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)