Refator kdb with new BayesNetwork

bayesclass/clfs.py

@@ -16,6 +16,7 @@ from pgmpy.base import DAG
 import matplotlib.pyplot as plt
 from fimdlp.mdlp import FImdlp
 from .cppSelectFeatures import CSelectKBestWeighted
+from .BayesNet import BayesNetwork
 from ._version import __version__
 
 
@@ -166,17 +167,27 @@ class BayesBase(BaseEstimator, ClassifierMixin):
         kwargs : dict
             fit parameters
         """
-        self.model_ = BayesianNetwork(
-            self.dag_.edges(), show_progress=self.show_progress
-        )
-        states = dict(state_names=kwargs.pop("state_names", []))
-        self.model_.fit(
-            self.dataset_,
-            estimator=BayesianEstimator,
-            prior_type="K2",
-            weighted=self.weighted_,
-            **states,
-        )
+        # self.model_ = BayesianNetwork(
+        #     self.dag_.edges(), show_progress=self.show_progress
+        # )
+        # states = dict(state_names=kwargs.pop("state_names", []))
+        # self.model_.fit(
+        #     self.dataset_,
+        #     estimator=BayesianEstimator,
+        #     prior_type="K2",
+        #     weighted=self.weighted_,
+        #     **states,
+        # )
+        self.model_ = BayesNetwork()
+        features = kwargs["features"]
+        for i, feature in enumerate(features):
+            maxf = max(self.X_[:, i] + 1)
+            self.model_.addNode(feature, maxf)
+        class_name = kwargs["class_name"]
+        self.model_.addNode(class_name, max(self.y_) + 1)
+        for source, destination in self.dag_.edges():
+            self.model_.addEdge(source, destination)
+        self.model_.fit(self.X_, self.y_, features, class_name)
 
     def predict(self, X):
         """A reference implementation of a prediction for a classifier.
@@ -228,10 +239,11 @@ class BayesBase(BaseEstimator, ClassifierMixin):
         check_is_fitted(self, ["X_", "y_", "fitted_"])
         # Input validation
         X = check_array(X)
-        dataset = pd.DataFrame(
-            X, columns=self.feature_names_in_, dtype=np.int32
-        )
-        return self.model_.predict(dataset).values.ravel()
+        # dataset = pd.DataFrame(
+        #     X, columns=self.feature_names_in_, dtype=np.int32
+        # )
+        # return self.model_.predict(dataset).values.ravel()
+        return self.model_.predict(X)
 
     def plot(self, title="", node_size=800):
         warnings.simplefilter("ignore", UserWarning)
@@ -398,7 +410,7 @@ class KDB(BayesBase):
         # 3. Let the used variable list, S, be empty.
         S_nodes = []
         # 4. Let the DAG being constructed, BN, begin with a single class node
-        dag = BayesianNetwork(show_progress=self.show_progress)
+        dag = BayesianNetwork()
         dag.add_node(self.class_name_)  # , state_names=self.classes_)
         # 5. Repeat until S includes all domain features
         # 5.1 Select feature Xmax which is not in S and has the largest value