support renormalize_features in multi-task gam

Author: csinva

imodels/algebraic/gam_multitask.py

@@ -16,6 +16,7 @@
 from collections import defaultdict
 import pandas as pd
 import json
+from sklearn.preprocessing import StandardScaler
 
 import imodels
 from interpret.glassbox import ExplainableBoostingClassifier, ExplainableBoostingRegressor
@@ -37,7 +38,8 @@ def __init__(
         multitask=True,
         interactions=0.95,
         linear_penalty='ridge',
-        onehot_prior=True,
+        onehot_prior=False,
+        renormalize_features=False,
         random_state=42,
     ):
         """
@@ -54,6 +56,7 @@ def __init__(
         self.random_state = random_state
         self.interactions = interactions
         self.onehot_prior = onehot_prior
+        self.renormalize_features = renormalize_features
 
         # override ebm_kwargs
         ebm_kwargs['random_state'] = random_state
@@ -90,9 +93,12 @@ def fit(self, X, y, sample_weight=None):
         self.term_names_list_ = [
             ebm_.term_names_ for ebm_ in self.ebms_]
         self.term_names_ = sum(self.term_names_list_, [])
-
         feats = self._extract_ebm_features(X)
 
+        if self.renormalize_features:
+            self.scaler_ = StandardScaler()
+            feats = self.scaler_.fit_transform(feats)
+
         # fit a linear model to the features
         if self.linear_penalty == 'ridge':
             self.lin_model = RidgeCV(alphas=np.logspace(-2, 3, 7))
@@ -126,13 +132,16 @@ def _extract_ebm_features(self, X):
             feats[:, offset: offset + n_features_ebm_num] = \
                 self.ebms_[ebm_num].eval_terms(X)
             offset += n_features_ebm_num
+
         return feats
 
     def predict(self, X):
         check_is_fitted(self)
         X = check_array(X, accept_sparse=False)
         if hasattr(self, 'ebms_'):
             feats = self._extract_ebm_features(X)
+            if hasattr(self, 'scaler_'):
+                feats = self.scaler_.transform(feats)
             return self.lin_model.predict(feats)
         else:
             return self.ebm_.predict(X)
@@ -183,7 +192,7 @@ def test_multitask_extraction():
 
 
 if __name__ == "__main__":
-    test_multitask_extraction()
+    # test_multitask_extraction()
     # X, y, feature_names = imodels.get_clean_dataset("heart")
     X, y, feature_names = imodels.get_clean_dataset("bike_sharing")
     # X, y, feature_names = imodels.get_clean_dataset("diabetes")
@@ -199,9 +208,10 @@ def test_multitask_extraction():
     for gam in tqdm([
             # AdaBoostRegressor(estimator=MultiTaskGAMRegressor(
         # multitask=True), n_estimators=2),
-        MultiTaskGAMRegressor(multitask=False, onehot_prior=True),
-        MultiTaskGAMRegressor(multitask=False, onehot_prior=False),
-        MultiTaskGAMRegressor(multitask=True),
+        # MultiTaskGAMRegressor(multitask=True, onehot_prior=True),
+        # MultiTaskGAMRegressor(multitask=True, onehot_prior=False),
+        MultiTaskGAMRegressor(multitask=True, renormalize_features=True),
+        MultiTaskGAMRegressor(multitask=True, renormalize_features=False),
         # ExplainableBoostingRegressor(n_jobs=1, interactions=0)
     ]):
         np.random.seed(42)