Hi, all,
I want to include a feature selector in a pipeline that I am feeding to
GridSearch and I am wondering if I am doing the right thing here. Technically
it works, but I want to make sure that I am understanding the implementation
correctly.
E.g., when I am using SelectKBest like so:
################################################
# Example 1
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import SelectKBest
from sklearn import datasets
from sklearn.cross_validation import StratifiedKFold
iris = datasets.load_iris()
X = iris.data
y = iris.target
pipeline = Pipeline([('scl', StandardScaler()),
('sel', SelectKBest()),
('clf', SVC(kernel='linear', random_state=1))])
param_grid = [{'sel__k': [1, 2, 3, 4],
'clf__C': [0.1, 1, 10, 100],
'clf__kernel': ['linear']}]
grid_search = GridSearchCV(pipeline,
param_grid=param_grid,
verbose=1,
cv=StratifiedKFold(y, n_folds=10),
scoring='accuracy',
n_jobs=1)
grid_search.fit(X, y)
print(grid_search.best_estimator_)
print(grid_search.best_score_)
# end example
################################################
I am wondering how SelectKBest determines what the "best" set of features is
since it happens before they are fed to the classifier. Does it have it's own
"scoring" function or does it use the classifier from the last fit?
Alternative, I would use recursive feature selection like so:
################################################
# Example 2
from sklearn.feature_selection import RFE
svm = SVC(kernel='linear', random_state=1)
param_grid = [{'n_features_to_select': [1, 2, 3, 4]}]
rfe = RFE(estimator=svm, step=1)
grid_search = GridSearchCV(rfe,
param_grid=param_grid,
verbose=1,
cv=StratifiedKFold(y, n_folds=10),
scoring='accuracy',
n_jobs=1)
grid_search.fit(X, y)
print(grid_search.best_estimator_)
print(grid_search.best_score_)
# end example
################################################
>From my understanding the approach would be different in terms of that the
>Example 2 is a greedy instead of a exhaustive search (like in SelectKBest), is
>this correct? Also, in Example 2, I am fitting it to an "untuned" SVM. In a
>linear case it might not make a huge difference, but say I am using a RBF
>kernel, is there a way to combine the feature selection with hyperparameter
>tuning via grid search? Or is Example 1 already the "right" approach to do it?
Thanks,
Sebastian
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