skip grid search cv in build leaf if param_dict is empty; version 0.2.4

Author: ZebinYang

examples/demo.ipynb

@@ -95,7 +95,7 @@ def build_leaf(self, sample_indice):
                 best_estimator = LogisticRegressionCV(Cs=self.reg_lambda, penalty="l1", solver="liblinear", scoring="roc_auc",
                                       cv=5, random_state=self.random_state)
             else:
-                best_estimator = LogisticRegression(alpha=self.reg_lambda[0], precompute=False, random_state=self.random_state)
+                best_estimator = LogisticRegression(C=self.reg_lambda[0], random_state=self.random_state)
 
             mx = self.x[sample_indice].mean(0)
             sx = self.x[sample_indice].std(0) + self.EPSILON

simtree/glmtree.py

@@ -485,7 +485,7 @@ def build_leaf(self, sample_indice):
 
         param_size = len(self.reg_lambda)
         if param_size == 1:
-            best_estimator = SimRegressor(reg_lambda=[self.reg_lambda[0]], reg_gamma=self.reg_gamma, degree=self.degree,
+            best_estimator = SimClassifier(reg_lambda=[self.reg_lambda[0]], reg_gamma=self.reg_gamma, degree=self.degree,
                           knot_num=self.knot_num, random_state=self.random_state)
             best_estimator.fit(self.x[sample_indice], self.y[sample_indice].ravel())
             predict_func = lambda x: best_estimator.decision_function(x)