Source code for mindspore.nn.metrics.precision

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import sys

import numpy as np

from mindspore._checkparam import Validator as validator
from ._evaluation import EvaluationBase

[docs]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. This value is ultimately returned as the precision, an idempotent operation that simply divides :math:`\text{true_positive}` by the sum of :math:`\text{true_positive}` and :math:`\text{false_positive}`. .. 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}` should be 0 or 1. Args: eval_type (str): Metric to calculate accuracy over a dataset, for classification or multilabel. Default: 'classification'. 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.Precision('classification') >>> metric.clear() >>> metric.update(x, y) >>> precision = metric.eval() """ def __init__(self, eval_type='classification'): super(Precision, 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._positives = np.empty(0) self._true_positives_average = 0 self._positives_average = 0 else: self._true_positives = 0 self._positives = 0
[docs] 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 input is not 2. """ if len(inputs) != 2: raise ValueError('Precision 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 self._type == 'classification' and y_pred.ndim == y.ndim and self._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('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 self._type == "classification": 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] 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
[docs] def eval(self, average=False): """ Computes the precision. Args: average (bool): Specify whether calculate the average precision. Default value is False. Returns: Float, the computed result. """ if self._class_num == 0: raise RuntimeError('Input number of samples can not be 0.') 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