Merge pull request #43 from msamsami/major-internal-refactor

feat: refactor `GeneralNB` and `GaussianWNB`, improve validation, and enhance scikit-learn compatibility

README.md

@@ -7,7 +7,7 @@
 
 <div align="center">
 
-![Lastest Release](https://img.shields.io/badge/release-v0.5.1-green)
+![Lastest Release](https://img.shields.io/badge/release-v0.6.0-green)
 [![PyPI Version](https://img.shields.io/pypi/v/wnb)](https://pypi.org/project/wnb/)
 ![Python Versions](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)<br>
 ![GitHub Workflow Status (build)](https://github.com/msamsami/wnb/actions/workflows/build.yml/badge.svg)
@@ -102,6 +102,7 @@ Both Scikit-learn classifiers and WNB classifiers share these well-known methods
 - `predict(X)`
 - `predict_proba(X)`
 - `predict_log_proba(X)`
+- `predict_joint_log_proba(X)`
 - `score(X, y)`
 - `get_params()`
 - `set_params(**params)`
@@ -123,18 +124,6 @@ These benchmarks highlight the potential of WNB classifiers to provide better pe
 
 The scripts used to generate these benchmark results are available in the _tests/benchmarks/_ directory.
 
-## Tests
-To run the tests, make sure to clone the repository and install the development requirements in addition to base requirements:
-```bash
-pip install -r requirements.txt
-pip install -r requirements-dev.txt
-```
-
-Then, run pytest:
-```bash
-pytest
-```
-
 ## Support us 💡
 You can support the project in the following ways:
 

tests/test_gnb.py

@@ -1,4 +1,5 @@
 import numpy as np
+import pandas as pd
 import pytest
 from sklearn.base import is_classifier
 from sklearn.naive_bayes import BernoulliNB, CategoricalNB, GaussianNB
@@ -281,3 +282,23 @@ def test_gnb_var_smoothing_non_numeric():
     clf = GeneralNB(distributions=[D.CATEGORICAL, D.CATEGORICAL], var_smoothing=1e-6)
     clf.fit(X, y)
     assert clf.epsilon_ == 0
+
+
+def test_gnb_attrs():
+    """
+    Test whether the attributes are properly set.
+    """
+    clf = GeneralNB().fit(X, y)
+    assert np.array_equal(clf.class_count_, np.array([3, 3]))
+    assert np.array_equal(clf.class_prior_, np.array([0.5, 0.5]))
+    assert np.array_equal(clf.classes_, np.array([1, 2]))
+    assert clf.n_classes_ == 2
+    assert clf.epsilon_ > 0
+    assert clf.n_features_in_ == 2
+    assert not hasattr(clf, "feature_names_in_")
+    assert clf.distributions_ == [D.NORMAL, D.NORMAL]
+    assert len(clf.likelihood_params_) == 2
+
+    feature_names = [f"x{i}" for i in range(X.shape[1])]
+    clf = GeneralNB().fit(pd.DataFrame(X, columns=feature_names), y)
+    assert np.array_equal(clf.feature_names_in_, np.array(feature_names))

tests/test_gwnb.py

@@ -1,6 +1,7 @@
 import re
 
 import numpy as np
+import pandas as pd
 import pytest
 from sklearn.base import is_classifier
 from sklearn.utils._testing import assert_array_almost_equal, assert_array_equal
@@ -194,3 +195,25 @@ def test_gwnb_no_cost_hist():
     clf = GaussianWNB(max_iter=10)
     clf.fit(X, y)
     assert clf.cost_hist_ is None
+
+
+def test_gwnb_attrs():
+    """
+    Test whether the attributes are properly set.
+    """
+    clf = GaussianWNB().fit(X, y)
+    assert np.array_equal(clf.class_count_, np.array([3, 3]))
+    assert np.array_equal(clf.class_prior_, np.array([0.5, 0.5]))
+    assert np.array_equal(clf.classes_, np.array([1, 2]))
+    assert clf.n_classes_ == 2
+    assert clf.n_features_in_ == 2
+    assert not hasattr(clf, "feature_names_in_")
+    assert np.array_equal(clf.error_weights_, np.array([[0, 1], [-1, 0]]))
+    assert clf.theta_.shape == (2, 2)
+    assert clf.std_.shape == (2, 2)
+    assert clf.var_.shape == (2, 2)
+    assert clf.coef_.shape == (2,)
+
+    feature_names = [f"x{i}" for i in range(X.shape[1])]
+    clf = GaussianWNB().fit(pd.DataFrame(X, columns=feature_names), y)
+    assert np.array_equal(clf.feature_names_in_, np.array(feature_names))

wnb/__init__.py

@@ -2,7 +2,7 @@
 Python library for the implementations of general and weighted naive Bayes (WNB) classifiers.
 """
 
-__version__ = "0.5.1"
+__version__ = "0.6.0"
 __author__ = "Mehdi Samsami"
 
 

wnb/_utils.py

@@ -2,11 +2,13 @@
 
 import sklearn
 from packaging import version
-from sklearn.utils import check_array
+from sklearn.utils.validation import check_array
+from sklearn.utils.validation import check_X_y as _check_X_y
 
 __all__ = [
     "SKLEARN_V1_6_OR_LATER",
     "validate_data",
+    "check_X_y",
     "_check_n_features",
     "_check_feature_names",
 ]
@@ -24,12 +26,20 @@ def validate_data(*args, **kwargs):
             kwargs["ensure_all_finite"] = kwargs.pop("force_all_finite")
         return _validate_data(*args, **kwargs)
 
+    def check_X_y(*args, **kwargs):
+        if kwargs.get("force_all_finite"):
+            kwargs["ensure_all_finite"] = kwargs.pop("force_all_finite")
+        return _check_X_y(*args, **kwargs)
+
 else:
 
     def validate_data(estimator, X, **kwargs: Any):
         kwargs.pop("reset", None)
         return check_array(X, estimator=estimator, **kwargs)
 
+    def check_X_y(*args, **kwargs):
+        return _check_X_y(*args, **kwargs)
+
     def _check_n_features(estimator, X, reset):
         return estimator._check_n_features(X, reset=reset)