Question

在sklearn中，可以定义一个串行管道，以便为管道的所有连续部分获得最佳的超参数组合。串行管道可以实现如下：

from sklearn.svm import SVC
from sklearn import decomposition, datasets
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

digits = datasets.load_digits()
X_train = digits.data
y_train = digits.target

#Use Principal Component Analysis to reduce dimensionality
# and improve generalization
pca = decomposition.PCA()
# Use a linear SVC
svm = SVC()
# Combine PCA and SVC to a pipeline
pipe = Pipeline(steps=[('pca', pca), ('svm', svm)])
# Check the training time for the SVC
n_components = [20, 40, 64]
params_grid = {
'svm__C': [1, 10, 100, 1000],
'svm__kernel': ['linear', 'rbf'],
'svm__gamma': [0.001, 0.0001],
'pca__n_components': n_components,
}

但是如果我想为管道的每个步骤尝试不同的算法呢？我怎么能... gridsearch over

主成分分析或奇异值分解和支持向量机或随机森林

这将需要某种第二级或＆＃34;元网格搜索＆＃34;，因为模型的类型将是超参数之一。这可能在sklearn？

Answer 1

Pipeline在其None（估算器列表）中支持steps，可以切换管道的某些部分。

您可以将None参数传递给管道的named_steps，以便通过设置传递给GridSearchCV的参数来使用该估算工具。

假设您要使用PCA和TruncatedSVD。

pca = decomposition.PCA()
svd = decomposition.TruncatedSVD()
svm = SVC()
n_components = [20, 40, 64]

在管道中添加svd

pipe = Pipeline(steps=[('pca', pca), ('svd', svd), ('svm', svm)])

# Change params_grid -> Instead of dict, make it a list of dict**
# In the first element, pass `svd = None`, and in second `pca = None`
params_grid = [{
'svm__C': [1, 10, 100, 1000],
'svm__kernel': ['linear', 'rbf'],
'svm__gamma': [0.001, 0.0001],
'pca__n_components': n_components,
'svd':[None]
},
{
'svm__C': [1, 10, 100, 1000],
'svm__kernel': ['linear', 'rbf'],
'svm__gamma': [0.001, 0.0001],
'pca':[None],
'svd__n_components': n_components,
'svd__algorithm':['randomized']
}]

现在只需将管道对象传递给gridsearchCV

grd = GridSearchCV(pipe, param_grid = params_grid)

调用grd.fit()将在params_grid列表的两个元素上搜索参数，一次使用一个值中的所有值。

如果参数具有相同的名称，则简化

如果您的＆＃34; OR＆＃34;中有两个估算器具有与此情况相同的参数名称，其中PCA和TruncatedSVD具有n_components（或者您只想搜索此参数，这可以简化为：

#Here I have changed the name to `preprocessor`
pipe = Pipeline(steps=[('preprocessor', pca), ('svm', svm)])

#Now assign both estimators to `preprocessor` as below:
params_grid = {
'svm__C': [1, 10, 100, 1000],
'svm__kernel': ['linear', 'rbf'],
'svm__gamma': [0.001, 0.0001],
'preprocessor':[pca, svd],
'preprocessor__n_components': n_components,
}

此计划的推广

我们可以创建一个功能，可以使用适当的值自动填充要提供给param_grid的{{1}}： -

GridSearchCV

在任意数量的变压器和估算器上使用此功能

def make_param_grids(steps, param_grids):

    final_params=[]

    # Itertools.product will do a permutation such that 
    # (pca OR svd) AND (svm OR rf) will become ->
    # (pca, svm) , (pca, rf) , (svd, svm) , (svd, rf)
    for estimator_names in itertools.product(*steps.values()):
        current_grid = {}

        # Step_name and estimator_name should correspond
        # i.e preprocessor must be from pca and select.
        for step_name, estimator_name in zip(steps.keys(), estimator_names):
            for param, value in param_grids.get(estimator_name).iteritems():
                if param == 'object':
                    # Set actual estimator in pipeline
                    current_grid[step_name]=[value]
                else:
                    # Set parameters corresponding to above estimator
                    current_grid[step_name+'__'+param]=value
        #Append this dictionary to final params            
        final_params.append(current_grid)

return final_params

现在使用上面# add all the estimators you want to "OR" in single key # use OR between `pca` and `select`, # use OR between `svm` and `rf` # different keys will be evaluated as serial estimator in pipeline pipeline_steps = {'preprocessor':['pca', 'select'], 'classifier':['svm', 'rf']} # fill parameters to be searched in this dict all_param_grids = {'svm':{'object':SVC(), 'C':[0.1,0.2] }, 'rf':{'object':RandomForestClassifier(), 'n_estimators':[10,20] }, 'pca':{'object':PCA(), 'n_components':[10,20] }, 'select':{'object':SelectKBest(), 'k':[5,10] } } # Call the method on the above declared variables param_grids_list = make_param_grids(pipeline_steps, all_param_grids)

中使用的名称初始化一个管道对象

pipeline_steps

现在，最后设置gridSearchCV对象并拟合数据

# The PCA() and SVC() used here are just to initialize the pipeline,
# actual estimators will be used from our `param_grids_list`
pipe = Pipeline(steps=[('preprocessor',PCA()), ('classifier', SVC())])

＆＃34;并行＆＃34;使用gridsearch获取最佳模型的管道

1 个答案:

如果参数具有相同的名称，则简化