TrainingContainer: Add train_test_split method

Calls the corresponding method on TrainingStorage.

pyiron_potentialfit/atomistics/job/trainingcontainer.py

@@ -624,6 +624,47 @@ def iter(self, *arrays, wrap_atoms=True):
         """
         yield from self._container.iter(*arrays, wrap_atoms=wrap_atoms)
 
+    def train_test_split(self, train_size: float, seed=None,
+                         run: bool = True,
+                         train_name: str = None, test_name: str = None, project=None):
+        """
+        Split into two random sub sets for training and testing.
+
+        Args:
+            train_size (float): fraction of data points for the training set, must be within (0, 1)
+            seed (optional): how to initialize the RNG, see numpy.random.default_rng() for details, but an int will do
+            run (bool, optional): whether to immediately run and save the new containers
+            train_name (str, optional): default is the name of this container with suffix _train
+            test_name (str, optional): default is the name of this container with suffix _test
+            project (Project, optional): where to create the new containers; defaults to this project
+
+        Returns:
+            (TrainingContainer, TrainingContainer): two training storages for training and testing
+
+        Raises:
+            ValueError: if either `train_name` or `test_name` already exist in `project`.
+            ValueError: from :meth:`.TrainingStorage.train_test_split` if `train_size` cannot be realized
+        """
+        if project is None:
+            project = self.project
+        if train_name is None:
+            train_name = f"{self.name}_train"
+        if test_name is None:
+            test_name = f"{self.name}_test"
+        if len({train_name, test_name}.intersection(project.list_nodes())) > 0:
+            raise ValueError("Target containers already exist!")
+
+        train, test = self._container.train_test_split(train_size, seed)
+        trainc = project.create.job.TrainingContainer(train_name)
+        trainc.include_storage(train)
+        testc = project.create.job.TrainingContainer(test_name)
+        testc.include_storage(test)
+
+        if run:
+            trainc.run()
+            testc.run()
+        return trainc, testc
+
 
 class TrainingPlots(StructurePlots):
     """