我正在试图找出如何使用GridSearchCV进行线性回归,但是我得到了一个令人讨厌的错误,如果这是一个估算器问题对GridSearchCV不正确或者如果这是我的“LogisticRegression”,我就不会得到设置不正确 . 我让它适用于随机森林和knn,但我坚持这个实现 .
我使用一个小数据集,这就是我想使用liblinear的原因(即使它是默认情况下,如文档中所述) .
tuned_parameters = {'C': [0.1, 0.5, 1, 5, 10, 50, 100]}
clf = GridSearchCV(LogisticRegression(solver='liblinear'), tuned_parameters, cv=5, scoring="accuracy")
clf.fit(X_train, y_train)
和错误:
StratifiedShuffleSplit(n_splits=1, random_state=0, test_size=0.4,
train_size=None)
Traceback (most recent call last):
File "linearRegression.py", line 105, in <module>
clf.fit(X_train, y_train)
File "/usr/local/lib/python2.7/dist-packages/sklearn/model_selection/_search.py", line 945, in fit
return self._fit(X, y, groups, ParameterGrid(self.param_grid))
File "/usr/local/lib/python2.7/dist-packages/sklearn/model_selection/_search.py", line 564, in _fit
for parameters in parameter_iterable
File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 758, in __call__
while self.dispatch_one_batch(iterator):
File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 608, in dispatch_one_batch
self._dispatch(tasks)
File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 571, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/_parallel_backends.py", line 109, in apply_async
result = ImmediateResult(func)
File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/_parallel_backends.py", line 326, in __init__
self.results = batch()
File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/usr/local/lib/python2.7/dist-packages/sklearn/model_selection/_validation.py", line 260, in _fit_and_score
test_score = _score(estimator, X_test, y_test, scorer)
File "/usr/local/lib/python2.7/dist-packages/sklearn/model_selection/_validation.py", line 288, in _score
score = scorer(estimator, X_test, y_test)
File "/usr/local/lib/python2.7/dist-packages/sklearn/metrics/scorer.py", line 91, in __call__
y_pred = estimator.predict(X)
File "/usr/local/lib/python2.7/dist-packages/sklearn/linear_model/base.py", line 336, in predict
scores = self.decision_function(X)
File "/usr/local/lib/python2.7/dist-packages/sklearn/linear_model/base.py", line 320, in decision_function
dense_output=True) + self.intercept_
File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/extmath.py", line 189, in safe_sparse_dot
return fast_dot(a, b)
TypeError: Cannot cast array data from dtype([('f0', 'f8'), ('f1','f8')]) to dtype('float64') according to the rule 'safe'
我阅读了文档:http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
和
谢谢你的帮助 .
编辑:X和Y的形状:
X = np.array(Xlist,np.dtype('float,float'))# - >两个浮点数作为特征y = np.array(ylist,np.dtype('int'))# - >标签0或1
例如:X_train是
[[(0.0,0.0)(3.85,0.0)] [(3.6,0.0)(2.45,0.0)] [(1.1,0.0)(1.35,0.0)] [(3.7,0.0)(1.85,0.0)] ]
Y_train是
[1 0 0 0 1 0 1 1]
2 回答
可能是你输入X数据集作为元组列表:(A,B),而不是数组列表:[A,B]?
我能够使用scikit-learn == 0.18.1运行以下代码:
注意:我不得不减少GridSearchCV的cv属性,因为没有足够大的数据集分为5个部分 .
好吧,我的一个朋友解决了它:
我用的是:
即使它正在使用这些分类器,它也无法很好地使用此估算器:
SVC(kernel ='rbf'),SVC(kernel ='linear'),SVC(kernel ='poly'),NeighborsClassifier(),DecisionTreeClassifier(),RandomForestClassifier()
所以我只是将这两行替换为: