def test_add_features_with_index():
X, y = make_classification(n_samples=1000,
n_features=100,
n_informative=2,
n_redundant=0,
n_repeated=0,
n_classes=2,
n_clusters_per_class=1,
weights=None,
class_sep=1.0,
hypercube=True,
scale=2.0,
shuffle=True,
random_state=0)
X_og = X.copy()
index = [0, 8, 9, 20]
X_two_less = np.delete(X_og, index, axis=1)
nb = CustomNaiveBayes(encode_data=True)
nb.fit(X_two_less, y)
nb.add_features(X_og[:, index], y, index=index)
independent = nb.indepent_term_
smoothed_log_counts_ = nb.smoothed_log_counts_
added = nb.predict_proba(X)
nb.fit(X, y)
og = nb.predict_proba(X)
assert np.allclose(nb.indepent_term_, independent)
assert np.allclose(nb.smoothed_log_counts_, smoothed_log_counts_)
assert np.allclose(og, added)
def test_remove_feature():
X, y = make_classification(n_samples=1000,
n_features=100,
n_informative=2,
n_redundant=0,
n_repeated=0,
n_classes=2,
n_clusters_per_class=1,
weights=None,
class_sep=1.0,
hypercube=True,
scale=2.0,
shuffle=True,
random_state=0)
nb = CustomNaiveBayes(encode_data=True)
nb.fit(X, y)
nb.remove_feature(0)
independent = nb.indepent_term_
smoothed_log_counts_ = nb.smoothed_log_counts_
removed = nb.predict_proba(np.delete(X, 0, axis=1))
nb.fit(np.delete(X, 0, axis=1), y)
og = nb.predict_proba(np.delete(X, 0, axis=1))
assert np.allclose(nb.smoothed_log_counts_, smoothed_log_counts_)
assert np.allclose(nb.indepent_term_, independent)
assert np.allclose(og, removed)