v0.2.49

bartzbeielstein · bartzbeielstein · commit e8d5a1212fe1 · 2023-06-27T12:17:00.000+02:00
diff --git a/pyproject.toml b/pyproject.toml
@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.2.48"
+version = "0.2.49"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]
diff --git a/src/spotPython/data/light_hyper_dict.json b/src/spotPython/data/light_hyper_dict.json
@@ -20,16 +20,32 @@
                     "lower": 1,
                     "upper": 4},
         "act_fn": {
-                "levels": ["Sigmoid", "Tanh", "ReLU", "LeakyReLU", "ELU", "Swish"],
+                "levels": ["Sigmoid",
+                           "Tanh",
+                           "ReLU",
+                           "LeakyReLU",
+                           "ELU",
+                           "Swish"],
                 "type": "factor",
                 "default": "ReLU",
                 "transform": "None",
-                "class_name": "spotPython.torch.activation", 
+                "class_name": "spotPython.torch.activation",
                 "core_model_parameter_type": "instance()",
                 "lower": 0,
                 "upper": 2},
         "optimizer": {
-            "levels": ["Adadelta", "Adagrad", "Adam", "AdamW", "SparseAdam", "Adamax", "ASGD", "NAdam", "RAdam", "RMSprop", "Rprop", "SGD"],
+            "levels": ["Adadelta",
+                       "Adagrad",
+                       "Adam",
+                       "AdamW",
+                       "SparseAdam",
+                       "Adamax",
+                       "ASGD",
+                       "NAdam",
+                       "RAdam",
+                       "RMSprop",
+                       "Rprop",
+                       "SGD"],
             "type": "factor",
             "default": "SGD",
             "transform": "None",
@@ -48,7 +64,14 @@
             "default": 1.0,
             "transform": "None",
             "lower": 0.1,
-            "upper": 10.0}
+            "upper": 10.0},
+        "patience": {
+            "type": "int",
+            "default": 2,
+            "transform": "transform_power_2_int",
+            "lower": 2,
+            "upper": 6
+        }
     },
     "LitModel":
     {
@@ -87,6 +110,6 @@
             "class_name": "torch.optim",
             "core_model_parameter_type": "str",
             "lower": 0,
-            "upper": 12}
+            "upper": 11}
     }
 }
diff --git a/src/spotPython/light/netlightbase.py b/src/spotPython/light/netlightbase.py
@@ -8,16 +8,18 @@
 
 
 class NetLightBase(L.LightningModule):
-    def __init__(self, l1, epochs, batch_size, act_fn, optimizer, dropout_prob, lr_mult, _L_in=64, _L_out=11):
+    def __init__(
+        self, l1, epochs, batch_size, act_fn, optimizer, dropout_prob, lr_mult, patience=3, _L_in=64, _L_out=11
+    ):
         super().__init__()
-
-        # We take in input dimensions as parameters and use those to dynamically build model.
+        self.save_hyperparameters()
         self._L_out = _L_out
         if l1 < 4:
             raise ValueError("l1 must be at least 4")
         self.l1 = l1
         hidden_sizes = [l1, l1 // 2, l1 // 2, l1 // 4]
         self.epochs = epochs
+        self.patience = patience
         self.batch_size = batch_size
         self.act_fn = act_fn
         self.optimizer = optimizer
@@ -63,6 +65,7 @@ def validation_step(self, batch, batch_idx, prog_bar=False):
         self.log("valid_mapk", self.valid_mapk, on_step=False, on_epoch=True, prog_bar=prog_bar)
         self.log("val_loss", loss, prog_bar=prog_bar)
         self.log("val_acc", acc, prog_bar=prog_bar)
+        self.log("hp_metric", loss)
 
     def test_step(self, batch, batch_idx, prog_bar=False):
         x, y = batch
@@ -75,6 +78,7 @@ def test_step(self, batch, batch_idx, prog_bar=False):
         self.log("test_mapk", self.test_mapk, on_step=True, on_epoch=True, prog_bar=prog_bar)
         self.log("val_loss", loss, prog_bar=prog_bar)
         self.log("val_acc", acc, prog_bar=prog_bar)
+        # self.log("hp_metric", loss, prog_bar=prog_bar)
         return loss, acc
 
     def configure_optimizers(self):
diff --git a/src/spotPython/light/traintest.py b/src/spotPython/light/traintest.py
@@ -7,6 +7,10 @@
 
 
 def train_model(config, fun_control):
+    if fun_control["enable_progress_bar"] is None:
+        enable_progress_bar = False
+    else:
+        enable_progress_bar = fun_control["enable_progress_bar"]
     config_id = generate_config_id(config)
     # Init DataModule
     dm = CSVDataModule(
@@ -21,8 +25,11 @@ def train_model(config, fun_control):
         max_epochs=model.epochs,
         accelerator="auto",
         devices=1,
-        logger=TensorBoardLogger(save_dir=fun_control["tensorboard_path"], version=config_id),
-        callbacks=[EarlyStopping(monitor="val_loss", patience=3, mode="min", strict=False, verbose=False)],
+        logger=TensorBoardLogger(save_dir=fun_control["tensorboard_path"], version=config_id, default_hp_metric=True),
+        callbacks=[
+            EarlyStopping(monitor="val_loss", patience=config["patience"], mode="min", strict=False, verbose=False)
+        ],
+        enable_progress_bar=enable_progress_bar,
     )
     # Pass the datamodule as arg to trainer.fit to override model hooks :)
     trainer.fit(model=model, datamodule=dm)
@@ -36,6 +43,10 @@ def train_model(config, fun_control):
 
 
 def test_model(config, fun_control):
+    if fun_control["enable_progress_bar"] is None:
+        enable_progress_bar = False
+    else:
+        enable_progress_bar = fun_control["enable_progress_bar"]
     config_id = generate_config_id(config)
     # Init DataModule
     dm = CSVDataModule(
@@ -50,7 +61,11 @@ def test_model(config, fun_control):
         max_epochs=model.epochs,
         accelerator="auto",
         devices=1,
-        logger=TensorBoardLogger(save_dir=fun_control["tensorboard_path"], version=config_id),
+        logger=TensorBoardLogger(save_dir=fun_control["tensorboard_path"], version=config_id, default_hp_metric=True),
+        callbacks=[
+            EarlyStopping(monitor="val_loss", patience=config["patience"], mode="min", strict=False, verbose=False)
+        ],
+        enable_progress_bar=enable_progress_bar,
     )
     # Pass the datamodule as arg to trainer.fit to override model hooks :)
     trainer.fit(model=model, datamodule=dm)
@@ -61,7 +76,11 @@ def test_model(config, fun_control):
 
 
 def cv_model(config, fun_control):
-    # config_id = generate_config_id(config)
+    config_id = generate_config_id(config)
+    if fun_control["enable_progress_bar"] is None:
+        enable_progress_bar = False
+    else:
+        enable_progress_bar = fun_control["enable_progress_bar"]
     results = []
     num_folds = 10
     split_seed = 12345
@@ -87,7 +106,13 @@ def cv_model(config, fun_control):
             max_epochs=model.epochs,
             accelerator="auto",
             devices=1,
-            # logger=TensorBoardLogger(save_dir=fun_control["tensorboard_path"], version=config_id),
+            logger=TensorBoardLogger(
+                save_dir=fun_control["tensorboard_path"], version=config_id, default_hp_metric=True
+            ),
+            callbacks=[
+                EarlyStopping(monitor="val_loss", patience=config["patience"], mode="min", strict=False, verbose=False)
+            ],
+            enable_progress_bar=enable_progress_bar,
         )
         # Pass the datamodule as arg to trainer.fit to override model hooks :)
         trainer.fit(model=model, datamodule=dm)
diff --git a/src/spotPython/utils/init.py b/src/spotPython/utils/init.py
@@ -4,7 +4,7 @@
 from torch.utils.tensorboard import SummaryWriter
 
 
-def fun_control_init(task, tensorboard_path=None, num_workers=0, device=None):
+def fun_control_init(task, enable_progress_bar=False, tensorboard_path=None, num_workers=0, device=None):
     """Initialize fun_control dictionary.
     Args:
         None
@@ -52,27 +52,28 @@ def fun_control_init(task, tensorboard_path=None, num_workers=0, device=None):
     fun_control = {
         "data": None,
         "data_dir": "./data",
-        "train": None,
-        "test": None,
+        "device": device,
+        "enable_progress_bar": enable_progress_bar,
+        "eval": None,
+        "k_folds": None,
         "loss_function": None,
-        "metric_sklearn": None,
         "metric_river": None,
+        "metric_sklearn": None,
         "metric_torch": None,
         "metric_params": {},
-        "num_workers": num_workers,
-        "prep_model": None,
         "n_samples": None,
-        "target_column": None,
-        "shuffle": None,
-        "eval": None,
-        "k_folds": None,
+        "num_workers": num_workers,
         "optimizer": None,
-        "device": device,
-        "show_batch_interval": 1_000_000,
         "path": None,
+        "prep_model": None,
+        "save_model": False,
+        "show_batch_interval": 1_000_000,
+        "shuffle": None,
+        "target_column": None,
+        "train": None,
+        "test": None,
         "task": task,
         "tensorboard_path": tensorboard_path,
-        "save_model": False,
         "weights": 1.0,
         "writer": writer,
     }

Original file line number	Diff line number	Diff line change
`@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"`
`7`	`7`
`8`	`8`	`[project]`
`9`	`9`	`name = "spotPython"`
`10`		`-version = "0.2.48"`
	`10`	`+version = "0.2.49"`
`11`	`11`	`authors = [`
`12`	`12`	`{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }`
`13`	`13`	`]`