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Source code for pytorch_lightning.metrics.classification.precision_recall_curve

# 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, Any, Union, Tuple, List

import torch

from pytorch_lightning.metrics import Metric
from pytorch_lightning.metrics.functional.precision_recall_curve import (
    _precision_recall_curve_update,
    _precision_recall_curve_compute
)
from pytorch_lightning.utilities import rank_zero_warn


[docs]class PrecisionRecallCurve(Metric): """ Computes precision-recall pairs for different thresholds. Works for both binary and multiclass problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach. Forward accepts - ``preds`` (float tensor): ``(N, ...)`` (binary) or ``(N, C, ...)`` (multiclass) where C is the number of classes - ``target`` (long tensor): ``(N, ...)`` Args: num_classes: integer with number of classes. Not nessesary to provide for binary problems. pos_label: integer determining the positive class. Default is ``None`` which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1] compute_on_step: Forward only calls ``update()`` and return None if this is set to False. default: True dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before returning the value at the step. default: False process_group: Specify the process group on which synchronization is called. default: None (which selects the entire world) Example (binary case): >>> pred = torch.tensor([0, 1, 2, 3]) >>> target = torch.tensor([0, 1, 1, 0]) >>> pr_curve = PrecisionRecallCurve(pos_label=1) >>> precision, recall, thresholds = pr_curve(pred, target) >>> precision tensor([0.6667, 0.5000, 0.0000, 1.0000]) >>> recall tensor([1.0000, 0.5000, 0.0000, 0.0000]) >>> thresholds tensor([1, 2, 3]) Example (multiclass case): >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.05, 0.75, 0.05]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> pr_curve = PrecisionRecallCurve(num_classes=5) >>> precision, recall, thresholds = pr_curve(pred, target) >>> precision [tensor([1., 1.]), tensor([1., 1.]), tensor([0.2500, 0.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])] >>> recall [tensor([1., 0.]), tensor([1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])] >>> thresholds # doctest: +NORMALIZE_WHITESPACE [tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])] """ def __init__( self, num_classes: Optional[int] = None, pos_label: Optional[int] = None, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, ): super().__init__( compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, ) self.num_classes = num_classes self.pos_label = pos_label self.add_state("preds", default=[], dist_reduce_fx=None) self.add_state("target", default=[], dist_reduce_fx=None) rank_zero_warn( 'Metric `PrecisionRecallCurve` will save all targets and predictions in buffer.' ' For large datasets this may lead to large memory footprint.' )
[docs] def update(self, preds: torch.Tensor, target: torch.Tensor): """ Update state with predictions and targets. Args: preds: Predictions from model target: Ground truth values """ preds, target, num_classes, pos_label = _precision_recall_curve_update( preds, target, self.num_classes, self.pos_label ) self.preds.append(preds) self.target.append(target) self.num_classes = num_classes self.pos_label = pos_label
[docs] def compute(self) -> Union[Tuple[torch.Tensor, torch.Tensor, torch.Tensor], Tuple[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]]: """ Compute the precision-recall curve Returns: 3-element tuple containing precision: tensor where element i is the precision of predictions with score >= thresholds[i] and the last element is 1. If multiclass, this is a list of such tensors, one for each class. recall: tensor where element i is the recall of predictions with score >= thresholds[i] and the last element is 0. If multiclass, this is a list of such tensors, one for each class. thresholds: Thresholds used for computing precision/recall scores """ preds = torch.cat(self.preds, dim=0) target = torch.cat(self.target, dim=0) return _precision_recall_curve_compute(preds, target, self.num_classes, self.pos_label)

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision c462b274.

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