mindspore.train.metrics.fbeta 源代码

# Copyright 2020-2021 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
# 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.
# ============================================================================
"""Fbeta."""
from __future__ import absolute_import

import sys
import numpy as np

from mindspore._checkparam import Validator as validator
from mindspore.train.metrics.metric import Metric, rearrange_inputs, _check_onehot_data


[文档]class Fbeta(Metric): r""" Calculates the Fbeta score. Fbeta score is a weighted mean of precision and recall. .. math:: F_\beta=\frac{(1+\beta^2) \cdot true\_positive} {(1+\beta^2) \cdot true\_positive +\beta^2 \cdot false\_negative + false\_positive} Args: beta (Union[float, int]): Beta coefficient in the F measure. `beta` should be greater than 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> from mindspore import Tensor >>> from mindspore.train import Fbeta >>> >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) >>> y = Tensor(np.array([1, 0, 1])) >>> metric = Fbeta(1) >>> metric.clear() >>> metric.update(x, y) >>> fbeta = metric.eval() >>> print(fbeta) [0.66666667 0.66666667] """ def __init__(self, beta): super(Fbeta, self).__init__() self.eps = sys.float_info.min if not beta > 0: raise ValueError("For 'Fbeta', the argument 'beta' must be greater than 0, but got {}.".format(beta)) self.beta = beta self.clear()
[文档] def clear(self): """Clears the internal evaluation result.""" self._true_positives = 0 self._actual_positives = 0 self._positives = 0 self._class_num = 0
[文档] @rearrange_inputs def update(self, *inputs): """ Updates the internal evaluation result `y_pred` and `y`. Args: inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray. `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]` and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C` is the number of categories. y contains values of integers. The shape is :math:`(N, C)` if one-hot encoding is used. Shape can also be :math:`(N,)` if category index is used. Raises: ValueError: class numbers of last input predicted data and current predicted data not match. ValueError: If the predicted value and true value contain different classes. """ if len(inputs) != 2: raise ValueError("For 'Fbeta.update', it needs 2 inputs (predicted value, true value), " "but got {}.".format(len(inputs))) y_pred = self._convert_data(inputs[0]) y = self._convert_data(inputs[1]) if y_pred.ndim == y.ndim and _check_onehot_data(y): y = y.argmax(axis=1) if self._class_num == 0: self._class_num = y_pred.shape[1] elif y_pred.shape[1] != self._class_num: raise ValueError("For 'Fbeta.update', class number not match, last input predicted data contain {} " "classes, but current predicted data contain {} classes, please check your " "predicted value(inputs[0]).".format(self._class_num, y_pred.shape[1])) class_num = self._class_num if y.max() + 1 > class_num: raise ValueError("For 'Fbeta.update', predicted value(inputs[0]) and true value(inputs[1]) " "should contain same classes, but got predicted value contains {} classes" " and true value contains {} classes.".format(class_num, y.max() + 1)) y = np.eye(class_num)[y.reshape(-1)] indices = y_pred.argmax(axis=1).reshape(-1) y_pred = np.eye(class_num)[indices] positives = y_pred.sum(axis=0) actual_positives = y.sum(axis=0) true_positives = (y * y_pred).sum(axis=0) self._true_positives += true_positives self._positives += positives self._actual_positives += actual_positives
[文档] def eval(self, average=False): """ Computes the fbeta. Args: average (bool): Whether to calculate the average fbeta. Default: False. Returns: numpy.ndarray or numpy.float64, the computed result. """ validator.check_value_type("average", average, [bool], self.__class__.__name__) if self._class_num == 0: raise RuntimeError("The 'Fbeta' can not be calculated, because the number of samples is 0, " "please check whether your inputs(predicted value, true value) are empty, " "or has called update method before calling eval method.") fbeta = (1.0 + self.beta ** 2) * self._true_positives / \ (self.beta ** 2 * self._actual_positives + self._positives + self.eps) if average: return fbeta.mean() return fbeta
[文档]class F1(Fbeta): r""" Calculates the F1 score. F1 is a special case of Fbeta when beta is 1. Refer to class :class:`mindspore.train.Fbeta` for more details. .. math:: F_1=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive} Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> from mindspore import Tensor >>> from mindspore.train import F1 >>> >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) >>> y = Tensor(np.array([1, 0, 1])) >>> metric = F1() >>> metric.update(x, y) >>> result = metric.eval() >>> print(result) [0.66666667 0.66666667] """ def __init__(self): super(F1, self).__init__(1.0)