Removed priors

mghasemi · Feb 21, 2019 · aacbd22 · aacbd22
1 parent d4ff507
commit aacbd22
Showing 1 changed file with 13 additions and 13 deletions.
diff --git a/SKSurrogate/aml.py b/SKSurrogate/aml.py
@@ -170,33 +170,33 @@ def Generate(self, l):
     },
     'sklearn.ensemble.GradientBoostingClassifier': {
        "loss": Categorical(['deviance', 'exponential']),
-       "learning_rate": Real(1.e-6, 1. - 1.e-6, prior='uniform'),
+       "learning_rate": Real(1.e-6, 1. - 1.e-6),
        "n_estimators": Integer(10, 500),
-       "subsample": Real(1.e-6, 1., prior='uniform'),
+       "subsample": Real(1.e-6, 1.),
        "criterion": Categorical(['friedman_mse', 'mse', 'mae']),
        # "min_samples_split": Integer(2, 20),
        # "min_samples_leaf": Integer(1, 20),
-       # "min_weight_fraction_leaf": Real(0., .5, prior='uniform'),
+       # "min_weight_fraction_leaf": Real(0., .5),
        # "max_depth": Integer(2, 20),
-       # "min_impurity_decrease": Real(0., 10., prior='uniform'),
-       # "min_impurity_split": Real(0., 10., prior='uniform'),
+       # "min_impurity_decrease": Real(0., 10.),
+       # "min_impurity_split": Real(0., 10.),
        # "max_features": Categorical(['auto', 'sqrt', 'log2', None]),
-       "tol": Real(1.e-6, .1, prior='uniform')
+       "tol": Real(1.e-6, .1)
     },
     "lightgbm.LGBMClassifier": {
         "boosting_type": Categorical(['gbdt', 'dart', 'goss', 'rf']),
         "num_leaves": Integer(2, 100),
-        "learning_rate": Real(1.e-7, 1. - 1.e-6),  # prior='uniform'),
+        "learning_rate": Real(1.e-7, 1. - 1.e-6),
         "n_estimators": Integer(5, 250),
-        "min_split_gain": Real(0., 1.),  # prior='uniform'),
-        # "min_child_weight": Real(1.e-6, 1.),# prior='uniform'),
+        "min_split_gain": Real(0., 1.),
+        # "min_child_weight": Real(1.e-6, 1.),
         # "min_child_samples": Integer(5, 200),
-        "subsample": Real(1.e-6, 1.),  # prior='uniform'),
+        "subsample": Real(1.e-6, 1.),
         # "subsample_freq": Integer(0, 10),
-        # "colsample_bytree": Real(1.e-5, 1.),# prior='uniform'),
+        # "colsample_bytree": Real(1.e-5, 1.),
         # "class_weight": HDReal((1.e-5, 1.e-5), (20., 20.)),
-        # "reg_alpha": Real(0., 10.),# prior='uniform'),
-        # "reg_lambda": Real(0., 10.),# prior='uniform'),
+        # "reg_alpha": Real(0., 10.),
+        # "reg_lambda": Real(0., 10.),
         "importance_type": Categorical(['split', 'gain'])
     },
     "xgboost.XGBClassifier": {