RandomizedSearchCV does not work in a Pipeline when evaluating alternative classifiers - machine-learning

Question

In Scikit Learn, RandomizedSearchCV can work for evaluating different parameters in a pipeline, but only in some cases where the classifiers share similar/same parameters. When you pass blocks of parameters for different classifiers it fails when GridSearchCV succeeds.
You will notice in the code below, the problem setup is the same for gridsearch and random search but only random search fails.
numpy.random.seed(52)
MY_RAND_SEED=numpy.random.seed(52)
pipe = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler' , StandardScaler()),
('classify', LogisticRegression())
])
X, y = make_classification(n_samples= 500, n_features=58, n_redundant=13, n_informative=7, n_clusters_per_class=2)
param_grid_linear = [
{'classify' : [LogisticRegression(),],
'classify__penalty' : ['l1', 'l2'],
'classify__C' : numpy.logspace(-4, 4, 50),
'classify__solver' : ['liblinear']},
{'classify' : [LogisticRegression(),],
'classify__penalty' : ['l2'],
'classify__C' : numpy.logspace(-4, 4, 50),
'classify__solver' : ['lbfgs']},
{'classify': [SVC(),],
'classify__kernel': ['linear',],
'classify__C': numpy.linspace(0.001,200, 10),},
]
innercv=StratifiedKFold(n_splits=5, shuffle=True, random_state=numpy.random.seed(52))
gridA = GridSearchCV(pipe, param_grid_linear, scoring='accuracy', iid=False, verbose=1, n_jobs=12)
gridA.fit(X, y)
print("finished grid search")
gridB = RandomizedSearchCV(pipe, param_grid_linear, scoring='accuracy', n_iter=5, iid=False, verbose=1, n_jobs=12)
gridB.fit(X, y)

Answer 1

Apparently, usually one passes only lists of dictionaries as parameters {dict, dict, dict}, but to do what I suggested above requires passing a list of lists of dictionaries, which is currently only accepted by GridSearchCV.
RandomizedSearchCV does not accept this now but will in a future release of sklearn. Here is a response I received on GitHub:
From: Thomas J Fan
Date: Wednesday, August 14, 2019 at 7:13 PM
This was addressed in #14549
This feature is not released yet, but you can try it out by installing the nightly build of scikit-learn:
pip install --pre -f https://sklearn-nightly.scdn8.secure.raxcdn.com scikit-learn