fixed some missing API reference in fs.core

federatedscope/core/configs/__init__.py

@@ -13,10 +13,19 @@
 if "config" in all_sub_configs:
     all_sub_configs.remove('config')
 
+
+from federatedscope.core.configs.config import CN, init_global_cfg
+__all__ = __all__ + \
+          [
+              'CN',
+              'init_global_cfg'
+          ]
+
 # reorder the config to ensure the base config will be registered first
 base_configs = [
     'cfg_data', 'cfg_fl_setting', 'cfg_model', 'cfg_training', 'cfg_evaluation'
 ]
 for base_config in base_configs:
     all_sub_configs.pop(all_sub_configs.index(base_config))
     all_sub_configs.insert(0, base_config)
+

federatedscope/core/monitors/__init__.py

@@ -0,0 +1,5 @@
+from federatedscope.core.monitors.early_stopper import EarlyStopper
+from federatedscope.core.monitors.metric_calculator import MetricCalculator
+from federatedscope.core.monitors.monitor import Monitor
+
+__all__ = ['EarlyStopper', 'MetricCalculator', 'Monitor']

federatedscope/core/trainers/__init__.py

@@ -1,15 +1,14 @@
 from federatedscope.core.trainers.trainer import Trainer, GeneralTorchTrainer
+from federatedscope.core.trainers.trainer_multi_model import GeneralMultiModelTrainer
 from federatedscope.core.trainers.trainer_pFedMe import wrap_pFedMeTrainer
+from federatedscope.core.trainers.trainer_Ditto import wrap_DittoTrainer
+from federatedscope.core.trainers.trainer_FedEM import FedEMTrainer
 from federatedscope.core.trainers.context import Context
 from federatedscope.core.trainers.trainer_fedprox import wrap_fedprox_trainer
 from federatedscope.core.trainers.trainer_nbafl import wrap_nbafl_trainer, wrap_nbafl_server
 
 __all__ = [
-    'Trainer',
-    'Context',
-    'GeneralTorchTrainer',
-    'wrap_pFedMeTrainer',
-    'wrap_fedprox_trainer',
-    'wrap_nbafl_trainer',
-    'wrap_nbafl_server'
+    'Trainer', 'Context', 'GeneralTorchTrainer', 'GeneralMultiModelTrainer',
+    'wrap_pFedMeTrainer', 'wrap_DittoTrainer', 'FedEMTrainer',
+    'wrap_fedprox_trainer', 'wrap_nbafl_trainer', 'wrap_nbafl_server'
 ]

federatedscope/mf/dataloader/__init__.py

@@ -1,6 +1,3 @@
 from federatedscope.mf.dataloader.dataloader import load_mf_dataset, MFDataLoader
 
-__all__ = [
-    'load_mf_dataset',
-    'MFDataLoader'
-]
+__all__ = ['load_mf_dataset', 'MFDataLoader']

federatedscope/mf/dataset/__init__.py

@@ -1,13 +1,6 @@
 from federatedscope.mf.dataset.movielens import *
 
 __all__ = [
-    'VMFDataset',
-    'HMFDataset',
-    'MovieLensData',
-    'MovieLens1M',
-    'MovieLens10M',
-    'VFLMovieLens1M',
-    'HFLMovieLens1M',
-    'VFLMovieLens10M',
-    'HFLMovieLens10M'
-]
+    'VMFDataset', 'HMFDataset', 'MovieLensData', 'MovieLens1M', 'MovieLens10M',
+    'VFLMovieLens1M', 'HFLMovieLens1M', 'VFLMovieLens10M', 'HFLMovieLens10M'
+]

federatedscope/mf/model/__init__.py

@@ -1,9 +1,4 @@
 from federatedscope.mf.model.model import BasicMFNet, VMFNet, HMFNet
 from federatedscope.mf.model.model_builder import get_mfnet
 
-__all__ = [
-    "get_mfnet",
-    "BasicMFNet",
-    "VMFNet",
-    "HMFNet"
-]
+__all__ = ["get_mfnet", "BasicMFNet", "VMFNet", "HMFNet"]

federatedscope/mf/trainer/__init__.py

@@ -2,9 +2,6 @@
 from federatedscope.mf.trainer.trainer_sgdmf import wrap_MFTrainer, init_sgdmf_ctx, embedding_clip, hook_on_batch_backward
 
 __all__ = [
-    'MFTrainer',
-    'wrap_MFTrainer',
-    'init_sgdmf_ctx',
-    'embedding_clip',
+    'MFTrainer', 'wrap_MFTrainer', 'init_sgdmf_ctx', 'embedding_clip',
     'hook_on_batch_backward'
-]
+]

scripts/parse_exp_results_wandb.py

@@ -95,7 +95,7 @@ def main():
                     if res['Role'] == 'Server #':
                         cur_round = res['Round']
                     res.pop('Role')
-                    if cur_round != "Final":
+                    if cur_round != "Final" and 'Results_raw' in res:
                         res.pop('Results_raw')
 
                     log_res = {}
@@ -110,6 +110,10 @@ def main():
 
                             else:
                                 exp_stop_normal = True
+                                if key == "Results_raw":
+                                    for final_type, final_type_dict in res["Results_raw"].items():
+                                        for inner_key, inner_val in final_type_dict.items():
+                                            log_res_best[f"{final_type}/{inner_key}"] = inner_val
                             #     log_res_best = {}
                             #     for best_res_type, val_dict in val.items():
                             #         for key_inner, val_inner in val_dict.items():