# 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"""
import numpy as np
[docs]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 (boolean): If True, assume that the curve is ascending in the case of ties, as for an ROC curve.
If the curve is non-ascending, the result will be wrong. Default: False.
Returns:
Scalar (float): Compute result.
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('The inputs must 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('At least 2 points are needed to compute the AUC, but x.shape = {}.'.format(x.shape))
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("Reordering is not turned on, and the x array is not increasing:{}".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("Bad input shape {0}.".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]))