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# Licensed under the Apache License, Version 2.0 (the "License");
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# ============================================================================
"""Metric base class."""
from abc import ABCMeta, abstractmethod
import functools
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):
self._indexes = None
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('The input data type must be a tensor, list or numpy.ndarray')
return data
def _check_onehot_data(self, data):
"""
Whether input data is one-hot encoding.
Args:
data (numpy.array): Input data.
Returns:
bool, return true, if input data is 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]
@property
def indexes(self):
"""The `_indexes` is a private attribute, and you can retrieve it by `self.indexes`.
"""
return getattr(self, '_indexes', None)
[docs] def set_indexes(self, indexes):
"""
The `_indexes` is a private attribute and you can modify it by this function.
This allows you to determine the order of logits and labels to be calculated in the
inputs, specially when you call the method `update` within this metrics.
Note:
It has been applied in subclass of Metric, eg. `Accuracy`, `BleuScore`, `ConfusionMatrix`,
`CosineSimilarity`, `MAE`, and `MSE`.
Args:
indexes (List(int)): The order of logits and labels to be rearranged.
Outputs:
:class:`Metric`, its original Class instance.
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]))
>>> y2 = Tensor(np.array([0, 0, 1]))
>>> metric = nn.Accuracy('classification').set_indexes([0, 2])
>>> metric.clear()
>>> metric.update(x, y, y2)
>>> accuracy = metric.eval()
>>> print(accuracy)
0.3333333333333333
"""
if not isinstance(indexes, list) or not all(isinstance(i, int) for i in indexes):
raise ValueError("The indexes should be a list and all its elements should be int")
self._indexes = indexes
return self
def __call__(self, *inputs):
"""
Evaluate input data once.
Args:
inputs (tuple): The first item is a predict array, the second item is a 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 must be equal to 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 must be equal to 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 a predicted array and the second item is a 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