如何从合适的 GridSearchCV
中提取最佳管道,以便将其传递给 cross_val_predict
?
直接传递适合的GridSearchCV
对象导致cross_val_predict
再次运行整个网格搜索,我只想让最好的pipeline受制于cross_val_predict
评估。
我的独立代码如下:
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_
手动重建管道。但是我的管道通常有更多的步骤,例如 SVD 或 PCA,有时我会添加或删除步骤并重新运行网格搜索来探索数据。在下面手动重建管道时,必须始终重复此步骤,这很容易出错。
有没有办法直接从拟合 GridSearchCV
中提取最佳管道,以便我可以将其传递给 cross_val_predict
?
最佳答案
y_predicted = cross_val_predict(gs_clf.best_estimator_, X, y)
作品与返回:
Fitting 3 folds for each of 12 candidates, totalling 36 fits
[Parallel(n_jobs=4)]: Done 36 out of 36 | elapsed: 43.6s finished
precision recall f1-score support
0 0.920 0.911 0.916 584
1 0.894 0.943 0.918 597
2 0.929 0.887 0.908 594
avg / total 0.914 0.914 0.914 1775
[编辑] 当我再次尝试仅传递 pipeline
(原始管道)的代码时,它返回相同的输出(传递 best_pipeline
也是如此)。因此,您可以只使用管道本身,但我并不是 100% 同意这一点。
关于python - 从 GridSearchCV 中为 cross_val_predict 提取最佳管道,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/45151043/