# 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.
# ============================================================================
"""Recall."""
from __future__ import absolute_import
import sys
import numpy as np
from mindspore import _checkparam as validator
from mindspore.train.metrics.metric import EvaluationBase, rearrange_inputs, _check_onehot_data
[docs]class Recall(EvaluationBase):
r"""
Calculates recall for classification and multilabel data.
The recall class creates two local variables, :math:`\text{true_positive}` and :math:`\text{false_negative}`,
that are used to compute the recall. The calculation formula is:
.. math::
\text{recall} = \frac{\text{true_positive}}{\text{true_positive} + \text{false_negative}}
Note:
In the multi-label cases, the elements of :math:`y` and :math:`y_{pred}` must be 0 or 1.
Args:
eval_type (str): ``'classification'`` or ``'multilabel'`` are supported. Default: ``'classification'`` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore.train import Recall
>>>
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> metric = Recall('classification')
>>> metric.clear()
>>> metric.update(x, y)
>>> recall = metric.eval()
>>> print(recall)
[1. 0.5]
"""
def __init__(self, eval_type='classification'):
super(Recall, self).__init__(eval_type)
self.eps = sys.float_info.min
self.clear()
[docs] def clear(self):
"""Clears the internal evaluation result."""
self._class_num = 0
if self._type == "multilabel":
self._true_positives = np.empty(0)
self._actual_positives = np.empty(0)
self._true_positives_average = 0
self._actual_positives_average = 0
else:
self._true_positives = 0
self._actual_positives = 0
[docs] @rearrange_inputs
def update(self, *inputs):
"""
Updates the internal evaluation result with `y_pred` and `y`.
Args:
inputs: Input `y_pred` and `y`. `y_pred` and `y` are a `Tensor`, a list or an array.
For 'classification' evaluation type, `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. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot
encoding is used or the shape is :math:`(N,)` with integer values if index of category is used.
For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with
values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y`
are both :math:`(N, C)`.
Raises:
ValueError: If the number of inputs is not 2.
"""
if len(inputs) != 2:
raise ValueError("For 'Recall.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 self._type == 'classification' and y_pred.ndim == y.ndim and _check_onehot_data(y):
y = y.argmax(axis=1)
self._check_shape(y_pred, y)
self._check_value(y_pred, y)
if self._class_num == 0:
self._class_num = y_pred.shape[1]
elif y_pred.shape[1] != self._class_num:
raise ValueError("For 'Recall.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 self._type == "classification":
if y.max() + 1 > class_num:
raise ValueError("For 'Recall.update', predicted value (input[0]) should have the same classes number "
"as true value (input[1]), but got predicted value classes {}, true value 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]
elif self._type == "multilabel":
y_pred = y_pred.swapaxes(1, 0).reshape(class_num, -1)
y = y.swapaxes(1, 0).reshape(class_num, -1)
actual_positives = y.sum(axis=0)
true_positives = (y * y_pred).sum(axis=0)
if self._type == "multilabel":
self._true_positives_average += np.sum(true_positives / (actual_positives + self.eps))
self._actual_positives_average += len(actual_positives)
self._true_positives = np.concatenate((self._true_positives, true_positives), axis=0)
self._actual_positives = np.concatenate((self._actual_positives, actual_positives), axis=0)
else:
self._true_positives += true_positives
self._actual_positives += actual_positives
[docs] def eval(self, average=False):
"""
Computes the recall.
Args:
average (bool): Specify whether calculate the average recall. Default: ``False`` .
Returns:
numpy.float64, the computed result.
"""
if self._class_num == 0:
raise RuntimeError("The 'Recall' 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.")
validator.check_value_type("average", average, [bool], self.__class__.__name__)
result = self._true_positives / (self._actual_positives + self.eps)
if average:
if self._type == "multilabel":
result = self._true_positives_average / (self._actual_positives_average + self.eps)
return result.mean()
return result