# 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 math import log from typing import Any, List, Optional, Sequence, Union, cast import torch from torch import Tensor from typing_extensions import Literal from torchmetrics.functional.regression.js_divergence import _jsd_compute, _jsd_update from torchmetrics.metric import Metric from torchmetrics.utilities.data import dim_zero_cat from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["JensenShannonDivergence.plot"] class JensenShannonDivergence(Metric): r"""Compute the `Jensen-Shannon divergence`_. .. math:: D_{JS}(P||Q) = \frac{1}{2} D_{KL}(P||M) + \frac{1}{2} D_{KL}(Q||M) Where :math:`P` and :math:`Q` are probability distributions where :math:`P` usually represents a distribution over data and :math:`Q` is often a prior or approximation of :math:`P`. :math:`D_{KL}` is the `KL divergence`_ and :math:`M` is the average of the two distributions. It should be noted that the Jensen-Shannon divergence is a symmetrical metric i.e. :math:`D_{JS}(P||Q) = D_{JS}(Q||P)`. As input to ``forward`` and ``update`` the metric accepts the following input: - ``p`` (:class:`~torch.Tensor`): a data distribution with shape ``(N, d)`` - ``q`` (:class:`~torch.Tensor`): prior or approximate distribution with shape ``(N, d)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``js_divergence`` (:class:`~torch.Tensor`): A tensor with the Jensen-Shannon divergence Args: log_prob: bool indicating if input is log-probabilities or probabilities. If given as probabilities, will normalize to make sure the distributes sum to 1. reduction: Determines how to reduce over the ``N``/batch dimension: - ``'mean'`` [default]: Averages score across samples - ``'sum'``: Sum score across samples - ``'none'`` or ``None``: Returns score per sample kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: TypeError: If ``log_prob`` is not an ``bool``. ValueError: If ``reduction`` is not one of ``'mean'``, ``'sum'``, ``'none'`` or ``None``. .. attention:: Half precision is only support on GPU for this metric. Example: >>> from torch import tensor >>> from torchmetrics.regression import JensenShannonDivergence >>> p = tensor([[0.1, 0.9], [0.2, 0.8], [0.3, 0.7]]) >>> q = tensor([[0.3, 0.7], [0.4, 0.6], [0.5, 0.5]]) >>> js_div = JensenShannonDivergence() >>> js_div(p, q) tensor(0.0259) """ is_differentiable: bool = True higher_is_better: bool = False full_state_update: bool = False plot_lower_bound: float = 0.0 plot_upper_bound: float = log(2) measures: Union[Tensor, List[Tensor]] total: Tensor def __init__( self, log_prob: bool = False, reduction: Literal["mean", "sum", "none", None] = "mean", **kwargs: Any, ) -> None: super().__init__(**kwargs) if not isinstance(log_prob, bool): raise TypeError(f"Expected argument `log_prob` to be bool but got {log_prob}") self.log_prob = log_prob allowed_reduction = ["mean", "sum", "none", None] if reduction not in allowed_reduction: raise ValueError(f"Expected argument `reduction` to be one of {allowed_reduction} but got {reduction}") self.reduction = reduction if self.reduction in ["mean", "sum"]: self.add_state("measures", torch.tensor(0.0), dist_reduce_fx="sum") else: self.add_state("measures", [], dist_reduce_fx="cat") self.add_state("total", torch.tensor(0), dist_reduce_fx="sum") def update(self, p: Tensor, q: Tensor) -> None: """Update the metric state.""" measures, total = _jsd_update(p, q, self.log_prob) if self.reduction is None or self.reduction == "none": cast(List[Tensor], self.measures).append(measures) else: self.measures = cast(Tensor, self.measures) + measures.sum() self.total += total def compute(self) -> Tensor: """Compute metric.""" measures: Tensor = ( dim_zero_cat(cast(List[Tensor], self.measures)) if self.reduction in ["none", None] else cast(Tensor, self.measures) ) return _jsd_compute(measures, self.total, self.reduction) 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 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import JensenShannonDivergence >>> metric = JensenShannonDivergence() >>> metric.update(randn(10,3).softmax(dim=-1), randn(10,3).softmax(dim=-1)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import JensenShannonDivergence >>> metric = JensenShannonDivergence() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,3).softmax(dim=-1), randn(10,3).softmax(dim=-1))) >>> fig, ax = metric.plot(values) """ return self._plot(val, ax)