从GridSearchCV中提取最佳管道以进行cross\u val\u预测

如何从拟合中提取最佳管道 GridSearchCV cross_val_predict ?

直接通过配合 对象原因 cross\u val\u预测 cross\u val\u预测 评价

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import StratifiedKFold
from sklearn import metrics

# fetch data data
newsgroups = fetch_20newsgroups(remove=('headers', 'footers', 'quotes'), categories=['comp.graphics', 'rec.sport.baseball', 'sci.med'])
X = newsgroups.data
y = newsgroups.target

# setup and run GridSearchCV
wordvect = TfidfVectorizer(analyzer='word', lowercase=True)
classifier = OneVsRestClassifier(SVC(kernel='linear', class_weight='balanced'))
pipeline = Pipeline([('vect', wordvect), ('classifier', classifier)])
scoring = 'f1_weighted'
parameters = {
            'vect__min_df': [1, 2],
            'vect__max_df': [0.8, 0.9],
            'classifier__estimator__C': [0.1, 1, 10]
            }
gs_clf = GridSearchCV(pipeline, parameters, n_jobs=8, scoring=scoring, verbose=1)
gs_clf = gs_clf.fit(X, y)

### outputs: Fitting 3 folds for each of 12 candidates, totalling 36 fits

# manually extract the best models from the grid search to re-build the pipeline
best_clf = gs_clf.best_estimator_.named_steps['classifier']
best_vectorizer = gs_clf.best_estimator_.named_steps['vect']
best_pipeline = Pipeline([('best_vectorizer', best_vectorizer), ('classifier', best_clf)])

# passing gs_clf here would run the grind search again inside cross_val_predict
y_predicted = cross_val_predict(pipeline, X, y)
print(metrics.classification_report(y, y_predicted, digits=3))

我目前正在做的是从 best_estimator_ 但是我的管道通常有更多的步骤,如奇异值分解或主成分分析,有时我会添加或删除步骤,并重新运行网格搜索以探索数据。然后,当手动重新构建管道时,必须在下面重复此步骤,这很容易出错。

GridSearchCV cross\u val\u预测