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# 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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# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
""""Class for Metric Recall"""
import sys
import numpy as np
from mindnlp.abc import Metric
from mindnlp.common.metrics import _check_onehot_data, _check_shape, _convert_data_type
[docs]class Recall(Metric):
r"""
Calculates the recall. Recall is also referred to as the true positive rate or
sensitivity. The function is shown as follows:
.. math::
\text{Recall} =\frac{\text{TP}} {\text{TP} + \text{FN}}
where `TP` is the number of true posistive cases, `FN` is the number of false negative cases.
Args:
name (str): Name of the metric.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindnlp.common.metrics import Recall
>>> preds = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
>>> labels = Tensor(np.array([1, 0, 1]), mindspore.int32)
>>> metric = Recall()
>>> metric.update(preds, labels)
>>> rec = metric.eval()
>>> print(rec)
[1. 0.5]
"""
def __init__(self, name='Recall'):
super().__init__()
self._name = name
self.actual_positives = 0
self.true_positives = 0
self.epsilon = sys.float_info.min
[docs] def clear(self):
"""Clears the internal evaluation results."""
self.actual_positives = 0
self.true_positives = 0
[docs] def update(self, *inputs):
"""
Updates local variables.
Args:
inputs: Input `preds` and `labels`.
- preds (Union[Tensor, list, np.ndarray]): Predicted value. `preds` is a list of
floating numbers in range :math:`[0, 1]` and the shape of `preds` is
:math:`(N, C)` in most cases (not strictly), where :math:`N` is the number
of cases and :math:`C` is the number of categories.
- labels (Union[Tensor, list, np.ndarray]): Ground truth. `labels` must be in
one-hot format that shape is :math:`(N, C)`, or can be transformed to
one-hot format that shape is :math:`(N,)`.
Raises:
ValueError: If the number of inputs is not 2.
ValueError: If `preds` doesn't have the same classes number as `labels`.
"""
if len(inputs) != 2:
raise ValueError(f'For `Recall.update`, it needs 2 inputs (`preds` and `labels`), '
f'but got {len(inputs)}.')
preds = inputs[0]
labels = inputs[1]
y_pred = _convert_data_type(preds)
y_true = _convert_data_type(labels)
if y_pred.ndim == y_true.ndim and _check_onehot_data(y_true):
y_true = y_true.argmax(axis=1)
_check_shape(y_pred, y_true)
class_num = y_pred.shape[1]
if y_true.max() + 1 > class_num:
raise ValueError(f'For `Recall.update`, `preds` should have the same classes number '
f'as `labels`, but got `preds` classes {class_num}, true value classes'
f' {y_true.max() + 1}.')
y_true = np.eye(class_num)[y_true.reshape(-1)]
indices = y_pred.argmax(axis=1).reshape(-1)
y_pred = np.eye(class_num)[indices]
self.actual_positives += y_true.sum(axis=0)
self.true_positives += (y_true * y_pred).sum(axis=0)
[docs] def eval(self):
"""
Computes and returns the recall.
Returns:
- **rec** (numpy.ndarray) - The computed result.
"""
rec = (self.true_positives / (self.actual_positives + self.epsilon))
return rec
[docs] def get_metric_name(self):
"""
Returns the name of the metric.
"""
return self._name