Source code for mindspore.nn.metrics.fbeta

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"""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)