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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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# ============================================================================
""""Class for Metric F1Score"""
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 F1Score(Metric):
r"""
Calculates the F1 score. Fbeta score is a weighted mean of precision and recall,
and F1 score is a special case of Fbeta when beta is 1. The function is shown
as follows:
.. math::
F_1=\frac{2\cdot TP}{2\cdot TP + FN + FP}
where `TP` is the number of true posistive cases, `FN` is the number of false negative cases,
`FP` is the number of false positive cases.
Args:
name (str): Name of the metric.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindnlp.engine.metrics import F1Score
>>> preds = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> labels = Tensor(np.array([1, 0, 1]))
>>> metric = F1Score()
>>> metric.update(preds, labels)
>>> f1_s = metric.eval()
>>> print(f1_s)
[0.6666666666666666 0.6666666666666666]
"""
def __init__(self, name='F1Score'):
super().__init__()
self._name = name
self.epsilon = sys.float_info.min
self._true_positives = 0
self._actual_positives = 0
self._positives = 0
self._class_num = 0
[docs] def clear(self):
"""Clears the internal evaluation results."""
self._true_positives = 0
self._actual_positives = 0
self._positives = 0
self._class_num = 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: class numbers of last input predicted data and current
predicted data not match.
ValueError: If `preds` doesn't have the same classes number as `labels`.
"""
if len(inputs) != 2:
raise ValueError(f'For `F1Score.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)
if self._class_num == 0:
self._class_num = y_pred.shape[1]
elif y_pred.shape[1] != self._class_num:
raise ValueError(f'For `F1Score.update`, class number not match, last input '
f'predicted data contain {self._class_num} classes, but '
f'current predicted data contain {y_pred.shape[1]} classes,'
f' please check your predicted value(`preds`).')
class_num = self._class_num
if y_true.max() + 1 > class_num:
raise ValueError(f'For `F1Score.update`, `preds` and `labels` should contain '
f'same classes, but got `preds` contains {class_num} classes '
f'and true value contains {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]
positives = y_pred.sum(axis=0)
actual_positives = y_true.sum(axis=0)
true_positives = (y_true * y_pred).sum(axis=0)
self._true_positives += true_positives
self._positives += positives
self._actual_positives += actual_positives
[docs] def eval(self):
"""
Computes and returns the F1 score.
Returns:
- **f1_s** (numpy.ndarray) - The computed result.
Raises:
RuntimeError: If the number of samples is 0.
"""
f1_s = (2 * self._true_positives / (self._actual_positives + self._positives + \
self.epsilon))
return f1_s
[docs] def get_metric_name(self):
"""
Returns the name of the metric.
"""
return self._name