# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""""Class for Metric Spearman"""
import numpy as np
from mindnlp.abc import Metric
from .utils import _convert_data_type, _get_rank
[docs]def spearman_correlation_fn(preds, labels):
r"""
Calculates the Spearman's rank correlation coefficient (SRCC). It is a nonparametric
measure of rank correlation (statistical dependence between the rankings of two
variables). It assesses how well the relationship between two variables can be
described using a monotonic function. If there are no repeated data values, a
perfect Spearman correlation of +1 or −1 occurs when each of the variables is
a perfect monotone function of the other.
Args:
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)`.
Returns:
- **s_r_c_c** (float) - The computed result.
Raises:
RuntimeError: If `preds` and `labels` have different lengths.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindnlp.common.metrics import spearman_correlation
>>> 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)
>>> s_r_c_c = spearman_correlation(preds, labels)
>>> print(s_r_c_c)
1.0
"""
def _spearman(y_pred, y_true):
preds_rank = _get_rank(y_pred)
labels_rank = _get_rank(y_true)
total = 0
n_pred = len(y_pred)
for i in range(n_pred):
total += pow((preds_rank[i] - labels_rank[i]), 2)
res = 1 - float(6 * total) / (n_pred * (pow(n_pred, 2) - 1))
return res
preds = _convert_data_type(preds)
labels = _convert_data_type(labels)
preds = np.squeeze(preds.reshape(-1, 1)).tolist()
labels = np.squeeze(labels.reshape(-1, 1)).tolist()
if len(preds) != len(labels):
raise RuntimeError(f'`preds` and `labels` should have the same length, but got `preds` '
f'length {len(preds)}, `labels` length {len(labels)})')
s_r_c_c = _spearman(preds, labels)
return s_r_c_c
[docs]class SpearmanCorrelation(Metric):
r"""
Calculates the Spearman's rank correlation coefficient (SRCC). It is a nonparametric measure
of rank correlation (statistical dependence between the rankings of two variables).
It assesses how well the relationship between two variables can be described
using a monotonic function. If there are no repeated data values, a perfect
Spearman correlation of +1 or −1 occurs when each of the variables is
a perfect monotone function of the other.
Args:
name (str): Name of the metric.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindnlp.engine.metrics import SpearmanCorrelation
>>> 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 = SpearmanCorrelation()
>>> metric.update(preds, labels)
>>> s_r_c_c = metric.eval()
>>> print(s_r_c_c)
1.0
"""
def __init__(self, name='SpearmanCorrelation'):
super().__init__()
self._name = name
self.preds = []
self.labels = []
[docs] def clear(self):
"""Clears the internal evaluation results."""
self.preds = []
self.labels = []
[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 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 `SpearmanCorrelation.update`, it needs 2 inputs (`preds` '
f'and `labels`), but got {len(inputs)}.')
preds = inputs[0]
labels = inputs[1]
preds = _convert_data_type(preds)
labels = _convert_data_type(labels)
preds = np.squeeze(preds.reshape(-1, 1)).tolist()
labels = np.squeeze(labels.reshape(-1, 1)).tolist()
if len(preds) != len(labels):
raise RuntimeError(f'For `SpearmanCorrelation.update`, `preds` and `labels` should have'
f' the same length, but got `preds` length {len(preds)}, `labels` '
f'length {len(labels)})')
self.preds.append(preds)
self.labels.append(labels)
[docs] def eval(self):
"""
Computes and returns the SRCC.
Returns:
- **s_r_c_c** (float) - The computed result.
"""
self.preds = [item for pred in self.preds for item in pred]
self.labels = [item for label in self.labels for item in label]
preds_rank = _get_rank(self.preds)
labels_rank = _get_rank(self.labels)
total = 0
n_preds = len(self.preds)
for i in range(n_preds):
total += pow((preds_rank[i] - labels_rank[i]), 2)
s_r_c_c = 1 - float(6 * total) / (n_preds * (pow(n_preds, 2) - 1))
return s_r_c_c
[docs] def get_metric_name(self):
"""
Returns the name of the metric.
"""
return self._name
__all__ = ['spearman_correlation_fn', 'SpearmanCorrelation']