Prune deprecated metrics for 1.3 (#6161)

* prune deprecated metrics for 1.3

* isort / yapf

Author: Borda

CHANGELOG.md

@@ -9,6 +9,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added a way to print to terminal without breaking up the progress bar ([#5470](https://github.com/PyTorchLightning/pytorch-lightning/pull/5470))
+
 
 ### Changed
 
@@ -24,6 +26,11 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed deprecated Trainer argument `enable_pl_optimizer` and `automatic_optimization` ([#6163](https://github.com/PyTorchLightning/pytorch-lightning/pull/6163))
 
 
+- Removed deprecated metrics ([#6161](https://github.com/PyTorchLightning/pytorch-lightning/pull/6161))
+    * from `pytorch_lightning.metrics.functional.classification` removed `to_onehot`, `to_categorical`, `get_num_classes`, `roc`, `multiclass_roc`, `average_precision`, `precision_recall_curve`, `multiclass_precision_recall_curve`
+    * from `pytorch_lightning.metrics.functional.reduction` removed `reduce`, `class_reduce`	
+
+
 ### Fixed
 
 - Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](https://github.com/PyTorchLightning/pytorch-lightning/pull/6011))
@@ -93,7 +100,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `Trainer` flag to activate Stochastic Weight Averaging (SWA) `Trainer(stochastic_weight_avg=True)` ([#6038](https://github.com/PyTorchLightning/pytorch-lightning/pull/6038))
 - Added DeepSpeed integration ([#5954](https://github.com/PyTorchLightning/pytorch-lightning/pull/5954),
     [#6042](https://github.com/PyTorchLightning/pytorch-lightning/pull/6042))
-- Added a way to print to terminal without breaking up the progress bar ([#5470](https://github.com/PyTorchLightning/pytorch-lightning/pull/5470))
 
 ### Changed
 

pytorch_lightning/metrics/functional/__init__.py

@@ -21,7 +21,6 @@
     multiclass_auroc,
     stat_scores_multiple_classes,
     to_categorical,
-    to_onehot,
 )
 from pytorch_lightning.metrics.functional.confusion_matrix import confusion_matrix  # noqa: F401
 from pytorch_lightning.metrics.functional.explained_variance import explained_variance  # noqa: F401

pytorch_lightning/metrics/functional/classification.py

@@ -18,70 +18,11 @@
 
 from pytorch_lightning.metrics.functional.auc import auc as __auc
 from pytorch_lightning.metrics.functional.auroc import auroc as __auroc
-from pytorch_lightning.metrics.functional.average_precision import average_precision as __ap
 from pytorch_lightning.metrics.functional.iou import iou as __iou
-from pytorch_lightning.metrics.functional.precision_recall_curve import _binary_clf_curve
-from pytorch_lightning.metrics.functional.precision_recall_curve import precision_recall_curve as __prc
-from pytorch_lightning.metrics.functional.roc import roc as __roc
-from pytorch_lightning.metrics.utils import class_reduce
-from pytorch_lightning.metrics.utils import get_num_classes as __gnc
-from pytorch_lightning.metrics.utils import reduce
-from pytorch_lightning.metrics.utils import to_categorical as __tc
-from pytorch_lightning.metrics.utils import to_onehot as __to
+from pytorch_lightning.metrics.utils import class_reduce, get_num_classes, reduce, to_categorical
 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,
@@ -122,6 +63,7 @@ def stat_scores(
     return tp, fp, tn, fn, sup
 
 
+# todo: remove in 1.4
 def stat_scores_multiple_classes(
     pred: torch.Tensor,
     target: torch.Tensor,
@@ -210,6 +152,7 @@ def _confmat_normalize(cm):
     return cm
 
 
+# todo: remove in 1.4
 def precision_recall(
     pred: torch.Tensor,
     target: torch.Tensor,
@@ -268,6 +211,7 @@ def precision_recall(
     return precision, recall
 
 
+# todo: remove in 1.4
 def precision(
     pred: torch.Tensor,
     target: torch.Tensor,
@@ -311,6 +255,7 @@ def precision(
     return precision_recall(pred=pred, target=target, num_classes=num_classes, class_reduction=class_reduction)[0]
 
 
+# todo: remove in 1.4
 def recall(
     pred: torch.Tensor,
     target: torch.Tensor,
@@ -353,128 +298,7 @@ def recall(
     return precision_recall(pred=pred, target=target, 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(
-    pred: torch.Tensor,
-    target: torch.Tensor,
-    sample_weight: Optional[Sequence] = None,
-    num_classes: Optional[int] = None,
-) -> Tuple[Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
-    """
-    Computes the Receiver Operating Characteristic (ROC) for multiclass predictors.
-
-    .. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.roc.roc`
-
-    Args:
-        pred: estimated probabilities
-        target: ground-truth labels
-        sample_weight: sample weights
-        num_classes: number of classes (default: None, computes automatically from data)
-
-    Return:
-        returns roc for each class.
-        Number of classes, false-positive rate (fpr), true-positive rate (tpr), thresholds
-
-    Example:
-
-        >>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05],
-        ...                      [0.05, 0.85, 0.05, 0.05],
-        ...                      [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
-        ((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(pred=pred_c, target=target, sample_weight=sample_weight, pos_label=c))
-
-    return tuple(class_roc_vals)
-
-
+# todo: remove in 1.4
 def auc(
     x: torch.Tensor,
     y: torch.Tensor,
@@ -508,6 +332,7 @@ def auc(
     return __auc(x, y)
 
 
+# todo: remove in 1.4
 def auc_decorator() -> Callable:
     rank_zero_warn("This `auc_decorator` was deprecated in v1.2.0." " It will be removed in v1.4.0", DeprecationWarning)
 
@@ -524,6 +349,7 @@ def new_func(*args, **kwargs) -> torch.Tensor:
     return wrapper
 
 
+# todo: remove in 1.4
 def multiclass_auc_decorator() -> Callable:
     rank_zero_warn(
         "This `multiclass_auc_decorator` was deprecated in v1.2.0."
@@ -546,6 +372,7 @@ def new_func(*args, **kwargs) -> torch.Tensor:
     return wrapper
 
 
+# todo: remove in 1.4
 def auroc(
     pred: torch.Tensor,
     target: torch.Tensor,
@@ -588,6 +415,7 @@ def auroc(
     )
 
 
+# todo: remove in 1.4
 def multiclass_auroc(
     pred: torch.Tensor,
     target: torch.Tensor,
@@ -767,68 +595,3 @@ def iou(
         num_classes=num_classes,
         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)

pytorch_lightning/metrics/functional/iou.py

@@ -16,8 +16,7 @@
 import torch
 
 from pytorch_lightning.metrics.functional.confusion_matrix import _confusion_matrix_update
-from pytorch_lightning.metrics.functional.reduction import reduce
-from pytorch_lightning.metrics.utils import get_num_classes
+from pytorch_lightning.metrics.utils import get_num_classes, reduce
 
 
 def _iou_from_confmat(