如何在不同参数模型中使用网格搜索和交叉验证

Question

我写了一个分类算法，使用了6个不同的模型。我想通过对每个模型使用微调参数来改进模型。我遇到的问题与我的"for循环"有关。事实上，我在三个不同的字典中循环，但由于字典没有排序，我在网格搜索中使用的模型和参数之间的对应关系没有保持；

我似乎没能找到另一个解决方案：

这里是我的代码和结果，正如你所看到的，model_name与param_name不同，所以我得到了多个错误，例如(ValueError：估计器LinearSVC的参数alpha无效(C=1.0，class_weight=None，dual=True，fit_intercept=True，)

低于代码

model1 = LinearSVC()
model2 = MultinomialNB()
model3 = LogisticRegression()
model4 = RandomForestClassifier()
model5 = KNeighborsClassifier()
model6 = MLPClassifier(max_iter=300, random_state=1)
models = {'Model_SVC': model1, 'Model_G_NB': model2, 'Model_LR': model3, 'Model_RF': model4, 'Model_KN': model5, 'Model_MLP': model6}
# list of parameters 

parameter_RF = {'min_samples_split': [3, 5, 10], 
'criterion': ['gini', 'entropy'],
'n_estimators' : [100, 300],
'max_features': ['auto', 'sqrt','log2'],
'bootstrap': ['True', 'False'],
'max_depth': [3, 5, 15, 25]
}
parameter_LinearSvc = {'C': [0.001, 0.01, 0.1, 1, 10, 100]
}
parameter_LR = {'C': [0.001, 0.01, 0.1, 1, 10, 100],
'penalty' : ['l1', 'l2'],
'solver' : ['liblinear', 'warn'],
'dual' : ['True','False'],
'max_iter' :[100, 110, 120, 130, 140]
}
parameter_NB = {'alpha':  [1, 0.1, 0.01, 0.001, 0.0001, 0.00001]
'loss': ['hinge', 'hinge_squarred'],
'penalty' : ['l1', 'l2']
}
k_range = list(range(1, 31))
weight_options = ['uniform', 'distance']
parameter_KNN = dict(n_neighbors=k_range, weights=weight_options)

parameter_MLP = { 'hidden_layer_sizes': [(50,50,50), (50,100,50), (100,)],
'activation': ['tanh', 'relu'],
'solver': ['sgd', 'adam'],
'alpha': [0.0001, 0.05],
'learning_rate': ['constant','adaptive'],
'max_iter' : [100, 200, 300]
}
parameters_dict = {'Model_SVC': parameter_LinearSvc, 'Model_G_NB': parameter_NB, 'Model_LR': parameter_LR, 'Model_RF': parameter_LR, 'Model_KN': parameter_KNN, 'Model_MLP': parameter_MLP}
cv_splitter = KFold(n_splits=10, shuffle=False, random_state=None)
for feature_name, feature in features.items():
for model_name, model in models.items():
for param_name, parameter in parameters_dict.items():
clf = GridSearchCV(estimator=model, param_grid=parameter, cv=cv_splitter, verbose = 1, n_jobs = -1, return_train_score=True)
best_model = clf.fit(feature, ylabels)

输出：正如你所看到的，有时它可以工作，但其他时候参数和模型不一样，这会导致错误

[5 rows x 7 columns]
Feature: vecteur_CV
Model: Model_SVC
Param: Model_SVC
Fitting 10 folds for each of 6 candidates, totalling 60 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.
[Parallel(n_jobs=-1)]: Done  58 out of  60 | elapsed:    2.8s remaining:    0.1s
/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
"the number of iterations.", ConvergenceWarning)
[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:    2.8s finished
Feature: vecteur_CV
Model: Model_SVC
Param: Model_G_NB
Fitting 10 folds for each of 24 candidates, totalling 240 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.
joblib.externals.loky.process_executor._RemoteTraceback: 
"""
Traceback (most recent call last):
File "/ho/anaconda3/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py", line 418, in _process_worker
r = call_item()
File "/ho/anaconda3/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py", line 272, in __call__
return self.fn(*self.args, **self.kwargs)
File "/ho/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py", line 567, in __call__
return self.func(*args, **kwargs)
File "/ho/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 225, in __call__
for func, args, kwargs in self.items]
File "/ho/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 225, in <listcomp>
for func, args, kwargs in self.items]
File "/home/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py", line 503, in _fit_and_score
estimator.set_params(**parameters)
File "/home/anaconda3/lib/python3.7/site-packages/sklearn/base.py", line 224, in set_params
(key, self))
ValueError: Invalid parameter alpha for estimator LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,

功能看起来像这个

`X_data, X_data_0, X_data_1, X_data_2 = features_fusion(verbatim, first_arg)

features = {'vecteur_CV': X_data, 'vecteur_NEG': X_data_0, 'Vecteur_NEG_lexique': X_data_1, 'Vecteur_NEG_CV': X_data_2}

Answer 1

我使用了Iris数据集的两个略有不同的版本，下面的代码运行(尽管在训练过程中有很多警告(：

from sklearn.model_selection import GridSearchCV,KFold
from sklearn.neural_network import MLPClassifier
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
data = load_iris()
model1 = LinearSVC()
model2 = MultinomialNB()
model3 = LogisticRegression()
model4 = RandomForestClassifier()
model5 = KNeighborsClassifier()
model6 = MLPClassifier(max_iter=300, random_state=1)
models = {'Model_SVC': model1, 'Model_G_NB': model2, 'Model_LR': model3, 
'Model_RF': model4, 'Model_KN': model5, 'Model_MLP': model6}
# list of parameters 

parameter_RF = {'max_depth': [2,3, 5, 15, 25],
'min_samples_split': [3, 5, 10],
'criterion': ['gini', 'entropy'],
'n_estimators' : [100, 300],
'max_features': ['auto', 'sqrt','log2'],
'bootstrap': ['True', 'False'],
}
parameter_LinearSvc = {'C': [0.001, 0.01, 0.1, 1, 10, 100]}
parameter_LR = {'C': [0.001, 0.01, 0.1, 1, 10, 100],
'penalty' : ['l1', 'l2'],
'solver' : ['liblinear', 'warn'],
#'dual' : ['True','False'],
'max_iter' :[100, 110, 120, 130, 140]
}
parameter_NB = {'alpha':  [1, 0.1, 0.01, 0.001, 0.0001, 0.00001],
#'loss': ['hinge', 'hinge_squarred'],
#'penalty' : ['l1', 'l2']
}
k_range = list(range(1, 31))
weight_options = ['uniform', 'distance']
parameter_KNN = dict(n_neighbors=k_range, weights=weight_options)

parameter_MLP = {'hidden_layer_sizes':[(50,50,50),(50,100,50),(100,)],
'activation': ['tanh', 'relu'],
'solver': ['sgd', 'adam'],
'alpha': [0.0001, 0.05],
'learning_rate': ['constant','adaptive'],
'max_iter' : [100, 200, 300]
}
parameters_dict = {'Model_SVC': parameter_LinearSvc,
'Model_G_NB': parameter_NB,
'Model_LR': parameter_LR, 'Model_RF': parameter_RF,
'Model_KN': parameter_KNN, 'Model_MLP': parameter_MLP}
cv_splitter = KFold(n_splits=10, shuffle=False, random_state=None)
#features = {'vecteur_CV': X_data, 'vecteur_NEG': X_data_0, 
#            'Vecteur_NEG_lexique': X_data_1, 'Vecteur_NEG_CV': X_data_2}
features = {'iris': data['data'],'iris_sub':data['data'][:,1:]}
ylabels = data['target']
for feature_name, feature in features.items():
#print(feature_name, feature)
for model_name in models:
print('Training model: ', model_name)
clf = GridSearchCV(estimator=models[model_name],
param_grid=parameters_dict[model_name], 
cv=cv_splitter, verbose = 1, n_jobs = -1,
return_train_score=True)
best_model = clf.fit(feature, ylabels)
#for feature_name, feature in features.items():
#    for model_name, model in models.items():
#        for param_name, parameter in parameters_dict.items():
#            print(model_name,model,param_name,parameter)
#            clf = GridSearchCV(estimator=model, param_grid=parameter, 
#                               cv=cv_splitter, verbose = 1, n_jobs = -1,
#                               return_train_score=True)
#            best_model = clf.fit(feature, ylabels)

我不得不评论一些模型参数，因为它们给出了错误。在你的代码段中也有一个拼写错误，'Model_RF': parameter_LR应该是'Model_RF': parameter_RF。我不确定这是否是你犯错的原因。我还删除了内部parameters_dict循环，因为我可以使用与models相同的密钥访问所有元素。