"Parallel" 使用网格搜索获得最佳模型的管道

Question

在 sklearn 中，可以定义串行管道，以获得管道所有连续部分的最佳超参数组合。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怎么办？我怎么能

的网格搜索

Principal Component Analysis OR Singular Value Decomposition AND Support Vector machines OR Random Forest

这需要某种第二级或 "meta-gridsearch"，因为模型类型将是超参数之一。这在sklearn中可能吗？

Answer 1

Pipeline 在其 steps（估算器列表）中支持 None，通过它可以关闭管道的某些部分。

您可以将 None 参数传递给管道的 named_steps 以不使用该估计器，方法是在传递给 GridSearchCV 的参数中设置该估计器。

假设您要使用 PCA and 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() 将搜索 params_grid 列表的两个元素的参数，一次使用一个元素的所有值。

如果参数同名则简化

如果您的 "OR" 中的两个估算器与本例中的参数名称相同，其中 PCA 和 TruncatedSVD 具有 n_components（或者您只想搜索在这个参数上，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,
}

此方案的推广

我们可以创建一个函数，它可以使用适当的值自动填充我们的 param_grid 以提供给 GridSearchCV：-

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

并在任意数量的转换器和估算器上使用此函数

# 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)

现在用上面使用的名称初始化管道对象pipeline_steps

# 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())])

现在，终于开始设置gridSearchCV对象和拟合数据了

grd = GridSearchCV(pipe, param_grid = param_grids_list)
grd.fit(X, y)

"Parallel" 使用网格搜索获得最佳模型的管道

"Parallel" pipeline to get best model using gridsearch

python

machine-learning

scikit-learn

grid-search

如果参数同名则简化