First implementation

bayesclass/__init__.py

@@ -1,4 +1,4 @@
-from .bayesclass import TAN, KDB
+from .bayesclass import TAN, KDB, AODE
 from ._version import __version__
 
 __author__ = "Ricardo Montañana Gómez"
@@ -6,4 +6,4 @@ __copyright__ = "Copyright 2020-2023, Ricardo Montañana Gómez"
 __license__ = "MIT License"
 __author_email__ = "ricardo.montanana@alu.uclm.es"
 
-__all__ = ["TAN", "KDB", "__version__"]
+__all__ = ["TAN", "KDB", "AODE", "__version__"]

bayesclass/bayesclass.py

@@ -4,7 +4,9 @@ This is a module to be used as a reference for building other modules
 import random
 import numpy as np
 import pandas as pd
+from scipy.stats import mode
 from sklearn.base import ClassifierMixin, BaseEstimator
+from sklearn.ensemble import BaseEnsemble
 from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
 from sklearn.utils.multiclass import unique_labels
 from sklearn.feature_selection import mutual_info_classif
@@ -37,6 +39,29 @@ class BayesBase(BaseEstimator, ClassifierMixin):
         """To keep compatiblity with the benchmark platform"""
         return 0, 0
 
+    def _check_params_fit(self, X, y, expected_args, kwargs):
+        """Check the common parameters passed to fit"""
+        # Check that X and y have correct shape
+        X, y = check_X_y(X, y)
+        # Store the classes seen during fit
+        self.classes_ = unique_labels(y)
+        self.n_classes_ = self.classes_.shape[0]
+        # Default values
+        self.class_name_ = "class"
+        self.features_ = [f"feature_{i}" for i in range(X.shape[1])]
+        for key, value in kwargs.items():
+            if key in expected_args:
+                setattr(self, f"{key}_", value)
+            else:
+                raise ValueError(f"Unexpected argument: {key}")
+        if self.random_state is not None:
+            random.seed(self.random_state)
+        if len(self.features_) != X.shape[1]:
+            raise ValueError(
+                "Number of features does not match the number of columns in X"
+            )
+        return X, y
+
     def fit(self, X, y, **kwargs):
         """A reference implementation of a fitting function for a classifier.
 
@@ -75,7 +100,7 @@ class BayesBase(BaseEstimator, ClassifierMixin):
         >>> model.fit(train_data, train_y, features=features, class_name='E')
         TAN(random_state=17)
         """
-        X_, y_ = self._check_params_fit(X, y, kwargs)
+        X_, y_ = self._check_params(X, y, kwargs)
         # Store the information needed to build the model
         self.X_ = X_
         self.y_ = y_
@@ -89,6 +114,16 @@ class BayesBase(BaseEstimator, ClassifierMixin):
         # Return the classifier
         return self
 
+    def _train(self):
+        self.model_ = BayesianNetwork(
+            self.dag_.edges(), show_progress=self.show_progress
+        )
+        self.model_.fit(
+            self.dataset_,
+            estimator=BayesianEstimator,
+            prior_type="K2",
+        )
+
     def predict(self, X):
         """A reference implementation of a prediction for a classifier.
 
@@ -193,30 +228,12 @@ class TAN(BayesBase):
             show_progress=show_progress, random_state=random_state
         )
 
-    def _check_params_fit(self, X, y, kwargs):
+    def _check_params(self, X, y, kwargs):
-        """Check the parameters passed to fit"""
-        # Check that X and y have correct shape
-        X, y = check_X_y(X, y)
-        # Store the classes seen during fit
-        self.classes_ = unique_labels(y)
-        # Default values
-        self.class_name_ = "class"
-        self.features_ = [f"feature_{i}" for i in range(X.shape[1])]
         self.head_ = 0
         expected_args = ["class_name", "features", "head"]
-        for key, value in kwargs.items():
+        X, y = self._check_params_fit(X, y, expected_args, kwargs)
-            if key in expected_args:
-                setattr(self, f"{key}_", value)
-            else:
-                raise ValueError(f"Unexpected argument: {key}")
-        if self.random_state is not None:
-            random.seed(self.random_state)
         if self.head_ == "random":
             self.head_ = random.randint(0, len(self.features_) - 1)
-        if len(self.features_) != X.shape[1]:
-            raise ValueError(
-                "Number of features does not match the number of columns in X"
-            )
         if self.head_ is not None and self.head_ >= len(self.features_):
             raise ValueError("Head index out of range")
         return X, y
@@ -229,16 +246,6 @@ class TAN(BayesBase):
             show_progress=self.show_progress,
         )
 
-    def _train(self):
-        self.model_ = BayesianNetwork(
-            self.dag_.edges(), show_progress=self.show_progress
-        )
-        self.model_.fit(
-            self.dataset_,
-            estimator=BayesianEstimator,
-            prior_type="K2",
-        )
-
 
 class KDB(BayesBase):
     def __init__(self, k, theta=0.03, show_progress=False, random_state=None):
@@ -248,29 +255,9 @@ class KDB(BayesBase):
             show_progress=show_progress, random_state=random_state
         )
 
-    def _check_params_fit(self, X, y, kwargs):
+    def _check_params(self, X, y, kwargs):
-        """Check the parameters passed to fit"""
-        # Check that X and y have correct shape
-        X, y = check_X_y(X, y)
-        # Store the classes seen during fit
-        self.classes_ = unique_labels(y)
-        # Default values
-        self.class_name_ = "class"
-        self.features_ = [f"feature_{i}" for i in range(X.shape[1])]
-        self.head_ = 0
         expected_args = ["class_name", "features"]
-        for key, value in kwargs.items():
+        return self._check_params_fit(X, y, expected_args, kwargs)
-            if key in expected_args:
-                setattr(self, f"{key}_", value)
-            else:
-                raise ValueError(f"Unexpected argument: {key}")
-        if self.random_state is not None:
-            random.seed(self.random_state)
-        if len(self.features_) != X.shape[1]:
-            raise ValueError(
-                "Number of features does not match the number of columns in X"
-            )
-        return X, y
 
     def _build(self):
         """
@@ -335,12 +322,56 @@ class KDB(BayesBase):
             S_nodes.append(idx)
         self.dag_ = dag
 
+
+class AODE(BayesBase, BaseEnsemble):
+    def __init__(self, min_data=30, show_progress=False, random_state=None):
+        super().__init__(
+            show_progress=show_progress, random_state=random_state
+        )
+        self.min_data = min_data
+
+    def _check_params(self, X, y, kwargs):
+        expected_args = ["class_name", "features"]
+        return self._check_params_fit(X, y, expected_args, kwargs)
+
+    def _build(self):
+
+        self.dag_ = None
+
     def _train(self):
-        self.model_ = BayesianNetwork(
+        """Build SPODE estimators (Super Parent One Dependent Estimator)"""
-            self.dag_.edges(), show_progress=self.show_progress
+        self.models_ = []
-        )
+        class_edges = [(self.class_name_, f) for f in self.features_]
-        self.model_.fit(
+        for idx in range(len(self.features_)):
-            self.dataset_,
+            feature_edges = [
-            estimator=BayesianEstimator,
+                (self.features_[idx], f)
-            prior_type="K2",
+                for f in self.features_
-        )
+                if f != self.features_[idx]
+            ]
+            feature_edges.extend(class_edges)
+            model = BayesianNetwork(
+                feature_edges, show_progress=self.show_progress
+            )
+            model.fit(
+                self.dataset_,
+                estimator=BayesianEstimator,
+                prior_type="K2",
+            )
+            self.models_.append(model)
+
+    def plot(self, title=""):
+        for idx, model in enumerate(self.models_):
+            self.model_ = model
+            super().plot(title=f"{idx} {title}")
+
+    def predict(self, X: np.ndarray) -> np.ndarray:
+        check_is_fitted(self, ["X_", "y_", "fitted_"])
+        # Input validation
+        X = self._validate_data(X, reset=False)
+        n_samples = X.shape[0]
+        n_estimators = len(self.models_)
+        result = np.empty((n_samples, n_estimators))
+        dataset = pd.DataFrame(X, columns=self.features_, dtype="int16")
+        for index, model in enumerate(self.models_):
+            result[:, index] = model.predict(dataset).values.ravel()
+        return mode(result, axis=1).mode.ravel()

bayesclass/tests/test_AODE.py

@@ -0,0 +1,83 @@
+import pytest
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import KBinsDiscretizer
+from matplotlib.testing.decorators import image_comparison
+from matplotlib.testing.conftest import mpl_test_settings
+
+
+from bayesclass import AODE
+from .._version import __version__
+
+
+@pytest.fixture
+def data():
+    X, y = load_iris(return_X_y=True)
+    enc = KBinsDiscretizer(encode="ordinal")
+    return enc.fit_transform(X), y
+
+
+@pytest.fixture
+def clf():
+    return AODE()
+
+
+def test_AODE_default_hyperparameters(data, clf):
+    # Test default values of hyperparameters
+    assert not clf.show_progress
+    assert clf.random_state is None
+    assert clf.min_data == 30
+    clf = AODE(show_progress=True, random_state=17, min_data=3)
+    assert clf.show_progress
+    assert clf.random_state == 17
+    assert clf.min_data == 3
+    clf.fit(*data)
+    assert clf.class_name_ == "class"
+    assert clf.features_ == [
+        "feature_0",
+        "feature_1",
+        "feature_2",
+        "feature_3",
+    ]
+
+
+def test_AODE_version(clf):
+    """Check AODE version."""
+    assert __version__ == clf.version()
+
+
+def test_AODE_nodes_leaves(clf):
+    assert clf.nodes_leaves() == (0, 0)
+
+
+def test_AODE_classifier(data, clf):
+    clf.fit(*data)
+    attribs = ["classes_", "X_", "y_", "features_", "class_name_"]
+    for attr in attribs:
+        assert hasattr(clf, attr)
+    X = data[0]
+    y = data[1]
+    y_pred = clf.predict(X)
+    assert y_pred.shape == (X.shape[0],)
+    assert sum(y == y_pred) == 147
+
+
+def test_AODE_wrong_num_features(data, clf):
+    with pytest.raises(
+        ValueError,
+        match="Number of features does not match the number of columns in X",
+    ):
+        clf.fit(*data, features=["feature_1", "feature_2"])
+
+
+def test_AODE_wrong_hyperparam(data, clf):
+    with pytest.raises(ValueError, match="Unexpected argument: wrong_param"):
+        clf.fit(*data, wrong_param="wrong_param")
+
+
+def test_AODE_error_size_predict(data, clf):
+    X, y = data
+    clf.fit(X, y)
+    with pytest.raises(ValueError):
+        X_diff_size = np.ones((10, X.shape[1] + 1))
+        clf.predict(X_diff_size)

bayesclass/tests/test_KDB.py

@@ -30,6 +30,7 @@ def test_KDB_default_hyperparameters(data, clf):
     clf = KDB(show_progress=True, random_state=17, k=3)
     assert clf.show_progress
     assert clf.random_state == 17
+    assert clf.k == 3
     clf.fit(*data)
     assert clf.class_name_ == "class"
     assert clf.features_ == [
@@ -41,7 +42,7 @@ def test_KDB_default_hyperparameters(data, clf):
 
 
 def test_KDB_version(clf):
-    """Check TAN version."""
+    """Check KDB version."""
     assert __version__ == clf.version()
 
 
@@ -58,7 +59,7 @@ def test_KDB_classifier(data, clf):
     y = data[1]
     y_pred = clf.predict(X)
     assert y_pred.shape == (X.shape[0],)
-    assert sum(y == y_pred) == 147
+    assert sum(y == y_pred) == 148
 
 
 @image_comparison(

bayesclass/tests/test_common.py

@@ -2,12 +2,13 @@ import pytest
 
 from sklearn.utils.estimator_checks import check_estimator
 
-from bayesclass import TAN
+from bayesclass import TAN, KDB, AODE
 
 
-@pytest.mark.parametrize("estimator", [TAN()])
+@pytest.mark.parametrize("estimator", [TAN(), KDB(k=2), AODE()])
+# @pytest.mark.parametrize("estimator", [AODE()])
 def test_all_estimators(estimator):
     i = 0
     for estimator, test in check_estimator(estimator, generate_only=True):
-        print(i := i + 1, test, "classes_")
+        print(i := i + 1, test)
         # test(estimator)