# 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.
# ============================================================================
"""ROC"""
import numpy as np
from mindspore._checkparam import Validator as validator
from .metric import Metric, rearrange_inputs
[docs]class ROC(Metric):
"""
Calculates the ROC curve. It is suitable for solving binary classification and multi classification problems.
In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.
Args:
class_num (int): The number of classes. It is not necessary to provide this argument under the binary
classification scenario. Default: None.
pos_label (int): Determine the integer of positive class. For binary problems, it is translated to 1 by default.
For multiclass problems, this argument should not be set, as it will
iteratively changed in the range [0,num_classes-1]. Default: None.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> # 1) binary classification example
>>> x = Tensor(np.array([3, 1, 4, 2]))
>>> y = Tensor(np.array([0, 1, 2, 3]))
>>> metric = nn.ROC(pos_label=2)
>>> metric.clear()
>>> metric.update(x, y)
>>> fpr, tpr, thresholds = metric.eval()
>>> print(fpr)
[0. 0. 0.33333333 0.6666667 1.]
>>> print(tpr)
[0. 1. 1. 1. 1.]
>>> print(thresholds)
[5 4 3 2 1]
>>>
>>> # 2) multiclass classification example
>>> x = Tensor(np.array([[0.28, 0.55, 0.15, 0.05], [0.10, 0.20, 0.05, 0.05], [0.20, 0.05, 0.15, 0.05],
... [0.05, 0.05, 0.05, 0.75]]))
>>> y = Tensor(np.array([0, 1, 2, 3]))
>>> metric = nn.ROC(class_num=4)
>>> metric.clear()
>>> metric.update(x, y)
>>> fpr, tpr, thresholds = metric.eval()
>>> print(fpr)
[array([0., 0., 0.33333333, 0.66666667, 1.]), array([0., 0.33333333, 0.33333333, 1.]),
array([0., 0.33333333, 1.]), array([0., 0., 1.])]
>>> print(tpr)
[array([0., 1., 1., 1., 1.]), array([0., 0., 1., 1.]), array([0., 1., 1.]), array([0., 1., 1.])]
>>> print(thresholds)
[array([1.28, 0.28, 0.2, 0.1, 0.05]), array([1.55, 0.55, 0.2, 0.05]), array([1.15, 0.15, 0.05]),
array([1.75, 0.75, 0.05])]
"""
def __init__(self, class_num=None, pos_label=None):
super().__init__()
self.class_num = class_num if class_num is None else validator.check_value_type("class_num", class_num, [int])
self.pos_label = pos_label if pos_label is None else validator.check_value_type("pos_label", pos_label, [int])
self.clear()
[docs] def clear(self):
"""Clear the internal evaluation result."""
self.y_pred = 0
self.y = 0
self.sample_weights = None
self._is_update = False
def _precision_recall_curve_update(self, y_pred, y, class_num, pos_label):
"""update curve"""
if not (len(y_pred.shape) == len(y.shape) or len(y_pred.shape) == len(y.shape) + 1):
raise ValueError(f"For 'ROC', predicted value (input[0]) and true value (input[1]) should have same "
f"dimensions, or the dimension of predicted value equal the dimension of true value add "
f"1, but got predicted value ndim: {len(y_pred.shape)}, true value ndim: {len(y.shape)}.")
# single class evaluation
if len(y_pred.shape) == len(y.shape):
if class_num is not None and class_num != 1:
raise ValueError(f"For 'ROC', when predicted value (input[0]) and true value (input[1]) have the same "
f"shape, the 'class_num' should be 1, but got {class_num}.")
class_num = 1
if pos_label is None:
pos_label = 1
y_pred = y_pred.flatten()
y = y.flatten()
# multi class evaluation
elif len(y_pred.shape) == len(y.shape) + 1:
if pos_label is not None:
raise ValueError(f"For 'ROC', when the dimension of predicted value (input[0]) equals the dimension "
f"of true value (input[1]) add 1, the 'pos_label' should be None, "
f"but got {pos_label}.")
if class_num != y_pred.shape[1]:
raise ValueError("For 'ROC', the 'class_num' should equal the number of classes from predicted value "
"(input[0]), but got 'class_num' {}, the number of classes from predicted value {}."
.format(class_num, y_pred.shape[1]))
y_pred = y_pred.transpose(0, 1).reshape(class_num, -1).transpose(0, 1)
y = y.flatten()
return y_pred, y, class_num, pos_label
[docs] @rearrange_inputs
def update(self, *inputs):
"""
Update state with predictions and targets.
Args:
inputs: Input `y_pred` and `y`. `y_pred` and `y` are `Tensor`, list or numpy.ndarray.
In most cases (not strictly), y_pred is a list of floating numbers in range :math:`[0, 1]`
and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
is the number of categories. y contains values of integers. The shape is :math:`(N,C)` if one-hot
encoding is used. Shape can also be :math:`(N,)` if category index is used.
"""
if len(inputs) != 2:
raise ValueError("For 'ROC.update', it needs 2 inputs (predicted value, true value), but got {}"
.format(len(inputs)))
y_pred = self._convert_data(inputs[0])
y = self._convert_data(inputs[1])
y_pred, y, class_num, pos_label = self._precision_recall_curve_update(y_pred, y, self.class_num, self.pos_label)
self.y_pred = y_pred
self.y = y
self.class_num = class_num
self.pos_label = pos_label
self._is_update = True
def _roc_eval(self, y_pred, y, class_num, pos_label, sample_weights=None):
"""Computes the ROC curve."""
if class_num == 1:
fps, tps, thresholds = self._binary_clf_curve(y_pred, y, sample_weights=sample_weights,
pos_label=pos_label)
tps = np.squeeze(np.hstack([np.zeros(1, dtype=tps.dtype), tps]))
fps = np.squeeze(np.hstack([np.zeros(1, dtype=fps.dtype), fps]))
thresholds = np.hstack([thresholds[0][None] + 1, thresholds])
if fps[-1] <= 0:
raise ValueError("For 'ROC.eval', there is no negative samples in true value, "
"false positive value is meaningless.")
fpr = fps / fps[-1]
if tps[-1] <= 0:
raise ValueError("For 'ROC.eval', there is no positive samples in true value, "
"true positive value is meaningless.")
tpr = tps / tps[-1]
return fpr, tpr, thresholds
fpr, tpr, thresholds = [], [], []
for c in range(class_num):
preds_c = y_pred[:, c]
res = self._roc(preds_c, y, class_num=1, pos_label=c, sample_weights=sample_weights)
fpr.append(res[0])
tpr.append(res[1])
thresholds.append(res[2])
return fpr, tpr, thresholds
def _roc(self, y_pred, y, class_num=None, pos_label=None, sample_weights=None):
"""
Update curve and return the result of the ROC curve.
Args:
y_pred (Union[Tensor, list, np.ndarray]): In most cases (not strictly), y_pred is a list of floating numbers
in range :math:`[0, 1]` and the shape is :math:`(N, C)`, where :math:`N` is the number of cases
and :math:`C` is the number of categories.
y (Union[Tensor, list, np.ndarray]): values of integers.
class_num (int): Integer with the number of classes. For the problem of binary classification, it is not
necessary to provide this argument. Default: None.
pos_label (int): Determine the integer of positive class. Default: None. For binary problems, it is
translated to 1. For multiclass problems, this argument should not be set, as it is iteratively changed
in the range [0,num_classes-1]. Default: None.
sample_weights (Union[None, np.ndarray]): If sample_weights is None, the weight value is 1.
If sample_weights is ndarray, the weight value is the ndarray value.
"""
y_pred, y, class_num, pos_label = self._precision_recall_curve_update(y_pred, y, class_num, pos_label)
return self._roc_eval(y_pred, y, class_num, pos_label, sample_weights)
[docs] def eval(self):
"""
Computes the ROC curve.
Returns:
A tuple, composed of `fpr`, `tpr`, and `thresholds`.
- **fpr** (np.array) - False positive rate. In binary classification case, a fpr numpy array under different
thresholds will be returned, otherwise in multiclass case, a list of
fpr numpy arrays will be returned and each element represents one category.
- **tpr** (np.array) - True positive rates. n binary classification case, a tps numpy array under different
thresholds will be returned, otherwise in multiclass case, a list of tps numpy arrays
will be returned and each element represents one category.
- **thresholds** (np.array) - Thresholds used for computing fpr and tpr.
Raises:
RuntimeError: If the update method is not called first, an error will be reported.
"""
if self._is_update is False:
raise RuntimeError("Please call the 'update' method before calling 'eval' method.")
y_pred = np.squeeze(np.vstack(self.y_pred))
y = np.squeeze(np.vstack(self.y))
return self._roc_eval(y_pred, y, self.class_num, self.pos_label)