# Copyright 2022 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
# distributed under the License is distributed on an "AS IS" BASIS,
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# ============================================================================
""""Class for Metric ConfusionMatrix"""
import numpy as np
from mindnlp.abc import Metric
from mindnlp.common.metrics import _check_value_type, _convert_data_type
[docs]class ConfusionMatrix(Metric):
r"""
Calculates the confusion matrix. Confusion matrix is commonly used to evaluate
the performance of classification models, including binary classification and
multiple classification.
Args:
class_num (int): Number of classes in the dataset. Default: 2.
name (str): Name of the metric.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindnlp.engine.metrics import ConfusionMatrix
>>> preds = Tensor(np.array([1, 0, 1, 0]))
>>> labels = Tensor(np.array([1, 0, 0, 1]))
>>> metric = ConfusionMatrix()
>>> metric.update(preds, labels)
>>> conf_mat = metric.eval()
>>> print(conf_mat)
[[1. 1.]
[1. 1.]]
"""
def __init__(self, class_num=2, name='ConfusionMatrix'):
super().__init__()
self._name = name
self.class_num = _check_value_type("class_num", class_num, [int])
self.conf_mat = np.zeros((self.class_num, self.class_num))
[docs] def clear(self):
"""Clears the internal evaluation results."""
self.conf_mat = np.zeros((self.class_num, self.class_num))
[docs] def update(self, *inputs):
"""
Updates local variables.
Args:
inputs: Input `preds` and `labels`.
- preds (Union[Tensor, list, np.ndarray]): Predicted value. `preds` is a list of
floating numbers and the shape of `preds` is :math:`(N, C)` or :math:`(N,)`.
- labels (Union[Tensor, list, np.ndarray]): Ground truth. The shape of `labels` is
:math:`(N,)`.
Raises:
ValueError: If the number of inputs is not 2.
ValueError: If `preds` and `labels` do not have valid dimensions.
"""
if len(inputs) != 2:
raise ValueError(f'For `ConfusionMatrix.update`, it needs 2 inputs (`preds` and '
f'`labels`), but got {len(inputs)}.')
preds = inputs[0]
labels = inputs[1]
preds = _convert_data_type(preds)
labels = _convert_data_type(labels)
if preds.ndim not in (labels.ndim, labels.ndim + 1):
raise ValueError(f'For `ConfusionMatrix.update`, `preds` and `labels` should have the '
f'same dimensions, or the dimension of `preds` equals the dimension '
f'of true value add 1, but got `preds` ndim: {preds.ndim}, `labels` '
f'ndim: {labels.ndim}.')
if preds.ndim == labels.ndim + 1:
preds = np.argmax(preds, axis=1)
trans = (labels.reshape(-1) * self.class_num + preds.reshape(-1)).astype(int)
bincount = np.bincount(trans, minlength=self.class_num ** 2)
conf_mat = bincount.reshape(self.class_num, self.class_num)
self.conf_mat += conf_mat
[docs] def eval(self):
"""
Computes and returns the Confusion Matrix.
Returns:
- **conf_mat** (np.ndarray) - The computed result.
"""
conf_mat = self.conf_mat.astype(float)
return conf_mat
[docs] def get_metric_name(self):
"""
Returns the name of the metric.
"""
return self._name