# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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
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# ============================================================================
"""Perplexity"""
from __future__ import absolute_import
import math
import numpy as np
from mindspore import _checkparam as validator
from mindspore.train.metrics.metric import Metric, rearrange_inputs
[docs]class Perplexity(Metric):
r"""
Computes perplexity. Perplexity is a measurement about how well a probability distribution or a model predicts a
sample. A low perplexity indicates the model can predict the sample well. The function is shown as follows:
.. math::
PP(W)=P(w_{1}w_{2}...w_{N})^{-\frac{1}{N}}=\sqrt[N]{\frac{1}{P(w_{1}w_{2}...w_{N})}}
Where :math:`w` represents words in corpus.
Args:
ignore_label (Union[int, None]): Index of an invalid label to be ignored when counting. If set to `None`,
it will include all entries. Default: None.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore.train import Perplexity
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> metric = Perplexity(ignore_label=None)
>>> metric.clear()
>>> metric.update(x, y)
>>> perplexity = metric.eval()
>>> print(perplexity)
2.231443166940565
"""
def __init__(self, ignore_label=None):
super(Perplexity, self).__init__()
if ignore_label is None:
self.ignore_label = ignore_label
else:
self.ignore_label = validator.check_value_type("ignore_label", ignore_label, [int])
self.clear()
[docs] def clear(self):
"""Clears the internal evaluation result."""
self._sum_metric = 0.0
self._num_inst = 0
[docs] @rearrange_inputs
def update(self, *inputs):
"""
Updates the internal evaluation result `preds` and `labels`.
Args:
inputs: Input `preds` and `labels`. `preds` and `labels` are a `Tensor`, list or numpy.ndarray.
`preds` is the predicted values, `labels` is the labels of the data.
The shape of `preds` and `labels` are both :math:`(N, C)`.
Raises:
ValueError: If the number of the inputs is not 2.
RuntimeError: If preds and labels have different lengths.
RuntimeError: If label shape is not equal to pred shape.
"""
if len(inputs) != 2:
raise ValueError("For 'Perplexity.update', it needs 2 inputs (predicted value, label), but got {}."
.format(len(inputs)))
preds = [self._convert_data(inputs[0])]
labels = [self._convert_data(inputs[1])]
if len(preds) != len(labels):
raise RuntimeError("For 'Perplexity.update', predicted value (input[0]) and label (input[1]) should have "
"the same length, but got predicted value length {}, label length {}."
.format(len(preds), len(labels)))
loss = 0.
num = 0
for label, pred in zip(labels, preds):
if label.size != pred.size / pred.shape[-1]:
raise RuntimeError("For 'Perplexity.update', predicted value (input[0]) and label (input[1]) should "
"have the same shape, but got predicted value shape {}, label shape {}."
.format(pred.shape, label.shape))
label = label.reshape((label.size,))
label_expand = label.astype(int)
label_expand = np.expand_dims(label_expand, axis=1)
first_indices = np.arange(label_expand.shape[0])[:, None]
pred = np.squeeze(pred[first_indices, label_expand])
if self.ignore_label is not None:
ignore = (label == self.ignore_label).astype(pred.dtype)
num -= np.sum(ignore)
pred = pred * (1 - ignore) + ignore
loss -= np.sum(np.log(np.maximum(1e-10, pred)))
num += pred.size
self._sum_metric += loss
self._num_inst += num
[docs] def eval(self):
r"""
Returns the current evaluation result.
Returns:
numpy.float64. The computed result.
Raises:
RuntimeError: If the sample size is 0.
"""
if self._num_inst == 0:
raise RuntimeError("The 'Perplexity' can not be calculated, because the number of samples is 0, please "
"check whether has called update method before calling eval method.")
return math.exp(self._sum_metric / self._num_inst)