def test_naive_bayes(nlp_20news):
X, y = nlp_20news
X = sparse_scipy_to_cp(X, cp.float32).astype(cp.float32)
y = y.astype(cp.int32)
with cupy_using_allocator(dummy_allocator):
model = MultinomialNB()
model.fit(X, y)
y_hat = model.predict(X)
y_hat = model.predict(X)
y_hat = model.predict_proba(X)
y_hat = model.predict_log_proba(X)
y_hat = model.score(X, y)
del y_hat
def test_predict_log_proba(x_dtype, y_dtype, nlp_20news):
X, y = nlp_20news
cu_X = sparse_scipy_to_cp(X, x_dtype).astype(x_dtype)
cu_y = y.astype(y_dtype)
cu_X = cu_X.tocsr()
y = y.get()
cuml_model = MultinomialNB()
sk_model = skNB()
cuml_model.fit(cu_X, cu_y)
sk_model.fit(X, y)
cuml_proba = cuml_model.predict_log_proba(cu_X).get()
sk_proba = sk_model.predict_log_proba(X)
assert_allclose(cuml_proba, sk_proba, atol=1e-2, rtol=1e-2)