0.14.21

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.14.20"
+version = "0.14.21"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

pyproject.toml

@@ -0,0 +1,85 @@
+import math
+import random
+
+
+class FriedmanDriftDataset:
+    """Friedman Drift Dataset."""
+
+    def __init__(self, n_samples=100, change_point1=50, change_point2=75, seed=None, constant=False):
+        """Constructor for the Friedman Drift Dataset.
+
+        Args:
+            n_samples (int): The number of samples to generate.
+            change_point1 (int): The index of the first change point.
+            change_point2 (int): The index of the second change point.
+            seed (int): The seed for the random number generator.
+            constant (bool): If True, only the first feature is set to 1 and all others are set to 0.
+
+        Returns:
+            None
+
+        Examples:
+            >>> from spotPython.data.friedman import FriedmanDriftDataset
+                data_generator = FriedmanDriftDataset(n_samples=100,
+                    seed=42, change_point1=50, change_point2=75, constant=False)
+                data = [data for data in data_generator]
+                indices = [i for _, _, i in data]
+                values = {f"x{i}": [] for i in range(5)}
+                values["y"] = []
+                for x, y, _ in data:
+                    for i in range(5):
+                        values[f"x{i}"].append(x[i])
+                    values["y"].append(y)
+                plt.figure(figsize=(10, 6))
+                for label, series in values.items():
+                    plt.plot(indices, series, label=label)
+                plt.xlabel('Index')
+                plt.ylabel('Value')
+                plt.title('')
+                plt.axvline(x=50, color='k', linestyle='--', label='Drift Point 1')
+                plt.axvline(x=75, color='r', linestyle='--', label='Drift Point 2')
+                plt.legend()
+                plt.grid(True)
+                plt.show()
+        """
+        self.n_samples = n_samples
+        self._change_point1 = change_point1
+        self._change_point2 = change_point2
+        self.seed = seed
+        self.index = 0
+        self.rng = random.Random(self.seed)
+        self.constant = constant
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.index >= self.n_samples:  # Specifying end of generation
+            raise StopIteration
+        if self.constant:
+            # x[0] is set to 1, all others to 0
+            x = {0: 1}
+            x.update({i: 0 for i in range(1, 10)})  # All x[i] are 0 for i > 0
+        else:
+            x = {i: self.rng.uniform(a=0, b=1) for i in range(10)}
+        y = self._global_recurring_abrupt_gen(x, self.index) + self.rng.gauss(mu=0, sigma=1)
+        result = (x, y, self.index)
+        self.index += 1
+        return result
+
+    def _global_recurring_abrupt_gen(self, x, index):
+        if index < self._change_point1 or index >= self._change_point2:
+            return 10 * math.sin(math.pi * x[0] * x[1]) + 20 * (x[2] - 0.5) ** 2 + 10 * x[3] + 5 * x[4]
+        else:
+            return 10 * math.sin(math.pi * x[3] * x[5]) + 20 * (x[1] - 0.5) ** 2 + 10 * x[0] + 5 * x[2]
+
+    def __len__(self) -> int:
+        """
+        Returns the length of the dataset.
+
+        Returns:
+            int: The length of the dataset.
+
+
+        """
+        return self.n_samples

src/spotPython/data/friedman.py

@@ -1511,4 +1511,4 @@ def get_metric_sklearn(metric_name):
         sklearn.metrics: The metric from the metric name.
     """
     metric_sklearn = getattr(sklearn.metrics, metric_name)
-    return metric_sklearn
+    return metric_sklearn

src/spotPython/hyperparameters/values.py

@@ -6,8 +6,12 @@
 import datetime
 from dateutil.tz import tzlocal
 from torch.utils.tensorboard import SummaryWriter
-from spotPython.hyperparameters.values import (add_core_model_to_fun_control,
-                                               get_core_model_from_name, get_metric_sklearn, get_prep_model)
+from spotPython.hyperparameters.values import (
+    add_core_model_to_fun_control,
+    get_core_model_from_name,
+    get_metric_sklearn,
+    get_prep_model,
+)
 
 
 def fun_control_init(