Merge branch 'dev-2.0.0-rc' into dev-2.0.0-sshe-nn

examples/pipeline/hetero_nn/test_nn_binary.py

@@ -48,7 +48,7 @@ def main(config="../../config.yaml", namespace=""):
             num_train_epochs=5,
             per_device_train_batch_size=16,
             logging_strategy='epoch',
-            no_cuda=True,
+            no_cuda=True
         )
 
     guest_conf = get_config_of_default_runner(
@@ -70,7 +70,7 @@ def main(config="../../config.yaml", namespace=""):
 
     hetero_nn_0 = HeteroNN(
         'hetero_nn_0',
-        train_data=psi_0.outputs['output_data']
+        train_data=psi_0.outputs['output_data'], validate_data=psi_0.outputs['output_data']
     )
 
     hetero_nn_0.guest.task_setting(runner_conf=guest_conf)
@@ -86,7 +86,7 @@ def main(config="../../config.yaml", namespace=""):
         'eval_0',
         runtime_roles=['guest'],
         metrics=['auc'],
-        input_data=[hetero_nn_1.outputs['predict_data_output'], hetero_nn_0.outputs['train_data_output']]
+        input_data=[hetero_nn_0.outputs['train_data_output']]
     )
 
     pipeline.add_task(psi_0)

examples/pipeline/hetero_nn/test_nn_binary_with_fedpass.py

@@ -46,11 +46,10 @@ def main(config="../../config.yaml", namespace=""):
                                                                         namespace="experiment"))
 
     training_args = TrainingArguments(
-            num_train_epochs=5,
+            num_train_epochs=1,
             per_device_train_batch_size=16,
             logging_strategy='epoch',
-            no_cuda=True,
-            disable_tqdm=False
+            no_cuda=True
         )
 
     guest_conf = get_config_of_default_runner(

examples/pipeline/hetero_secureboost/test_hetero_sbt_binary.py

@@ -21,7 +21,7 @@ def main(config="../config.yaml", namespace=""):
     psi_0.hosts[0].task_setting(input_data=DataWarehouseChannel(name="breast_hetero_host",
                                                                         namespace="experiment"))
 
-    hetero_sbt_0 = HeteroSecureBoost('sbt_0', num_trees=3, max_bin=32, max_depth=3, goss=True,
+    hetero_sbt_0 = HeteroSecureBoost('sbt_0', num_trees=2, max_bin=32, max_depth=3, goss=True, top_rate=0.2,
                                     he_param={'kind': 'paillier', 'key_length': 1024}, train_data=psi_0.outputs['output_data'],)
     evaluation_0 = Evaluation(
         'eval_0',

python/fate/components/components/hetero_nn.py

@@ -55,7 +55,6 @@ def train(
         train_model_input
     )
 
-
 @hetero_nn.predict()
 def predict(
         ctx: Context,

python/fate/components/components/nn/component_utils.py

@@ -136,13 +136,17 @@ def train_procedure(
 
     logger.info('Predicting Train & Validate Data')
     train_pred = runner.predict(train_data_, saved_model_path)
+    validate_pred = None
+    if validate_data_ is not None:
+        validate_pred = runner.predict(validate_data_)
+
+    logger.info('predicting done')
     if train_pred is not None:
         assert isinstance(
             train_pred, DataFrame), "train predict result should be a DataFrame"
         add_dataset_type(train_pred, consts.TRAIN_SET)
 
-        if validate_data_ is not None:
-            validate_pred = runner.predict(validate_data_)
+        if validate_pred is not None:
             assert isinstance(
                 validate_pred, DataFrame), "validate predict result should be a DataFrame"
             add_dataset_type(validate_pred, consts.VALIDATE_SET)

python/fate/components/components/nn/runner/hetero_default_runner.py

@@ -271,7 +271,6 @@ def predict(self,
                 dataframe_format='fate_std',
                 task_type=self.task_type,
                 classes=classes)
-
             return rs_df
 
         elif self.is_host():

python/fate/ml/ensemble/algo/secureboost/hetero/guest.py

@@ -85,6 +85,7 @@ def _extend_score(s: pd.Series, class_num, dim):
         acc_scores = acc_scores + extend_scores * learning_rate
     return acc_scores
 
+
 class HeteroSecureBoostGuest(HeteroBoostingTree):
     def __init__(
         self,
@@ -404,7 +405,7 @@ def from_model(self, model: dict):
         self._init_score = float(model["init_score"]) if model["init_score"] is not None else None
         # initialize
         self._tree_dim = self.num_class if self.objective == MULTI_CE else 1
-        self._loss_func = _get_loss_func(self.objective)
+        self._loss_func = _get_loss_func(self.objective, class_num=self.num_class)
         # for warmstart
         self._model_loaded = True
         # load loss

python/fate/ml/glm/homo/lr/client.py

@@ -5,7 +5,7 @@
 from fate.arch import Context
 import logging
 import torch as t
-from fate.ml.nn.homo import FedAVGCLient, TrainingArguments, FedAVGArguments
+from fate.ml.nn.homo.fedavg import FedAVGClient, TrainingArguments, FedAVGArguments
 from transformers import default_data_collator
 import functools
 import tempfile
@@ -408,7 +408,7 @@ def fit(self, ctx: Context, train_data: DataFrame,
             num_train_epochs=self.max_iter,
             per_device_train_batch_size=self.batch_size,
             per_device_eval_batch_size=self.batch_size)
-        self.trainer = FedAVGCLient(
+        self.trainer = FedAVGClient(
             ctx,
             model=self.model,
             loss_fn=loss_fn,
@@ -436,7 +436,7 @@ def predict(self, ctx: Context, predict_data: DataFrame) -> DataFrame:
             train_arg = TrainingArguments(
                 num_train_epochs=self.max_iter,
                 per_device_eval_batch_size=batch_size)
-            trainer = FedAVGCLient(
+            trainer = FedAVGClient(
                 ctx,
                 train_set=self.predict_set,
                 model=self.model,

python/fate/ml/glm/homo/lr/server.py

@@ -2,7 +2,7 @@
 from fate.arch.dataframe import DataFrame
 from fate.arch import Context
 import logging
-from fate.ml.nn.homo import FedAVGServer
+from fate.ml.nn.homo.fedavg import FedAVGServer
 
 
 logger = logging.getLogger(__name__)
@@ -25,4 +25,4 @@ def predict(
             ctx: Context,
             predict_data: DataFrame = None) -> DataFrame:
 
-        logger.info('kkip prediction stage')
+        logger.info('skip prediction stage')

python/fate/ml/nn/model_zoo/agg_layer/fedpass/_passport_block.py

@@ -44,10 +44,13 @@ def set_key(self, skey, bkey):
     def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                               missing_keys, unexpected_keys, error_msgs):
 
-        if 'skey' in state_dict:
-            self.register_buffer('skey', t.randn(*state_dict['skey'].size()))
-        if 'bkey' in state_dict:
-            self.register_buffer('bkey', t.randn(*state_dict['bkey'].size()))
+        skey = '_agg_layer._model.skey'
+        bkey = '_agg_layer._model.bkey'
+
+        if skey in state_dict:
+            self.register_buffer('skey', t.randn(*state_dict[skey].size()))
+        if bkey in state_dict:
+            self.register_buffer('bkey', t.randn(*state_dict[bkey].size()))
 
         if '_out_scale' in state_dict:
             self.scale = nn.Parameter(t.randn(*state_dict['_out_scale'].size()))