def test_multinomial_basic_fit_predict_dense_numpy(x_dtype, y_dtype,
nlp_20news):
"""
Cupy Test
"""
X, y = nlp_20news
n_rows = 500
X = sparse_scipy_to_cp(X, cp.float32).tocsr()[:n_rows]
y = y[:n_rows].astype(y_dtype)
model = MultinomialNB()
model.fit(np.ascontiguousarray(cp.asnumpy(X.todense()).astype(x_dtype)), y)
y_hat = model.predict(X).get()
modelsk = skNB()
modelsk.fit(X.get(), y.get())
y_sk = model.predict(X.get())
assert_allclose(y_hat, y_sk)
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)
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
print(f_report)
print("Sklearn Linear SVC metrices: ")
print(s_report)
#3.6 Bernoulli Naive Bayes
TARGET = "bernoulli_naive_bayes"
FrovedisServer.initialize("mpirun -np 8 " + os.environ["FROVEDIS_SERVER"])
f_est = frovNB(alpha=1.0)
E_NM = TARGET + "_frovedis_" + frovedis.__version__
f_report = evaluate(f_est, E_NM, x_train, y_train, x_test, y_test)
f_est.release()
FrovedisServer.shut_down()
s_est = skNB(alpha=1.0)
E_NM = TARGET + "_sklearn_" + sklearn.__version__
s_report = evaluate(s_est, E_NM, x_train, y_train, x_test, y_test)
# Precision, Recall and F1 score for each class
print("Frovedis Bernoulli Naive Bayes metrices: ")
print(f_report)
print("Sklearn Bernoulli Naive Bayes metrices: ")
print(s_report)
#3.7 Nearest Neighbor Classification
TARGET = "nearest_neighbor_classification"
FrovedisServer.initialize("mpirun -np 8 " + os.environ["FROVEDIS_SERVER"])
f_est = frovKNC(n_neighbors=3, algorithm='brute', metric='euclidean')
E_NM = TARGET + "_frovedis_" + frovedis.__version__
