This code raises a PicklingError:
from sklearn.datasets import load_boston
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.grid_search import RandomizedSearchCV
from sklearn.externals import joblib
from scipy.stats import randint
X, y = load_boston().data, load_boston().target
pipe = Pipeline([("rf", RandomForestRegressor())])
params = {"rf__n_estimators": randint(2,3)}
random_search = RandomizedSearchCV(pipe, params, n_iter=1).fit(X, y)
joblib.dump(random_search, "final_model.pkl", compress=3)
In params, if randint(2,3) is changed to range(2,3), no pickling error occurs.
In 0.16.2, changing all the parameters in a large grid search to ranges causes
a memory error (due to all possible combinations being saved to an array), so
this is not a workable solution.
Pickling just the best_estimator_ works (which is now what I do), but currently
there does not seem to be a way to pickle a gridsearch that has a large number
of hyper-parameters (very common with RandomizedSearchCV) in 0.16.2.
You all do amazing work. Thank you all so much for your contributions to the
project.
Jason
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