v0.2.45

cleanup

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.2.45"
+version = "0.2.46"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/light/csvmodel.py

@@ -28,17 +28,6 @@ def __init__(
         self.valid_mapk = MAPK(k=3)
         self.test_mapk = MAPK(k=3)
 
-        # self.model = nn.Sequential(
-        #     nn.Flatten(),
-        #     nn.Linear(_L_in, l1),
-        #     nn.ReLU(),
-        #     nn.Dropout(0.1),
-        #     nn.Linear(l1, l1),
-        #     nn.ReLU(),
-        #     nn.Dropout(0.1),
-        #     nn.Linear(l1, _L_out),
-        # )
-
         # Create the network based on the specified hidden sizes
         layers = []
         layer_sizes = [_L_in] + hidden_sizes

src/spotPython/light/hyperlight.py

@@ -70,7 +70,7 @@ def fun(self, X, fun_control=None):
             try:
                 df_eval = train_model(config, self.fun_control)
             except Exception as err:
-                print(f"Error in fun_light(). Call to train_model failed. {err=}, {type(err)=}")
+                print(f"Error in fun(). Call to train_model failed. {err=}, {type(err)=}")
                 print("Setting df_eval to np.nan")
                 df_eval = np.nan
             z_val = fun_control["weights"] * df_eval

src/spotPython/utils/device.py

@@ -4,16 +4,15 @@
 def getDevice(device=None):
     """Get cpu, gpu or mps device for training.
     Args:
-        device (str): Device for training. If None,
-        the device is selected automatically.
+        device (str): Device for training. If None or "auto" the device is selected automatically.
     Returns:
         device (str): Device for training.
         Example:
             >>> from spotPython.utils.device import getDevice
             >>> getDevice()
             'cuda:0'
     """
-    if device is None:
+    if device is None or device == "auto":
         device = "cpu"
         if torch.cuda.is_available():
             device = "cuda:0"

src/spotPython/utils/file.py

@@ -1,12 +1,43 @@
 import torchvision
 import torchvision.transforms as transforms
+import socket
+from datetime import datetime
+from dateutil.tz import tzlocal
 
 
 def load_data(data_dir="./data"):
+    """Loads the CIFAR10 dataset.
+    Args:
+        data_dir (str, optional): Directory to save the data. Defaults to "./data".
+    Returns:
+        trainset (torchvision.datasets.CIFAR10): Training dataset.
+    Examples:
+        >>> from spotPython.utils.file import load_data
+        >>> trainset = load_data(data_dir="./data")
+
+    """
     transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
     trainset = torchvision.datasets.CIFAR10(root=data_dir, train=True, download=True, transform=transform)
 
     testset = torchvision.datasets.CIFAR10(root=data_dir, train=False, download=True, transform=transform)
 
     return trainset, testset
+
+
+def get_experiment_name(prefix: str = "00") -> str:
+    """Returns a unique experiment name with a given prefix.
+    Args:
+        prefix (str, optional): Prefix for the experiment name. Defaults to "00".
+    Returns:
+        str: Unique experiment name.
+    Examples:
+        >>> from spotPython.utils.file import get_experiment_name
+        >>> get_experiment_name(prefix="00")
+        00_ubuntu_2021-08-31_14-30-00
+    """
+    start_time = datetime.now(tzlocal())
+    HOSTNAME = socket.gethostname().split(".")[0]
+    experiment_name = prefix + "_" + HOSTNAME + "_" + str(start_time).split(".", 1)[0].replace(" ", "_")
+    experiment_name = experiment_name.replace(":", "-")
+    return experiment_name