grid search over multiple classifiers

Although the solution from dubek is more straight forward, it does not help with interactions between parameters of pipeline elements that come before the classfier. Therefore, I have written a helper class to deal with it, and can be included in the default Pipeline setting of scikit. A minimal example:

from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler, MaxAbsScaler
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
from pipelinehelper import PipelineHelper

iris = datasets.load_iris()
X_iris = iris.data
y_iris = iris.target
pipe = Pipeline([
    ('scaler', PipelineHelper([
        ('std', StandardScaler()),
        ('max', MaxAbsScaler()),
    ])),
    ('classifier', PipelineHelper([
        ('svm', LinearSVC()),
        ('rf', RandomForestClassifier()),
    ])),
])

params = {
    'scaler__selected_model': pipe.named_steps['scaler'].generate({
        'std__with_mean': [True, False],
        'std__with_std': [True, False],
        'max__copy': [True],  # just for displaying
    }),
    'classifier__selected_model': pipe.named_steps['classifier'].generate({
        'svm__C': [0.1, 1.0],
        'rf__n_estimators': [100, 20],
    })
}
grid = GridSearchCV(pipe, params, scoring='accuracy', verbose=1)
grid.fit(X_iris, y_iris)
print(grid.best_params_)
print(grid.best_score_)

It can also be used for other elements of the pipeline, not just the classifier.
Code is on github if anyone wants to check it out.

Edit: I have published this on PyPI if anyone is interested, just install ti using pip install pipelinehelper.