[英]How to use GridSearchCV for comparing multiple models along with pipeline and hyper-parameter tuning in python
I am using two estimators, Randomforest and SVM我正在使用两个估计器,随机森林和 SVM
random_forest_pipeline=Pipeline([
('vectorizer',CountVectorizer(stop_words='english')),
('random_forest',RandomForestClassifier())
])
svm_pipeline=Pipeline([
('vectorizer',CountVectorizer(stop_words='english')),
('svm',LinearSVC())
])
I want to first vectorize the data and then use the estimator, I was going through this online tutorial .我想首先对数据进行矢量化,然后使用估计器,我正在阅读这个在线教程。 then I use the hyper parameter as follows然后我使用超参数如下
parameters=[
{
'vectorizer__max_features':[500,1000,1500],
'random_forest__min_samples_split':[50,100,250,500]
},
{
'vectorizer__max_features':[500,1000,1500],
'svm__C':[1,3,5]
}
]
and passed to the GridSearchCV
并传递给GridSearchCV
pipelines=[random_forest_pipeline,svm_pipeline]
grid_search=GridSearchCV(pipelines,param_grid=parameters,cv=3,n_jobs=-1)
grid_search.fit(x_train,y_train)
but, when I run the code I get an error但是,当我运行代码时出现错误
TypeError: estimator should be an estimator implementing 'fit' method TypeError:估计器应该是实现“拟合”方法的估计器
Don't know why am I getting this error不知道为什么我会收到这个错误
It is quite possible to do it in a single Pipeline
/ GridSearchCV
, based on an example here .根据此处的示例,很有可能在单个Pipeline
/ GridSearchCV
中执行此操作。
You just have to explicitly mention the scoring
method for the pipeline since we are not declaring the final estimator initially.您只需明确提及管道的scoring
方法,因为我们最初并未声明最终估算器。
Example:例子:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
my_pipeline = Pipeline([
('vectorizer', CountVectorizer(stop_words='english')),
('clf', 'passthrough')
])
parameters = [
{
'vectorizer__max_features': [500, 1000],
'clf':[RandomForestClassifier()],
'clf__min_samples_split':[50, 100,]
},
{
'vectorizer__max_features': [500, 1000],
'clf':[LinearSVC()],
'clf__C':[1, 3]
}
]
grid_search = GridSearchCV(my_pipeline, param_grid=parameters, cv=3, n_jobs=-1, scoring='accuracy')
grid_search.fit(X, y)
grid_search.best_params_
> # {'clf': RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
# criterion='gini', max_depth=None, max_features='auto',
# max_leaf_nodes=None, max_samples=None,
# min_impurity_decrease=0.0, min_impurity_split=None,
# min_samples_leaf=1, min_samples_split=100,
# min_weight_fraction_leaf=0.0, n_estimators=100,
# n_jobs=None, oob_score=False, random_state=None,
# verbose=0, warm_start=False),
# 'clf__min_samples_split': 100,
# 'vectorizer__max_features': 1000}
pd.DataFrame(grid_search.cv_results_)[['param_vectorizer__max_features',
'param_clf__min_samples_split',
'param_clf__C','mean_test_score',
'rank_test_score']]
The problem is the pipelines=[random_forest_pipeline,svm_pipeline]
that is a list not having the fit
method.问题是pipelines=[random_forest_pipeline,svm_pipeline]
这是一个没有fit
方法的列表。
Even if you could make it work this way, at some point the 'random_forest__min_samples_split':[50,100,250,500]
would be passed in the svm_pipeline
and this would raise an error.即使您可以使其以这种方式工作,在某些时候'random_forest__min_samples_split':[50,100,250,500]
也会在svm_pipeline
中传递,这会引发错误。
ValueError: Invalid parameter svm for estimator Pipeline ValueError:估计器管道的参数 svm 无效
You cannot mix this way 2 pipelines because at some point you request the svm_pipeline
to be evaluated using the values of random_forest__min_samples_split
and this is INVALID.您不能以这种方式混合 2 个管道,因为在某些时候您请求使用svm_pipeline
的值评估random_forest__min_samples_split
,这是无效的。
Solution: Fit a GridSearch object for the Random forest model and another GridSearch object for the SVC model
解决方案: Fit a GridSearch object for the Random forest model and another GridSearch object for the SVC model
pipelines=[random_forest_pipeline,svm_pipeline]
grid_search_1=GridSearchCV(pipelines[0],param_grid=parameters[0],cv=3,n_jobs=-1)
grid_search_1.fit(X,y)
grid_search_2=GridSearchCV(pipelines[1],param_grid=parameters[1],cv=3,n_jobs=-1)
grid_search_2.fit(X,y)
Full code:完整代码:
random_forest_pipeline=Pipeline([
('vectorizer',CountVectorizer(stop_words='english')),
('random_forest',RandomForestClassifier())
])
svm_pipeline=Pipeline([
('vectorizer',CountVectorizer(stop_words='english')),
('svm',LinearSVC())
])
parameters=[
{
'vectorizer__max_features':[500,1000,1500],
'random_forest__min_samples_split':[50,100,250,500]
},
{
'vectorizer__max_features':[500,1000,1500],
'svm__C':[1,3,5]
}
]
pipelines=[random_forest_pipeline,svm_pipeline]
# gridsearch only for the Random Forest model
grid_search_1 =GridSearchCV(pipelines[0],param_grid=parameters[0],cv=3,n_jobs=-1)
grid_search_1.fit(X,y)
# gridsearch only for the SVC model
grid_search_2 =GridSearchCV(pipelines[1],param_grid=parameters[1],cv=3,n_jobs=-1)
grid_search_2.fit(X,y)
If you explicitly define the models into the param_grid
list then it is possible based on the documentation.如果您将模型明确定义到param_grid
列表中,则可以根据文档进行。
Link: https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html?highlight=pipeline%20gridsearch链接: https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html?highlight=pipeline%20gridsearch
Code from doc:来自文档的代码:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from sklearn.decomposition import PCA, NMF
from sklearn.feature_selection import SelectKBest, chi2
print(__doc__)
pipe = Pipeline([
# the reduce_dim stage is populated by the param_grid
('reduce_dim', 'passthrough'),
('classify', LinearSVC(dual=False, max_iter=10000))
])
N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
{
'reduce_dim': [PCA(iterated_power=7), NMF()],
'reduce_dim__n_components': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
},
{
'reduce_dim': [SelectKBest(chi2)],
'reduce_dim__k': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
},
]
reducer_labels = ['PCA', 'NMF', 'KBest(chi2)']
grid = GridSearchCV(pipe, n_jobs=1, param_grid=param_grid)
X, y = load_digits(return_X_y=True)
grid.fit(X, y)
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