add tree_gam minimal

imodels/algebraic/tree_gam_minimal.py

@@ -1,23 +1,15 @@
 from copy import deepcopy
 import numpy as np
-import pandas as pd
 from sklearn.base import BaseEstimator
-from sklearn.linear_model import ElasticNetCV, LinearRegression, RidgeCV
 from sklearn.tree import DecisionTreeRegressor
-from sklearn.utils.validation import check_is_fitted
 from sklearn.utils import check_array
 from sklearn.utils.multiclass import check_classification_targets
-from sklearn.utils.validation import check_X_y
-from sklearn.utils.validation import _check_sample_weight
-from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor
+from sklearn.utils.validation import check_X_y, check_is_fitted, _check_sample_weight
 from sklearn.model_selection import train_test_split
+from sklearn.base import RegressorMixin, ClassifierMixin
 from sklearn.metrics import accuracy_score, roc_auc_score
-from tqdm import tqdm
-
 import imodels
 
-from sklearn.base import RegressorMixin, ClassifierMixin
-
 
 class TreeGAMMinimal(BaseEstimator):
     """Tree-based GAM classifier.
@@ -31,7 +23,6 @@ def __init__(
         self,
         n_boosting_rounds=100,
         max_leaf_nodes=3,
-        reg_param=0.0,
         learning_rate: float = 0.01,
         boosting_strategy="cyclic",
         validation_frac=0.15,
@@ -44,8 +35,6 @@ def __init__(
             Number of boosting rounds for the cyclic boosting.
         max_leaf_nodes : int
             Maximum number of leaf nodes for the trees in the cyclic boosting.
-        reg_param : float
-            Regularization parameter for the cyclic boosting.
         learning_rate: float
             Learning rate for the cyclic boosting.
         boosting_strategy : str ["cyclic", "greedy"]
@@ -57,7 +46,6 @@ def __init__(
         """
         self.n_boosting_rounds = n_boosting_rounds
         self.max_leaf_nodes = max_leaf_nodes
-        self.reg_param = reg_param
         self.learning_rate = learning_rate
         self.boosting_strategy = boosting_strategy
         self.validation_frac = validation_frac
@@ -129,9 +117,6 @@ def _cyclic_boost(
                 )
                 if not succesfully_split_on_feature:
                     continue
-                if self.reg_param > 0:
-                    est = imodels.HSTreeRegressor(
-                        est, reg_param=self.reg_param)
                 self.estimators_.append(est)
                 residuals_train_new = (
                     residuals_train - self.learning_rate * est.predict(X_train)