Add version and example

bayesclass/__init__.py

@@ -1,5 +1,9 @@
 from .bayesclass import TAN
-
 from ._version import __version__
 
+__author__ = "Ricardo Montañana Gómez"
+__copyright__ = "Copyright 2020-2023, Ricardo Montañana Gómez"
+__license__ = "MIT License"
+__author_email__ = "ricardo.montanana@alu.uclm.es"
+
 __all__ = ["TAN", "__version__"]

bayesclass/bayesclass.py

@@ -11,6 +11,7 @@ import networkx as nx
 from pgmpy.estimators import TreeSearch, BayesianEstimator
 from pgmpy.models import BayesianNetwork
 import matplotlib.pyplot as plt
+from ._version import __version__
 
 
 class TAN(ClassifierMixin, BaseEstimator):
@@ -40,6 +41,21 @@ class TAN(ClassifierMixin, BaseEstimator):
         self.show_progress = show_progress
         self.random_state = random_state
 
+    def _more_tags(self):
+        import numpy as np
+
+        return {
+            "requires_positive_X": True,
+            "requires_positive_y": True,
+            "preserve_dtype": [np.int64, np.int32],
+            "requires_y": True,
+        }
+
+    @staticmethod
+    def version() -> str:
+        """Return the version of the package."""
+        return __version__
+
     def __check_params_fit(self, X, y, kwargs):
         # Check that X and y have correct shape
         X, y = check_X_y(X, y)
@@ -48,7 +64,7 @@ class TAN(ClassifierMixin, BaseEstimator):
         # Default values
         self.class_name_ = "class"
         self.features_ = [f"feature_{i}" for i in range(X.shape[1])]
-        self.head_ = None
+        self.head_ = 0
         expected_args = ["class_name", "features", "head"]
         for key, value in kwargs.items():
             if key in expected_args:
@@ -65,6 +81,7 @@ class TAN(ClassifierMixin, BaseEstimator):
             )
         if self.head_ is not None and self.head_ >= len(self.features_):
             raise ValueError("Head index out of range")
+        return X, y
 
     def fit(self, X, y, **kwargs):
         """A reference implementation of a fitting function for a classifier.
@@ -104,13 +121,11 @@ class TAN(ClassifierMixin, BaseEstimator):
         >>> model.fit(train_data, train_y, features=features, class_name='E')
         TAN(random_state=17)
         """
-        self.__check_params_fit(X, y, kwargs)
+        X_, y_ = self.__check_params_fit(X, y, kwargs)
         # Store the information needed to build the model
-        self.X_ = X
-        self.y_ = y.astype(int)
-        self.dataset_ = pd.DataFrame(
-            self.X_, columns=self.features_, dtype="int16"
-        )
+        self.X_ = X_
+        self.y_ = y_
+        self.dataset_ = pd.DataFrame(self.X_, columns=self.features_)
         self.dataset_[self.class_name_] = self.y_
         # Build the DAG
         self.__build()
@@ -136,7 +151,7 @@ class TAN(ClassifierMixin, BaseEstimator):
         List
             List of edges
         """
-        head = 0 if self.head_ is None else self.head_
+        head = self.head_
         if self.simple_init:
             first_node = self.features_[head]
             return [
@@ -158,15 +173,12 @@ class TAN(ClassifierMixin, BaseEstimator):
         # initialize a complete network with all edges
         self.model_.add_edges_from(self.__initial_edges())
         # learn graph structure
-        root_node = None if self.head_ is None else self.features_[self.head_]
-        est = TreeSearch(self.dataset_, root_node=root_node)
+        est = TreeSearch(self.dataset_, root_node=self.features_[self.head_])
         self.dag_ = est.estimate(
             estimator_type="tan",
             class_node=self.class_name_,
             show_progress=self.show_progress,
         )
-        if self.head_ is None:
-            self.head_ = est.root_node
 
     def __train(self):
         self.model_ = BayesianNetwork(
@@ -174,11 +186,13 @@ class TAN(ClassifierMixin, BaseEstimator):
         )
         self.model_.fit(
             self.dataset_,
-            # estimator=MaximumLikelihoodEstimator,
             estimator=BayesianEstimator,
             prior_type="K2",
         )
 
+    def nodes_leaves(self):
+        return 0, 0
+
     def plot(self, title=""):
         nx.draw_circular(
             self.model_,

bayesclass/tests/test_bayesclass.py

@@ -7,6 +7,7 @@ from matplotlib.testing.conftest import mpl_test_settings
 
 
 from bayesclass import TAN
+from .._version import __version__
 
 
 @pytest.fixture
@@ -16,7 +17,7 @@ def data():
     return enc.fit_transform(X), y
 
 
-def test_TAN_constructor():
+def test_TAN_default_hyperparameters(data):
     clf = TAN()
     # Test default values of hyperparameters
     assert clf.simple_init
@@ -26,6 +27,21 @@ def test_TAN_constructor():
     assert clf.simple_init
     assert clf.show_progress
     assert clf.random_state == 17
+    clf.fit(*data)
+    assert clf.head_ == 0
+    assert clf.class_name_ == "class"
+    assert clf.features_ == [
+        "feature_0",
+        "feature_1",
+        "feature_2",
+        "feature_3",
+    ]
+
+
+def test_TAN_version():
+    """Check TAN version."""
+    clf = TAN()
+    assert __version__ == clf.version()
 
 
 def test_TAN_random_head(data):

bayesclass/tests/test_common.py

@@ -7,5 +7,4 @@ from bayesclass import TAN
 
 @pytest.mark.parametrize("estimator", [TAN()])
 def test_all_estimators(estimator):
-    # return check_estimator(estimator)
-    assert True
+    return check_estimator(estimator)

example.py

@@ -1,7 +1,7 @@
 import sys
 import time
 from sklearn.model_selection import cross_val_score, StratifiedKFold
-from benchmark import Discretizer
+from benchmark import Datasets
 from bayesclass import TAN
 import warnings
 
@@ -15,7 +15,7 @@ start = time.time()
 random_state = 17
 name = sys.argv[1]
 n_folds = int(sys.argv[2]) if len(sys.argv) == 3 else 5
-dt = Discretizer()
+dt = Datasets()
 name_list = list(dt) if name == "all" else [name]
 print(f"Accuracy in {n_folds} folds stratified crossvalidation")
 for name in name_list: