def load_model(self, model_dict):
LOGGER.debug("Start Loading model")
result_obj = list(model_dict.get('model').values())[0].get(
self.model_param_name)
meta_obj = list(model_dict.get('model').values())[0].get(
self.model_meta_name)
# self.fit_intercept = meta_obj.fit_intercept
if self.init_param_obj is None:
self.init_param_obj = InitParam()
self.init_param_obj.fit_intercept = meta_obj.fit_intercept
self.header = list(result_obj.header)
# For hetero-lr arbiter predict function
if self.header is None:
return
need_one_vs_rest = result_obj.need_one_vs_rest
LOGGER.debug(
"in _load_model need_one_vs_rest: {}".format(need_one_vs_rest))
if need_one_vs_rest:
one_vs_rest_result = result_obj.one_vs_rest_result
self.one_vs_rest_obj = one_vs_rest_factory(classifier=self,
role=self.role,
mode=self.mode,
has_arbiter=True)
self.one_vs_rest_obj.load_model(one_vs_rest_result)
self.need_one_vs_rest = True
else:
self.load_single_model(result_obj)
self.need_one_vs_rest = False
def load_model(self, model_dict):
LOGGER.debug("Start Loading model")
result_obj = list(model_dict.get('model').values())[0].get(
self.model_param_name)
meta_obj = list(model_dict.get('model').values())[0].get(
self.model_meta_name)
if self.init_param_obj is None:
self.init_param_obj = InitParam()
self.init_param_obj.fit_intercept = meta_obj.fit_intercept
self.model_param.reveal_strategy = meta_obj.reveal_strategy
LOGGER.debug(
f"reveal_strategy: {self.model_param.reveal_strategy}, {self.is_respectively_reveal}"
)
self.header = list(result_obj.header)
need_one_vs_rest = result_obj.need_one_vs_rest
LOGGER.info(
"in _load_model need_one_vs_rest: {}".format(need_one_vs_rest))
if need_one_vs_rest:
one_vs_rest_result = result_obj.one_vs_rest_result
self.one_vs_rest_obj = one_vs_rest_factory(classifier=self,
role=self.role,
mode=self.mode,
has_arbiter=False)
self.one_vs_rest_obj.load_model(one_vs_rest_result)
self.need_one_vs_rest = True
else:
self.load_single_model(result_obj)
self.need_one_vs_rest = False
def __init__(self):
self.guest_X = np.array([[1, 2, 3, 4, 5], [3, 2, 4, 5, 1],
[
2,
2,
3,
1,
1,
]]) / 10
self.guest_Y = np.array([[1], [1], [-1]])
self.values = []
for idx, x in enumerate(self.guest_X):
inst = Instance(inst_id=idx, features=x, label=self.guest_Y[idx])
self.values.append((idx, inst))
self.host_X = np.array([[1, 1.2, 3.1, 4, 5], [2.3, 2, 4, 5.3, 1],
[
2,
2.2,
1.3,
1,
1.6,
]]) / 10
self.host_Y = np.array([[-1], [1], [-1]])
self.host_values = []
for idx, x in enumerate(self.host_X):
inst = Instance(inst_id=idx, features=x, label=self.host_Y[idx])
self.values.append((idx, inst))
self.max_iter = 10
self.alpha = 0.01
self.learning_rate = 0.01
optimizer = 'SGD'
self.gradient_operator = LogisticGradient()
self.initializer = Initializer()
self.fit_intercept = True
self.init_param_obj = InitParam(fit_intercept=self.fit_intercept)
self.updater = L2Updater(self.alpha, self.learning_rate)
self.optimizer = Optimizer(learning_rate=self.learning_rate,
opt_method_name=optimizer)
self.__init_model()