ScikitLearn GridSearchCV和管道使用不同的方法

Question

我正在尝试使用 GridSearchCV 和管道将这些机器学习方法评估为相同的数据，当我在同一方法中改变参数时，它可以工作，但是当我放置多个方法时，它会给出错误

pipe_steps = [
('scaler', StandardScaler()), 
('logistic', LogisticRegression()),
('SVM',SVC()),
('KNN',KNeighborsClassifier())]
check_params={
'logistic__C':[1,1e5],
'SVM__C':[1,1e5],
'KNN__n_neighbors':[3,5],
'KNN__metric':['euclidean','manhattan']
}
pipeline = Pipeline(pipe_steps)
GridS = GridSearchCV(pipeline, param_grid=check_params)
GridS.fit(X, y)
print('Score %3.2f' %GridS.score(X, y))
print('Best Fit')
print(GridS.best_params_)

在下面的管道线上给出错误消息

TypeError                                 Traceback (most recent call last)
<ipython-input-139-75960299bc1c> in <module>
13     }
14 
---> 15 pipeline = Pipeline(pipe_steps)
16 
17 BCX_Grid = GridSearchCV(pipeline, param_grid=check_params)
C:ProgramDataAnaconda3libsite-packagessklearnpipeline.py in __init__(self, steps, memory, verbose)
133     def __init__(self, steps, memory=None, verbose=False):
134         self.steps = steps
--> 135         self._validate_steps()
136         self.memory = memory
137         self.verbose = verbose
C:ProgramDataAnaconda3libsite-packagessklearnpipeline.py in _validate_steps(self)
183                                 "transformers and implement fit and transform "
184                                 "or be the string 'passthrough' "
--> 185                                 "'%s' (type %s) doesn't" % (t, type(t)))
186 
187         # We allow last estimator to be None as an identity transformation
TypeError: All intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' 'LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)' (type <class 'sklearn.linear_model.logistic.LogisticRegression'>) doesn't

谢谢

Answer 1

您需要将管道拆分为多个管道，为此我有一个解决方案，该解决方案需要一个网格参数列表来确定管道的每个步骤。

pipeline = Pipeline([
('transformer', StandardScaler(),),
('model', 'passthrough',),
])
params = [
{
'model': (LogisticRegression(),),
'model__C': (1, 1e5,),
},
{
'model': (SVC(),),
'model__C': (1, 1e5,),
},
{
'model': (KNeighborsClassifier(),),
'model__n_neighbors': (3, 5,),
'model__metric': ('euclidean', 'manhattan',),
}
]
grid_Search = GridSearchCV(pipeline, params)

使用此策略，可以动态定义管道的步骤。

Answer 2

您的问题不在于超参数，因为它们的定义正确。问题是所有中间步骤都应该transformers，正如错误所示。在您的管道中，SVM不是变压器。

看这篇文章