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Prune metrics: precision & recall 6/n (#6573)
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* avg precision

* precision
* recall

* curve

* tests

* chlog

* isort

* fix
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Borda authored Mar 18, 2021
1 parent 8853a36 commit 38a2119
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2 changes: 2 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -79,6 +79,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

[#6572](https://github.com/PyTorchLightning/pytorch-lightning/pull/6572),

[#6573](https://github.com/PyTorchLightning/pytorch-lightning/pull/6573),

)


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103 changes: 8 additions & 95 deletions pytorch_lightning/metrics/classification/average_precision.py
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Expand Up @@ -11,64 +11,16 @@
# 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 Any, List, Optional, Union
from typing import Any, Optional

import torch
from torchmetrics import Metric
from torchmetrics import AveragePrecision as _AveragePrecision

from pytorch_lightning.metrics.functional.average_precision import _average_precision_compute, _average_precision_update
from pytorch_lightning.utilities import rank_zero_warn
from pytorch_lightning.utilities.deprecation import deprecated


class AveragePrecision(Metric):
"""
Computes the average precision score, which summarises the precision recall
curve into one number. 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) tensor
with probabilities, where C is the number of classes.
- ``target`` (long tensor): ``(N, ...)`` with integer labels
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, 1])
>>> average_precision = AveragePrecision(pos_label=1)
>>> average_precision(pred, target)
tensor(1.)
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])
>>> average_precision = AveragePrecision(num_classes=5)
>>> average_precision(pred, target)
[tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)]
"""
class AveragePrecision(_AveragePrecision):

@deprecated(target=_AveragePrecision, ver_deprecate="1.3.0", ver_remove="1.5.0")
def __init__(
self,
num_classes: Optional[int] = None,
Expand All @@ -77,48 +29,9 @@ def __init__(
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 `AveragePrecision` will save all targets and predictions in buffer.'
' For large datasets this may lead to large memory footprint.'
)

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 = _average_precision_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

def compute(self) -> Union[torch.Tensor, List[torch.Tensor]]:
"""
Compute the average precision score
Returns:
tensor with average precision. If multiclass will return list
of such tensors, one for each class
This implementation refers to :class:`~torchmetrics.AveragePrecision`.
.. deprecated::
Use :class:`~torchmetrics.AveragePrecision`. Will be removed in v1.5.0.
"""
preds = torch.cat(self.preds, dim=0)
target = torch.cat(self.target, dim=0)
return _average_precision_compute(preds, target, self.num_classes, self.pos_label)
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