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_score(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_score = cuml_model.score(cu_X, cu_y)
sk_score = sk_model.score(X, y)
THRES = 1e-4
assert sk_score - THRES <= cuml_score <= sk_score + THRES