我试图从sklearn.svm.LinearSVC
创建一个子类,用作sklearn.model_selection.GridSearchCV
的估算器。子类有一个额外的函数,在本例中它什么都不做。然而,当我运行这个程序时,我最终遇到了一个我似乎无法调试的错误。如果复制粘贴代码并运行,它将复制以ValueError: Input contains NaN, infinity or a value too large for dtype('float64')
结尾的完整错误
一旦我让他开始工作,我希望为方法transform_this()
添加更多功能
有人能告诉我哪里出了问题吗?基于this我最初认为这是由于我的数据出现了一些问题。然而,由于我使用sklearn内置数据集复制了它,所以情况似乎并非如此。此外,我相信我正在根据我对上一个问题here的回答正确地将其子类化。此外,我还了解到GridSearchCV似乎没有以不同的方式初始化估计器(不知何故,它首先使用默认参数,正如我从this post中看到的)
from sklearn.datasets import load_breast_cancer
from sklearn.svm import LinearSVC
from sklearn.model_selection import GridSearchCV
RANDOM_STATE = 123
class LinearSVCSub(LinearSVC):
def __init__(self, penalty='l2', loss='squared_hinge', additional_parameter1=1, additional_parameter2=100,
dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1,
class_weight=None, verbose=0, random_state=None, max_iter=1000):
super(LinearSVCSub, self).__init__(penalty=penalty, loss=loss, dual=dual, tol=tol,
C=C, multi_class=multi_class, fit_intercept=fit_intercept,
intercept_scaling=intercept_scaling, class_weight=class_weight,
verbose=verbose, random_state=random_state, max_iter=max_iter)
self.additional_parameter1 = additional_parameter1
self.additional_parameter2 = additional_parameter2
def fit(self, X, y, sample_weight=None):
X = self.transform_this(X)
super(LinearSVCSub, self).fit(X, y, sample_weight)
def predict(self, X):
X = self.transform_this(X)
super(LinearSVCSub, self).predict(X)
def score(self, X, y, sample_weight=None):
X = self.transform_this(X)
super(LinearSVCSub, self).score(X, y, sample_weight)
def decision_function(self, X):
X = self.transform_this(X)
super(LinearSVCSub, self).decision_function(X)
def transform_this(self, X):
return X
if __name__ == '__main__':
data = load_breast_cancer()
X, y = data.data, data.target
# Parameter tuning with custom LinearSVC
param_grid = {'C': [0.00001, 0.0001, 0.0005],
'dual': (True, False), 'random_state': [RANDOM_STATE],
'additional_parameter1': [0.90, 0.80, 0.60, 0.30],
'additional_parameter2': [20, 30]}
gs_model = GridSearchCV(estimator=LinearSVCSub(), verbose=1, param_grid=param_grid,
scoring='roc_auc', n_jobs=-1)
gs_model.fit(X, y)
你有两个问题:
LinearSVC
收敛一旦你纠正了这些错误,你就可以走了:
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