问题描述
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超参数的最佳组合。 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？ 我如何例如进行网格搜索
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“元网格搜索”，因为模型的类型将是超参数之一。 在sklearn中可能吗？
1楼
管道在其steps （估计器列表）中支持“ None ，通过该steps可以关闭管道的某些部分。
您可以通过在传递给GridSearchCV的参数中进行设置，将None参数传递给管道的named_steps以不使用该估算器。
假设您要使用和 。
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()会一次使用一个中的所有值在params_grid列表的两个元素上搜索参数。
如果参数名称相同，则简化
如果“ OR”中的两个估计量都具有与本例相同的参数名称，其中PCA和TruncatedSVD具有n_components （或者您只想搜索该参数，则可以简化为：
#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,
}
该方案的推广
我们可以创建一个函数，该函数可以使用适当的值自动填充要提供给GridSearchCV param_grid ：
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
并在任意数量的变压器和估计器上使用此功能
# 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)
现在使用上面pipeline_steps使用的名称初始化管道对象
# 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，最后列出gridSearchCV对象并拟合数据
grd = GridSearchCV(pipe, param_grid = param_grids_list)
grd.fit(X, y)