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- # 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,
- # 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.
- # ============================================================================
- """"Class for Metric ConfusionMatrix"""
-
-
- import numpy as np
-
- from mindnlp.abc import Metric
- from .utils import _check_value_type, _convert_data_type
-
- def confusion_matrix_fn(preds, labels, class_num=2):
- r"""
- Calculates the confusion matrix. Confusion matrix is commonly used to evaluate
- the performance of classification models, including binary classification and
- multiple classification.
-
- Args:
- 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,)`.
- class_num (int): Number of classes in the dataset. Default: 2.
-
- Returns:
- - **conf_mat** (np.ndarray) - The computed result.
-
- Raises:
- ValueError: If `preds` and `labels` do not have valid dimensions.
-
- Example:
- >>> import numpy as np
- >>> import mindspore
- >>> from mindspore import Tensor
- >>> from mindnlp.common.metrics import confusion_matrix
- >>> preds = Tensor(np.array([1, 0, 1, 0]))
- >>> labels = Tensor(np.array([1, 0, 0, 1]))
- >>> conf_mat = confusion_matrix(preds, labels)
- >>> print(conf_mat)
- [[1. 1.]
- [1. 1.]]
-
- """
- class_num = _check_value_type("class_num", class_num, [int])
-
- preds = _convert_data_type(preds)
- labels = _convert_data_type(labels)
-
- if preds.ndim not in (labels.ndim, labels.ndim + 1):
- raise ValueError(f'`preds` and `labels` should have the same dimensions, or the dimension '
- f'of `preds` equals the dimension of true value add 1, but got `preds` '
- f'ndim: {preds.ndim}, `labels` ndim: {labels.ndim}.')
-
- if preds.ndim == labels.ndim + 1:
- preds = np.argmax(preds, axis=1)
-
- trans = (labels.reshape(-1) * class_num + preds.reshape(-1)).astype(int)
- bincount = np.bincount(trans, minlength=class_num ** 2)
- conf_mat = bincount.reshape(class_num, class_num)
-
- conf_mat = conf_mat.astype(float)
-
- return conf_mat
-
-
- 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))
-
- def clear(self):
- """Clears the internal evaluation results."""
- self.conf_mat = np.zeros((self.class_num, self.class_num))
-
- 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
-
- 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
-
- def get_metric_name(self):
- """
- Returns the name of the metric.
- """
- return self._name
-
-
- __all__ = ['confusion_matrix_fn', 'ConfusionMatrix']
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