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Merge pull request #11 from msamsami/refactor-gwnb
Refactor GaussianWNB classifier
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,142 @@ | ||
| import numpy as np | ||
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| import pytest | ||
| from sklearn.utils.estimator_checks import check_estimator | ||
| from sklearn.base import is_classifier | ||
| from sklearn.utils._testing import assert_array_equal, assert_array_almost_equal | ||
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| from wnb import GaussianWNB | ||
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| # Data is just 6 separable points in the plane | ||
| X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]) | ||
| y = np.array([1, 1, 1, 2, 2, 2]) | ||
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| @pytest.fixture | ||
| def global_random_seed(): | ||
| return np.random.randint(0, 1000) | ||
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| def get_random_normal_x_binary_y(global_random_seed): | ||
| # A bit more random tests | ||
| rng = np.random.RandomState(global_random_seed) | ||
| X1 = rng.normal(size=(10, 3)) | ||
| y1 = (rng.normal(size=10) > 0).astype(int) | ||
| return X1, y1 | ||
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| def test_gwnb(): | ||
| """Binary Gaussian MLD-WNB classification | ||
| Checks that GaussianWNB implements fit and predict and returns correct values for a simple toy dataset. | ||
| """ | ||
| clf = GaussianWNB() | ||
| y_pred = clf.fit(X, y).predict(X) | ||
| assert_array_equal(y_pred, y) | ||
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| y_pred_proba = clf.predict_proba(X) | ||
| y_pred_log_proba = clf.predict_log_proba(X) | ||
| assert_array_almost_equal(np.log(y_pred_proba), y_pred_log_proba, 8) | ||
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| def test_gwnb_estimator(): | ||
| """ | ||
| Test whether GaussianWNB estimator adheres to scikit-learn conventions. | ||
| """ | ||
| check_estimator(GaussianWNB()) | ||
| assert is_classifier(GaussianWNB) | ||
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| def test_gwnb_prior(global_random_seed): | ||
| """ | ||
| Test whether class priors are properly set. | ||
| """ | ||
| clf = GaussianWNB().fit(X, y) | ||
| assert_array_almost_equal(np.array([3, 3]) / 6.0, clf.class_prior_, 8) | ||
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| X1, y1 = get_random_normal_x_binary_y(global_random_seed) | ||
| clf = GaussianWNB().fit(X1, y1) | ||
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| # Check that the class priors sum to 1 | ||
| assert_array_almost_equal(clf.class_prior_.sum(), 1) | ||
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| def test_gwnb_neg_priors(): | ||
| """ | ||
| Test whether an error is raised in case of negative priors. | ||
| """ | ||
| clf = GaussianWNB(priors=np.array([-1.0, 2.0])) | ||
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| msg = "Priors must be non-negative" | ||
| with pytest.raises(ValueError, match=msg): | ||
| clf.fit(X, y) | ||
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| def test_gwnb_priors(): | ||
| """ | ||
| Test whether the class priors override is properly used. | ||
| """ | ||
| clf = GaussianWNB(priors=np.array([0.3, 0.7])).fit(X, y) | ||
| assert_array_almost_equal( | ||
| clf.predict_proba([[-0.1, -0.1]]), | ||
| np.array([[0.823571, 0.176429]]), | ||
| 8, | ||
| ) | ||
| assert_array_almost_equal(clf.class_prior_, np.array([0.3, 0.7])) | ||
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| def test_gwnb_wrong_nb_priors(): | ||
| """ | ||
| Test whether an error is raised if the number of priors is different from the number of classes. | ||
| """ | ||
| clf = GaussianWNB(priors=np.array([0.25, 0.25, 0.25, 0.25])) | ||
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| msg = "Number of priors must match the number of classes" | ||
| with pytest.raises(ValueError, match=msg): | ||
| clf.fit(X, y) | ||
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| def test_gwnb_prior_greater_one(): | ||
| """ | ||
| Test if an error is raised if the sum of priors greater than one. | ||
| """ | ||
| clf = GaussianWNB(priors=np.array([2.0, 1.0])) | ||
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| msg = "The sum of the priors should be 1" | ||
| with pytest.raises(ValueError, match=msg): | ||
| clf.fit(X, y) | ||
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| def test_gwnb_prior_large_bias(): | ||
| """ | ||
| Test if good prediction when class priors favor largely one class. | ||
| """ | ||
| clf = GaussianWNB(priors=np.array([0.01, 0.99])) | ||
| clf.fit(X, y) | ||
| assert clf.predict(np.array([[-0.1, -0.1]])) == np.array([2]) | ||
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| def test_gwnb_non_binary(): | ||
| """ | ||
| Test if an error is raised when given non-binary targets. | ||
| """ | ||
| X_ = np.array( | ||
| [ | ||
| [-1, -1], | ||
| [-2, -1], | ||
| [-3, -2], | ||
| [-4, -5], | ||
| [-5, -4], | ||
| [1, 1], | ||
| [2, 1], | ||
| [3, 2], | ||
| [4, 4], | ||
| [5, 5], | ||
| ] | ||
| ) | ||
| y_ = np.array([1, 2, 3, 4, 4, 3, 2, 1, 1, 2]) | ||
| clf = GaussianWNB() | ||
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| msg = "Unknown label type: non-binary" | ||
| with pytest.raises(ValueError, match=msg): | ||
| clf.fit(X_, y_) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,4 @@ | ||
| __version__ = "0.1.11" | ||
| __version__ = "0.1.12" | ||
| __author__ = "Mehdi Samsami" | ||
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| __all__ = [ | ||
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