# Copyright 2020 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."""
import sys
import numpy as np
from mindspore._checkparam import ParamValidator as validator
from .metric import Metric
[docs]class Fbeta(Metric):
r"""
Calculates the fbeta score.
Fbeta score is a weighted mean of precison 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 (float): The weight of precision.
Examples:
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> metric = nn.Fbeta(1)
>>> metric.update(x, y)
>>> fbeta = metric.eval()
[0.66666667 0.66666667]
"""
def __init__(self, beta):
super(Fbeta, self).__init__()
self.eps = sys.float_info.min
if not beta > 0:
raise ValueError('`beta` must greater than zero, but got {}'.format(beta))
self.beta = beta
self.clear()
[docs] def clear(self):
"""Clears the internal evaluation result."""
self._true_positives = 0
self._actual_positives = 0
self._positives = 0
self._class_num = 0
[docs] 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, 1)` if category index is used.
"""
if len(inputs) != 2:
raise ValueError('Fbeta need 2 inputs (y_pred, y), 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 self._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('Class number not match, last input data contain {} classes, but current data contain {} '
'classes'.format(self._class_num, y_pred.shape[1]))
class_num = self._class_num
if y.max() + 1 > class_num:
raise ValueError('y_pred contains {} classes less than y 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
[docs] def eval(self, average=False):
"""
Computes the fbeta.
Args:
average (bool): Whether to calculate the average fbeta. Default value is False.
Returns:
Float, computed result.
"""
validator.check_type("average", average, [bool])
if self._class_num == 0:
raise RuntimeError('Input number of samples can not be 0.')
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
[docs]class F1(Fbeta):
r"""
Calculates the F1 score. F1 is a special case of Fbeta when beta is 1.
Refer to class `Fbeta` for more details.
.. math::
F_\beta=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive}
Examples:
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> metric = nn.F1()
>>> metric.update(x, y)
>>> fbeta = metric.eval()
"""
def __init__(self):
super(F1, self).__init__(1.0)