CODEWITHSUNDEEP
python-3.xmachine-learningscikit-learngridsearchcv
Nick Carraway
Nick Carraway

Reputation: 77

How to tune quantile_range in RobustScaler in sklearn Pipeline?

pipeline = Pipeline([
                     ('scale', RobustScaler(quantile_range=()))
                     ('classify', OneVsRestClassifier(SVC()))
                      ],
                     memory=self.memory)

Given that pipeline, how to tune the quantile_range in RobustScaler using GridSearchCV? The default quantile_range is (25.0, 75.0). Alternatives I want to try are something like (5.0, 95.0), (10.0, 90.0), ..., (25.0, 75.0). How to achieve that? I guess, the params_grid should look this:

params_grid = [{'scale__quantile_range': ??}]

But I don't know what to put into the question mark placeholder.

Upvotes: 3

Views: 418

Answers (1)

Sergey Bushmanov
Sergey Bushmanov

Reputation: 25189

The hyperparameters to try from should be an iterable. Try:

from sklearn.preprocessing import RobustScaler
from sklearn.pipeline import Pipeline
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import make_classification

pipeline = Pipeline([
                     ('scale', RobustScaler(quantile_range=())),
                     ('classify', OneVsRestClassifier(SVC()))
                      ],
                     memory=None)

params = {"scale__quantile_range":[(25.0,75.0),(10.0,90.0),(1.0,99.0)]}

grid_cf = GridSearchCV(pipeline, param_grid=params)

X,y = make_classification(1000,10,n_classes=2,random_state=42)

grid_cf.fit(X,y)

grid_cf.best_params_

{'scale__quantile_range': (1.0, 99.0)}

Upvotes: 3

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