def test_regressor_mg_train_sg_predict(datatype, keys, data_size,
fit_intercept, client):
nrows, ncols, n_info = data_size
X_train, y_train, X_test = make_dataset(datatype, nrows, ncols, n_info)
X_test_local = X_test.compute()
dist_model = LinearRegression(fit_intercept=fit_intercept, client=client)
dist_model.fit(X_train, y_train)
expected = dist_model.predict(X_test).compute()
local_model = dist_model.get_combined_model()
actual = local_model.predict(X_test_local)
assert_equal(expected.get(), actual.get())
def test_get_combined_model(datatype, keys, data_size, fit_intercept, client):
nrows, ncols, n_info = data_size
X_train, y_train, X_test = make_dataset(datatype, nrows, ncols, n_info)
model = LinearRegression(fit_intercept=fit_intercept, client=client)
model.fit(X_train, y_train)
combined_model = model.get_combined_model()
assert combined_model.coef_ is not None
assert combined_model.intercept_ is not None
y_hat = combined_model.predict(X_train.compute())
np.testing.assert_allclose(y_hat.get(),
y_train.compute().get(),
atol=1e-3,
rtol=1e-3)
def test_check_internal_model_failures(client):
# Test model not trained yet
model = LinearRegression(client=client)
assert model.get_combined_model() is None
# Test single Int future fails
int_future = client.submit(lambda: 1)
with pytest.raises(ValueError):
model._set_internal_model(int_future)
# Test list Int future fails
with pytest.raises(ValueError):
model._set_internal_model([int_future])
# Test directly setting Int fails
with pytest.raises(ValueError):
model._set_internal_model(1)