Add KDBNew model and fit_feature hyperparameter

benchmark/Arguments.py

@@ -112,6 +112,17 @@ class Arguments(argparse.ArgumentParser):
                     "help": "Generate Excel File",
                 },
             ],
+            "fit_features": [
+                ("--fit_features",),
+                {
+                    "action": EnvDefault,
+                    "envvar": "fit_features",
+                    "required": True,
+                    "help": "Include features in fit call",
+                    "const": "1",
+                    "nargs": "?",
+                },
+            ],
             "grid_paramfile": [
                 ("-g", "--grid_paramfile"),
                 {

benchmark/Experiments.py

@@ -113,8 +113,10 @@ class Experiment:
         title,
         progress_bar=True,
         ignore_nan=True,
+        fit_features=None,
         folds=5,
     ):
+        env_data = EnvData.load()
         today = datetime.now()
         self.time = today.strftime("%H:%M:%S")
         self.date = today.strftime("%Y-%m-%d")
@@ -134,6 +136,11 @@ class Experiment:
         self.title = title
         self.ignore_nan = ignore_nan
         self.stratified = stratified == "1"
+        self.fit_features = (
+            env_data["fit_features"] == "1"
+            if fit_features is None
+            else fit_features == "1"
+        )
         self.stratified_class = StratifiedKFold if self.stratified else KFold
         self.datasets = datasets
         dictionary = json.loads(hyperparams_dict)
@@ -187,11 +194,14 @@ class Experiment:
         self.depths = []
 
     def _build_fit_params(self, name):
+        if not self.fit_features:
+            return None
+        res = dict(features=self.datasets.get_features())
         states = self.datasets.get_states(name)
         if states is None:
-            return None
-        features = self.datasets.get_features()
-        return {"state_names": states, "features": features}
+            return res
+        res["state_names"] = states
+        return res
 
     def _n_fold_crossval(self, name, X, y, hyperparameters):
         if self.scores != []:

benchmark/Models.py

@@ -8,7 +8,7 @@ from sklearn.ensemble import (
 )
 from sklearn.svm import SVC
 from stree import Stree
-from bayesclass.clfs import TAN, KDB, AODE
+from bayesclass.clfs import TAN, KDB, AODE, KDBNew
 from wodt import Wodt
 from odte import Odte
 from xgboost import XGBClassifier
@@ -41,6 +41,7 @@ class Models:
             "STree": Stree(random_state=random_state),
             "TAN": TAN(random_state=random_state),
             "KDB": KDB(k=2),
+            "KDBNew": KDBNew(k=2),
             "AODE": AODE(random_state=random_state),
             "Cart": DecisionTreeClassifier(random_state=random_state),
             "ExtraTree": ExtraTreeClassifier(random_state=random_state),

benchmark/scripts/be_main.py

@@ -14,6 +14,7 @@ def main(args_test=None):
     arguments.xset("stratified").xset("score").xset("model", mandatory=True)
     arguments.xset("n_folds").xset("platform").xset("quiet").xset("title")
     arguments.xset("report").xset("ignore_nan").xset("discretize")
+    arguments.xset("fit_features")
     arguments.add_exclusive(
         ["grid_paramfile", "best_paramfile", "hyperparameters"]
     )
@@ -40,6 +41,7 @@ def main(args_test=None):
             ignore_nan=args.ignore_nan,
             title=args.title,
             folds=args.n_folds,
+            fit_features=args.fit_features,
         )
         job.do_experiment()
     except ValueError as e: