Reorg/naive bayes (#145)

* reorg algorithm
* reorg: test
* fix: var calc

tests/classification/naive_bayes/test_gaussian_naive_bayes.py → tests/classification/test_naive_bayes.py

@@ -1,10 +1,15 @@
 import math
 
+import numpy as np
 import pytest
 
-from sklearn.naive_bayes import GaussianNB
+from sklearn.naive_bayes import CategoricalNB, GaussianNB, MultinomialNB
 
-from toyml.classification.naive_bayes import GaussianNaiveBayes
+from toyml.classification.naive_bayes import (
+    CategoricalNaiveBayes,
+    GaussianNaiveBayes,
+    MultinomialNaiveBayes,
+)
 
 
 @pytest.fixture
@@ -36,6 +41,17 @@ def wikipedia_person_classification_sample() -> list[float]:
     return [6, 130, 8]
 
 
+@pytest.fixture
+def sklearn_example_random_dataset_label() -> tuple[list[list[int]], list[int]]:
+    """
+    References: https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.MultinomialNB.html#multinomialnb
+    """
+    rng = np.random.RandomState(1)
+    dataset = rng.randint(5, size=(6, 100)).tolist()
+    label = np.array([1, 2, 3, 4, 5, 6]).tolist()
+    return dataset, label
+
+
 class TestGaussianNaiveBayesIntegration:
     def test_same_result_with_wikipedia(
         self,
@@ -79,3 +95,63 @@ def test_same_result_with_sklearn(
         sklearn_prob = sklearn_clf.predict_proba([wikipedia_person_classification_sample])
         assert math.isclose(sut_prob[0], sklearn_prob[0][0])
         assert math.isclose(sut_prob[1], sklearn_prob[0][1])
+
+
+class TestMultinomialNaiveBayesIntegration:
+    def test_same_result_with_sklearn(
+        self,
+        sklearn_example_random_dataset_label: tuple[list[list[float]], list[int]],
+    ) -> None:
+        dataset, label = sklearn_example_random_dataset_label
+        sklearn_clf = MultinomialNB()
+        sklearn_clf.fit(dataset, label)
+        # use the same variance calculation config with sklearn
+        sut = MultinomialNaiveBayes(alpha=1).fit(dataset, label)
+        # test same labels
+        test_sample = dataset[2]
+        sklearn_label = sklearn_clf.predict([test_sample])
+        sut_label = sut.predict(test_sample)
+
+        assert sut_label == sklearn_label[0]
+
+        # test same log probs
+        sut_log_prob = sut.predict_log_proba(test_sample)
+        sklearn_log_prob = sklearn_clf.predict_log_proba([test_sample])
+        for i in range(6):
+            assert math.isclose(sut_log_prob[i + 1], sklearn_log_prob[0][i])
+
+        # # test same probs
+        sut_prob = sut.predict_proba(test_sample)
+        sklearn_prob = sklearn_clf.predict_proba([test_sample])
+        for i in range(6):
+            assert math.isclose(sut_prob[i + 1], sklearn_prob[0][i])
+
+
+class TestCategoricalNaiveBayesIntegration:
+    def test_same_result_with_sklearn(
+        self,
+        sklearn_example_random_dataset_label: tuple[list[list[float]], list[int]],
+    ) -> None:
+        dataset, label = sklearn_example_random_dataset_label
+        sklearn_clf = CategoricalNB(alpha=1)
+        sklearn_clf.fit(dataset, label)
+        # use the same variance calculation config with sklearn
+        sut = CategoricalNaiveBayes(alpha=1).fit(dataset, label)
+        # test same labels
+        test_sample = dataset[2]
+        sklearn_label = sklearn_clf.predict([test_sample])
+        sut_label = sut.predict(test_sample)
+
+        assert sut_label == sklearn_label[0]
+
+        # test same log probs
+        sut_log_prob = sut.predict_log_proba(test_sample)
+        sklearn_log_prob = sklearn_clf.predict_log_proba([test_sample])
+        for i in range(6):
+            assert math.isclose(sut_log_prob[i + 1], sklearn_log_prob[0][i])
+
+        # # test same probs
+        sut_prob = sut.predict_proba(test_sample)
+        sklearn_prob = sklearn_clf.predict_proba([test_sample])
+        for i in range(6):
+            assert math.isclose(sut_prob[i + 1], sklearn_prob[0][i])