# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# ============================================================================
""""Class for Metric PearsonCorrelation"""
import math
import numpy as np
from mindnlp.abc import Metric
from mindnlp.common.metrics import _convert_data_type
[文档]class PearsonCorrelation(Metric):
r"""
Calculates the Pearson correlation coefficient (PCC). PCC is a measure of linear
correlation between two sets of data. It is the ratio between the covariance of
two variables and the product of their standard deviations; thus, it is essentially
a normalized measurement of the covariance, such that the result always has a value
between −1 and 1.
Args:
name (str): Name of the metric.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindnlp.engine.metrics import PearsonCorrelation
>>> preds = Tensor(np.array([[0.1], [1.0], [2.4], [0.9]]), mindspore.float32)
>>> labels = Tensor(np.array([[0.0], [1.0], [2.9], [1.0]]), mindspore.float32)
>>> metric = PearsonCorrelation()
>>> metric.update(preds, labels)
>>> p_c_c = metric.eval()
>>> print(p_c_c)
0.9985229081857804
"""
def __init__(self, name='PearsonCorrelation'):
super().__init__()
self._name = name
self.preds = []
self.labels = []
[文档] def clear(self):
"""Clears the internal evaluation results."""
self.preds = []
self.labels = []
[文档] 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 and the shape of `preds` is :math:`(N, 1)`.
- labels (Union[Tensor, list, np.ndarray]): Ground truth. `labels` is a list of
floating numbers and the shape of `preds` is :math:`(N, 1)`.
Raises:
ValueError: If the number of inputs is not 2.
RuntimeError: If `preds` and `labels` have different lengths.
"""
if len(inputs) != 2:
raise ValueError(f'For `PearsonCorrelation.update`, it needs 2 inputs (`preds` '
f'and `labels`), but got {len(inputs)}.')
preds = inputs[0]
labels = inputs[1]
y_pred = _convert_data_type(preds)
y_true = _convert_data_type(labels)
y_pred = np.squeeze(y_pred.reshape(-1, 1)).tolist()
y_true = np.squeeze(y_true.reshape(-1, 1)).tolist()
if len(y_pred) != len(y_true):
raise RuntimeError(f'For `PearsonCorrelation.update`, `preds` and `labels` should have '
f'the same length, but got `preds` length {len(y_pred)}, `labels` '
f'length {len(y_true)})')
self.preds.append(y_pred)
self.labels.append(y_true)
[文档] def eval(self):
"""
Computes and returns the PCC.
Returns:
- **p_c_c** (float) - The computed result.
"""
preds = [item for pred in self.preds for item in pred]
labels = [item for label in self.labels for item in label]
n_preds = len(preds)
# simple sums
sum1 = sum(float(preds[i]) for i in range(n_preds))
sum2 = sum(float(labels[i]) for i in range(n_preds))
# sum up the squares
sum1_pow = sum(pow(v, 2.0) for v in preds)
sum2_pow = sum(pow(v, 2.0) for v in labels)
# sum up the products
p_sum = sum(preds[i] * labels[i] for i in range(n_preds))
numerator = p_sum - (sum1 * sum2 / n_preds)
denominator = math.sqrt(
(sum1_pow - pow(sum1, 2) / n_preds) * (sum2_pow - pow(sum2, 2) / n_preds))
if denominator == 0:
return 0.0
p_c_c = numerator / denominator
return p_c_c
[文档] def get_metric_name(self):
"""
Returns the name of the metric.
"""
return self._name