unit test for gaussianNB

numpy_ml/tests/test_naive_bayes.py

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+import numpy as np
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+
+from sklearn import naive_bayes
+
+from numpy_ml.naive_bayes.naive_bayes import GaussianNB
+
+def test_GaussianNB():
+ iris = datasets.load_iris()
+ X = iris.data
+ y = iris.target
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
+
+ NB = GaussianNB()
+ NB.fit(X_train, y_train)
+
+ probs=NB.predict(X_test)
+ pred = np.argmax(probs, 1)
+ accuracy = sum(pred==y_test)/X_test.shape[0]
+
+ sklearn_NB = naive_bayes.GaussianNB()
+ sklearn_NB.fit(X_train, y_train)
+
+ sk_pred=sklearn_NB.predict(X_test)
+ sk_accuracy = sum(sk_pred==y_test)/X_test.shape[0]
+
+
+ try:
+ np.testing.assert_almost_equal(accuracy, sk_accuracy)
+ print("\Accuracies are equal")
+ except AssertionError as e:
+ print("\Accuracies are not equal:\n{}".format(e))
+
+
+