def test_gaussian_fit_predict(x_dtype, y_dtype, is_sparse, nlp_20news):
"""
Cupy Test
"""
X, y = nlp_20news
model = GaussianNB()
n_rows = 500
n_cols = int(2e5)
X = sparse_scipy_to_cp(X, x_dtype)
X = X.tocsr()[:n_rows, :n_cols]
if is_sparse:
y = y.astype(y_dtype)[:n_rows]
model.fit(X, y)
else:
X = X.todense()
y = y[:n_rows].astype(y_dtype)
model.fit(np.ascontiguousarray(cp.asnumpy(X).astype(x_dtype)), y)
y_hat = model.predict(X)
y_hat = cp.asnumpy(y_hat)
y = cp.asnumpy(y)
assert accuracy_score(y, y_hat) >= 0.99
def test_gaussian_parameters(priors, var_smoothing, nlp_20news):
x_dtype = cp.float32
y_dtype = cp.int32
nrows = 150
X, y = nlp_20news
X = sparse_scipy_to_cp(X[:nrows], x_dtype).todense()
y = y.astype(y_dtype)[:nrows]
if priors == 'balanced':
priors = cp.array([1 / 20] * 20)
elif priors == 'unbalanced':
priors = cp.linspace(0.01, 0.09, 20)
model = GaussianNB(priors=priors, var_smoothing=var_smoothing)
model_sk = skGNB(priors=priors.get() if priors is not None else None,
var_smoothing=var_smoothing)
model.fit(X, y)
model_sk.fit(X.get(), y.get())
y_hat = model.predict(X)
y_hat_sk = model_sk.predict(X.get())
y_hat = cp.asnumpy(y_hat)
y = cp.asnumpy(y)
assert_allclose(model.epsilon_.get(), model_sk.epsilon_, rtol=1e-4)
assert_array_equal(y_hat, y_hat_sk)
def test_gaussian_basic():
# Data is just 6 separable points in the plane
X = cp.array([[-2, -1, -1], [-1, -1, -1], [-1, -2, -1], [1, 1, 1],
[1, 2, 1], [2, 1, 1]],
dtype=cp.float32)
y = cp.array([1, 1, 1, 2, 2, 2])
skclf = skGNB()
skclf.fit(X.get(), y.get())
clf = GaussianNB()
clf.fit(X, y)
assert_array_almost_equal(clf.theta_.get(), skclf.theta_, 6)
assert_array_almost_equal(clf.sigma_.get(), skclf.sigma_, 6)
y_pred = clf.predict(X)
y_pred_proba = clf.predict_proba(X)
y_pred_log_proba = clf.predict_log_proba(X)
y_pred_proba_sk = skclf.predict_proba(X.get())
y_pred_log_proba_sk = skclf.predict_log_proba(X.get())
assert_array_equal(y_pred.get(), y.get())
assert_array_almost_equal(y_pred_proba.get(), y_pred_proba_sk, 8)
assert_allclose(y_pred_log_proba.get(),
y_pred_log_proba_sk,
atol=1e-2,
rtol=1e-2)