Source code for mindspore.nn.metrics.accuracy

# 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.
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# ============================================================================
import numpy as np
from ._evaluation import EvaluationBase

[docs]class Accuracy(EvaluationBase): r""" Calculates the accuracy for classification and multilabel data. The accuracy class creates two local variables, correct number and total number that are used to compute the frequency with which predictions matches labels. This frequency is ultimately returned as the accuracy: an idempotent operation that simply divides correct number by total number. .. math:: \text{accuracy} =\frac{\text{true_positive} + \text{true_negative}} {\text{true_positive} + \text{true_negative} + \text{false_positive} + \text{false_negative}} Args: eval_type (str): Metric to calculate the accuracy over a dataset, for classification (single-label), and multilabel (multilabel classification). Default: 'classification'. Examples: >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32) >>> y = Tensor(np.array([1, 0, 1]), mindspore.float32) >>> metric = nn.Accuracy('classification') >>> metric.clear() >>> metric.update(x, y) >>> accuracy = metric.eval() """ def __init__(self, eval_type='classification'): super(Accuracy, self).__init__(eval_type) self.clear()
[docs] def clear(self): """Clears the internal evaluation result.""" self._correct_num = 0 self._total_num = 0 self._class_num = 0
[docs] def update(self, *inputs): """ Updates the internal evaluation result :math:`y_{pred}` and :math:`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 the input is not 2. """ if len(inputs) != 2: raise ValueError('Accuracy 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])) if self._type == 'classification': indices = y_pred.argmax(axis=1) result = (np.equal(indices, y) * 1).reshape(-1) elif self._type == 'multilabel': dimension_index = y_pred.ndim - 1 y_pred = y_pred.swapaxes(1, dimension_index).reshape(-1, self._class_num) y = y.swapaxes(1, dimension_index).reshape(-1, self._class_num) result = np.equal(y_pred, y).all(axis=1) * 1 self._correct_num += result.sum() self._total_num += result.shape[0]
[docs] def eval(self): """ Computes the accuracy. Returns: Float, the computed result. Raises: RuntimeError: If the sample size is 0. """ if self._total_num == 0: raise RuntimeError('Accuary can not be calculated, because the number of samples is 0.') return self._correct_num / self._total_num