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bootstrapping.py
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bootstrapping.py
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# Copyright The 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 copy import deepcopy
from typing import Any, Dict, Optional, Sequence, Union
import torch
from torch import Tensor
from torch.nn import ModuleList
from torchmetrics.metric import Metric
from torchmetrics.utilities import apply_to_collection
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["BootStrapper.plot"]
def _bootstrap_sampler(
size: int,
sampling_strategy: str = "poisson",
) -> Tensor:
"""Resample a tensor along its first dimension with replacement.
Args:
size: number of samples
sampling_strategy: the strategy to use for sampling, either ``'poisson'`` or ``'multinomial'``
Returns:
resampled tensor
"""
if sampling_strategy == "poisson":
p = torch.distributions.Poisson(1)
n = p.sample((size,))
return torch.arange(size).repeat_interleave(n.long(), dim=0)
if sampling_strategy == "multinomial":
return torch.multinomial(torch.ones(size), num_samples=size, replacement=True)
raise ValueError("Unknown sampling strategy")
class BootStrapper(Metric):
r"""Using `Turn a Metric into a Bootstrapped`_.
That can automate the process of getting confidence intervals for metric values. This wrapper
class basically keeps multiple copies of the same base metric in memory and whenever ``update`` or
``forward`` is called, all input tensors are resampled (with replacement) along the first dimension.
Args:
base_metric: base metric class to wrap
num_bootstraps: number of copies to make of the base metric for bootstrapping
mean: if ``True`` return the mean of the bootstraps
std: if ``True`` return the standard diviation of the bootstraps
quantile: if given, returns the quantile of the bootstraps. Can only be used with pytorch version 1.6 or higher
raw: if ``True``, return all bootstrapped values
sampling_strategy:
Determines how to produce bootstrapped samplings. Either ``'poisson'`` or ``multinomial``.
If ``'possion'`` is chosen, the number of times each sample will be included in the bootstrap
will be given by :math:`n\sim Poisson(\lambda=1)`, which approximates the true bootstrap distribution
when the number of samples is large. If ``'multinomial'`` is chosen, we will apply true bootstrapping
at the batch level to approximate bootstrapping over the hole dataset.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example::
>>> from pprint import pprint
>>> from torchmetrics.wrappers import BootStrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> _ = torch.manual_seed(123)
>>> base_metric = MulticlassAccuracy(num_classes=5, average='micro')
>>> bootstrap = BootStrapper(base_metric, num_bootstraps=20)
>>> bootstrap.update(torch.randint(5, (20,)), torch.randint(5, (20,)))
>>> output = bootstrap.compute()
>>> pprint(output)
{'mean': tensor(0.2205), 'std': tensor(0.0859)}
"""
full_state_update: Optional[bool] = True
def __init__(
self,
base_metric: Metric,
num_bootstraps: int = 10,
mean: bool = True,
std: bool = True,
quantile: Optional[Union[float, Tensor]] = None,
raw: bool = False,
sampling_strategy: str = "poisson",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if not isinstance(base_metric, Metric):
raise ValueError(
f"Expected base metric to be an instance of torchmetrics.Metric but received {base_metric}"
)
self.metrics = ModuleList([deepcopy(base_metric) for _ in range(num_bootstraps)])
self.num_bootstraps = num_bootstraps
self.mean = mean
self.std = std
self.quantile = quantile
self.raw = raw
allowed_sampling = ("poisson", "multinomial")
if sampling_strategy not in allowed_sampling:
raise ValueError(
f"Expected argument ``sampling_strategy`` to be one of {allowed_sampling}"
f" but recieved {sampling_strategy}"
)
self.sampling_strategy = sampling_strategy
def update(self, *args: Any, **kwargs: Any) -> None:
"""Update the state of the base metric.
Any tensor passed in will be bootstrapped along dimension 0.
"""
for idx in range(self.num_bootstraps):
args_sizes = apply_to_collection(args, Tensor, len)
kwargs_sizes = list(apply_to_collection(kwargs, Tensor, len))
if len(args_sizes) > 0:
size = args_sizes[0]
elif len(kwargs_sizes) > 0:
size = kwargs_sizes[0]
else:
raise ValueError("None of the input contained tensors, so could not determine the sampling size")
sample_idx = _bootstrap_sampler(size, sampling_strategy=self.sampling_strategy).to(self.device)
new_args = apply_to_collection(args, Tensor, torch.index_select, dim=0, index=sample_idx)
new_kwargs = apply_to_collection(kwargs, Tensor, torch.index_select, dim=0, index=sample_idx)
self.metrics[idx].update(*new_args, **new_kwargs)
def compute(self) -> Dict[str, Tensor]:
"""Compute the bootstrapped metric values.
Always returns a dict of tensors, which can contain the following keys: ``mean``, ``std``, ``quantile`` and
``raw`` depending on how the class was initialized.
"""
computed_vals = torch.stack([m.compute() for m in self.metrics], dim=0)
output_dict = {}
if self.mean:
output_dict["mean"] = computed_vals.mean(dim=0)
if self.std:
output_dict["std"] = computed_vals.std(dim=0)
if self.quantile is not None:
output_dict["quantile"] = torch.quantile(computed_vals, self.quantile)
if self.raw:
output_dict["raw"] = computed_vals
return output_dict
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.wrappers import BootStrapper
>>> from torchmetrics.regression import MeanSquaredError
>>> metric = BootStrapper(MeanSquaredError(), num_bootstraps=20)
>>> metric.update(torch.randn(100,), torch.randn(100,))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.wrappers import BootStrapper
>>> from torchmetrics.regression import MeanSquaredError
>>> metric = BootStrapper(MeanSquaredError(), num_bootstraps=20)
>>> values = [ ]
>>> for _ in range(3):
... values.append(metric(torch.randn(100,), torch.randn(100,)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)