def fit(self, df, y, param_grid=None):
from sklearn.model_selection import GridSearchCV
X = df.drop(y, axis=1).values
y = df[y].values
meta_X = self.get_meta(X)
if param_grid is not None:
model = self.stacked_model_class()
gridsearch = GridSearchCV(model, param_grid)
gridsearch.fit(meta_X, y)
self.stacked_model = self.stacked_model_class(**gridsearch.best_params_)
else:
self.stacked_model = self.stacked_model_class()
self.stacked_model.fit(meta_X, y)
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