Update state_names hyperparameter to fit tests

Add computed nodes to classifiers

bayesclass/_version.py

@@ -1 +1 @@
-__version__ = "0.1.0"
+__version__ = "0.1.1"

bayesclass/clfs.py

@@ -34,7 +34,8 @@ class BayesBase(BaseEstimator, ClassifierMixin):
 
     def nodes_leaves(self):
         """To keep compatiblity with the benchmark platform"""
-        return 0, 0
+        nodes = len(self.dag_) if hasattr(self, "dag_") else 0
+        return nodes, 0
 
     def _check_params_fit(self, X, y, expected_args, kwargs):
         """Check the common parameters passed to fit"""
@@ -57,6 +58,7 @@ class BayesBase(BaseEstimator, ClassifierMixin):
             raise ValueError(
                 "Number of features does not match the number of columns in X"
             )
+        self.n_features_in_ = X.shape[1]
         return X, y
 
     def fit(self, X, y, **kwargs):
@@ -115,11 +117,12 @@ class BayesBase(BaseEstimator, ClassifierMixin):
         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",
-            state_names=kwargs["state_names"],
+            **states,
         )
 
     def predict(self, X):
@@ -267,7 +270,7 @@ class KDB(BayesBase):
         5.1. Select feature Xmax which is not in S and has the largest value I(Xmax;C).
         5.2. Add a node to BN representing Xmax.
         5.3. Add an arc from C to Xmax in BN.
-        5.4. Add m =min(lSl,/c) arcs from m distinct features Xj in S with the highest value for I(Xmax;X,jC).
+        5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with the highest value for I(Xmax;X,jC).
         5.5. Add Xmax to S.
         Compute the conditional probabilility infered by the structure of BN by using counts from DB, and output BN.
         """
@@ -331,8 +334,14 @@ class AODE(BayesBase, BaseEnsemble):
         expected_args = ["class_name", "features", "state_names"]
         return self._check_params_fit(X, y, expected_args, kwargs)
 
-    def _build(self):
+    def nodes_leaves(self):
+        """To keep compatiblity with the benchmark platform"""
+        nodes = 0
+        if hasattr(self, "fitted_"):
+            nodes = sum([len(x) for x in self.models_])
+        return nodes, 0
 
+    def _build(self):
         self.dag_ = None
 
     def _train(self, kwargs):
@@ -349,11 +358,12 @@ class AODE(BayesBase, BaseEnsemble):
             model = BayesianNetwork(
                 feature_edges, show_progress=self.show_progress
             )
+            states = dict(state_names=kwargs.pop("state_names", []))
             model.fit(
                 self.dataset_,
                 estimator=BayesianEstimator,
                 prior_type="K2",
-                state_names=kwargs["state_names"],
+                **states,
             )
             self.models_.append(model)
 

bayesclass/tests/test_AODE.py

@@ -55,8 +55,10 @@ def test_AODE_version(clf):
     assert __version__ == clf.version()
 
 
-def test_AODE_nodes_leaves(clf):
+def test_AODE_nodes_leaves(clf, data):
     assert clf.nodes_leaves() == (0, 0)
+    clf.fit(*data)
+    assert clf.nodes_leaves() == (20, 0)
 
 
 def test_AODE_classifier(data, clf):

bayesclass/tests/test_KDB.py

@@ -46,8 +46,10 @@ def test_KDB_version(clf):
     assert __version__ == clf.version()
 
 
-def test_KDB_nodes_leaves(clf):
+def test_KDB_nodes_leaves(clf, data):
     assert clf.nodes_leaves() == (0, 0)
+    clf.fit(*data)
+    assert clf.nodes_leaves() == (5, 0)
 
 
 def test_KDB_classifier(data, clf):

bayesclass/tests/test_TAN.py

@@ -45,8 +45,11 @@ def test_TAN_version(clf):
     assert __version__ == clf.version()
 
 
-def test_TAN_nodes_leaves(clf):
+def test_TAN_nodes_leaves(clf, data):
     assert clf.nodes_leaves() == (0, 0)
+    clf = TAN(random_state=17)
+    clf.fit(*data, head="random")
+    assert clf.nodes_leaves() == (5, 0)
 
 
 def test_TAN_random_head(data):

bayesclass/tests/test_common.py

@@ -5,10 +5,12 @@ from sklearn.utils.estimator_checks import check_estimator
 from bayesclass.clfs import TAN, KDB, AODE
 
 
-@pytest.mark.parametrize("estimator", [TAN(), KDB(k=2), AODE()])
-# @pytest.mark.parametrize("estimator", [AODE()])
-def test_all_estimators(estimator):
+@pytest.mark.parametrize("estimators", [TAN(), KDB(k=2), AODE()])
+# @pytest.mark.parametrize("estimators", [TAN()])
+
+
+def test_all_estimators(estimators):
     i = 0
-    for estimator, test in check_estimator(estimator, generate_only=True):
+    for estimator, test in check_estimator(estimators, generate_only=True):
         print(i := i + 1, test)
         # test(estimator)