# Source code for mindspore.nn.metrics.precision

# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# you may not use this file except in compliance with the License.
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# Unless required by applicable law or agreed to in writing, software
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# ============================================================================
"""Precision."""
from __future__ import absolute_import

import sys
import numpy as np

from mindspore._checkparam import Validator as validator
from .metric import EvaluationBase, rearrange_inputs, _check_onehot_data

[文档]class Precision(EvaluationBase):
r"""
Calculates precision for classification and multilabel data.

The precision function creates two local variables, :math:\text{true_positive} and
:math:\text{false_positive}, that are used to compute the precision. The calculation formula is:

.. math::
\text{precision} = \frac{\text{true_positive}}{\text{true_positive} + \text{false_positive}}

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 nn, Tensor
>>>
>>> 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.Precision('classification')
>>> metric.clear()
>>> metric.update(x, y)
>>> precision = metric.eval()
>>> print(precision)
[0.5 1. ]

"""
def __init__(self, eval_type='classification'):
super(Precision, self).__init__(eval_type)
self.eps = sys.float_info.min
self.clear()

[文档]    def clear(self):
"""Clears the internal evaluation result."""
self._class_num = 0
if self._type == "multilabel":
self._true_positives = np.empty(0)
self._positives = np.empty(0)
self._true_positives_average = 0
self._positives_average = 0
else:
self._true_positives = 0
self._positives = 0

[文档]    @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 Tensor, list or numpy.ndarray.
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 'Precision.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 'Precision.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 'Precision.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)

positives = y_pred.sum(axis=0)
true_positives = (y * y_pred).sum(axis=0)

if self._type == "multilabel":
self._true_positives_average += np.sum(true_positives / (positives + self.eps))
self._positives_average += len(positives)
self._true_positives = np.concatenate((self._true_positives, true_positives), axis=0)
self._positives = np.concatenate((self._positives, positives), axis=0)

else:
self._true_positives += true_positives
self._positives += positives

[文档]    def eval(self, average=False):
"""
Computes the precision.

Args:
average (bool): Specify whether calculate the average precision. Default: False.

Returns:
numpy.float64, the computed result.
"""
if self._class_num == 0:
raise RuntimeError("The 'Precision' 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._positives + self.eps)

if average:
if self._type == "multilabel":
result = self._true_positives_average / (self._positives_average + self.eps)
return result.mean()
return result