Source code for mindspore.nn.metrics.precision

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"""Precision."""
from __future__ import absolute_import

import sys
import numpy as np

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


[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}`, which 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()
[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] @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
[docs] 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