fix: replace mutable default evals_result=dict() across 27 contrib models

qlib/contrib/model/catboost_model.py

@@ -31,10 +31,13 @@ def fit(
         num_boost_round=1000,
         early_stopping_rounds=50,
         verbose_eval=20,
-        evals_result=dict(),
+        evals_result=None,
         reweighter=None,
         **kwargs,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         df_train, df_valid = dataset.prepare(
             ["train", "valid"],
             col_set=["feature", "label"],

qlib/contrib/model/pytorch_adarnn.py

@@ -242,9 +242,12 @@ def log_metrics(self, mode, metrics):
     def fit(
         self,
         dataset: DatasetH,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         df_train, df_valid = dataset.prepare(
             ["train", "valid"],
             col_set=["feature", "label"],

qlib/contrib/model/pytorch_add.py

@@ -363,9 +363,12 @@ def fit_thresh(self, train_label):
     def fit(
         self,
         dataset: DatasetH,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         label_train, label_valid = dataset.prepare(
             ["train", "valid"],
             col_set=["label"],

qlib/contrib/model/pytorch_alstm.py

@@ -209,9 +209,12 @@ def test_epoch(self, data_x, data_y):
     def fit(
         self,
         dataset: DatasetH,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         df_train, df_valid, df_test = dataset.prepare(
             ["train", "valid", "test"],
             col_set=["feature", "label"],

qlib/contrib/model/pytorch_alstm_ts.py

@@ -206,10 +206,13 @@ def test_epoch(self, data_loader):
     def fit(
         self,
         dataset,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
         reweighter=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         if dl_train.empty or dl_valid.empty:

qlib/contrib/model/pytorch_gats.py

@@ -224,9 +224,12 @@ def test_epoch(self, data_x, data_y):
     def fit(
         self,
         dataset: DatasetH,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         df_train, df_valid, df_test = dataset.prepare(
             ["train", "valid", "test"],
             col_set=["feature", "label"],

qlib/contrib/model/pytorch_gats_ts.py

@@ -233,9 +233,12 @@ def test_epoch(self, data_loader):
     def fit(
         self,
         dataset,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         if dl_train.empty or dl_valid.empty:

qlib/contrib/model/pytorch_general_nn.py

@@ -235,10 +235,13 @@ def test_epoch(self, data_loader):
     def fit(
         self,
         dataset: Union[DatasetH, TSDatasetH],
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
         reweighter=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         ists = isinstance(dataset, TSDatasetH)  # is this time series dataset
 
         dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)

qlib/contrib/model/pytorch_gru.py

@@ -209,9 +209,12 @@ def test_epoch(self, data_x, data_y):
     def fit(
         self,
         dataset: DatasetH,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         # prepare training and validation data
         dfs = {
             k: dataset.prepare(

qlib/contrib/model/pytorch_gru_ts.py

@@ -200,10 +200,13 @@ def test_epoch(self, data_loader):
     def fit(
         self,
         dataset,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
         reweighter=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         if dl_train.empty or dl_valid.empty:

qlib/contrib/model/pytorch_hist.py

@@ -244,9 +244,12 @@ def test_epoch(self, data_x, data_y, stock_index):
     def fit(
         self,
         dataset: DatasetH,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         df_train, df_valid, df_test = dataset.prepare(
             ["train", "valid", "test"],
             col_set=["feature", "label"],

qlib/contrib/model/pytorch_igmtf.py

@@ -248,9 +248,12 @@ def test_epoch(self, data_x, data_y, train_hidden, train_hidden_day):
     def fit(
         self,
         dataset: DatasetH,
-        evals_result=dict(),
+        evals_result=None,
         save_path=None,
     ):
+        if evals_result is None:
+            evals_result = {}
+
         df_train, df_valid = dataset.prepare(
             ["train", "valid"],
             col_set=["feature", "label"],