Source code for mindspore.nn.metrics.metric

# Copyright 2020 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Metric base class."""
from abc import ABCMeta, abstractmethod
import numpy as np
from mindspore.common.tensor import Tensor

_eval_types = {'classification', 'multilabel'}


[docs]class Metric(metaclass=ABCMeta): """ Base class of metric. Note: For examples of subclasses, please refer to the definition of class `MAE`, 'Recall' etc. """ def __init__(self): pass def _convert_data(self, data): """ Convert data type to numpy array. Args: data (Object): Input data. Returns: Ndarray, data with `np.ndarray` type. """ if isinstance(data, Tensor): data = data.asnumpy() elif isinstance(data, list): data = np.array(data) elif isinstance(data, np.ndarray): pass else: raise TypeError('Input data type must be tensor, list or numpy.ndarray') return data def _check_onehot_data(self, data): """ Whether input data are one-hot encoding. Args: data (numpy.array): Input data. Returns: bool, return true, if input data are one-hot encoding. """ if data.ndim > 1 and np.equal(data ** 2, data).all(): shp = (data.shape[0],) + data.shape[2:] if np.equal(np.ones(shp), data.sum(axis=1)).all(): return True return False def _binary_clf_curve(self, preds, target, sample_weights=None, pos_label=1): """Calculate True Positives and False Positives per binary classification threshold.""" if sample_weights is not None and not isinstance(sample_weights, np.ndarray): sample_weights = np.array(sample_weights) if preds.ndim > target.ndim: preds = preds[:, 0] desc_score_indices = np.argsort(-preds) preds = preds[desc_score_indices] target = target[desc_score_indices] if sample_weights is not None: weight = sample_weights[desc_score_indices] else: weight = 1. distinct_value_indices = np.where(preds[1:] - preds[:-1])[0] threshold_idxs = np.pad(distinct_value_indices, (0, 1), constant_values=target.shape[0] - 1) target = np.array(target == pos_label).astype(np.int64) tps = np.cumsum(target * weight, axis=0)[threshold_idxs] if sample_weights is not None: fps = np.cumsum((1 - target) * weight, axis=0)[threshold_idxs] else: fps = 1 + threshold_idxs - tps return fps, tps, preds[threshold_idxs] def __call__(self, *inputs): """ Evaluate input data once. Args: inputs (tuple): The first item is predict array, the second item is target array. Returns: Float, compute result. """ self.clear() self.update(*inputs) return self.eval()
[docs] @abstractmethod def clear(self): """ An interface describes the behavior of clearing the internal evaluation result. Note: All subclasses must override this interface. """ raise NotImplementedError('Must define clear function to use this base class')
[docs] @abstractmethod def eval(self): """ An interface describes the behavior of computing the evaluation result. Note: All subclasses must override this interface. """ raise NotImplementedError('Must define eval function to use this base class')
[docs] @abstractmethod def update(self, *inputs): """ An interface describes the behavior of updating the internal evaluation result. Note: All subclasses must override this interface. Args: inputs: A variable-length input argument list. """ raise NotImplementedError('Must define update function to use this base class')
class EvaluationBase(Metric): """ Base class of evaluation. Note: Please refer to the definition of class `Accuracy`. Args: eval_type (str): Type of evaluation must be in {'classification', 'multilabel'}. Raises: TypeError: If the input type is not classification or multilabel. """ def __init__(self, eval_type): super(EvaluationBase, self).__init__() if eval_type not in _eval_types: raise TypeError('Type must be in {}, but got {}'.format(_eval_types, eval_type)) self._type = eval_type def _check_shape(self, y_pred, y): """ Checks the shapes of y_pred and y. Args: y_pred (Tensor): Predict array. y (Tensor): Target array. """ if self._type == 'classification': if y_pred.ndim != y.ndim + 1: raise ValueError('Classification case, dims of y_pred equal dims of y add 1, ' 'but got y_pred: {} dims and y: {} dims'.format(y_pred.ndim, y.ndim)) if y.shape != (y_pred.shape[0],) + y_pred.shape[2:]: raise ValueError('Classification case, y_pred shape and y shape can not match. ' 'got y_pred shape is {} and y shape is {}'.format(y_pred.shape, y.shape)) else: if y_pred.ndim != y.ndim: raise ValueError('{} case, dims of y_pred need equal with dims of y, but got y_pred: {} ' 'dims and y: {} dims.'.format(self._type, y_pred.ndim, y.ndim)) if y_pred.shape != y.shape: raise ValueError('{} case, y_pred shape need equal with y shape, but got y_pred: {} and y: {}'. format(self._type, y_pred.shape, y.shape)) def _check_value(self, y_pred, y): """ Checks the values of y_pred and y. Args: y_pred (Tensor): Predict array. y (Tensor): Target array. """ if self._type != 'classification' and not (np.equal(y_pred ** 2, y_pred).all() and np.equal(y ** 2, y).all()): raise ValueError('For multilabel case, input value must be 1 or 0.') def clear(self): """ A interface describes the behavior of clearing the internal evaluation result. Note: All subclasses must override this interface. """ raise NotImplementedError def update(self, *inputs): """ A interface describes the behavior of updating the internal evaluation result. Note: All subclasses must override this interface. Args: inputs: The first item is predicted array and the second item is target array. """ raise NotImplementedError def eval(self): """ A interface describes the behavior of computing the evaluation result. Note: All subclasses must override this interface. """ raise NotImplementedError