add back compatibility for deprecated metrics 1/n (#5067)

* add back compatibility for metrics * tests * Add deprecated metric utility functions back to functional (#5062) * add back *deprecated* metric utility functions to functional * pep * pep * suggestions * move Co-authored-by: Jirka Borovec <[email protected]> * more * fix * import * docs * tests * fix Co-authored-by: Teddy Koker <[email protected]>
Lightning-AI · Dec 11, 2020 · 4a3f906 · 4a3f906
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -85,9 +85,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
-- Removed `multiclass_roc` and `multiclass_precision_recall_curve`, use `roc` and `precision_recall_curve` instead ([#4549](https://github.com/PyTorchLightning/pytorch-lightning/pull/4549))
 - Tuner algorithms will be skipped if `fast_dev_run=True` ([#3903](https://github.com/PyTorchLightning/pytorch-lightning/pull/3903))
-- WandbLogger does not force wandb `reinit` arg to True anymore and creates a run only when needed ([#4648](https://github.com/PyTorchLightning/pytorch-lightning/pull/4648))
+- `WandbLogger` does not force wandb `reinit` arg to True anymore and creates a run only when needed ([#4648](https://github.com/PyTorchLightning/pytorch-lightning/pull/4648))
 - Changed `automatic_optimization` to be a model attribute ([#4602](https://github.com/PyTorchLightning/pytorch-lightning/pull/4602))
 - Changed `Simple Profiler` report to order by percentage time spent + num calls ([#4880](https://github.com/PyTorchLightning/pytorch-lightning/pull/4880))
 - Simplify optimization Logic ([#4984](https://github.com/PyTorchLightning/pytorch-lightning/pull/4984))
@@ -105,6 +104,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Removed
 
 - Removed `reorder` parameter of the `auc` metric ([#5004](https://github.com/PyTorchLightning/pytorch-lightning/pull/5004))
+- Removed `multiclass_roc` and `multiclass_precision_recall_curve`, use `roc` and `precision_recall_curve` instead ([#4549](https://github.com/PyTorchLightning/pytorch-lightning/pull/4549))
 
 ### Fixed
 

diff --git a/pytorch_lightning/metrics/functional/__init__.py b/pytorch_lightning/metrics/functional/__init__.py
@@ -17,13 +17,16 @@
     auc,
     auroc,
     dice_score,
+    get_num_classes,
+    iou,
     multiclass_auroc,
     precision,
     precision_recall,
     recall,
     stat_scores,
     stat_scores_multiple_classes,
-    iou,
+    to_categorical,
+    to_onehot,
 )
 from pytorch_lightning.metrics.functional.confusion_matrix import confusion_matrix
 # TODO: unify metrics between class and functional, add below

diff --git a/pytorch_lightning/metrics/functional/classification.py b/pytorch_lightning/metrics/functional/classification.py
@@ -16,12 +16,73 @@
 
 import torch
 
-from pytorch_lightning.metrics.functional.roc import roc
-from pytorch_lightning.metrics.functional.precision_recall_curve import _binary_clf_curve
-from pytorch_lightning.metrics.utils import to_categorical, get_num_classes, reduce, class_reduce
+from pytorch_lightning.metrics.functional.average_precision import average_precision as __ap
+from pytorch_lightning.metrics.functional.precision_recall_curve import _binary_clf_curve, precision_recall_curve as __prc
+from pytorch_lightning.metrics.functional.roc import roc as __roc
+from pytorch_lightning.metrics.utils import (
+    to_categorical as __tc,
+    to_onehot as __to,
+    get_num_classes as __gnc,
+    reduce,
+    class_reduce,
+)
 from pytorch_lightning.utilities import rank_zero_warn
 
 
+def to_onehot(
+        tensor: torch.Tensor,
+        num_classes: Optional[int] = None,
+) -> torch.Tensor:
+    """
+    Converts a dense label tensor to one-hot format
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.utils.to_onehot`
+    """
+    rank_zero_warn(
+        "This `to_onehot` was deprecated in v1.1.0 in favor of"
+        " `from pytorch_lightning.metrics.utils import to_onehot`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    return __to(tensor, num_classes)
+
+
+def to_categorical(
+        tensor: torch.Tensor,
+        argmax_dim: int = 1
+) -> torch.Tensor:
+    """
+    Converts a tensor of probabilities to a dense label tensor
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.utils.to_categorical`
+
+    """
+    rank_zero_warn(
+        "This `to_categorical` was deprecated in v1.1.0 in favor of"
+        " `from pytorch_lightning.metrics.utils import to_categorical`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    return __tc(tensor)
+
+
+def get_num_classes(
+        pred: torch.Tensor,
+        target: torch.Tensor,
+        num_classes: Optional[int] = None,
+) -> int:
+    """
+    Calculates the number of classes for a given prediction and target tensor.
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.utils.get_num_classes`
+
+    """
+    rank_zero_warn(
+        "This `get_num_classes` was deprecated in v1.1.0 in favor of"
+        " `from pytorch_lightning.metrics.utils import get_num_classes`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    return __gnc(pred,target, num_classes)
+
+
 def stat_scores(
         pred: torch.Tensor,
         target: torch.Tensor,
@@ -333,8 +394,79 @@ def recall(
                             num_classes=num_classes, class_reduction=class_reduction)[1]
 
 
+# todo: remove in 1.3
+def roc(
+        pred: torch.Tensor,
+        target: torch.Tensor,
+        sample_weight: Optional[Sequence] = None,
+        pos_label: int = 1.,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Computes the Receiver Operating Characteristic (ROC). It assumes classifier is binary.
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.roc.roc`
+    """
+    rank_zero_warn(
+        "This `multiclass_roc` was deprecated in v1.1.0 in favor of"
+        " `from pytorch_lightning.metrics.functional.roc import roc`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    return __roc(preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label)
+
+
+# TODO: deprecated in favor of general ROC in pytorch_lightning/metrics/functional/roc.py
+def _roc(
+        pred: torch.Tensor,
+        target: torch.Tensor,
+        sample_weight: Optional[Sequence] = None,
+        pos_label: int = 1.,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Computes the Receiver Operating Characteristic (ROC). It assumes classifier is binary.
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.roc.roc`
+
+    Example:
+
+        >>> x = torch.tensor([0, 1, 2, 3])
+        >>> y = torch.tensor([0, 1, 1, 1])
+        >>> fpr, tpr, thresholds = _roc(x, y)
+        >>> fpr
+        tensor([0., 0., 0., 0., 1.])
+        >>> tpr
+        tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000])
+        >>> thresholds
+        tensor([4, 3, 2, 1, 0])
+
+    """
+    rank_zero_warn(
+        "This `multiclass_roc` was deprecated in v1.1.0 in favor of"
+        " `from pytorch_lightning.metrics.functional.roc import roc`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    fps, tps, thresholds = _binary_clf_curve(pred, target, sample_weights=sample_weight, pos_label=pos_label)
+
+    # Add an extra threshold position
+    # to make sure that the curve starts at (0, 0)
+    tps = torch.cat([torch.zeros(1, dtype=tps.dtype, device=tps.device), tps])
+    fps = torch.cat([torch.zeros(1, dtype=fps.dtype, device=fps.device), fps])
+    thresholds = torch.cat([thresholds[0][None] + 1, thresholds])
+
+    if fps[-1] <= 0:
+        raise ValueError("No negative samples in targets, false positive value should be meaningless")
+
+    fpr = fps / fps[-1]
+
+    if tps[-1] <= 0:
+        raise ValueError("No positive samples in targets, true positive value should be meaningless")
+
+    tpr = tps / tps[-1]
+
+    return fpr, tpr, thresholds
+
+
 # TODO: deprecated in favor of general ROC in pytorch_lightning/metrics/functional/roc.py
-def __multiclass_roc(
+def multiclass_roc(
         pred: torch.Tensor,
         target: torch.Tensor,
         sample_weight: Optional[Sequence] = None,
@@ -343,7 +475,7 @@ def __multiclass_roc(
     """
     Computes the Receiver Operating Characteristic (ROC) for multiclass predictors.
 
-    .. warning:: Deprecated
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.roc.roc`
 
     Args:
         pred: estimated probabilities
@@ -362,19 +494,24 @@ def __multiclass_roc(
         ...                      [0.05, 0.05, 0.85, 0.05],
         ...                      [0.05, 0.05, 0.05, 0.85]])
         >>> target = torch.tensor([0, 1, 3, 2])
-        >>> __multiclass_roc(pred, target)   # doctest: +NORMALIZE_WHITESPACE
+        >>> multiclass_roc(pred, target)   # doctest: +NORMALIZE_WHITESPACE
         ((tensor([0., 0., 1.]), tensor([0., 1., 1.]), tensor([1.8500, 0.8500, 0.0500])),
          (tensor([0., 0., 1.]), tensor([0., 1., 1.]), tensor([1.8500, 0.8500, 0.0500])),
          (tensor([0.0000, 0.3333, 1.0000]), tensor([0., 0., 1.]), tensor([1.8500, 0.8500, 0.0500])),
          (tensor([0.0000, 0.3333, 1.0000]), tensor([0., 0., 1.]), tensor([1.8500, 0.8500, 0.0500])))
     """
+    rank_zero_warn(
+        "This `multiclass_roc` was deprecated in v1.1.0 in favor of"
+        " `from pytorch_lightning.metrics.functional.roc import roc`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
     num_classes = get_num_classes(pred, target, num_classes)
 
     class_roc_vals = []
     for c in range(num_classes):
         pred_c = pred[:, c]
 
-        class_roc_vals.append(roc(preds=pred_c, target=target, sample_weights=sample_weight, pos_label=c, num_classes=1))
+        class_roc_vals.append(_roc(pred=pred_c, target=target, sample_weight=sample_weight, pos_label=c))
 
     return tuple(class_roc_vals)
 
@@ -472,7 +609,7 @@ def auroc(
 
     @auc_decorator()
     def _auroc(pred, target, sample_weight, pos_label):
-        return roc(preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label, num_classes=1)
+        return _roc(pred, target, sample_weight, pos_label)
 
     return _auroc(pred=pred, target=target, sample_weight=sample_weight, pos_label=pos_label)
 
@@ -525,7 +662,7 @@ def multiclass_auroc(
 
     @multiclass_auc_decorator()
     def _multiclass_auroc(pred, target, sample_weight, num_classes):
-        return __multiclass_roc(pred=pred, target=target, sample_weight=sample_weight, num_classes=num_classes)
+        return multiclass_roc(pred, target, sample_weight, num_classes)
 
     class_aurocs = _multiclass_auroc(pred=pred, target=target,
                                      sample_weight=sample_weight,
@@ -672,3 +809,65 @@ def iou(
         ])
 
     return reduce(scores, reduction=reduction)
+
+
+# todo: remove in 1.3
+def precision_recall_curve(
+        pred: torch.Tensor,
+        target: torch.Tensor,
+        sample_weight: Optional[Sequence] = None,
+        pos_label: int = 1.,
+):
+    """
+    Computes precision-recall pairs for different thresholds.
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.precision_recall_curve.precision_recall_curve`
+    """
+    rank_zero_warn(
+        "This `precision_recall_curve` was deprecated in v1.1.0 in favor of"
+        " `from pytorch_lightning.metrics.functional.precision_recall_curve import precision_recall_curve`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    return __prc(preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label)
+
+
+# todo: remove in 1.3
+def multiclass_precision_recall_curve(
+        pred: torch.Tensor,
+        target: torch.Tensor,
+        sample_weight: Optional[Sequence] = None,
+        num_classes: Optional[int] = None,
+):
+    """
+    Computes precision-recall pairs for different thresholds given a multiclass scores.
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.precision_recall_curve.precision_recall_curve`
+    """
+    rank_zero_warn(
+        "This `multiclass_precision_recall_curve` was deprecated in v1.1.0 in favor of"
+        " `from pytorch_lightning.metrics.functional.precision_recall_curve import precision_recall_curve`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    if num_classes is None:
+        num_classes = get_num_classes(pred, target, num_classes)
+    return __prc(preds=pred, target=target, sample_weights=sample_weight, num_classes=num_classes)
+
+
+# todo: remove in 1.3
+def average_precision(
+        pred: torch.Tensor,
+        target: torch.Tensor,
+        sample_weight: Optional[Sequence] = None,
+        pos_label: int = 1.,
+):
+    """
+    Compute average precision from prediction scores.
+
+    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.average_precision.average_precision`
+    """
+    rank_zero_warn(
+        "This `average_precision` was deprecated in v1.1.0 in favor of"
+        " `pytorch_lightning.metrics.functional.average_precision import average_precision`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    return __ap(preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label)
diff --git a/pytorch_lightning/metrics/functional/reduction.py b/pytorch_lightning/metrics/functional/reduction.py
@@ -0,0 +1,35 @@
+# 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 torch
+
+from pytorch_lightning.metrics.utils import reduce as __reduce, class_reduce as __cr
+from pytorch_lightning.utilities import rank_zero_warn
+
+
+def reduce(to_reduce: torch.Tensor, reduction: str) -> torch.Tensor:
+    rank_zero_warn(
+        "This `reduce` was deprecated in v1.1.0 in favor of"
+        " `pytorch_lightning.metrics.utils import reduce`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    return __reduce(to_reduce=to_reduce, reduction=reduction)
+
+
+def class_reduce(num: torch.Tensor, denom: torch.Tensor, weights: torch.Tensor, class_reduction: str = 'none'):
+    rank_zero_warn(
+        "This `class_reduce` was deprecated in v1.1.0 in favor of"
+        " `pytorch_lightning.metrics.utils import class_reduce`."
+        " It will be removed in v1.3.0", DeprecationWarning
+    )
+    return __cr(num=num, denom=denom, weights=weights, class_reduction=class_reduction)