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- # 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
-
-
- 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
-
-
- 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 = []
-
- def clear(self):
- """Clears the internal evaluation results."""
- self.preds = []
- self.labels = []
-
- 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)
-
- 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
-
- def get_metric_name(self):
- """
- Returns the name of the metric.
- """
- return self._name
-
- __all__ = ['spearman_correlation_fn', 'SpearmanCorrelation']
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