0.18.11

Author: bartzbeielstein

RELEASE_NOTES.txt

@@ -1,3 +1,8 @@
+spotpython 0.18.11:
+
+- testmodel, predictmodel, and cvmodel functions updated, so that they can handle DataModules specified by the user in fun_control.
+
+
 spotpython 0.18.8:
 
 - lightdatamodule.py:

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.18.10"
+version = "0.18.11"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/light/cvmodel.py

@@ -56,19 +56,22 @@ def cv_model(config: dict, fun_control: dict) -> float:
 
         model = fun_control["core_model"](**config, _L_in=_L_in, _L_out=_L_out, _L_cond=_L_cond, _torchmetric=_torchmetric)
 
-        dm = LightCrossValidationDataModule(
-            k=k,
-            num_splits=num_folds,
-            split_seed=split_seed,
-            dataset=fun_control["data_set"],
-            data_full_train=fun_control["data_full_train"],
-            data_test=fun_control["data_test"],
-            num_workers=fun_control["num_workers"],
-            batch_size=config["batch_size"],
-            data_dir=fun_control["DATASET_PATH"],
-            scaler=fun_control["scaler"],
-            verbosity=fun_control["verbosity"],
-        )
+        if fun_control["data_module"] is None:
+            dm = LightCrossValidationDataModule(
+                k=k,
+                num_splits=num_folds,
+                split_seed=split_seed,
+                dataset=fun_control["data_set"],
+                data_full_train=fun_control["data_full_train"],
+                data_test=fun_control["data_test"],
+                num_workers=fun_control["num_workers"],
+                batch_size=config["batch_size"],
+                data_dir=fun_control["DATASET_PATH"],
+                scaler=fun_control["scaler"],
+                verbosity=fun_control["verbosity"],
+            )
+        else:
+            dm = fun_control["data_module"]
         dm.setup()
         dm.prepare_data()
 

src/spotpython/light/predictmodel.py

@@ -64,17 +64,20 @@ def predict_model(config: dict, fun_control: dict) -> Tuple[float, float]:
     # the config id is generated here without a timestamp. This differs from
     # the config id generated in cvmodel.py and trainmodel.py.
     config_id = generate_config_id(config, timestamp=False) + "_TEST"
-    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"],
-    )
+    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"]
     # TODO: Check if this is necessary:
     # dm.setup(stage="train")
     # Init model from datamodule's attributes

src/spotpython/light/testmodel.py

@@ -65,17 +65,20 @@ def test_model(config: dict, fun_control: dict) -> Tuple[float, float]:
     # the config id is generated here without a timestamp. This differs from
     # the config id generated in cvmodel.py and trainmodel.py.
     config_id = generate_config_id(config, timestamp=False) + "_TEST"
-    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"],
-    )
+    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"]
     # TODO: Check if this is necessary:
     # dm.setup()
     # Init model from datamodule's attributes