[Fixbug] Fix twitter related bugs and merge_test_data

benchmark/FedHPOB/scripts/lr/run_hpo_twitter_lr.sh

@@ -0,0 +1,42 @@
+set -e
+
+cudaid=$1
+dataset=$2
+
+cd ../..
+
+out_dir=out_${dataset} 
+
+if [ ! -d $out_dir ];then
+  mkdir $out_dir
+fi
+
+echo "HPO starts..."
+
+sample_rates=(0.01)
+lrs=(0.00001 0.0001 0.001 0.01 0.1 1.0)
+wds=(0.0 0.001 0.01 0.1)
+steps=(1 2 3 4)
+batch_sizes=(64)
+
+for (( sr=0; sr<${#sample_rates[@]}; sr++ ))
+do
+    for (( l=0; l<${#lrs[@]}; l++ ))
+    do
+        for (( w=0; w<${#wds[@]}; w++ ))
+        do
+            for (( s=0; s<${#steps[@]}; s++ ))
+            do
+                for (( b=0; b<${#batch_sizes[@]}; b++ ))
+                do
+                    for k in {1..3}
+                    do
+                        python federatedscope/main.py --cfg fedhpo/openml/openml_lr.yaml device $cudaid optimizer.lr ${lrs[$l]} optimizer.weight_decay ${wds[$w]} federate.local_update_steps ${steps[$s]} data.type ${dataset}@openml data.batch_size ${batch_sizes[$b]} federate.sample_client_rate ${sample_rates[$sr]} model.out_channels $out_channels seed $k outdir lr/${out_dir}_${sample_rates[$sr]} expname lr${lrs[$l]}_wd${wds[$w]}_dropout0_step${steps[$s]}_batch${batch_sizes[$b]}_seed${k} >/dev/null 2>&1
+                    done
+                done
+            done
+        done
+    done
+done
+
+echo "HPO ends."

benchmark/FedHPOB/scripts/lr/twitter.yaml

@@ -0,0 +1,36 @@
+use_gpu: True
+device: 0
+early_stop:
+  patience: 100
+federate:
+  mode: standalone
+  total_round_num: 500
+  sample_client_rate: 0.01
+  make_global_eval: True
+  merge_test_data: True
+  share_local_model: True
+  online_aggr: True
+data:
+  root: data/
+  type: twitter
+  batch_size: 5
+  subsample: 0.005
+  num_workers: 0
+model:
+  type: lr
+  out_channels: 2
+  dropout: 0.0
+train:
+  local_update_steps: 10
+  optimizer:
+    lr: 0.0003
+    weight_decay: 0.0
+criterion:
+  type: CrossEntropyLoss
+trainer:
+  type: nlptrainer
+eval:
+  freq: 1
+  metrics: ['acc', 'correct', 'f1']
+  split: [ 'test' ]
+  best_res_update_round_wise_key: 'test_loss'

federatedscope/core/auxiliaries/data_builder.py

@@ -644,28 +644,44 @@ def merge_data(all_data, merged_max_data_id, specified_dataset_name=None):
     assert len(dataset_names) >= 1, \
         "At least one sub-dataset is required in client 1"
     data_name = "test" if "test" in dataset_names else dataset_names[0]
-    if isinstance(all_data[1][data_name], dict):
-        data_elem_names = list(all_data[1][data_name].keys())  # e.g., x, y
+    id_has_key = 1
+    while "test" not in all_data[id_has_key]:
+        id_has_key += 1
+        if len(all_data) <= id_has_key:
+            raise KeyError(f'All data do not key {data_name}.')
+    if isinstance(all_data[id_has_key][data_name], dict):
+        data_elem_names = list(
+            all_data[id_has_key][data_name].keys())  # e.g., x, y
         merged_data = {name: defaultdict(list) for name in dataset_names}
         for data_id in range(1, merged_max_data_id):
             for d_name in dataset_names:
+                if d_name not in all_data[data_id]:
+                    continue
                 for elem_name in data_elem_names:
                     merged_data[d_name][elem_name].append(
                         all_data[data_id][d_name][elem_name])
         for d_name in dataset_names:
             for elem_name in data_elem_names:
                 merged_data[d_name][elem_name] = np.concatenate(
                     merged_data[d_name][elem_name])
-    elif issubclass(type(all_data[1][data_name]), torch.utils.data.DataLoader):
-        merged_data = {name: all_data[1][name] for name in dataset_names}
-        for data_id in range(2, merged_max_data_id):
+    elif issubclass(type(all_data[id_has_key][data_name]),
+                    torch.utils.data.DataLoader):
+        merged_data = {
+            name: all_data[id_has_key][name]
+            for name in dataset_names
+        }
+        for data_id in range(1, merged_max_data_id):
+            if data_id == id_has_key:
+                continue
             for d_name in dataset_names:
+                if d_name not in all_data[data_id]:
+                    continue
                 merged_data[d_name].dataset.extend(
                     all_data[data_id][d_name].dataset)
     else:
         raise NotImplementedError(
             "Un-supported type when merging data across different clients."
-            f"Your data type is {type(all_data[1][data_name])}. "
+            f"Your data type is {type(all_data[id_has_key][data_name])}. "
             f"Currently we only support the following forms: "
             " 1): {data_id: {train: {x:ndarray, y:ndarray}} }"
             " 2): {data_id: {train: DataLoader }")

federatedscope/core/monitors/monitor.py

@@ -541,7 +541,8 @@ def update_best_result(self,
                 if round_wise_update_key not in [
                         "val_loss", "test_loss", "loss", "val_avg_loss",
                         "test_avg_loss", "avg_loss", "test_acc", "test_std",
-                        "val_acc", "val_std", "val_imp_ratio"
+                        "val_acc", "val_std", "val_imp_ratio", "train_loss",
+                        "train_avg_loss"
                 ]:
                     raise NotImplementedError(
                         f"We currently support round_wise_update_key as one "

federatedscope/cv/dataset/leaf.py

@@ -91,7 +91,7 @@ def process(self):
 
 class LocalDataset(Dataset):
     """
-    Convert data list to torch Dataset to save memory usage.
+        Convert data list to torch Dataset to save memory usage.
     """
     def __init__(self,
                  Xs,
@@ -122,3 +122,7 @@ def __getitem__(self, idx):
             target = self.target_transform(target)
 
         return data, target
+
+    def extend(self, dataset):
+        self.Xs = np.vstack((self.Xs, dataset.Xs))
+        self.targets = np.hstack((self.targets, dataset.targets))

federatedscope/nlp/baseline/fedavg_lr_on_twitter.yaml

@@ -6,6 +6,8 @@ federate:
   mode: standalone
   total_round_num: 100
   sample_client_num: 10
+  share_local_model: True
+  online_aggr: True
 data:
   root: data/
   type: twitter
@@ -26,7 +28,7 @@ criterion:
 trainer:
   type: nlptrainer
 eval:
-  freq: 10
-  metrics: ['acc', 'correct']
-  split: ['train']
+  freq: 1
+  metrics: ['acc', 'correct', 'f1']
+  split: [ 'train' ]
   best_res_update_round_wise_key: 'train_loss'