class HeteroBoosting(Boosting, ABC):
def __init__(self):
super(HeteroBoosting, self).__init__()
self.encrypter = None
self.encrypted_calculator = None
self.early_stopping_rounds = None
self.binning_class = QuantileBinning
self.model_param = HeteroBoostingParam()
self.transfer_variable = HeteroBoostingTransferVariable()
self.mode = consts.HETERO
def _init_model(self, param: HeteroBoostingParam):
LOGGER.debug('in hetero boosting, objective param is {}'.format(
param.objective_param.objective))
super(HeteroBoosting, self)._init_model(param)
self.encrypt_param = param.encrypt_param
self.re_encrypt_rate = param.encrypted_mode_calculator_param
self.calculated_mode = param.encrypted_mode_calculator_param.mode
self.re_encrypted_rate = param.encrypted_mode_calculator_param.re_encrypted_rate
self.early_stopping_rounds = param.early_stopping_rounds
self.use_first_metric_only = param.use_first_metric_only
def generate_encrypter(self):
LOGGER.info("generate encrypter")
if self.encrypt_param.method.lower() == consts.PAILLIER.lower():
self.encrypter = PaillierEncrypt()
self.encrypter.generate_key(self.encrypt_param.key_length)
elif self.encrypt_param.method.lower() == consts.ITERATIVEAFFINE.lower(
):
self.encrypter = IterativeAffineEncrypt()
self.encrypter.generate_key(key_size=self.encrypt_param.key_length,
randomized=False)
elif self.encrypt_param.method.lower(
) == consts.RANDOM_ITERATIVEAFFINE.lower():
self.encrypter = IterativeAffineEncrypt()
self.encrypter.generate_key(key_size=self.encrypt_param.key_length,
randomized=True)
else:
raise NotImplementedError("encrypt method not supported yes!!!")
self.encrypted_calculator = EncryptModeCalculator(
self.encrypter, self.calculated_mode, self.re_encrypted_rate)
def check_label(self):
LOGGER.info("check label")
classes_ = []
num_classes, booster_dim = 1, 1
if self.task_type == consts.CLASSIFICATION:
num_classes, classes_ = ClassifyLabelChecker.validate_label(
self.data_bin)
if num_classes > 2:
booster_dim = num_classes
range_from_zero = True
for _class in classes_:
try:
if 0 <= _class < len(classes_) and isinstance(_class, int):
continue
else:
range_from_zero = False
break
except:
range_from_zero = False
classes_ = sorted(classes_)
if not range_from_zero:
class_mapping = dict(zip(classes_, range(num_classes)))
self.y = self.y.mapValues(lambda _class: class_mapping[_class])
else:
RegressionLabelChecker.validate_label(self.data_bin)
return classes_, num_classes, booster_dim
class HeteroSecureBoostingTreeGuest(BoostingTree):
def __init__(self):
super(HeteroSecureBoostingTreeGuest, self).__init__()
self.convegence = None
self.y = None
self.F = None
self.predict_F = None
self.data_bin = None
self.loss = None
self.init_score = None
self.classes_dict = {}
self.classes_ = []
self.num_classes = 0
self.classify_target = "binary"
self.feature_num = None
self.encrypter = None
self.grad_and_hess = None
self.tree_dim = 1
self.tree_meta = None
self.trees_ = []
self.history_loss = []
self.bin_split_points = None
self.bin_sparse_points = None
self.encrypted_mode_calculator = None
self.feature_importances_ = {}
self.role = consts.GUEST
self.transfer_variable = HeteroSecureBoostingTreeTransferVariable()
def set_loss(self, objective_param):
loss_type = objective_param.objective
params = objective_param.params
LOGGER.info("set objective, objective is {}".format(loss_type))
if self.task_type == consts.CLASSIFICATION:
if loss_type == "cross_entropy":
if self.num_classes == 2:
self.loss = SigmoidBinaryCrossEntropyLoss()
else:
self.loss = SoftmaxCrossEntropyLoss()
else:
raise NotImplementedError("objective %s not supported yet" %
(loss_type))
elif self.task_type == consts.REGRESSION:
if loss_type == "lse":
self.loss = LeastSquaredErrorLoss()
elif loss_type == "lae":
self.loss = LeastAbsoluteErrorLoss()
elif loss_type == "huber":
self.loss = HuberLoss(params[0])
elif loss_type == "fair":
self.loss = FairLoss(params[0])
elif loss_type == "tweedie":
self.loss = TweedieLoss(params[0])
elif loss_type == "log_cosh":
self.loss = LogCoshLoss()
else:
raise NotImplementedError("objective %s not supported yet" %
(loss_type))
else:
raise NotImplementedError("objective %s not supported yet" %
(loss_type))
def convert_feature_to_bin(self, data_instance):
LOGGER.info("convert feature to bins")
param_obj = FeatureBinningParam(bin_num=self.bin_num)
if self.use_missing:
binning_obj = QuantileBinning(param_obj,
abnormal_list=[NoneType()])
else:
binning_obj = QuantileBinning(param_obj)
binning_obj.fit_split_points(data_instance)
self.data_bin, self.bin_split_points, self.bin_sparse_points = binning_obj.convert_feature_to_bin(
data_instance)
LOGGER.info("convert feature to bins over")
def set_y(self):
LOGGER.info("set label from data and check label")
self.y = self.data_bin.mapValues(lambda instance: instance.label)
self.check_label()
def generate_flowid(self, round_num, tree_num):
LOGGER.info("generate flowid, flowid {}".format(self.flowid))
return ".".join(map(str, [self.flowid, round_num, tree_num]))
def check_label(self):
LOGGER.info("check label")
if self.task_type == consts.CLASSIFICATION:
self.num_classes, self.classes_ = ClassifyLabelChecker.validate_label(
self.data_bin)
if self.num_classes > 2:
self.classify_target = "multinomial"
self.tree_dim = self.num_classes
range_from_zero = True
for _class in self.classes_:
try:
if _class >= 0 and _class < self.num_classes and isinstance(
_class, int):
continue
else:
range_from_zero = False
break
except:
range_from_zero = False
self.classes_ = sorted(self.classes_)
if not range_from_zero:
class_mapping = dict(
zip(self.classes_, range(self.num_classes)))
self.y = self.y.mapValues(lambda _class: class_mapping[_class])
else:
RegressionLabelChecker.validate_label(self.data_bin)
self.set_loss(self.objective_param)
def generate_encrypter(self):
LOGGER.info("generate encrypter")
if self.encrypt_param.method.lower() == consts.PAILLIER.lower():
self.encrypter = PaillierEncrypt()
self.encrypter.generate_key(self.encrypt_param.key_length)
elif self.encrypt_param.method.lower() == consts.ITERATIVEAFFINE.lower(
):
self.encrypter = IterativeAffineEncrypt()
self.encrypter.generate_key(self.encrypt_param.key_length)
else:
raise NotImplementedError("encrypt method not supported yes!!!")
self.encrypted_calculator = EncryptModeCalculator(
self.encrypter, self.calculated_mode, self.re_encrypted_rate)
@staticmethod
def accumulate_f(f_val, new_f_val, lr=0.1, idx=0):
f_val[idx] += lr * new_f_val
return f_val
def update_feature_importance(self, tree_feature_importance):
for fid in tree_feature_importance:
if fid not in self.feature_importances_:
self.feature_importances_[fid] = 0
self.feature_importances_[fid] += tree_feature_importance[fid]
def update_f_value(self, new_f=None, tidx=-1, mode="train"):
LOGGER.info("update tree f value, tree idx is {}".format(tidx))
if mode == "train" and self.F is None:
if self.tree_dim > 1:
self.F, self.init_score = self.loss.initialize(
self.y, self.tree_dim)
else:
self.F, self.init_score = self.loss.initialize(self.y)
else:
accumulate_f = functools.partial(self.accumulate_f,
lr=self.learning_rate,
idx=tidx)
if mode == "train":
self.F = self.F.join(new_f, accumulate_f)
else:
self.predict_F = self.predict_F.join(new_f, accumulate_f)
def compute_grad_and_hess(self):
LOGGER.info("compute grad and hess")
loss_method = self.loss
if self.task_type == consts.CLASSIFICATION:
self.grad_and_hess = self.y.join(self.F, lambda y, f_val: \
(loss_method.compute_grad(y, loss_method.predict(f_val)), \
loss_method.compute_hess(y, loss_method.predict(f_val))))
else:
self.grad_and_hess = self.y.join(
self.F, lambda y, f_val: (loss_method.compute_grad(y, f_val),
loss_method.compute_hess(y, f_val)))
def compute_loss(self):
LOGGER.info("compute loss")
if self.task_type == consts.CLASSIFICATION:
loss_method = self.loss
y_predict = self.F.mapValues(lambda val: loss_method.predict(val))
loss = loss_method.compute_loss(self.y, y_predict)
elif self.task_type == consts.REGRESSION:
if self.objective_param.objective in [
"lse", "lae", "logcosh", "tweedie", "log_cosh", "huber"
]:
loss_method = self.loss
loss = loss_method.compute_loss(self.y, self.F)
else:
loss_method = self.loss
y_predict = self.F.mapValues(
lambda val: loss_method.predict(val))
loss = loss_method.compute_loss(self.y, y_predict)
return float(loss)
def get_grad_and_hess(self, tree_idx):
LOGGER.info("get grad and hess of tree {}".format(tree_idx))
grad_and_hess_subtree = self.grad_and_hess.mapValues(
lambda grad_and_hess:
(grad_and_hess[0][tree_idx], grad_and_hess[1][tree_idx]))
return grad_and_hess_subtree
def check_convergence(self, loss):
LOGGER.info("check convergence")
if self.convegence is None:
self.convegence = converge_func_factory("diff", self.tol)
return self.convegence.is_converge(loss)
def sample_valid_features(self):
LOGGER.info("sample valid features")
if self.feature_num is None:
self.feature_num = self.bin_split_points.shape[0]
choose_feature = random.choice(range(0, self.feature_num), \
max(1, int(self.subsample_feature_rate * self.feature_num)), replace=False)
valid_features = [False for i in range(self.feature_num)]
for fid in choose_feature:
valid_features[fid] = True
return valid_features
def sync_tree_dim(self):
LOGGER.info("sync tree dim to host")
self.transfer_variable.tree_dim.remote(self.tree_dim,
role=consts.HOST,
idx=-1)
def sync_stop_flag(self, stop_flag, num_round):
LOGGER.info(
"sync stop flag to host, boosting round is {}".format(num_round))
self.transfer_variable.stop_flag.remote(stop_flag,
role=consts.HOST,
idx=-1,
suffix=(num_round, ))
def fit(self, data_inst, validate_data=None):
LOGGER.info("begin to train secureboosting guest model")
self.gen_feature_fid_mapping(data_inst.schema)
data_inst = self.data_alignment(data_inst)
self.convert_feature_to_bin(data_inst)
self.set_y()
self.update_f_value()
self.generate_encrypter()
self.sync_tree_dim()
self.callback_meta(
"loss", "train",
MetricMeta(name="train",
metric_type="LOSS",
extra_metas={"unit_name": "iters"}))
validation_strategy = self.init_validation_strategy(
data_inst, validate_data)
for i in range(self.num_trees):
self.compute_grad_and_hess()
for tidx in range(self.tree_dim):
tree_inst = HeteroDecisionTreeGuest(self.tree_param)
tree_inst.set_inputinfo(self.data_bin,
self.get_grad_and_hess(tidx),
self.bin_split_points,
self.bin_sparse_points)
valid_features = self.sample_valid_features()
tree_inst.set_valid_features(valid_features)
tree_inst.set_encrypter(self.encrypter)
tree_inst.set_encrypted_mode_calculator(
self.encrypted_calculator)
tree_inst.set_flowid(self.generate_flowid(i, tidx))
tree_inst.set_host_party_idlist(
self.component_properties.host_party_idlist)
tree_inst.set_runtime_idx(
self.component_properties.local_partyid)
tree_inst.fit()
tree_meta, tree_param = tree_inst.get_model()
self.trees_.append(tree_param)
if self.tree_meta is None:
self.tree_meta = tree_meta
self.update_f_value(new_f=tree_inst.predict_weights, tidx=tidx)
self.update_feature_importance(
tree_inst.get_feature_importance())
loss = self.compute_loss()
self.history_loss.append(loss)
LOGGER.info("round {} loss is {}".format(i, loss))
LOGGER.debug("type of loss is {}".format(type(loss).__name__))
self.callback_metric("loss", "train", [Metric(i, loss)])
if validation_strategy:
validation_strategy.validate(self, i)
if self.n_iter_no_change is True:
if self.check_convergence(loss):
self.sync_stop_flag(True, i)
break
else:
self.sync_stop_flag(False, i)
LOGGER.debug("history loss is {}".format(min(self.history_loss)))
self.callback_meta(
"loss", "train",
MetricMeta(name="train",
metric_type="LOSS",
extra_metas={"Best": min(self.history_loss)}))
LOGGER.info("end to train secureboosting guest model")
def predict_f_value(self, data_inst):
LOGGER.info("predict tree f value, there are {} trees".format(
len(self.trees_)))
tree_dim = self.tree_dim
init_score = self.init_score
self.predict_F = data_inst.mapValues(lambda v: init_score)
rounds = len(self.trees_) // self.tree_dim
for i in range(rounds):
for tidx in range(self.tree_dim):
tree_inst = HeteroDecisionTreeGuest(self.tree_param)
tree_inst.load_model(self.tree_meta,
self.trees_[i * self.tree_dim + tidx])
# tree_inst.set_tree_model(self.trees_[i * self.tree_dim + tidx])
tree_inst.set_flowid(self.generate_flowid(i, tidx))
tree_inst.set_runtime_idx(
self.component_properties.local_partyid)
tree_inst.set_host_party_idlist(
self.component_properties.host_party_idlist)
predict_data = tree_inst.predict(data_inst)
self.update_f_value(new_f=predict_data,
tidx=tidx,
mode="predict")
def predict(self, data_inst):
LOGGER.info("start predict")
data_inst = self.data_alignment(data_inst)
self.predict_f_value(data_inst)
if self.task_type == consts.CLASSIFICATION:
loss_method = self.loss
if self.num_classes == 2:
predicts = self.predict_F.mapValues(
lambda f: float(loss_method.predict(f)))
else:
predicts = self.predict_F.mapValues(
lambda f: loss_method.predict(f).tolist())
elif self.task_type == consts.REGRESSION:
if self.objective_param.objective in [
"lse", "lae", "huber", "log_cosh", "fair", "tweedie"
]:
predicts = self.predict_F
else:
raise NotImplementedError(
"objective {} not supprted yet".format(
self.objective_param.objective))
if self.task_type == consts.CLASSIFICATION:
classes_ = self.classes_
if self.num_classes == 2:
threshold = self.predict_param.threshold
predict_result = data_inst.join(
predicts, lambda inst, pred: [
inst.label, classes_[1]
if pred > threshold else classes_[0], pred, {
"0": 1 - pred,
"1": pred
}
])
else:
predict_label = predicts.mapValues(
lambda preds: classes_[np.argmax(preds)])
predict_result = data_inst.join(
predicts, lambda inst, preds: [
inst.label, classes_[np.argmax(preds)],
np.max(preds),
dict(zip(map(str, classes_), preds))
])
elif self.task_type == consts.REGRESSION:
predict_result = data_inst.join(
predicts, lambda inst, pred:
[inst.label,
float(pred),
float(pred), {
"label": float(pred)
}])
else:
raise NotImplementedError("task type {} not supported yet".format(
self.task_type))
LOGGER.info("end predict")
return predict_result
def get_feature_importance(self):
return self.feature_importances_
def get_model_meta(self):
model_meta = BoostingTreeModelMeta()
model_meta.tree_meta.CopyFrom(self.tree_meta)
model_meta.learning_rate = self.learning_rate
model_meta.num_trees = self.num_trees
model_meta.quantile_meta.CopyFrom(QuantileMeta(bin_num=self.bin_num))
model_meta.objective_meta.CopyFrom(
ObjectiveMeta(objective=self.objective_param.objective,
param=self.objective_param.params))
model_meta.task_type = self.task_type
# model_meta.tree_dim = self.tree_dim
model_meta.n_iter_no_change = self.n_iter_no_change
model_meta.tol = self.tol
# model_meta.num_classes = self.num_classes
# model_meta.classes_.extend(map(str, self.classes_))
# model_meta.need_run = self.need_run
meta_name = "HeteroSecureBoostingTreeGuestMeta"
return meta_name, model_meta
def set_model_meta(self, model_meta):
self.tree_meta = model_meta.tree_meta
self.learning_rate = model_meta.learning_rate
self.num_trees = model_meta.num_trees
self.bin_num = model_meta.quantile_meta.bin_num
self.objective_param.objective = model_meta.objective_meta.objective
self.objective_param.params = list(model_meta.objective_meta.param)
self.task_type = model_meta.task_type
# self.tree_dim = model_meta.tree_dim
# self.num_classes = model_meta.num_classes
self.n_iter_no_change = model_meta.n_iter_no_change
self.tol = model_meta.tol
# self.classes_ = list(model_meta.classes_)
# self.set_loss(self.objective_param)
def get_model_param(self):
model_param = BoostingTreeModelParam()
model_param.tree_num = len(list(self.trees_))
model_param.tree_dim = self.tree_dim
model_param.trees_.extend(self.trees_)
model_param.init_score.extend(self.init_score)
model_param.losses.extend(self.history_loss)
model_param.classes_.extend(map(str, self.classes_))
model_param.num_classes = self.num_classes
feature_importances = list(self.get_feature_importance().items())
feature_importances = sorted(feature_importances,
key=itemgetter(1),
reverse=True)
feature_importance_param = []
for (sitename, fid), _importance in feature_importances:
feature_importance_param.append(
FeatureImportanceInfo(sitename=sitename,
fid=fid,
importance=_importance))
model_param.feature_importances.extend(feature_importance_param)
model_param.feature_name_fid_mapping.update(
self.feature_name_fid_mapping)
param_name = "HeteroSecureBoostingTreeGuestParam"
return param_name, model_param
def set_model_param(self, model_param):
self.trees_ = list(model_param.trees_)
self.init_score = np.array(list(model_param.init_score))
self.history_loss = list(model_param.losses)
self.classes_ = list(model_param.classes_)
self.tree_dim = model_param.tree_dim
self.num_classes = model_param.num_classes
self.feature_name_fid_mapping.update(
model_param.feature_name_fid_mapping)
def get_metrics_param(self):
if self.task_type == consts.CLASSIFICATION:
if self.num_classes == 2:
return EvaluateParam(eval_type="binary",
pos_label=self.classes_[1])
else:
return EvaluateParam(eval_type="multi")
else:
return EvaluateParam(eval_type="regression")
def export_model(self):
if self.need_cv:
return None
meta_name, meta_protobuf = self.get_model_meta()
param_name, param_protobuf = self.get_model_param()
self.model_output = {
meta_name: meta_protobuf,
param_name: param_protobuf
}
return self.model_output
def load_model(self, model_dict):
model_param = None
model_meta = None
for _, value in model_dict["model"].items():
for model in value:
if model.endswith("Meta"):
model_meta = value[model]
if model.endswith("Param"):
model_param = value[model]
LOGGER.info("load model")
self.set_model_meta(model_meta)
self.set_model_param(model_param)
self.set_loss(self.objective_param)