forked from huawei/mindspore2022
137 lines
4.9 KiB
Python
Executable File
137 lines
4.9 KiB
Python
Executable File
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Fbeta."""
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import sys
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import numpy as np
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from mindspore._checkparam import ParamValidator as validator
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from .metric import Metric
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class Fbeta(Metric):
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r"""
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Calculates the fbeta score.
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Fbeta score is a weighted mean of precison and recall.
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.. math::
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F_\beta=\frac{(1+\beta^2) \cdot true\_positive}
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{(1+\beta^2) \cdot true\_positive +\beta^2 \cdot false\_negative + false\_positive}
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Args:
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beta (float): The weight of precision.
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Examples:
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>>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
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>>> y = mindspore.Tensor(np.array([1, 0, 1]))
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>>> metric = nn.Fbeta(1)
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>>> metric.update(x, y)
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>>> fbeta = metric.eval()
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[0.66666667 0.66666667]
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"""
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def __init__(self, beta):
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super(Fbeta, self).__init__()
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self.eps = sys.float_info.min
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if not beta > 0:
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raise ValueError('`beta` must greater than zero, but got {}'.format(beta))
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self.beta = beta
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self.clear()
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def clear(self):
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"""Clears the internal evaluation result."""
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self._true_positives = 0
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self._actual_positives = 0
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self._positives = 0
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self._class_num = 0
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def update(self, *inputs):
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"""
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Updates the internal evaluation result `y_pred` and `y`.
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Args:
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inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray.
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`y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
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and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
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is the number of categories. y contains values of integers. The shape is :math:`(N, C)`
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if one-hot encoding is used. Shape can also be :math:`(N, 1)` if category index is used.
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"""
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if len(inputs) != 2:
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raise ValueError('Fbeta need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
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y_pred = self._convert_data(inputs[0])
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y = self._convert_data(inputs[1])
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if y_pred.ndim == y.ndim and self._check_onehot_data(y):
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y = y.argmax(axis=1)
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if self._class_num == 0:
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self._class_num = y_pred.shape[1]
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elif y_pred.shape[1] != self._class_num:
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raise ValueError('Class number not match, last input data contain {} classes, but current data contain {} '
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'classes'.format(self._class_num, y_pred.shape[1]))
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class_num = self._class_num
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if y.max() + 1 > class_num:
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raise ValueError('y_pred contains {} classes less than y contains {} classes.'.
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format(class_num, y.max() + 1))
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y = np.eye(class_num)[y.reshape(-1)]
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indices = y_pred.argmax(axis=1).reshape(-1)
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y_pred = np.eye(class_num)[indices]
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positives = y_pred.sum(axis=0)
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actual_positives = y.sum(axis=0)
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true_positives = (y * y_pred).sum(axis=0)
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self._true_positives += true_positives
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self._positives += positives
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self._actual_positives += actual_positives
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def eval(self, average=False):
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"""
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Computes the fbeta.
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Args:
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average (bool): Whether to calculate the average fbeta. Default value is False.
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Returns:
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Float, computed result.
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"""
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validator.check_type("average", average, [bool])
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if self._class_num == 0:
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raise RuntimeError('Input number of samples can not be 0.')
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fbeta = (1.0 + self.beta ** 2) * self._true_positives / \
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(self.beta ** 2 * self._actual_positives + self._positives + self.eps)
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if average:
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return fbeta.mean()
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return fbeta
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class F1(Fbeta):
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r"""
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Calculates the F1 score. F1 is a special case of Fbeta when beta is 1.
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Refer to class `Fbeta` for more details.
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.. math::
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F_\beta=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive}
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Examples:
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>>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
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>>> y = mindspore.Tensor(np.array([1, 0, 1]))
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>>> metric = nn.F1()
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>>> metric.update(x, y)
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>>> fbeta = metric.eval()
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"""
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def __init__(self):
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super(F1, self).__init__(1.0)
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