0.18.8

cleanup

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.18.7"
+version = "0.18.8"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/data/lightdatamodule.py

@@ -28,6 +28,8 @@ class LightDataModule(L.LightningDataModule):
             The number of workers. Defaults to 0.
         scaler (object, optional):
             The spot scaler object (e.g. TorchStandardScaler). Defaults to None.
+        verbosity (int):
+            The verbosity level. Defaults to 0.
 
     Examples:
         >>> from spotpython.data.lightdatamodule import LightDataModule

src/spotpython/light/cvmodel.py

@@ -67,6 +67,7 @@ def cv_model(config: dict, fun_control: dict) -> float:
             batch_size=config["batch_size"],
             data_dir=fun_control["DATASET_PATH"],
             scaler=fun_control["scaler"],
+            verbosity=fun_control["verbosity"],
         )
         dm.setup()
         dm.prepare_data()

src/spotpython/light/predictmodel.py

@@ -73,6 +73,7 @@ def predict_model(config: dict, fun_control: dict) -> Tuple[float, float]:
         test_size=fun_control["test_size"],
         test_seed=fun_control["test_seed"],
         scaler=fun_control["scaler"],
+        verbosity=fun_control["verbosity"],
     )
     # TODO: Check if this is necessary:
     # dm.setup(stage="train")

src/spotpython/light/testmodel.py

@@ -74,6 +74,7 @@ def test_model(config: dict, fun_control: dict) -> Tuple[float, float]:
         test_size=fun_control["test_size"],
         test_seed=fun_control["test_seed"],
         scaler=fun_control["scaler"],
+        verbosity=fun_control["verbosity"],
     )
     # TODO: Check if this is necessary:
     # dm.setup()

src/spotpython/light/trainmodel.py

@@ -105,18 +105,21 @@ def train_model(config: dict, fun_control: dict, timestamp: bool = True) -> floa
                 pprint.pprint(config)
                 y = train_model(config, fun_control)
     """
-    config_id = generate_config_id_with_timestamp(config=config, timestamp=timestamp)
+    if fun_control["data_module"] is None:
+        dm = LightDataModule(
+            dataset=fun_control["data_set"],
+            data_full_train=fun_control["data_full_train"],
+            data_test=fun_control["data_test"],
+            batch_size=config["batch_size"],
+            num_workers=fun_control["num_workers"],
+            test_size=fun_control["test_size"],
+            test_seed=fun_control["test_seed"],
+            scaler=fun_control["scaler"],
+            verbosity=fun_control["verbosity"],
+        )
+    else:
+        dm = fun_control["data_module"]
     model = build_model_instance(config, fun_control)
-    dm = LightDataModule(
-        dataset=fun_control["data_set"],
-        data_full_train=fun_control["data_full_train"],
-        data_test=fun_control["data_test"],
-        batch_size=config["batch_size"],
-        num_workers=fun_control["num_workers"],
-        test_size=fun_control["test_size"],
-        test_seed=fun_control["test_seed"],
-        scaler=fun_control["scaler"],
-    )
     # TODO: Check if this is necessary or if this is handled by the trainer
     # dm.setup()
     # print(f"train_model(): Test set size: {len(dm.data_test)}")
@@ -168,7 +171,7 @@ def train_model(config: dict, fun_control: dict, timestamp: bool = True) -> floa
     #   Can be set to 'link' on a local filesystem to create a symbolic link.
     #   This allows accessing the latest checkpoint in a deterministic manner.
     #   Default: None.
-
+    config_id = generate_config_id_with_timestamp(config=config, timestamp=timestamp)
     callbacks = [EarlyStopping(monitor="val_loss", patience=config["patience"], mode="min", strict=False, verbose=False)]
     if not timestamp:
         # add ModelCheckpoint only if timestamp is False