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如何将GridSearchCV与SVC估计器一起用于OneVsRestClassifier？

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我正在尝试将OneVsRestClassifier与SVC一起用于图像的多分类问题-我从CellProfiler获得了图像的数字特征。我想使用GridSearchCV来找到要使用的超参数，但我陷入了困境。

有人对此有解决方案/建议吗？

我已经阅读过Google，但似乎无法解决我的问题。

    grid = GridSearchCV(pipe,scoring='f1',param_grid=param_grid,cv=5,return_train_score=True,iid=False,n_jobs=-1
                       )
    grid.fit(X_train,np.ravel(y_train))
    return grid

from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import classification_report

pipe = make_pipeline(StandardScaler(),OneVsRestClassifier(SVC(probability=True)))

param_grid = {
    'estimator__C': [0.001,0.01,0.1,1,10,100],'estimator__kernel': ['linear','rbf','poly'],'estimator__degree': [2,3,4,5,7,10],'estimator__gamma': [0.01,0.02,0.03,0.04,0.05,1]
}

clf = grid_search_fit(pipe,param_grid)

preds = clf.predict(X_test)
print(classification_report(y_test,preds,target_names = ['empty','good','blurred']))

ValueError: Invalid parameter estimator for estimator Pipeline(memory=None,steps=[('standardscaler',StandardScaler(copy=True,with_mean=True,with_std=True)),('onevsrestclassifier',OneVsRestClassifier(estimator=SVC(C=1.0,cache_size=200,class_weight=None,coef0=0.0,decision_function_shape='ovr',degree=3,gamma='auto_deprecated',kernel='rbf',max_iter=-1,probability=True,random_state=None,shrinking=True,tol=0.001,verbose=False),n_jobs=None))],verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.
sinochun 回答：如何将GridSearchCV与SVC估计器一起用于OneVsRestClassifier？

我对您的代码做了如下修改：

  1. 删除了选项 iid=False
  2. 我稍微改变了你的流水线和 GridSearchCV 的形状

更改后的代码如下。您可以或多或少像这样构建流水线和网格搜索。

from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import classification_report

pipe = Pipeline([
    ("scale",StandardScaler()),('classify',OneVsRestClassifier(SVC(probability=True)))
])
    
param_grid = {
    'classify__estimator__C': [0.001,0.01,0.1,1,10,100],'classify__estimator__kernel': ['linear','rbf','poly'],'classify__estimator__degree': [2,3,4,5,7,10],'classify__estimator__gamma': [0.01,0.02,0.03,0.04,0.05,1]
}

grid_search = GridSearchCV(
    pipe,param_grid,cv=5,scoring='f1',verbose=1,return_train_score=True,n_jobs=-1)

grid_search = grid_search.fit(X_train,np.ravel(y_train))

preds = clf.predict(X_test)
print(classification_report(y_test,preds,target_names = ['empty','good','blurred']))
classificationgridsearchcvsvc
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