ``reduce`` parameter applies as usual.
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.classification import StatScores
>>> preds = torch.tensor([1, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> stat_scores = StatScores(reduce='macro', num_classes=3)
>>> stat_scores(preds, target)
tensor([[0, 1, 2, 1, 1],
[1, 1, 1, 1, 2],
[1, 0, 3, 0, 1]])
>>> stat_scores = StatScores(reduce='micro')
>>> stat_scores(preds, target)
tensor([2, 2, 6, 2, 4])
"""
def __init__(
self,
threshold: float = 0.5,
top_k: Optional[int] = None,
reduce: str = "micro",
num_classes: Optional[int] = None,
ignore_index: Optional[int] = None,
mdmc_reduce: Optional[str] = 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,
):
super().__init__(
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.reduce = reduce
self.mdmc_reduce = mdmc_reduce
self.num_classes = num_classes
self.threshold = threshold
self.is_multiclass = is_multiclass
self.ignore_index = ignore_index
self.top_k = top_k
if not 0 < threshold < 1:
raise ValueError(f"The `threshold` should be a float in the (0,1) interval, got {threshold}")
if reduce not in ["micro", "macro", "samples"]:
raise ValueError(f"The `reduce` {reduce} is not valid.")
if mdmc_reduce not in [None, "samplewise", "global"]:
raise ValueError(f"The `mdmc_reduce` {mdmc_reduce} is not valid.")
if reduce == "macro" and (not num_classes or num_classes < 1):
raise ValueError("When you set `reduce` as 'macro', 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")
if mdmc_reduce != "samplewise" and reduce != "samples":
if reduce == "micro":
zeros_shape = []
elif reduce == "macro":
zeros_shape = (num_classes, )
default, reduce_fn = lambda: torch.zeros(zeros_shape, dtype=torch.long), "sum"
else:
default, reduce_fn = lambda: [], None
for s in ("tp", "fp", "tn", "fn"):
self.add_state(s, default=default(), dist_reduce_fx=reduce_fn)
[docs] def update(self, preds: torch.Tensor, target: torch.Tensor):
"""
Update state with predictions and targets. See :ref:`extensions/metrics:input types` for more information
on input types.
Args:
preds: Predictions from model (probabilities or labels)
target: Ground truth values
"""
tp, fp, tn, fn = _stat_scores_update(
preds,
target,
reduce=self.reduce,
mdmc_reduce=self.mdmc_reduce,
threshold=self.threshold,
num_classes=self.num_classes,
top_k=self.top_k,
is_multiclass=self.is_multiclass,
ignore_index=self.ignore_index,
)
# 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 _get_final_stats(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Performs concatenation on the stat scores if neccesary,
before passing them to a compute function.
"""
if isinstance(self.tp, list):
tp = torch.cat(self.tp)
fp = torch.cat(self.fp)
tn = torch.cat(self.tn)
fn = torch.cat(self.fn)
else:
tp, fp, tn, fn = self.tp, self.fp, self.tn, self.fn
return tp, fp, tn, fn
[docs] def compute(self) -> torch.Tensor:
"""
Computes the stat scores based on inputs passed in to ``update`` previously.
Return:
The metric returns a tensor of shape ``(..., 5)``, where the last dimension corresponds
to ``[tp, fp, tn, fn, sup]`` (``sup`` stands for support and equals ``tp + fn``). The
shape depends on the ``reduce`` and ``mdmc_reduce`` (in case of multi-dimensional
multi-class data) parameters:
- If the data is not multi-dimensional multi-class, then
- If ``reduce='micro'``, the shape will be ``(5, )``
- If ``reduce='macro'``, the shape will be ``(C, 5)``,
where ``C`` stands for the number of classes
- If ``reduce='samples'``, the shape will be ``(N, 5)``, where ``N`` stands for
the number of samples
- If the data is multi-dimensional multi-class and ``mdmc_reduce='global'``, then
- If ``reduce='micro'``, the shape will be ``(5, )``
- If ``reduce='macro'``, the shape will be ``(C, 5)``
- If ``reduce='samples'``, the shape will be ``(N*X, 5)``, where ``X`` stands for
the product of sizes of all "extra" dimensions of the data (i.e. all dimensions
except for ``C`` and ``N``)
- If the data is multi-dimensional multi-class and ``mdmc_reduce='samplewise'``, then
- If ``reduce='micro'``, the shape will be ``(N, 5)``
- If ``reduce='macro'``, the shape will be ``(N, C, 5)``
- If ``reduce='samples'``, the shape will be ``(N, X, 5)``
"""
tp, fp, tn, fn = self._get_final_stats()
return _stat_scores_compute(tp, fp, tn, fn)
```