Source code for mindspore.nn.metrics.auc

# Copyright 2021 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.
# ============================================================================
"""auc"""
from __future__ import absolute_import

import numpy as np


[文档]def auc(x, y, reorder=False): """ Computes the AUC(Area Under the Curve) using the trapezoidal rule. This is a general function, given points on a curve. For computing the area under the ROC-curve. Args: x (Union[np.array, list]): From the ROC curve(fpr), np.array with false positive rates. If multiclass, this is a list of such np.array, one for each class. The shape :math:`(N)`. y (Union[np.array, list]): From the ROC curve(tpr), np.array with true positive rates. If multiclass, this is a list of such np.array, one for each class. The shape :math:`(N)`. reorder (bool): If False, x must rise or fall monotonously. If True, x will be sorted in ascending order. Default: False. Returns: float, the area under the ROC-curve. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> from mindspore import nn >>> >>> y_pred = np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]) >>> y = np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]) >>> metric = nn.ROC(pos_label=2) >>> metric.clear() >>> metric.update(y_pred, y) >>> fpr, tpr, thre = metric.eval() >>> output = nn.auc(fpr, tpr) >>> print(output) 0.5357142857142857 """ if not isinstance(x, np.ndarray) or not isinstance(y, np.ndarray): raise TypeError("For 'auc', the argument 'x' and 'y' must all be np.ndarray, but got {}, {}" .format(type(x), type(y))) _check_consistent_length(x, y) x = _column_or_1d(x) y = _column_or_1d(y) if x.shape[0] < 2: raise ValueError("For 'auc', the shape of the argument 'x' in axis 0 must be greater than 2, " "but got {}.".format(x.shape[0])) direction = 1 if reorder: order = np.lexsort((y, x)) x, y = x[order], y[order] else: dx = np.diff(x) if np.any(dx < 0): if np.all(dx <= 0): direction = -1 else: raise ValueError("For 'auc', if the argument is False, the argument 'x' array must be increasing " "or decreasing, but got 'x': {}".format(x)) area = direction * np.trapz(y, x) if isinstance(area, np.memmap): area = area.dtype.type(area) return area
def _column_or_1d(y): """ Ravel column or 1D numpy array, otherwise raise a ValueError. """ shape = np.shape(y) if len(shape) == 1 or (len(shape) == 2 and shape[1] == 1): return np.ravel(y) raise ValueError("For 'auc', the input must be a 1-dimensional array, or a 2-dimensional array with the second " "dimension of 1, but got shape {}.".format(shape)) def _num_samples(x): """Return the number of samples in array-like x.""" if hasattr(x, 'fit') and callable(x.fit): raise TypeError('Expected sequence or array-like, got estimator {}.'.format(x)) if not hasattr(x, '__len__') and not hasattr(x, 'shape'): if hasattr(x, '__array__'): x = np.asarray(x) else: raise TypeError("Expected sequence or array-like, got {}." .format(type(x))) if hasattr(x, 'shape'): if x.ndim == 0: raise TypeError("Singleton array {} cannot be considered as a valid collection.".format(x)) res = x.shape[0] else: res = x.size return res def _check_consistent_length(*arrays): r""" Check that all arrays have consistent first dimensions. Check whether all objects in arrays have the same shape or length. Args: - **(*arrays)** - (Union[tuple, list]): list or tuple of input objects. Objects that will be checked for consistent length. """ lengths = [_num_samples(array) for array in arrays if array is not None] uniques = np.unique(lengths) if len(uniques) > 1: raise ValueError("Found input variables with inconsistent numbers of samples: {}." .format([int(length) for length in lengths]))