def run(model, train_data, validate_data=None):
if not model.need_run:
return train_data
if model.mode == consts.HETERO:
step_obj = HeteroStepwise()
else:
raise ValueError("stepwise currently only support Hetero mode.")
stepwise_param = _get_stepwise_param(model)
step_obj.run(stepwise_param, train_data, validate_data, model)
pred_result = HeteroStepwise.predict(train_data, model)
LOGGER.info("Finish running Stepwise")
return pred_result
def test_add_one(self):
real_masks = [np.array([1, 1, 1, 1, 0], dtype=bool), np.array([1, 0, 1, 1, 1], dtype=bool)]
mask_generator = HeteroStepwise.add_one(self.mask)
i = 0
for mask in mask_generator:
np.testing.assert_array_equal(mask, real_masks[i],
f"In stepwise_test add one: mask{mask} not equal to expected {real_masks[i]}")
i += 1
def setUp(self):
self.job_id = str(uuid.uuid1())
self.session = Session.create(0, 0).init_computing(self.job_id).computing
model = HeteroStepwise()
model.__setattr__('role', consts.GUEST)
model.__setattr__('fit_intercept', True)
self.model = model
data_num = 100
feature_num = 5
bool_list = [True, False, True, True, False]
self.str_mask = "10110"
self.header = ["x1", "x2", "x3", "x4", "x5"]
self.mask = self.prepare_mask(bool_list)
self.table = self.prepare_data(data_num, feature_num, self.header, "id", "y")
def test_get_dfe(self):
real_dfe = 4
dfe = HeteroStepwise.get_dfe(self.model, self.str_mask)
self.assertEqual(dfe, real_dfe)
def test_string2mask(self):
real_mask = np.array([1, 0, 1, 1, 0], dtype=bool)
mask = HeteroStepwise.string2mask(self.str_mask)
np.testing.assert_array_equal(mask, real_mask)
def test_mask2string(self):
real_str_mask = "1011010110"
str_mask = HeteroStepwise.mask2string(self.mask, self.mask)
self.assertTrue(str_mask == real_str_mask)