mindspore.train.metrics.roc 源代码

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"""ROC"""
from __future__ import absolute_import

import numpy as np

from mindspore import _checkparam as validator
from mindspore.train.metrics.metric import Metric, rearrange_inputs, _binary_clf_curve


[文档]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 Tensor >>> from mindspore.train import ROC >>> >>> # 1) binary classification example >>> x = Tensor(np.array([3, 1, 4, 2])) >>> y = Tensor(np.array([0, 1, 2, 3])) >>> metric = 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 = 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()
[文档] def clear(self): """Clear the internal evaluation result.""" self.y_pred = 0 self.y = 0 self.sample_weights = None self._is_update = False
[文档] @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 = _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 = _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 = _precision_recall_curve_update(y_pred, y, class_num, pos_label) return self._roc_eval(y_pred, y, class_num, pos_label, sample_weights)
[文档] 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(self.y_pred) y = np.squeeze(self.y) return self._roc_eval(y_pred, y, self.class_num, self.pos_label)
def _precision_recall_curve_update(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]) must 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' must 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' must be None, " f"but got {pos_label}.") if class_num != y_pred.shape[1]: raise ValueError("For 'ROC', the 'class_num' must 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