From f7726933feff036bf7245c30625e993e348c8862 Mon Sep 17 00:00:00 2001 From: Github Actions Date: Fri, 23 Feb 2024 13:35:07 +0000 Subject: [PATCH] Sarah Segel: Merge pull request #94 from automl/bug/pre-commit-hooks --- development/.buildinfo | 2 +- development/.doctrees/api.doctree | Bin 7466 -> 8079 bytes .../.doctrees/api/deepcave.evaluators.doctree | Bin 7658 -> 10734 bytes .../api/deepcave.evaluators.epm.doctree | Bin 8114 -> 11845 bytes ...pcave.evaluators.epm.fanova_forest.doctree | Bin 18491 -> 36551 bytes ...pcave.evaluators.epm.random_forest.doctree | Bin 36048 -> 50551 bytes ...uators.epm.random_forest_surrogate.doctree | Bin 14766 -> 15967 bytes .../api/deepcave.evaluators.epm.utils.doctree | Bin 11361 -> 15333 bytes .../api/deepcave.evaluators.fanova.doctree | Bin 32178 -> 37483 bytes .../api/deepcave.evaluators.footprint.doctree | Bin 4780 -> 41365 bytes .../api/deepcave.evaluators.lpi.doctree | Bin 6514 -> 36750 bytes .../.doctrees/api/deepcave.layouts.doctree | Bin 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wmyP#|ZfI}b*!?53$$kHau&e#;7h#h-`c?Pszkh7smY@C)yk7kN5=oo?7oE<}tks+2PKY62+Hkc(n`Jt5N=J^u5OpHF0*GlO#`fR={WyHwn Qv{^vr6c?kz5N1~Q46!Wv$@Sv$lfBt_7~MCQNmwy4dQaXcrO)WS`Jt2%BctPH5t&n5jP{f7 HtJwnpb4(#w diff --git a/development/.doctrees/examples/api/parallel_coordinates.doctree b/development/.doctrees/examples/api/parallel_coordinates.doctree index cb10b2a643817a64376d8d76b90257d16e42abd2..52d9bf8ee5d7b39df14ba78fd0cdf5ee02cc9258 100644 GIT binary patch delta 64 zcmews_#=>|fpzNJjV!mg8QV7(3Y4(O8)$MV0D-bXT4s7_QL0{XMt-@5=9CQf*eMx; P5fDX;2AiLX2#Npz&f$`V`M)3e|W)=|!5D3Z%kNA;L68@Qmf#LH;@fhaKA2{k6Cx2v-(lgNHQUC&F zg|y7{(xOzo;*9)q4NWdoy}7$(MFLnE7y>q%vqrNLrziO4e$H<|`@U`7&-H^v2A}zx zFY)KAF#{dB*~RQ4GngS^HHC*4#o3d0x*5xZMBt7AI!GGqJqWR+QF$`AyOuRb4cu~| z1GrGstO0Tp3o`W!QVR46Dg(S3nM9c3fi$_mU7G0>|Kx}KlG$f$`u3M(F@=W)=|!5K!<4jR-vY>Cy)l1_lrontZ`cbfbYY^JYE1I>yPLSfmU! zxfFmvSs^Vmy|gG*uQ(&WTtgGpgqwVuoBUZB7(iH>VX~mR?B*C&OV-J}tO~p!8_r(6 z&iNH+`q#~exqh%rp2sD@3>4mcoj+d<%wvpy@;*0Xd5{^Xc7PoZ zCYCfROcrq03P-UAXcrek3B!`c6+mucL8g8|N`YQMWq>y$lL#{+1j3eOPPbKKU;trp u6n#f0Pjm;naq=#AX-4zO*W49A4xRkdU7Cs8YO;=%)MNt>0k#EhAYA}!JX+oW diff --git a/development/_downloads/e839af13aa37ae3d835848af3db8e085/pdp.py b/development/_downloads/e839af13aa37ae3d835848af3db8e085/pdp.py index 83479592..957e8d96 100644 --- a/development/_downloads/e839af13aa37ae3d835848af3db8e085/pdp.py +++ b/development/_downloads/e839af13aa37ae3d835848af3db8e085/pdp.py @@ -34,4 +34,4 @@ # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available. figure = plugin.load_outputs(run, inputs, outputs) # plotly.go figure figure.write_image("examples/api/pdp.png") - #figure.show() + # figure.show() diff --git a/development/_downloads/e9c545ae14a903164c9a627d1b3a375a/parallel_coordinates.ipynb b/development/_downloads/e9c545ae14a903164c9a627d1b3a375a/parallel_coordinates.ipynb index bf52afe1..accf8606 100644 --- a/development/_downloads/e9c545ae14a903164c9a627d1b3a375a/parallel_coordinates.ipynb +++ b/development/_downloads/e9c545ae14a903164c9a627d1b3a375a/parallel_coordinates.ipynb @@ -15,7 +15,7 @@ }, "outputs": [], "source": [ - "from deepcave.plugins.objective.parallel_coordinates import ParallelCoordinates\nfrom deepcave.runs.converters.deepcave import DeepCAVERun\nfrom pathlib import Path\n\n\nif __name__ == \"__main__\":\n # Instantiate the run\n run = DeepCAVERun.from_path(Path(\"logs/DeepCAVE/minimal/run_2\"))\n objective_id = run.get_objective_ids()[0]\n budget_id = run.get_budget_ids()[-1]\n\n # Instantiate the plugin\n plugin = ParallelCoordinates()\n inputs = plugin.generate_inputs(\n hyperparameter_names=run.configspace.get_hyperparameter_names(),\n objective_id=objective_id,\n budget_id=budget_id,\n show_important_only=True,\n show_unsuccessful=False,\n hide_hps=False,\n n_hps=3,\n )\n # Note: Filter variables are not considered.\n outputs = plugin.generate_outputs(run, inputs)\n\n # Finally, you can load the figure. Here, the filter variables play a role.\n # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available.\n figure = plugin.load_outputs(run, inputs, outputs) # plotly.go figure\n figure.write_image(\"examples/api/parallel_coordinates.png\", scale=2.)\n # figure.show()" + "from deepcave.plugins.objective.parallel_coordinates import ParallelCoordinates\nfrom deepcave.runs.converters.deepcave import DeepCAVERun\nfrom pathlib import Path\n\n\nif __name__ == \"__main__\":\n # Instantiate the run\n run = DeepCAVERun.from_path(Path(\"logs/DeepCAVE/minimal/run_2\"))\n objective_id = run.get_objective_ids()[0]\n budget_id = run.get_budget_ids()[-1]\n\n # Instantiate the plugin\n plugin = ParallelCoordinates()\n inputs = plugin.generate_inputs(\n hyperparameter_names=run.configspace.get_hyperparameter_names(),\n objective_id=objective_id,\n budget_id=budget_id,\n show_important_only=True,\n show_unsuccessful=False,\n hide_hps=False,\n n_hps=3,\n )\n # Note: Filter variables are not considered.\n outputs = plugin.generate_outputs(run, inputs)\n\n # Finally, you can load the figure. Here, the filter variables play a role.\n # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available.\n figure = plugin.load_outputs(run, inputs, outputs) # plotly.go figure\n figure.write_image(\"examples/api/parallel_coordinates.png\", scale=2.0)\n # figure.show()" ] } ], diff --git a/development/_downloads/f0660e643860808c88e59e2b47bd432b/importances.py b/development/_downloads/f0660e643860808c88e59e2b47bd432b/importances.py index 981f5d87..be61312d 100644 --- a/development/_downloads/f0660e643860808c88e59e2b47bd432b/importances.py +++ b/development/_downloads/f0660e643860808c88e59e2b47bd432b/importances.py @@ -26,7 +26,7 @@ budget_ids=budget_ids, method="global", n_hps=3, - n_trees=10 + n_trees=10, ) # Note: Filter variables are not considered. outputs = plugin.generate_outputs(run, inputs) @@ -34,5 +34,5 @@ # Finally, you can load the figure. Here, the filter variables play a role. # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available. figure = plugin.load_outputs(run, inputs, outputs) # plotly.go figure - figure.write_image("examples/api/importances.png", scale=2.) - # figure.show() \ No newline at end of file + figure.write_image("examples/api/importances.png", scale=2.0) + # figure.show() diff --git a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip index c1a5e58b0ba0bbab201d35a2893162c3c8c7dd05..84ef2de36b7ff3cc37a6828d573356096b1e6923 100644 GIT binary patch delta 292 zcmZ3ulX2-zM)3e|W)=|!5D3Z%k9gv9HcgO&fkAMicq7Z^38Ed0ljYf@84Wf+l{&_jOZpoO}5Q1`M8*vff_cON}kpNGnidIuz?xh zyjTRlX2ETn%pPr~fMPn(967M7AjFbJ7mmqs914?bqj}^(3gMOk74o7ed^34Pvix@ZC zun92-Ovy0ZNK&W){?B+x(V~lZhE9 zzS&apv=*4b?(%^R%=qQSA^_F`w|g>ow3$4nEnr82i6xCrlM|wK!D`?Z0WIT2C}CLA z_;T`^Xc>?dlTSxWGipqJ9<2%1!NU4=#z8g)1`vk$9HMPWyYj<;;Kf}+! S0K(E}rWVHNvAv4|2?7A#OifDw diff --git a/development/_modules/deepcave/evaluators/epm/fanova_forest.html b/development/_modules/deepcave/evaluators/epm/fanova_forest.html index 9b9948dc..55db2109 100644 --- a/development/_modules/deepcave/evaluators/epm/fanova_forest.html +++ b/development/_modules/deepcave/evaluators/epm/fanova_forest.html @@ -702,7 +702,20 @@

Source code for deepcave.evaluators.epm.fanova_forest

-from typing import List, Optional, Tuple
+# noqa: D400
+"""
+# FanovaForest
+
+The module provides utilities for creating a fANOVA forest.
+
+It includes a FanovaForest wrapper for pyrfr.
+fANOVA can be used for analyzing the importances of Hyperparameters.
+
+## Classes
+    - FanovaForest: A fANOVA forest wrapper for pyrfr.
+"""
+
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import itertools as it
 
@@ -719,7 +732,32 @@ 
Source code for deepcave.evaluators.epm.fanova_forest
[docs]
 class FanovaForest(RandomForest):
     """
-    A fanova forest wrapper for pyrfr.
+    A fANOVA forest wrapper for pyrfr.
+
+    Properties
+    ----------
+    cutoffs : Tuple[float, float]
+        The cutoffs of the model.
+    percentiles : NDArray[floating]
+        The percentiles of the data points Y.
+    all_midpoints : List
+        All midpoints tree wise for the whole forest.
+    all_sizes : List
+        All interval sizes tree wise for the whole forest.
+    bounds : List[Tuple[float, float]
+        Stores feature bounds.
+    trees_total_variances : List
+        The total variances of the trees.
+    trees_total_variance : Any
+        The total variance of a tree.
+    trees_variance_fractions : Dict
+        The variance fractions of the trees.
+    V_U_total : Dict[Tuple[int, ...], List[Any]]
+        Store variance-related information across all trees.
+    V_U_individual : Dict[Tuple[int, ...], List[Any]]
+        Store variance-related information for individual subsets.
+    n_params : int
+        The number of Hyperparameters to sample.
     """
 
     def __init__(
@@ -758,7 +796,7 @@ 
Source code for deepcave.evaluators.epm.fanova_forest
def _get_model(self) -> regression.base_tree:
         """
-        Returns the internal model.
+        Return the internal model.
 
         Returns
         -------
@@ -769,7 +807,7 @@ 
Source code for deepcave.evaluators.epm.fanova_forest
def _train(self, X: np.ndarray, Y: np.ndarray) -> None:
         """
-        Trains the random forest on X and Y.
+        Train the Random Forest on X and Y.
 
         Parameters
         ----------
@@ -781,7 +819,7 @@ 
Source code for deepcave.evaluators.epm.fanova_forest
super()._train(X, Y)
         self.percentiles = np.percentile(Y, range(0, 100))
 
-        # all midpoints and interval sizes treewise for the whole forest
+        # all midpoints and interval sizes tree wise for the whole forest
         self.all_midpoints = []
         self.all_sizes = []
 
@@ -790,8 +828,8 @@ 
Source code for deepcave.evaluators.epm.fanova_forest
# compute midpoints and interval sizes for variables in each tree
         for tree_split_values in forest_split_values:
-            sizes = []
-            midpoints = []
+            sizes: List = []
+            midpoints: List = []
             for i, split_vals in enumerate(tree_split_values):
                 if np.isnan(self.bounds[i][1]):  # categorical parameter
                     # check if the tree actually splits on this parameter
@@ -814,14 +852,14 @@ 
Source code for deepcave.evaluators.epm.fanova_forest
self.all_sizes.append(sizes)
 
         # capital V in the paper
-        self.trees_total_variances = []
+        self.trees_total_variances: list = []
 
         # dict of lists where the keys are tuples of the dimensions
         # and the value list contains \hat{f}_U for the individual trees
         # reset all the variance fractions computed
-        self.trees_variance_fractions = {}
-        self.V_U_total = {}
-        self.V_U_individual = {}
+        self.trees_variance_fractions: dict = {}
+        self.V_U_total: Dict[Tuple[int, ...], List[Any]] = {}
+        self.V_U_individual: Dict[Tuple[int, ...], List[Any]] = {}
 
         # Set cut-off
         self._model.set_cutoffs(self.cutoffs[0], self.cutoffs[1])
@@ -831,16 +869,29 @@ 
Source code for deepcave.evaluators.epm.fanova_forest

 [docs]
-    def compute_marginals(self, hp_ids: List[int], depth=1):
+    def compute_marginals(
+        self, hp_ids: Union[List[int], Tuple[int, ...]], depth: int = 1
+    ) -> Tuple[Dict[Tuple[int, ...], List[Any]], Dict[Tuple[int, ...], List[Any]],]:
         """
-        Returns the marginal of selected parameters.
+        Return the marginal of selected Hyperparameters.
 
         Parameters
         ----------
-        hp_ids: List[int]
-            Contains the indices of the configspace for the selected parameters (starts with 0).
+        hp_ids: Union[List[int], Tuple[int, ...]]
+            Contains the indices of the configspace for the selected Hyperparameters
+            (starts with 0).
+        depth: int
+            The depth of the marginalization.
+            Default value is 1.
+
+        Returns
+        -------
+        Tuple[Dict[Tuple[int, ...], List[Any]],
+        Dict[Tuple[int, ...], List[Any]],
+            The marginal of selected Hyperparameters.
         """
-        hp_ids = tuple(hp_ids)
+        if not isinstance(hp_ids, tuple):
+            hp_ids = tuple(hp_ids)
 
         # check if values has been previously computed
         if hp_ids in self.V_U_individual:
@@ -872,7 +923,7 @@ 
Source code for deepcave.evaluators.epm.fanova_forest
prod_midpoints = it.product(*midpoints)
             prod_sizes = it.product(*sizes)
 
-            sample = np.full(self.n_params, np.nan, dtype=float)
+            sample: np.ndarray = np.full(self.n_params, np.nan, dtype=float)
 
             # make prediction for all midpoints and weigh them by the corresponding size
             for i, (m, s) in enumerate(zip(prod_midpoints, prod_sizes)):
@@ -886,7 +937,7 @@ 
Source code for deepcave.evaluators.epm.fanova_forest
# line 10 in algorithm 2
             # note that V_U^2 can be computed by var(\hat a)^2 - \sum_{subU} var(f_subU)^2
             # which is why, \hat{f} is never computed in the code, but
-            # appears in the pseudocode
+            # appears in the pseudo code
             V_U_total = np.nan
             V_U_individual = np.nan
 
diff --git a/development/_modules/deepcave/evaluators/epm/random_forest.html b/development/_modules/deepcave/evaluators/epm/random_forest.html
new file mode 100644
index 00000000..75509889
--- /dev/null
+++ b/development/_modules/deepcave/evaluators/epm/random_forest.html
@@ -0,0 +1,1291 @@
+
+
+
+  
+    
+    
+    deepcave.evaluators.epm.random_forest — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.evaluators.epm.random_forest
+# noqa: D400
+"""
+# RandomForest
+
+This module can be used for training and using a Random Forest Regression model.
+
+A pyrfr wrapper is used for simplification.
+
+## Classes
+    - RandomForest: A random forest wrapper for pyrfr.
+
+## Constants
+    VERY_SMALL_NUMBER : float
+    PYRFR_MAPPING : Dict[str, str]
+"""
+
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import functools
+import warnings
+
+import numpy as np
+import pyrfr.regression as regression
+from ConfigSpace import ConfigurationSpace
+from ConfigSpace.hyperparameters import (
+    CategoricalHyperparameter,
+    Constant,
+    UniformFloatHyperparameter,
+    UniformIntegerHyperparameter,
+)
+from sklearn.decomposition import PCA
+from sklearn.exceptions import NotFittedError
+from sklearn.preprocessing import MinMaxScaler
+
+from deepcave.evaluators.epm.utils import get_types
+
+VERY_SMALL_NUMBER = 1e-10
+PYRFR_MAPPING = {
+    "n_trees": "num_trees",
+    "bootstrapping": "do_bootstrapping",
+    "max_features": "tree_opts.max_features",
+    "min_samples_split": "tree_opts.min_samples_to_split",
+    "min_samples_leaf": "tree_opts.min_samples_in_leaf",
+    "max_depth": "tree_opts.max_depth",
+    "eps_purity": "tree_opts.epsilon_purity",
+    "max_nodes": "tree_opts.max_num_nodes",
+}
+
+
+

+[docs]
+class RandomForest:
+    """
+    A random forest wrapper for pyrfr.
+
+    This is handy because only the configuration space needs to be passed.
+    and have a working version without specifying e.g. types and bounds.
+
+    Note
+    ----
+    This wrapper also supports instances.
+
+    Properties
+    ----------
+    cs : ConfigurationSpace
+        The configuration space.
+    log_y : bool
+        Whether y should be treated as a logarithmic transformation.
+    seed : int
+        The seed. If not provided, it is random.
+    types : List[int]
+        The types of the Hyperparameters.
+    bounds : List[Tuple[float, float]]
+        The bounds of the Hyperparameters.
+    n_params : int
+        The number of Hyperparameters in the configuration space.
+    n_features : int
+        The number of features.
+    pca_components : int
+        The number of components to keep for the principal component analysis (PCA).
+    pca : PCA
+        The principal component analysis (PCA) object.
+    scaler : MinMaxScaler
+        A MinMaxScaler to scale the features.
+    instance_features : ndarray
+        The instance features.
+    """
+
+    def __init__(
+        self,
+        configspace: ConfigurationSpace,
+        n_trees: int = 16,
+        ratio_features: float = 5.0 / 6.0,
+        min_samples_split: int = 3,
+        min_samples_leaf: int = 3,
+        max_depth: int = 2**20,
+        max_nodes: int = 2**20,
+        eps_purity: float = 1e-8,
+        bootstrapping: bool = True,
+        instance_features: Optional[np.ndarray] = None,
+        pca_components: Optional[int] = 2,
+        log_y: bool = False,
+        seed: Optional[int] = 0,
+    ):
+        self.cs = configspace
+        self.log_y = log_y
+        self.seed = seed
+
+        # Set types and bounds automatically
+        types, self.bounds = get_types(configspace, instance_features)
+        self.types = np.array(types)
+
+        # Prepare everything for PCA
+        self.n_params = len(configspace.get_hyperparameters())
+        self.n_features = 0
+        if instance_features is not None:
+            self.n_features = instance_features.shape[1]
+
+        self._pca_applied = False
+        self.pca_components = pca_components
+        self.pca = PCA(n_components=self.pca_components)
+        self.scaler = MinMaxScaler()
+        self.instance_features = instance_features
+
+        # Calculate max number of features
+        max_features = max(1, int(len(self.types) * ratio_features)) if ratio_features <= 1.0 else 0
+
+        # Prepare the model
+        self._model = self._get_model()
+        self._model.options = self._get_model_options(
+            n_trees=n_trees,
+            max_features=max_features,
+            min_samples_split=min_samples_split,
+            min_samples_leaf=min_samples_leaf,
+            max_depth=max_depth,
+            max_nodes=max_nodes,
+            eps_purity=eps_purity,
+            bootstrapping=bootstrapping,
+        )
+
+    def _get_model(self) -> regression.base_tree:
+        """
+        Return the internal model.
+
+        Returns
+        -------
+        model : regression.base_tree
+            Model which is used internally.
+        """
+        return regression.binary_rss_forest()
+
+    def _get_model_options(self, **kwargs: Union[int, float, bool]) -> regression.forest_opts:
+        """
+        Get model options from kwargs.
+
+        The mapping `PYRFR_MAPPING` is used in combination with
+        a recursive attribute setter to set the options for the pyrfr model.
+
+        Parameters
+        ----------
+        **kwargs : Dict[str, Any]
+            The key word arguments for the model options.
+
+        Returns
+        -------
+        options : regression.forest_opts
+            Random forest options.
+        """
+        # Now the options are set
+        options = regression.forest_opts()
+
+        def rgetattr(obj: object, attr: str, *args: Any) -> Any:
+            def _getattr(obj: object, attr: object) -> Any:
+                attr = str(attr)
+                return getattr(obj, attr, *args)
+
+            return functools.reduce(_getattr, [obj] + attr.split("."))
+
+        def rsetattr(obj: object, attr: str, val: Any) -> None:
+            pre, _, post = attr.rpartition(".")
+            return setattr(rgetattr(obj, pre) if pre else obj, post, val)
+
+        for k, v in kwargs.items():
+            new_k = PYRFR_MAPPING[k]
+            rsetattr(options, new_k, v)
+
+        return options
+
+    def _impute_inactive(self, X: np.ndarray) -> np.ndarray:
+        """
+        Impute inactive values in X.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            Data points.
+
+        Returns
+        -------
+        np.ndarray
+            Imputed data points.
+
+        Raises
+        ------
+        ValueError
+            If Hyperparameter is not supported.
+        """
+        conditional: Dict[int, bool] = {}
+        impute_values: Dict[int, float] = {}
+
+        X = X.copy()
+        for idx, hp in enumerate(self.cs.get_hyperparameters()):
+            if idx not in conditional:
+                parents = self.cs.get_parents_of(hp.name)
+                if len(parents) == 0:
+                    conditional[idx] = False
+                else:
+                    conditional[idx] = True
+                    if isinstance(hp, CategoricalHyperparameter):
+                        impute_values[idx] = len(hp.choices)
+                    elif isinstance(hp, (UniformFloatHyperparameter, UniformIntegerHyperparameter)):
+                        impute_values[idx] = -1
+                    elif isinstance(hp, Constant):
+                        impute_values[idx] = 1
+                    else:
+                        raise ValueError
+
+            if conditional[idx] is True:
+                nonfinite_mask = ~np.isfinite(X[:, idx])
+                X[nonfinite_mask, idx] = impute_values[idx]
+
+        return X
+
+    def _check_dimensions(self, X: np.ndarray, Y: Optional[np.ndarray] = None) -> None:
+        """
+        Check if the dimensions of X and Y are correct with respect to features.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            Input data points.
+        Y : Optional[np.ndarray], optional
+            Target values. By default None.
+
+        Raises
+        ------
+        ValueError
+            If any dimension of X or Y is incorrect or unsuitable.
+        """
+        if len(X.shape) != 2:
+            raise ValueError(f"Expected 2d array, got {len(X.shape)}d array.")
+
+        if X.shape[1] != self.n_params + self.n_features:
+            raise ValueError(
+                f"Feature mismatch: X should have {self.n_params} features, but has {X.shape[1]}"
+            )
+
+        if Y is not None:
+            if X.shape[0] != Y.shape[0]:
+                raise ValueError(f"X.shape[0] ({X.shape[0]}) != y.shape[0] ({Y.shape[0]})")
+
+    def _get_data_container(
+        self, X: np.ndarray, y: np.ndarray
+    ) -> regression.default_data_container:
+        """
+        Fill a pyrfr default data container.
+
+        The goal here is, that the forest knows categoricals and bounds for continuous data.
+
+        Parameters
+        ----------
+        X : np.ndarray [n_samples, n_features]
+            Input data points.
+        y : np.ndarray [n_samples, ]
+            Target values.
+
+        Returns
+        -------
+        data : regression.default_data_container
+            The filled data container that pyrfr can interpret.
+        """
+        # retrieve the types and the bounds from the ConfigSpace
+        data = regression.default_data_container(X.shape[1])
+
+        for i, (mn, mx) in enumerate(self.bounds):
+            if np.isnan(mx):
+                data.set_type_of_feature(i, mn)
+            else:
+                data.set_bounds_of_feature(i, mn, mx)
+
+        for row_X, row_y in zip(X, y):
+            data.add_data_point(row_X, row_y)
+
+        return data
+
+

+[docs]
+    def train(self, X: np.ndarray, Y: np.ndarray) -> None:
+        """
+        Train the random forest on X and Y.
+
+        Transform X if principal component analysis (PCA) is applied.
+        Afterwards, `_train` is called.
+
+        Parameters
+        ----------
+        X : np.ndarray [n_samples, n_features (config + instance features)]
+            Input data points.
+        Y : np.ndarray [n_samples, n_objectives]
+            Target values. `n_objectives` must match the number of target names specified in
+            the constructor.
+        """
+        self._check_dimensions(X, Y)
+
+        # Reduce dimensionality of features of larger than PCA_DIM
+        self._pca_applied = False
+        if (
+            self.pca_components
+            and X.shape[0] > self.pca.n_components
+            and self.n_features >= self.pca_components
+        ):
+            X_features = X[:, -self.n_features :]
+
+            # Scale features
+            X_features = self.scaler.fit_transform(X_features)
+            X_features = np.nan_to_num(X_features)  # if features with max == min
+
+            # PCA
+            X_features = self.pca.fit_transform(X_features)
+            X = np.hstack((X[:, : self.n_params], X_features))
+
+            # Adopt types
+            self.types = np.array(
+                np.hstack((self.types[: self.n_params], np.zeros((X_features.shape[1])))),
+                dtype=np.uint,
+            )
+            self._pca_applied = True
+
+        self._train(X, Y)

+
+
+    def _train(self, X: np.ndarray, Y: np.ndarray) -> None:
+        """
+        Train the random forest on X and Y.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            Input data points.
+        Y : np.ndarray
+            Target values.
+        """
+        # Now we can start to prepare the data for the pyrfr
+        data = self._get_data_container(X, Y.flatten())
+        seed = self.seed
+
+        rng = regression.default_random_engine(seed)
+
+        # Set more specific model options and finally fit it
+        self._model.options.num_data_points_per_tree = X.shape[0]
+        self._model.fit(data, rng=rng)
+
+

+[docs]
+    def predict(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Predict means and variances for a given X.
+
+        Parameters
+        ----------
+        X : np.ndarray [n_samples, n_features (config + instance features)]
+            Training samples.
+
+        Returns
+        -------
+        means : np.ndarray [n_samples, n_objectives]
+            Predictive mean.
+        vars : np.ndarray [n_samples, n_objectives] or [n_samples, n_samples]
+            Predictive variance or standard deviation.
+        """
+        self._check_dimensions(X)
+
+        if self._pca_applied:
+            try:
+                X_features = X[:, -self.n_features :]
+                X_features = self.scaler.transform(X_features)
+                X_features = self.pca.transform(X_features)
+                X = np.hstack((X[:, : self.n_params], X_features))
+            except NotFittedError:
+                pass  # PCA not fitted if only one training sample
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "Predicted variances are smaller than 0. Setting those variances to 0."
+            )
+            mean, var = self._predict(X)
+
+        if len(mean.shape) == 1:
+            mean = mean.reshape((-1, 1))
+
+        if var is not None and len(var.shape) == 1:
+            var = var.reshape((-1, 1))
+
+        return mean, var

+
+
+    def _predict(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Predict means and variances for a given X.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            [n_samples, n_features (config + instance features)]
+
+        Returns
+        -------
+        means : np.ndarray [n_samples, 1]
+            Predictive mean.
+        vars : np.ndarray [n_samples, 1]
+            Predictive variance.
+        """
+        self._check_dimensions(X)
+        X = self._impute_inactive(X)
+
+        if self.log_y:
+            all_preds = []
+            third_dimension = 0
+
+            # Gather data in a list of 2d arrays and get statistics about the required size of the
+            # 3d array
+            for row_X in X:
+                preds_per_tree = self._model.all_leaf_values(row_X)
+                all_preds.append(preds_per_tree)
+                max_num_leaf_data = max(map(len, preds_per_tree))
+                third_dimension = max(max_num_leaf_data, third_dimension)
+
+            # Transform list of 2d arrays into a 3d array
+            num_trees = self._model.options.num_trees
+            shape = (X.shape[0], num_trees, third_dimension)
+            preds_as_array = np.zeros(shape) * np.NaN
+            for i, preds_per_tree in enumerate(all_preds):
+                for j, pred in enumerate(preds_per_tree):
+                    preds_as_array[i, j, : len(pred)] = pred
+
+            # Do all necessary computation with vectorized functions
+            preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER)
+
+            # Compute the mean and the variance across the different trees
+            means = preds_as_array.mean(axis=1)
+            vars_ = preds_as_array.var(axis=1)
+        else:
+            means, vars_ = [], []
+            for row_X in X:
+                mean_, var = self._model.predict_mean_var(row_X)
+                means.append(mean_)
+                vars_.append(var)
+
+        means = np.array(means)
+        vars_ = np.array(vars_)
+
+        return means.reshape((-1, 1)), vars_.reshape((-1, 1))
+
+

+[docs]
+    def predict_marginalized(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Predict mean and variance marginalized over all instances.
+
+        Return the predictive mean and variance marginalized over all
+        instances for a set of configurations.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            [n_samples, n_features (config)]
+
+        Returns
+        -------
+        means : np.ndarray of shape = [n_samples, 1]
+            Predictive mean
+        vars : np.ndarray  of shape = [n_samples, 1]
+            Predictive variance
+        """
+        self._check_dimensions(X)
+
+        if self.instance_features is None or len(self.instance_features) == 0:
+            mean_, var = self.predict(X)
+            assert var is not None  # please mypy
+
+            var[var < VERY_SMALL_NUMBER] = VERY_SMALL_NUMBER
+            var[np.isnan(var)] = VERY_SMALL_NUMBER
+            return mean_, var
+
+        X = self._impute_inactive(X)
+
+        # marginalized predictions for each tree
+        dat_ = np.zeros((X.shape[0], self._model.options.num_trees))
+        for i, x in enumerate(X):
+            # marginalize over instances
+            # 1. get all leaf values for each tree
+            preds_trees: List[List[float]] = [[] for i in range(self._model.options.num_trees)]
+
+            for feat in self.instance_features:
+                x_ = np.concatenate([x, feat])
+                preds_per_tree = self._model.all_leaf_values(x_)
+                for tree_id, preds in enumerate(preds_per_tree):
+                    preds_trees[tree_id] += preds
+
+            # 2. average in each tree
+            if self.log_y:
+                for tree_id in range(self._model.options.num_trees):
+                    dat_[i, tree_id] = np.log(np.exp(np.array(preds_trees[tree_id])).mean())
+            else:
+                for tree_id in range(self._model.options.num_trees):
+                    dat_[i, tree_id] = np.array(preds_trees[tree_id]).mean()
+
+        # 3. compute statistics across trees
+        mean_ = dat_.mean(axis=1)
+        var = dat_.var(axis=1)
+
+        var[var < VERY_SMALL_NUMBER] = VERY_SMALL_NUMBER
+
+        if len(mean_.shape) == 1:
+            mean_ = mean_.reshape((-1, 1))
+        if len(var.shape) == 1:
+            var = var.reshape((-1, 1))
+
+        return mean_, var

+
+
+

+[docs]
+    def get_leaf_values(self, x: np.ndarray) -> regression.binary_rss_forest:
+        """
+        Get the leaf values of the model.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            Input data array.
+
+        Returns
+        -------
+        regression.binary_rss_forest
+            The leaf values of the model.
+        """
+        return self._model.all_leaf_values(x)

+

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/evaluators/epm/random_forest_surrogate.html b/development/_modules/deepcave/evaluators/epm/random_forest_surrogate.html
index 49cac1d1..9a7a67fa 100644
--- a/development/_modules/deepcave/evaluators/epm/random_forest_surrogate.html
+++ b/development/_modules/deepcave/evaluators/epm/random_forest_surrogate.html
@@ -702,7 +702,19 @@
               

                 
   
Source code for deepcave.evaluators.epm.random_forest_surrogate
-from typing import Tuple
+# noqa: D400
+"""
+# RandomForest Surrogate
+
+This module provides a RandomForest Surrogate model.
+
+Mean and standard deviation values can be predicted for a given input with this module.
+
+## Classes
+    - RandomForestSurrogate: Random forest surrogate for the pyPDP package.
+"""
+
+from typing import Optional, Tuple
 
 import ConfigSpace as CS
 import numpy as np
@@ -716,12 +728,14 @@ 
Source code for deepcave.evaluators.epm.random_forest_surrogate
class RandomForestSurrogate(SurrogateModel):
     """
     Random forest surrogate for the pyPDP package.
+
+    Predict deviations and fit the model.
     """
 
     def __init__(
         self,
         configspace: CS.ConfigurationSpace,
-        seed: int = None,
+        seed: Optional[int] = None,
     ):
         super().__init__(configspace, seed=seed)
         self._model = RandomForest(configspace=configspace, seed=seed)
@@ -729,11 +743,34 @@ 
Source code for deepcave.evaluators.epm.random_forest_surrogate

 [docs]
     def predict(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Predict the deviations.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            The data points on which to predict.
+
+        Returns
+        -------
+        Tuple[np.ndarray, np.ndarray]
+            The means and standard deviation.
+        """
         means, stds = self._model.predict(X)
         return means[:, 0], stds[:, 0]

 
 
-    def _fit(self, X: np.ndarray, y: np.ndarray):
+    def _fit(self, X: np.ndarray, y: np.ndarray) -> None:
+        """
+        Train the surrogate model.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            Input data points.
+        y : np.ndarray
+            Corresponding target values.
+        """
         self._model.train(X, y)

 
 

diff --git a/development/_modules/deepcave/evaluators/epm/utils.html b/development/_modules/deepcave/evaluators/epm/utils.html
index 97b53b44..716e2be9 100644
--- a/development/_modules/deepcave/evaluators/epm/utils.html
+++ b/development/_modules/deepcave/evaluators/epm/utils.html
@@ -702,7 +702,15 @@
               

                 
   
Source code for deepcave.evaluators.epm.utils
-import typing
+#  noqa: D400
+"""
+# Utils
+
+This module provides a utility to get the types
+as well as the bounds of the Hyperparameters.
+"""
+
+import typing
 
 import numpy as np
 from ConfigSpace import ConfigurationSpace
@@ -725,8 +733,30 @@ 
Source code for deepcave.evaluators.epm.utils
config_space: ConfigurationSpace,
     instance_features: typing.Optional[np.ndarray] = None,
 ) -> typing.Tuple[typing.List[int], typing.List[typing.Tuple[float, float]]]:
-    """Return the types of the hyperparameters and the bounds of the
-    hyperparameters and instance features.
+    """
+    Return the types of the Hyperparameters.
+
+    Also return the bounds of the Hyperparameters and instance features.
+
+    Parameters
+    ----------
+    config_space : ConfigurationSpace
+        The configuration space.
+    instance_features : Optional[np.ndarray], optional
+        The instance features.
+        Default is None.
+
+    Returns
+    -------
+    Tuple[typing.List[int], List[Tuple[float, float]]]
+        The types of the Hyperparameters, as well as the bounds and instance features.
+
+    Raises
+    ------
+    ValueError
+        Inactive parameters not supported for Beta and Normal Hyperparameters.
+    TypeError
+        If the Hyperparameter Type is unknown.
     """
     # Extract types vector for rf from config space and the bounds
     types = [0] * len(config_space.get_hyperparameters())
@@ -753,7 +783,7 @@ 
Source code for deepcave.evaluators.epm.utils
else:
                 bounds[i] = (0, int(n_cats) - 1)
         elif isinstance(param, Constant):
-            # for constants we simply set types to 0 which makes it a numerical
+            # for constants types are simply set to 0 which makes it a numerical
             # parameter
             if can_be_inactive:
                 bounds[i] = (2, np.nan)
@@ -761,7 +791,7 @@ 
Source code for deepcave.evaluators.epm.utils
else:
                 bounds[i] = (0, np.nan)
                 types[i] = 0
-            # and we leave the bounds to be 0 for now
+            # and the bounds are left to be 0 for now
         elif isinstance(param, UniformFloatHyperparameter):
             # Are sampled on the unit hypercube thus the bounds
             # are always 0.0, 1.0
diff --git a/development/_modules/deepcave/evaluators/fanova.html b/development/_modules/deepcave/evaluators/fanova.html
index d06d514c..0d10d2e3 100644
--- a/development/_modules/deepcave/evaluators/fanova.html
+++ b/development/_modules/deepcave/evaluators/fanova.html
@@ -702,7 +702,19 @@
               

                 
   
Source code for deepcave.evaluators.fanova
-from typing import Dict, List, Optional, Tuple, Union
+#  noqa: D400
+"""
+# fANOVA
+
+This module provides a tool for assessing the importance of an algorithms Hyperparameters.
+
+Utilities provide calculation of the data wrt the budget and train the forest on the encoded data.
+
+## Classes
+    - fANOVA: Calculate and provide midpoints and sizes.
+"""
+
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import itertools as it
 
@@ -718,8 +730,22 @@ 
Source code for deepcave.evaluators.fanova
 [docs]
 class fANOVA:
     """
-    Calculate and provide midpoints and sizes from the forest's
-    split values in order to get the marginals.
+    Calculate and provide midpoints and sizes.
+
+    They are generated from the forest's split values in order to get the marginals.
+
+    Properties
+    ----------
+    run : AbstractRun
+        The Abstract Run used for the calculation.
+    cs : ConfigurationSpace
+        The configuration space of the run.
+    hps : List[Hyperparameters]
+        The Hyperparameters of the configuration space.
+    hp_names : List[str]
+        The corresponding names of the Hyperparameters.
+    n_trees : int
+        The number of trees.
     """
 
     def __init__(self, run: AbstractRun):
@@ -741,7 +767,7 @@ 
Source code for deepcave.evaluators.fanova
         seed: int = 0,
     ) -> None:
         """
-        Get the data with respect to budget and trains the forest on the encoded data.
+        Get the data with respect to budget and train the forest on the encoded data.
 
         Note
         ----
@@ -750,7 +776,7 @@ 
Source code for deepcave.evaluators.fanova
         Parameters
         ----------
         objectives : Optional[Union[Objective, List[Objective]]], optional
-            Considerd objectives. By default None. If None, all objectives are considered.
+            Considered objectives. By default None. If None, all objectives are considered.
         budget : Optional[Union[int, float]], optional
             Considered budget. By default None. If None, the highest budget is chosen.
         n_trees : int, optional
@@ -762,7 +788,7 @@ 
Source code for deepcave.evaluators.fanova
             objectives = self.run.get_objectives()
 
         if budget is None:
-            budget = self.get_highest_budget()
+            budget = self.run.get_highest_budget()
 
         self.n_trees = n_trees
 
@@ -785,7 +811,7 @@ 
Source code for deepcave.evaluators.fanova
         self, hp_names: Optional[List[str]] = None, depth: int = 1, sort: bool = True
     ) -> Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]]:
         """
-        Returns the importance scores from the passed hyperparameter names.
+        Return the importance scores from the passed Hyperparameter names.
 
         Warning
         -------
@@ -794,17 +820,17 @@ 
Source code for deepcave.evaluators.fanova
         Parameters
         ----------
         hp_names : Optional[List[str]]
-            Selected hyperparameter names to get the importance scores from. If None, all
-            hyperparameters of the configspace are used.
+            Selected Hyperparameter names to get the importance scores from. If None, all
+            Hyperparameters of the configuration space are used.
         depth : int, optional
             How often dimensions should be combined. By default 1.
         sort : bool, optional
-            Whether the hyperparameters should be sorted by importance. By default True.
+            Whether the Hyperparameters should be sorted by importance. By default True.
 
         Returns
         -------
         Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]]
-            Dictionary with hyperparameter names and the corresbonding importance scores.
+            Dictionary with Hyperparameter names and the corresponding importance scores.
             The values are tuples of the form (mean individual, var individual, mean total,
             var total). Note that individual and total are the same if depth is 1.
 
@@ -823,14 +849,13 @@ 
Source code for deepcave.evaluators.fanova
         # Calculate the marginals
         vu_individual, vu_total = self._model.compute_marginals(hp_ids, depth)
 
-        importances = {}
+        importances: Dict[Tuple[Any, ...], Tuple[float, float, float, float]] = {}
         for k in range(1, len(hp_ids) + 1):
             if k > depth:
                 break
 
             for sub_hp_ids in it.combinations(hp_ids, k):
                 sub_hp_ids = tuple(sub_hp_ids)
-                importances[sub_hp_ids] = {}
 
                 # clean here to catch zero variance in a trees
                 non_zero_idx = np.nonzero(
@@ -866,16 +891,17 @@ 
Source code for deepcave.evaluators.fanova
                 k: v for k, v in sorted(importances.items(), key=lambda item: item[1][2])
             }
 
-        # We want to replace the ids with hyperparameter names again
+        # The ids get replaced with hyperparameter names again
         all_hp_names = self.cs.get_hyperparameter_names()
-        importances_ = {}
-        for hp_ids, values in importances.items():
-            hp_names = [all_hp_names[hp_id] for hp_id in hp_ids]
+        importances_: Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]] = {}
+        for hp_ids_importances, values in importances.items():
+            hp_names = [all_hp_names[hp_id] for hp_id in hp_ids_importances]
+            hp_names_key: Union[Tuple[str, ...], str]
             if len(hp_names) == 1:
-                hp_names = hp_names[0]
+                hp_names_key = hp_names[0]
             else:
-                hp_names = tuple(hp_names)
-            importances_[hp_names] = values
+                hp_names_key = tuple(hp_names)
+            importances_[hp_names_key] = values
 
         return importances_

 
@@ -883,7 +909,7 @@ 
Source code for deepcave.evaluators.fanova
     '''
     def marginal_mean_variance_for_values(self, dimlist, values_to_predict):
         """
-        Returns the marginal of selected parameters for specific values
+        Return the marginal of selected parameters for specific values
 
         Parameters
         ----------
@@ -906,7 +932,7 @@ 
Source code for deepcave.evaluators.fanova
 
     def get_most_important_pairwise_marginals(self, params=None, n=10):
         """
-        Returns the n most important pairwise marginals from the whole ConfigSpace.
+        Return the n most important pairwise marginals from the whole ConfigSpace.
 
         Parameters
         ----------
@@ -953,7 +979,7 @@ 
Source code for deepcave.evaluators.fanova
 
     def get_triple_marginals(self, params=None):
         """
-        Returns the n most important pairwise marginals from the whole ConfigSpace
+        Return the n most important pairwise marginals from the whole ConfigSpace
 
         Parameters
         ----------
diff --git a/development/_modules/deepcave/evaluators/footprint.html b/development/_modules/deepcave/evaluators/footprint.html
new file mode 100644
index 00000000..3b1a291c
--- /dev/null
+++ b/development/_modules/deepcave/evaluators/footprint.html
@@ -0,0 +1,1374 @@
+
+
+
+  
+    
+    
+    deepcave.evaluators.footprint — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.evaluators.footprint
+#  noqa: D400
+"""
+# Footprint
+
+This module provides utilities for creating a footprint of a run.
+It uses multidimensional scaling (MDS).
+It also provides utilities to get the surface and the points of the plot.
+
+
+## Classes
+    - Footprint: Can train and create a footprint of a run.
+"""
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+from ConfigSpace.hyperparameters import (
+    CategoricalHyperparameter,
+    Constant,
+    Hyperparameter,
+)
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.manifold import MDS
+from tqdm import tqdm
+
+from deepcave.constants import BORDER_CONFIG_ID, RANDOM_CONFIG_ID
+from deepcave.runs import AbstractRun, Status
+from deepcave.runs.objective import Objective
+from deepcave.utils.configspace import sample_border_config, sample_random_config
+from deepcave.utils.logs import get_logger
+
+logger = get_logger(__name__)
+
+
+

+[docs]
+class Footprint:
+    """
+    Can train and create a footprint of a run.
+
+    It uses multidimensional scaling (MDS).
+    Provides utilities to get the surface and the points of the plot.
+
+    Properties
+    ----------
+    run : AbstractRun
+        The AbstractRun used for the calculation of the footprint.
+    cs : ConfigurationSpace
+        The configuration space of the run.
+    """
+
+    def __init__(self, run: AbstractRun):
+        if run.configspace is None:
+            raise RuntimeError("The run needs to be initialized.")
+
+        self.run = run
+        self.cs = run.configspace
+
+        # Important parameters
+        is_categorical, depth = [], []
+        for hp in self.cs.get_hyperparameters():
+            if isinstance(hp, CategoricalHyperparameter):
+                is_categorical.append(True)
+            else:
+                is_categorical.append(False)
+            depth.append(self._get_depth(hp))
+
+        self._is_categorical = np.array(is_categorical)
+        self._depth = np.array(depth)
+
+        # Global variables
+        self._distances: Optional[np.ndarray] = None
+        self._trained = False
+        self._reset()
+
+    def _reset(self) -> None:
+        """Reset the footprint."""
+        self._objective_model: Optional[RandomForestRegressor] = None
+        self._area_model: Optional[RandomForestRegressor] = None
+        self._config_ids: Optional[List[int]] = None
+        self._incumbent_id: Optional[int] = None
+
+        # Those are used to fit the MDS (consists of random and border configs).
+        self._X: Optional[np.ndarray] = None
+        # Those are the fitted configs with shape (x, 2).
+        self._MDS_X: Optional[np.ndarray] = None
+
+

+[docs]
+    def calculate(
+        self,
+        objective: Objective,
+        budget: Union[int, float],
+        support_discretization: Optional[int] = 10,
+        rejection_rate: float = 0.01,
+        retries: int = 3,
+        exclude_configs: bool = False,
+    ) -> None:
+        """
+        Calculate the distances and train the model.
+
+        Parameters
+        ----------
+        objective : Objective
+            Objective and color to show.
+        budget : Union[int, float]
+            All configurations with this budget are considered.
+        support_discretization : Optional[int], optional
+            Discretization steps for integer and float hyperparameter (HP) values.
+            Default is set to 10.
+        rejection_rate : float, optional
+            Rejection rate whether a configuration should be rejected or not. Internally,
+            the max distance is calculated and if a configuration has a distance smaller than
+            max distance * rejection_rate, the configuration is rejected.
+            Default is set to 0.01.
+        retries : int, optional
+            How many times to retry adding a new configuration.
+            Default is set to 3.
+        exclude_configs : bool, optional
+            Whether the configurations from the run should be excluded
+            in the multidimensional scaling (MDS).
+            This is particularly interesting if only the search space should be plotted.
+            Default is set to False.
+        """
+        # Reset everything
+        self._reset()
+        self.cs.seed(0)
+
+        # Get config rejection threshold
+        # If the distance between two configs is smaller than the threshold, the config
+        # is rejected
+        rejection_threshold = self._get_max_distance() * rejection_rate
+
+        # Get encoded configs
+        data = self.run.get_encoded_data(
+            objective, budget, statuses=Status.SUCCESS, specific=False, include_config_ids=True
+        )
+        hp_names = self.run.configspace.get_hyperparameter_names()
+
+        # Make numpy arrays
+        X = data[hp_names].to_numpy()
+        Y = data[objective.name].to_numpy()
+        config_ids = data["config_id"].values.tolist()
+
+        # Get the incumbent
+        incumbent_config, _ = self.run.get_incumbent(objective, budget)
+        self._incumbent_id = self.run.get_config_id(incumbent_config)
+
+        # Reshape Y to 2D
+        Y = Y.reshape(-1, 1)
+
+        # Init distances
+        self._init_distances(X, config_ids, exclude_configs=exclude_configs)
+        assert self._distances is not None
+
+        border_generator = sample_border_config(self.cs)
+        random_generator = sample_random_config(self.cs, d=support_discretization)
+
+        # Now the border and random configs are added
+        count_border = 0
+        count_random = 0
+        tries = 0
+        logger.info("Starting to calculate distances and add border and random configurations...")
+        while True:
+            _configs = []
+            _config_ids = []
+
+            try:
+                _configs += [next(border_generator)]
+                _config_ids += [BORDER_CONFIG_ID]
+            except StopIteration:
+                pass
+
+            try:
+                _configs += [next(random_generator)]
+                _config_ids += [RANDOM_CONFIG_ID]
+            except StopIteration:
+                pass
+
+            counter = 0
+            for config, config_id in zip(_configs, _config_ids):
+                if config is None:
+                    continue
+
+                # Encode config
+                config_array = np.array(self.run.encode_config(config))
+                rejected = self._update_distances(config_array, config_id, rejection_threshold)
+                if not rejected:
+                    # Count
+                    if config_id == BORDER_CONFIG_ID:
+                        count_border += 1
+
+                    if config_id == RANDOM_CONFIG_ID:
+                        count_random += 1
+
+                    counter += 1
+
+            # Abort criteria
+            # If there are no new configs
+            if counter == 0:
+                tries += 1
+            else:
+                tries = 0
+
+            if tries >= retries:
+                break
+
+            # Or if reach more than 4000 are reached (otherwise it takes too long)
+            if self._distances.shape[0] % 100 == 0:
+                logger.info(f"Found {self._distances.shape[0]} configurations...")
+
+            if self._distances.shape[0] > 4000:
+                break
+
+        logger.info(f"Added {count_border} border configs and {count_random} random configs.")
+        logger.info(f"Total configurations: {self._distances.shape[0]}.")
+        logger.info("Getting MDS data...")
+
+        # Calculate MDS now to get 2D coordinates and set those points to reach them later.
+        MDS_X = self._get_mds()
+        self._MDS_X = MDS_X
+
+        # But here's the catch: Get rid of border and random configs because
+        # the y values for them are not known.
+        # However, it makes no sense to train the RF if the configs are excluded
+        # which were evaluated.
+        if not exclude_configs:
+            self._train_on_objective(MDS_X[: len(X)], Y.ravel())
+            self._trained = True
+        else:
+            self._trained = False
+
+        # Train on areas can be done anytime.
+        self._train_on_areas()

+
+
+

+[docs]
+    def get_surface(
+        self, details: float = 0.5, performance: bool = True
+    ) -> Tuple[List, List, List]:
+        """
+        Get surface of the multidimensional scaling (MDS) plot.
+
+        Parameters
+        ----------
+        details : float, optional
+            Steps to create the meshgrid. By default 0.5.
+        performance : bool, optional
+            Whether to get the surface from the performance or the valid areas.
+            Default is set to True.
+
+        Returns
+        -------
+        Tuple[List, List, List]
+            x (1D), y (1D) and z (2D) arrays for heatmap.
+
+        Raises
+        ------
+        RuntimeError
+            If `calculate` was not called before.
+        RuntimeError
+            If evaluated configs weren't included.
+        """
+        X = self._MDS_X
+        if X is None:
+            raise RuntimeError("You need to call `calculate` first.")
+
+        if performance and not self._trained:
+            raise RuntimeError(
+                "You can not get the surface" "if you do not include evaluated configs."
+            )
+
+        # Create meshgrid
+        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+
+        num = int(20 * details) + 10
+        x = np.linspace(x_min, x_max, num)
+        y = np.linspace(y_min, y_max, num)
+        x_mesh, y_mesh = np.meshgrid(x, y)
+        conc = np.c_[x_mesh.ravel(), y_mesh.ravel()]
+
+        if performance:
+            model = self._objective_model
+        else:
+            model = self._area_model
+
+        if model is None:
+            raise RuntimeError("You need to call `calculate` first.")
+
+        z = model.predict(conc)
+        z = z.reshape(x_mesh.shape)
+
+        return x.tolist(), y.tolist(), z.tolist()

+
+
+

+[docs]
+    def get_points(self, category: str = "configs") -> Tuple[List[float], List[float], List[int]]:
+        """
+        Return the points of the multidimensional scaling (MDS) plot.
+
+        Parameters
+        ----------
+        category : str, optional
+            Points of a specific category. Chose between `configs`, `borders`, `supports`
+            or `incumbents`. By default `configs`.
+
+        Returns
+        -------
+        Tuple[List[float], List[float], List[int]]
+            X, Y and config_ids as lists.
+
+        Raises
+        ------
+        RuntimeError
+            If category is not supported.
+        RuntimeError
+            If calculated wasn't called before.
+        """
+        if category not in ["configs", "borders", "supports", "incumbents"]:
+            raise RuntimeError("Unknown category.")
+
+        if self._MDS_X is None or self._config_ids is None:
+            raise RuntimeError("You need to call `calculate` first.")
+
+        X = []
+        Y = []
+        config_ids = []
+        for x, config_id in zip(self._MDS_X, self._config_ids):
+            if (
+                (category == "configs" and config_id >= 0)
+                or (category == "borders" and config_id == BORDER_CONFIG_ID)
+                or (category == "incumbents" and config_id == self._incumbent_id)
+                or (category == "supports" and config_id == RANDOM_CONFIG_ID)
+            ):
+                x = x.tolist()
+                X += [x[0]]
+                Y += [x[1]]
+                config_ids += [config_id]
+
+        return X, Y, config_ids

+
+
+    def _get_max_distance(self) -> float:
+        """
+        Calculate the maximum distance between all configs.
+
+        Basically, the number of Hyperparameters are just counted.
+
+        Returns
+        -------
+        float
+            Maximal distance between two configurations.
+        """
+        # The number of hps is just counted
+        # Since X is normalized, 1 can just be added
+        max_distance = 0
+        for hp in self.cs.get_hyperparameters():
+            if isinstance(hp, CategoricalHyperparameter) or isinstance(hp, Constant):
+                continue
+
+            max_distance += 1
+
+        return max_distance
+
+    def _get_distance(self, x: np.ndarray, y: np.ndarray) -> float:
+        """
+        Calculate distance between x and y. Both arrays must have the same length.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            Configuration 1.
+        y : np.ndarray
+            Configuration 2.
+
+        Returns
+        -------
+        float
+            Distance from configuration 1 and configuration 2.
+
+        Raises
+        ------
+        RuntimeError
+            If calculate wasn't called first.
+        """
+        if self._depth is None or self._is_categorical is None:
+            raise RuntimeError("You need to call `calculate` first.")
+
+        d = np.abs(x - y)
+        d[np.isnan(d)] = 1
+        d[np.logical_and(self._is_categorical, d != 0)] = 1
+        d = np.sum(d / self._depth)
+
+        return d
+
+    def _get_distances(self, X: np.ndarray) -> np.ndarray:
+        """
+        Get the distances between the configurations.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            The configurations.
+
+        Returns
+        -------
+        np.ndarray
+            The calculated distances.
+        """
+        n_configs = X.shape[0]
+
+        # The distances are initiated
+        distances = np.zeros((n_configs, n_configs))
+
+        for i in tqdm(range(n_configs)):
+            for j in range(i + 1, n_configs):
+                d = self._get_distance(X[i, :], X[j, :])
+                distances[i, j] = d
+                distances[j, i] = d
+
+        return distances
+
+    def _init_distances(
+        self, X: np.ndarray, config_ids: List[int], exclude_configs: bool = False
+    ) -> None:
+        """
+        Initialize the distances.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            Encoded data.
+        config_ids : List[int]
+            Corresponding configuration ids.
+        exclude_configs : bool, optional
+            Whether the passed X should be used or not. By default False.
+        """
+        if not exclude_configs:
+            self._X = X.copy()
+            self._config_ids = config_ids
+            self._distances = self._get_distances(X)
+            logger.info(f"Added {len(config_ids)} configurations.")
+        else:
+            self._X = None
+            self._config_ids = []
+            self._distances = np.zeros((0, 0))
+
+    def _update_distances(
+        self,
+        config: np.ndarray,
+        config_id: int,
+        rejection_threshold: Optional[float] = 0.0,
+    ) -> bool:
+        """
+        Update the internal distance if the passed configuration is not rejected.
+
+        Parameters
+        ----------
+        config : np.ndarray
+            Configuration, which is tried to be added.
+        config_id : int
+            Corresponding configuration id. This is important for later identification
+            as the configuration might be a border or random configuration.
+        rejection_threshold : Optional[float], optional
+            Threshold for rejecting the configuration. By default 0.0.
+
+        Returns
+        -------
+        rejected : bool
+            Whether the configuration was rejected or not.
+        """
+        X = self._X
+
+        assert self._distances is not None
+        distances = self._distances
+
+        if X is None:
+            X = np.array([[]])
+            n_configs = 0
+        else:
+            n_configs = X.shape[0]
+
+        rejected = False
+        new_distances = np.zeros((n_configs + 1, n_configs + 1))
+
+        # In case X is not set
+        if n_configs == 0:
+            new_distances[0, 0] = 0
+        else:
+            # Calculate distance to all configs
+            new_distances[:n_configs, :n_configs] = distances[:, :]
+            for j in range(n_configs):
+                d = self._get_distance(X[j, :], config)
+                if rejection_threshold is not None:
+                    if d < rejection_threshold:
+                        rejected = True
+                        break
+
+                # Add to new distances
+                new_distances[n_configs, j] = d
+                new_distances[j, n_configs] = d
+
+        if not rejected:
+            # Add to X here
+            if X.shape[1] == 0:
+                X = np.array([config])
+            else:
+                X = np.concatenate((X, np.array([config])), axis=0)
+
+            self._X = X
+            # There is no += to a None, an Issue has already been created
+            if self._config_ids is not None:
+                self._config_ids += [config_id]
+            self._distances = new_distances
+
+        return rejected
+
+    def _get_depth(self, hp: Hyperparameter) -> int:
+        """
+        Get depth (generations above) in configuration space of a given hyperparameter (HP).
+
+        Parameters
+        ----------
+        hp: Hyperparameter
+            name of Hyperparameter to inspect
+
+        Returns
+        -------
+        int
+            Depth of the Hyperparameter.
+        """
+        parents = self.cs.get_parents_of(hp)
+        if not parents:
+            return 1
+
+        new_parents = parents
+        d = 1
+        while new_parents:
+            d += 1
+            old_parents = new_parents
+            new_parents = []
+            for p in old_parents:
+                pp = self.cs.get_parents_of(p)
+                if pp:
+                    new_parents.extend(pp)
+                else:
+                    return d
+
+        return d
+
+    def _get_mds(self) -> np.ndarray:
+        """
+        Perform multidimensional scaling (MDS) on the internal distances.
+
+        Returns
+        -------
+        np.ndarray
+            Numpy array with multidimensional scaling (MDS) coordinates in 2D.
+
+        Raises
+        ------
+        RuntimeError
+            When calculated wasn't called first.
+        """
+        if self._distances is None:
+            raise RuntimeError("You need to call `calculate` first.")
+
+        mds = MDS(n_components=2, dissimilarity="precomputed", random_state=0)
+        return mds.fit_transform(self._distances)
+
+    def _train_on_objective(self, X: np.ndarray, Y: np.ndarray) -> None:
+        """
+        Train the random forest on the performance.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            Numpy array with multidimensional scaling (MDS) coordinates in 2D.
+        Y : np.ndarray
+            Numpy array with costs.
+        """
+        logger.info("Training on objective...")
+        self._objective_model = RandomForestRegressor(random_state=0)
+        self._objective_model.fit(X, Y)
+
+    def _train_on_areas(self) -> None:
+        """
+        Train the random forest on the "valid" areas.
+
+        Raises
+        ------
+        RuntimeError
+            If calculated wasn't called first.
+        """
+        if self._MDS_X is None:
+            raise RuntimeError("You need to call `calculate` first.")
+
+        logger.info("Training on area...")
+        MDS_X = self._MDS_X
+
+        # Basically, a grid has to be created here
+        x_min, x_max = MDS_X[:, 0].min() - 1, MDS_X[:, 0].max() + 1
+        y_min, y_max = MDS_X[:, 1].min() - 1, MDS_X[:, 1].max() + 1
+
+        x = np.linspace(x_min, x_max, 20)
+        y = np.linspace(y_min, y_max, 20)
+
+        X = []
+        Y = []
+        for x1, x2 in zip(x, x[1:]):
+            for y1, y2 in zip(y, y[1:]):
+                # Find center
+                center = [(x2 - x1) / 2 + x1, (y2 - y1) / 2 + y1]
+                value = 0
+                for a, b in MDS_X:
+                    # If it's in center add
+                    if a >= x1 and a <= x2 and b >= y1 and b <= y2:
+                        value = 1
+                        break
+
+                X.append(center)
+                Y.append(value)
+
+        X_array = np.array(X)
+        Y_array = np.array(Y)
+
+        # Train the model
+        self._area_model = RandomForestRegressor(random_state=0)
+        self._area_model.fit(X_array, Y_array)

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
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+
+
+
+  
+    
+    
+    deepcave.evaluators.lpi — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.evaluators.lpi
+#  noqa: D400
+"""
+# LPI
+
+This module provides utilities to calculate the local parameter importance (LPI).
+
+## Classes
+    - LPI: This class calculates the local parameter importance (LPI).
+"""
+
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+from ConfigSpace import Configuration
+from ConfigSpace.c_util import change_hp_value, check_forbidden
+from ConfigSpace.exceptions import ForbiddenValueError
+from ConfigSpace.hyperparameters import CategoricalHyperparameter
+from ConfigSpace.util import impute_inactive_values
+
+from deepcave.constants import COMBINED_COST_NAME
+from deepcave.evaluators.epm.fanova_forest import FanovaForest
+from deepcave.runs import AbstractRun
+from deepcave.runs.objective import Objective
+
+
+# https://github.com/automl/ParameterImportance/blob/f4950593ee627093fc30c0847acc5d8bf63ef84b/pimp/evaluator/local_parameter_importance.py#L27
+

+[docs]
+class LPI:
+    """
+    Calculate the local parameter importance (LPI).
+
+    Properties
+    ----------
+    run : AbstractRun
+        The AbstractRun to get the importance from.
+    cs : ConfigurationSpace
+        The configuration space of the run.
+    hp_names : List[str]
+        The names of the Hyperparameters.
+    variances : Dict[Any, list]
+        The overall variances per tree.
+    importances : dict
+        The importances of the Hyperparameters.
+    continuous_neighbors : int
+        The number of neighbors chosen for continuous Hyperparameters.
+    incumbent : Configuration
+        The incumbent of the run.
+    default : Configuration
+        A configuration containing Hyperparameters with default values.
+    incumbent_array : numpy.ndarray
+        The internal vector representation of the incumbent.
+    seed : int
+        The seed. If not provided it will be random.
+    rs : RandomState
+        A random state with a given seed value.
+    """
+
+    def __init__(self, run: AbstractRun):
+        self.run = run
+        self.cs = run.configspace
+        self.hp_names = self.cs.get_hyperparameter_names()
+        self.variances: Optional[Dict[Any, List[Any]]] = None
+        self.importances: Optional[Dict[Any, Any]] = None
+
+

+[docs]
+    def calculate(
+        self,
+        objectives: Optional[Union[Objective, List[Objective]]] = None,
+        budget: Optional[Union[int, float]] = None,
+        continous_neighbors: int = 500,
+        n_trees: int = 10,
+        seed: int = 0,
+    ) -> None:
+        """
+        Prepare the data and train a RandomForest model.
+
+        Parameters
+        ----------
+        objectives : Optional[Union[Objective, List[Objective]]], optional
+            Considered objectives. By default, None. If None, all objectives are considered.
+        budget : Optional[Union[int, float]], optional
+            Considered budget. By default, None. If None, the highest budget is chosen.
+        continuous_neighbors : int, optional
+            How many neighbors should be chosen for continuous hyperparameters (HPs).
+            By default, 500.
+        n_trees : int, optional
+            The number of trees for the fanova forest.
+            Default is 10.
+        seed : Optional[int], optional
+            The seed. By default None. If None, a random seed is chosen.
+        """
+        if objectives is None:
+            objectives = self.run.get_objectives()
+
+        if budget is None:
+            budget = self.run.get_highest_budget()
+
+        # Set variables
+        self.continous_neighbors = continous_neighbors
+        self.incumbent, _ = self.run.get_incumbent(budget=budget)
+        self.default = self.cs.get_default_configuration()
+        self.incumbent_array = self.incumbent.get_array()
+
+        self.seed = seed
+        self.rs = np.random.RandomState(seed)
+
+        # Get data
+        df = self.run.get_encoded_data(
+            objectives=objectives, budget=budget, specific=True, include_combined_cost=True
+        )
+        X = df[self.hp_names].to_numpy()
+        Y = df[COMBINED_COST_NAME].to_numpy()
+
+        # Get model and train it
+        # Use same forest as for fanova
+        self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed)
+        self._model.train(X, Y)
+
+        # Get neighborhood sampled on an unit-hypercube.
+        neighborhood = self._get_neighborhood()
+
+        # The delta performance is needed from the default configuration and the incumbent
+        def_perf, def_var = self._predict_mean_var(self.default)
+        inc_perf, inc_var = self._predict_mean_var(self.incumbent)
+        delta = def_perf - inc_perf
+
+        # These are used for plotting and hold the predictions for each neighbor of each parameter.
+        # That means performances holds the mean, variances the variance of the forest.
+        performances: Dict[str, List[np.ndarray]] = {}
+        variances: Dict[str, List[np.ndarray]] = {}
+        # These are used for importance and hold the corresponding importance/variance over
+        # neighbors. Only import if NOT quantifying importance via performance-variance across
+        # neighbors.
+        importances = {}
+        # Nested list of values per tree in random forest.
+        predictions: Dict[str, List[List[np.ndarray]]] = {}
+
+        # Iterate over parameters
+        for hp_idx, hp_name in enumerate(self.incumbent.keys()):
+            if hp_name not in neighborhood:
+                continue
+
+            performances[hp_name] = []
+            variances[hp_name] = []
+            predictions[hp_name] = []
+            incumbent_added = False
+            incumbent_idx = 0
+
+            # Iterate over neighbors
+            for unit_neighbor, neighbor in zip(neighborhood[hp_name][0], neighborhood[hp_name][1]):
+                if not incumbent_added:
+                    # Detect incumbent
+                    if unit_neighbor > self.incumbent_array[hp_idx]:
+                        performances[hp_name].append(inc_perf)
+                        variances[hp_name].append(inc_var)
+                        incumbent_added = True
+                    else:
+                        incumbent_idx += 1
+
+                # Create the neighbor-Configuration object
+                new_array = self.incumbent_array.copy()
+                new_array = change_hp_value(self.cs, new_array, hp_name, unit_neighbor, hp_idx)
+                new_config = impute_inactive_values(Configuration(self.cs, vector=new_array))
+
+                # Get the leaf values
+                x = np.array(new_config.get_array())
+                leaf_values = self._model.get_leaf_values(x)
+
+                # And the prediction/performance/variance
+                predictions[hp_name].append([np.mean(tree_pred) for tree_pred in leaf_values])
+                performances[hp_name].append(np.mean(predictions[hp_name][-1]))
+                variances[hp_name].append(np.var(predictions[hp_name][-1]))
+
+            if len(neighborhood[hp_name][0]) > 0:
+                neighborhood[hp_name][0] = np.insert(
+                    neighborhood[hp_name][0], incumbent_idx, self.incumbent_array[hp_idx]
+                )
+                neighborhood[hp_name][1] = np.insert(
+                    neighborhood[hp_name][1], incumbent_idx, self.incumbent[hp_name]
+                )
+            else:
+                neighborhood[hp_name][0] = np.array(self.incumbent_array[hp_idx])
+                neighborhood[hp_name][1] = [self.incumbent[hp_name]]
+
+            if not incumbent_added:
+                performances[hp_name].append(inc_perf)
+                variances[hp_name].append(inc_var)
+
+            # After all neighbors are estimated, look at all performances except the incumbent
+            perf_before = performances[hp_name][:incumbent_idx]
+            perf_after = performances[hp_name][incumbent_idx + 1 :]
+            tmp_perf = perf_before + perf_after
+
+            # Avoid division by zero
+            if delta == 0:
+                delta = 1
+
+            imp_over_mean = (np.mean(tmp_perf) - performances[hp_name][incumbent_idx]) / delta
+            imp_over_median = (np.median(tmp_perf) - performances[hp_name][incumbent_idx]) / delta
+            imp_over_max = (np.max(tmp_perf) - performances[hp_name][incumbent_idx]) / delta
+
+            importances[hp_name] = np.array([imp_over_mean, imp_over_median, imp_over_max])
+
+        # Creating actual importance value (by normalizing over sum of vars)
+        num_trees = len(list(predictions.values())[0][0])
+        hp_names = list(performances.keys())
+
+        overall_var_per_tree = {}
+        for hp_name in hp_names:
+            hp_variances = []
+            for tree_idx in range(num_trees):
+                variance = np.var([neighbor[tree_idx] for neighbor in predictions[hp_name]])
+                hp_variances += [variance]
+
+            overall_var_per_tree[hp_name] = hp_variances
+
+        # Sum up variances per tree across parameters
+        sum_var_per_tree = [
+            sum([overall_var_per_tree[hp_name][tree_idx] for hp_name in hp_names])
+            for tree_idx in range(num_trees)
+        ]
+
+        # Normalize
+        overall_var_per_tree = {
+            p: [t / sum_var_per_tree[idx] for idx, t in enumerate(trees)]
+            for p, trees in overall_var_per_tree.items()
+        }
+        self.variances = overall_var_per_tree
+        self.importances = importances

+
+
+

+[docs]
+    def get_importances(self, hp_names: List[str]) -> Dict[str, Tuple[float, float]]:
+        """
+        Return the importances.
+
+        Parameters
+        ----------
+        hp_names : List[str]
+            Selected Hyperparameter names to get the importance scores from.
+
+        Returns
+        -------
+        importances : Dict[str, Tuple[float, float]]
+            Hyperparameter name and mean+var importance.
+
+        Raises
+        ------
+        RuntimeError
+            If the important scores are not calculated.
+        """
+        if self.importances is None or self.variances is None:
+            raise RuntimeError("Importance scores must be calculated first.")
+
+        importances: Dict[str, Tuple[float, float]] = {}
+        for hp_name in hp_names:
+            mean = 0
+            std = 0
+
+            if hp_name in self.importances:
+                mean = self.importances[hp_name][0]
+                std = np.var(self.variances[hp_name])
+
+            # Use this to quantify importance via variance
+            # mean = np.mean(overall_var_per_tree[hp_name])
+
+            # Sometimes there is an ugly effect if default is better than
+            # incumbent.
+            if mean < 0:
+                mean = 0
+                std = 0
+
+            importances[hp_name] = (mean, std)
+
+        return importances

+
+
+    def _get_neighborhood(self) -> Dict[str, List[Union[np.ndarray, List[np.ndarray]]]]:
+        """
+        Slight modification of ConfigSpace's get_one_exchange neighborhood.
+
+        This orders the parameter values and samples more neighbors in one go.
+        Further each and every neighbor needs to be rigorously checked if it is forbidden or not.
+
+        Returns
+        -------
+        neighborhood : Dict[str, List[Union[np.ndarray, List[np.ndarray]]]]
+            The neighborhood.
+        """
+        hp_names = self.cs.get_hyperparameter_names()
+
+        neighborhood: Dict[str, List[Union[np.ndarray, List[np.ndarray]]]] = {}
+        for hp_idx, hp_name in enumerate(hp_names):
+            # Check if hyperparameter is active
+            if not np.isfinite(self.incumbent_array[hp_idx]):
+                continue
+
+            hp_neighborhood = []
+            checked_neighbors = []  # On unit cube
+            checked_neighbors_non_unit_cube = []  # Not on unit cube
+            hp = self.cs.get_hyperparameter(hp_name)
+            num_neighbors = hp.get_num_neighbors(self.incumbent[hp_name])
+
+            if num_neighbors == 0:
+                continue
+            elif np.isinf(num_neighbors):
+                if hp.log:
+                    base = np.e
+                    log_lower = np.log(hp.lower) / np.log(base)
+                    log_upper = np.log(hp.upper) / np.log(base)
+                    neighbors_range = np.logspace(
+                        start=log_lower,
+                        stop=log_upper,
+                        num=self.continous_neighbors,
+                        endpoint=True,
+                        base=base,
+                    )
+                else:
+                    neighbors_range = np.linspace(hp.lower, hp.upper, self.continous_neighbors)
+                neighbors = list(map(lambda x: hp._inverse_transform(x), neighbors_range))
+            else:
+                neighbors = hp.get_neighbors(self.incumbent_array[hp_idx], self.rs)
+
+            for neighbor in neighbors:
+                if neighbor in checked_neighbors:
+                    continue
+
+                new_array = self.incumbent_array.copy()
+                new_array = change_hp_value(self.cs, new_array, hp_name, neighbor, hp_idx)
+
+                try:
+                    new_config = Configuration(self.cs, vector=new_array)
+                    hp_neighborhood.append(new_config)
+                    new_config.is_valid_configuration()
+                    check_forbidden(self.cs.forbidden_clauses, new_array)
+
+                    checked_neighbors.append(neighbor)
+                    checked_neighbors_non_unit_cube.append(new_config[hp_name])
+                except (ForbiddenValueError, ValueError):
+                    pass
+
+            sort_idx = list(
+                map(lambda x: x[0], sorted(enumerate(checked_neighbors), key=lambda y: y[1]))
+            )
+            if isinstance(self.cs.get_hyperparameter(hp_name), CategoricalHyperparameter):
+                checked_neighbors_non_unit_cube_categorical = list(
+                    np.array(checked_neighbors_non_unit_cube)[sort_idx]
+                )
+                neighborhood[hp_name] = [
+                    np.array(checked_neighbors)[sort_idx],
+                    checked_neighbors_non_unit_cube_categorical,
+                ]
+            else:
+                checked_neighbors_non_unit_cube_non_categorical = np.array(
+                    checked_neighbors_non_unit_cube
+                )[sort_idx]
+                neighborhood[hp_name] = [
+                    np.array(checked_neighbors)[sort_idx],
+                    checked_neighbors_non_unit_cube_non_categorical,
+                ]
+
+        return neighborhood
+
+    def _predict_mean_var(self, config: Configuration) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Small wrapper to predict marginalized over instances.
+
+        Parameter
+        ---------
+        config:Configuration
+            The self.incumbent of which the performance across the whole instance set is to be
+            estimated.
+
+        Returns
+        -------
+        mean: np.ndarray
+            The mean performance over the instance set.
+        var: np.ndarray
+            The variance over the instance set. If logged values are used, the variance might not
+            be able to be used.
+        """
+        config = impute_inactive_values(config)
+        array = np.array([config.get_array()])
+        mean, var = self._model.predict_marginalized(array)
+
+        return mean.squeeze(), var.squeeze()

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/layouts.html b/development/_modules/deepcave/layouts.html
index 37786064..5ec88402 100644
--- a/development/_modules/deepcave/layouts.html
+++ b/development/_modules/deepcave/layouts.html
@@ -702,7 +702,14 @@
               

                 
   
Source code for deepcave.layouts
-from abc import ABC, abstractmethod
+#  noqa: D400
+"""
+# Layout
+
+This module provides a foundation to create layouts.
+"""
+
+from abc import ABC, abstractmethod
 from typing import List, Union
 
 from dash.development.base_component import Component
@@ -714,18 +721,27 @@ 
Source code for deepcave.layouts
 

 [docs]
 class Layout(ABC):
+    """
+    A foundation for creating layouts.
+
+    Properties
+    ----------
+    logger : Logger
+        A logger for the class.
+    """
+
     def __init__(self) -> None:
         self.register_callbacks()
         self.logger = get_logger(self.__class__.__name__)
 
     @interactive
-    def register_callbacks(self) -> None:
+    def register_callbacks(self) -> None:  # noqa: D102
         pass
 
 

 [docs]
     @abstractmethod
-    def __call__(self) -> Union[List[Component], Component]:
+    def __call__(self) -> Union[List[Component], Component]:  # noqa: D102
         pass

 

 
diff --git a/development/_modules/deepcave/layouts/not_found.html b/development/_modules/deepcave/layouts/not_found.html
index a4f42193..a1ea3cdc 100644
--- a/development/_modules/deepcave/layouts/not_found.html
+++ b/development/_modules/deepcave/layouts/not_found.html
@@ -702,7 +702,16 @@
               

                 
   
Source code for deepcave.layouts.not_found
-from typing import List
+#  noqa: D400
+"""
+# Not_Found
+
+This module defines a custom layout for displaying a "NotFound" page.
+
+## Classes
+    - NotFoundLayout: Define a custom "NotFound" layout.
+"""
+from typing import List
 
 from dash import html
 from dash.development.base_component import Component
@@ -713,13 +722,22 @@ 
Source code for deepcave.layouts.not_found
 

 [docs]
 class NotFoundLayout(Layout):
-    def __init__(self, url) -> None:
+    """
+    Define a custom "NotFound" layout.
+
+    Properties
+    ----------
+    url : str
+        The url that could not be reached.
+    """
+
+    def __init__(self, url: str) -> None:
         super().__init__()
         self.url = url
 
 

 [docs]
-    def __call__(self) -> List[Component]:
+    def __call__(self) -> List[Component]:  # noqa: D102
         return [
             html.H2("This page does not exists."),
             html.Div(f"Tried to reach {self.url}."),
diff --git a/development/_modules/deepcave/layouts/sidebar.html b/development/_modules/deepcave/layouts/sidebar.html
index 61120660..e79a536f 100644
--- a/development/_modules/deepcave/layouts/sidebar.html
+++ b/development/_modules/deepcave/layouts/sidebar.html
@@ -702,7 +702,19 @@
               

                 
   
Source code for deepcave.layouts.sidebar
-from typing import Dict, List, Tuple, Union
+#  noqa: D400
+"""
+# SidebarLayout
+
+This module defines layout for the sidebar.
+
+Callbacks are registered and handled.
+
+## Classes
+    - SidebarLayout: Customize the Sidebar Layout.
+"""
+
+from typing import Dict, List, Tuple, Union
 
 import dash_bootstrap_components as dbc
 from dash import ALL, html
@@ -718,6 +730,19 @@ 
Source code for deepcave.layouts.sidebar
 

 [docs]
 class SidebarLayout(Layout):
+    """
+    Customize the Sidebar Layout.
+
+    Callbacks are registered and handled.
+
+    Properties
+    ----------
+    plugins : Dict[str, List[Plugin]]
+        A dictionary of all categorized plugins.
+    nav_points : Dict[str, List[Tuple[str, str, str]]]
+        A dictionary with plugins attributes corresponding to their category.
+    """
+
     def __init__(self, categorized_plugins: Dict[str, List[Plugin]]) -> None:
         super().__init__()
         self.plugins = categorized_plugins
@@ -731,13 +756,29 @@ 
Source code for deepcave.layouts.sidebar
 
         self.nav_points = nav_points
 
+

+[docs]
     def register_callbacks(self) -> None:
+        """Register the callbacks for the sidebar layout."""
         # Update navigation items
         output = Output("navigation-items", "children")
         input = Input("on-page-load", "pathname")
 
         @app.callback(output, input)  # type: ignore
-        def update_navigation_items(pathname):  # type: ignore
+        def update_navigation_items(pathname: str) -> List[Component]:
+            """
+            Update the navigation items.
+
+            Parameters
+            ----------
+            pathname : str
+                The pathname.
+
+            Returns
+            -------
+            List[Component]
+                The navigation items.
+            """
             layouts = []
             for category, points in self.nav_points.items():
                 layouts += [
@@ -749,7 +790,7 @@ 
Source code for deepcave.layouts.sidebar
                 ]
 
                 point_layouts = []
-                for (id, name, icon) in points:
+                for id, name, icon in points:
                     href = f"/plugins/{id}"
                     point_layouts += [
                         html.Li(
@@ -766,8 +807,6 @@ 
Source code for deepcave.layouts.sidebar
 
                 layouts += [html.Ul(className="nav flex-column", children=point_layouts)]
 
-            icon = {"data-feather": "file-text"}
-
             return html.Div(
                 className="position-sticky pt-3",
                 children=[
@@ -792,6 +831,7 @@ 
Source code for deepcave.layouts.sidebar
             State({"type": "cancel-job", "index": ALL}, "name"),
         )
         def delete_job(n_clicks, job_ids):  # type: ignore
+            """Delete the job from the queue."""
             for n_click, job_id in zip(n_clicks, job_ids):
                 if n_click is not None:
                     queue.delete_job(job_id)
@@ -801,6 +841,7 @@ 
Source code for deepcave.layouts.sidebar
 
         @app.callback(output, Trigger("global-update", "n_intervals"))  # type: ignore
         def update_queue_info() -> List[Component]:
+            """Update the information of the queue."""
             try:
                 all_jobs = [
                     queue.get_finished_jobs(),
@@ -812,7 +853,6 @@ 
Source code for deepcave.layouts.sidebar
 
                 collect = []
                 for jobs, status in zip(all_jobs, job_stati):
-
                     for job in jobs:
                         name = job.meta["display_name"]
                         job_id = job.id
@@ -873,11 +913,12 @@ 
Source code for deepcave.layouts.sidebar
 
                 return []
             except Exception:
-                return []
+                return []

+
 
 

 [docs]
-    def __call__(self) -> Union[List[Component], Component]:
+    def __call__(self) -> Union[List[Component], Component]:  # noqa: D102
         return html.Nav(
             className="col-md-3 col-lg-2 d-md-block sidebar collapse",
             id="sidebarMenu",
diff --git a/development/_modules/deepcave/plugins.html b/development/_modules/deepcave/plugins.html
index 798146ec..4e1fb92c 100644
--- a/development/_modules/deepcave/plugins.html
+++ b/development/_modules/deepcave/plugins.html
@@ -702,7 +702,18 @@
               

                 
   
Source code for deepcave.plugins
-from abc import ABC
+#  noqa: D400
+"""
+# Plugins
+
+This module provides a base class for all the available plugins.
+It provides different utilities to handle the plugins and check for compatibility in the runs.
+
+## Classes
+    - Plugin: Base class for all plugins.
+"""
+
+from abc import ABC
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import copy
@@ -737,24 +748,35 @@ 
Source code for deepcave.plugins
     """
     Base class for all plugins.
 
-    Attributes
+    Provides different utilities to handle the plugins and check for compatibility in the runs.
+
+    Properties
     ----------
-    id : int
-        Unique identifier for the plugin.
-    name : str
-        Name of the plugin. It is shown in the navigation and in the title.
-    description : str, optional
-        Description of the plugin. Displayed below the title.
-    icon : str, optional
-        FontAwesome icon. Shown in the navigation.
-    help : str, optional
-        Path to the help file.
-    button_caption : str, optional
-        Caption of the button. Shown only, if `StaticPlugin` is used.
-    activate_run_selection : bool, optional
+    inputs : List[Tuple[str, str, bool, Any]]
+        The registered inputs.
+    outputs : List[Tuple[str, str, bool]]
+        The registered outputs.
+    previous_inputs : Dict[str, Dict[str, str]]
+        The previous inputs.
+    raw_outputs : Optional[Dict[str, Any]]
+        The raw outputs.
+    activate_run_selection : bool
         Shows a dropdown to select a run in the inputs layout.
         This feature is useful if only one run could be viewed at a time.
         Moreover, it prevents the plugin to calculate results across all runs.
+    id : str
+        The unique identifier for the plugin.
+    runs : List[AbstractRun]
+        A list of the abstract runs.
+    groups : List[Group]
+        A list of the groups.
+    help : str
+        The path to the documentation.
+    name : str
+        The name of the plugin.
+        It is shown in the navigation and title.
+    button_caption : str
+        Caption of the button. Shown only, if `StaticPlugin` is used.
     """
 
     id: str
@@ -774,7 +796,7 @@ 
Source code for deepcave.plugins
         self.previous_inputs: Dict[str, Dict[str, str]] = {}
         self.raw_outputs: Optional[Dict[str, Any]] = None
 
-        # We have to call the output layout one time to register
+        # The output layout has to be called one time to register
         # the values
         # Problem: Inputs/Outputs can't be changed afterwards anymore.
 
@@ -794,7 +816,7 @@ 
Source code for deepcave.plugins
     @interactive
     def get_base_url(cls) -> str:
         """
-        Generates the url for the plugin.
+        Generate the url for the plugin.
 
         Returns
         -------
@@ -811,8 +833,9 @@ 
Source code for deepcave.plugins
     @staticmethod
     def check_run_compatibility(run: AbstractRun) -> bool:
         """
-        Checks if a run is compatible with this plugin. If a plugin is not compatible,
-        you can not select the run.
+        Check if a run is compatible with this plugin.
+
+        If a plugin is not compatible, you can not select the run.
 
         Note
         ----
@@ -835,7 +858,8 @@ 
Source code for deepcave.plugins
 [docs]
     def check_runs_compatibility(self, runs: List[AbstractRun]) -> None:
         """
-        This function is needed if all selected runs need something in common
+        Needed if all selected runs need something in common.
+
         (e.g. budget or objective). Since this function is called before the layout is created,
         it can be also used to set common values for the plugin.
 
@@ -862,8 +886,10 @@ 
Source code for deepcave.plugins
         type: Any = None,
     ) -> str:
         """
-        Registers an input variable for the plugin. It is important to register the inputs
-        because callbacks have to be defined before the server is started.
+        Register an input variable for the plugin.
+
+        It is important to register the inputs.
+        This is, because callbacks have to be defined before the server is started.
         After registering all inputs, an internal mapping is created.
 
         Parameters
@@ -871,7 +897,7 @@ 
Source code for deepcave.plugins
         id : str
             Specifies the id of the input.
         attributes : Union[str, List[str]], optional
-            Attributes which should be passed to the (dash) component, by default ("value",)
+            Attributes which should be passed to the (dash) component, by default ("value",).
         filter : bool, optional
             Specifies if the input is a filter. By default False.
         type : Any, optional
@@ -895,9 +921,9 @@ 
Source code for deepcave.plugins
             if key not in self.inputs:
                 self.inputs.append(key)
 
-        # We have to rearrange the inputs because `State`
-        # must follow all `Input`. Since all filters are `Input`, we have to
-        # shift them to the front.
+        # The inputs have to be rearranged, because `State`
+        # must follow all `Input`. Since all filters are `Input`, they have to
+        # be shifted to the front.
         self.inputs.sort(key=lambda x: x[2], reverse=True)
 
         return self.get_internal_input_id(id)

@@ -909,7 +935,7 @@ 
Source code for deepcave.plugins
         self, id: str, attributes: Union[str, List[str]] = "value", mpl: bool = False
     ) -> str:
         """
-        Registers an output variable for the plugin.
+        Register an output variable for the plugin.
 
         Parameters
         ----------
@@ -939,21 +965,35 @@ 
Source code for deepcave.plugins
         return self.get_internal_output_id(id)

 
 
+

+[docs]
     def get_internal_id(self, id: str) -> str:
-        return f"{self.id}-{id}"
+        """Get the internal id."""
+        return f"{self.id}-{id}"

 
+
+

+[docs]
     def get_internal_input_id(self, id: str) -> str:
-        return f"{self.id}-{id}-input"
+        """Get the internal input id."""
+        return f"{self.id}-{id}-input"

+
 
+

+[docs]
     def get_internal_output_id(self, id: str) -> str:
-        return f"{self.id}-{id}-output"
+        """Get the internal output id."""
+        return f"{self.id}-{id}-output"

+
 
 

 [docs]
     @interactive
     def register_callbacks(self) -> None:
         """
-        Registers basic callbacks for the plugin. Following callbacks are registered:
+        Register basic callbacks for the plugin.
+
+        Following callbacks are registered:
         - If inputs changes, the changes are pasted back. This is in particular
         interest if input dependencies are used.
         - Raw data dialog to display raw data.
@@ -962,7 +1002,7 @@ 
Source code for deepcave.plugins
         Raises
         ------
         RuntimeError
-            _description_
+            If no run id is found.
         """
         from deepcave import app, c, run_handler
 
@@ -980,7 +1020,8 @@ 
Source code for deepcave.plugins
         if len(outputs) > 0:
 
             @app.callback(outputs, inputs)  # type: ignore
-            def plugin_input_update(pathname: str, *inputs_list: str) -> List[str]:
+            def plugin_input_update(pathname: str, *inputs_list: str) -> List[Optional[str]]:
+                """Update the input of the plugin."""
                 # Simple check if page was loaded for the first time
                 init = all(input is None for input in inputs_list)
 
@@ -996,10 +1037,10 @@ 
Source code for deepcave.plugins
                         # Overwrite/set the passed inputs
                         update_dict(inputs, passed_inputs)
 
-                        # Then we have to take care of the run_selection
+                        # Then the run_selection has to be taken care of
                         selected_run: Optional[AbstractRun] = None
                         if self.activate_run_selection:
-                            # If run_selection is active and we don't have an id, then
+                            # If run_selection is active and the id is not known, then
                             # the passed inputs have no use.
                             try:
                                 run_id = passed_inputs["run"]["value"]
@@ -1037,14 +1078,14 @@ 
Source code for deepcave.plugins
                             update_dict(inputs, new_inputs)
 
                         # Set not used inputs
-                        for (id, attribute, _, _) in self.inputs:
+                        for id, attribute, _, _ in self.inputs:
                             if id not in inputs:
                                 inputs[id] = {}
 
                             if attribute not in inputs[id]:
                                 inputs[id][attribute] = None
                     elif inputs is not None:
-                        # We have to update the options of the run selection here.
+                        # The options of the run selection have to be updated here.
                         # This is important if the user have added/removed runs.
                         if self.activate_run_selection:
                             run_value = inputs["run"]["value"]
@@ -1059,13 +1100,16 @@ 
Source code for deepcave.plugins
                             inputs["run"]["value"] = run_value
                 else:
                     # Map the list `inputs` to a dict.
-                    inputs = self._list_to_dict(inputs_list)  # type: ignore
+                    # inputs_list_as_list is necessary as new variable,
+                    # because inputs_list is a tuple and cant be passed to _list_to_dict.
+                    inputs_list_as_list = list(inputs_list)
+                    inputs = self._list_to_dict(inputs_list_as_list)
 
                     if len(self.previous_inputs) == 0:
                         self.previous_inputs = inputs.copy()
 
                     # Only work on copies.
-                    # We don't want the inputs dict to be changed by the user.
+                    # The inputs dict should not be changed by the user.
                     _previous_inputs = self.previous_inputs.copy()
                     _inputs = inputs.copy()
 
@@ -1079,9 +1123,9 @@ 
Source code for deepcave.plugins
 
                         # Reset everything if run name changed.
                         if _previous_run_id is not None and _previous_run_id != _run_id:
-                            # We can't use load_inputs here only
+                            # load_inputs cannot be used here, only
                             # because `run` would be removed.
-                            # Also: We want to keep the current run name.
+                            # Also: The current run name does not need to be kept.
                             update_dict(_inputs, self.load_inputs())
                             # Reset inputs
                             if "objective_id" in _inputs.keys():
@@ -1103,17 +1147,17 @@ 
Source code for deepcave.plugins
                         )
 
                         # Update dict
-                        # dict.update() remove keys, so we use our own method to do so
+                        # dict.update() removes keys, so our own method is used to do so
                         update_dict(inputs, user_dependencies_inputs)  # inplace operation
 
                 # Let's cast the inputs
                 inputs = self._cast_inputs(inputs)
 
                 # From dict to list
-                inputs_list = self._dict_to_list(inputs, input=True)  # type: ignore
+                inputs_list_from_dict = self._dict_to_list(inputs, input=True)
                 self.previous_inputs = inputs
 
-                return list(inputs_list)
+                return inputs_list_from_dict
 
         # Register modal for raw data here
         @app.callback(  # type: ignore
@@ -1125,6 +1169,7 @@ 
Source code for deepcave.plugins
             State(self.get_internal_id("raw_data"), "is_open"),
         )
         def toggle_raw_data_modal(n: Optional[int], is_open: bool) -> Tuple[bool, str]:
+            """Toggle the raw data modal."""
             code = ""
             if n:
                 if (out := self.raw_outputs) is not None:
@@ -1143,21 +1188,22 @@ 
Source code for deepcave.plugins
             Input(self.get_internal_id("show_help"), "n_clicks"),
             State(self.get_internal_id("help"), "is_open"),
         )
-        def toggle_help_modal(n: Optional[int], is_open: bool) -> Tuple[bool, str]:
+        def toggle_help_modal(n: Optional[int], is_open: bool) -> bool:
+            """Toggle the help modal."""
             if n:
                 return not is_open
-
             return is_open
 
         # Register callback to click on configurations
-        for (id, *_) in self.outputs:
+        for id, *_ in self.outputs:
             internal_id = self.get_internal_output_id(id)
 
             @app.callback(
                 Output(internal_id, "clickData"),
                 Input(internal_id, "clickData"),
-            )
-            def go_to_configuration(click_data):  # type: ignore
+            )  # type: ignore
+            def go_to_configuration(click_data: Any):
+                """Open link from hovertext."""
                 if click_data is not None:
                     # Get hovertext
                     try:
@@ -1179,7 +1225,7 @@ 
Source code for deepcave.plugins
         self, inputs: Dict[str, Dict[str, str]], last_inputs: Dict[str, Dict[str, str]]
     ) -> Tuple[bool, bool]:
         """
-        Checks if the inputs have changed.
+        Check if the inputs have changed.
 
         Parameters
         ----------
@@ -1197,11 +1243,10 @@ 
Source code for deepcave.plugins
         inputs_changed = False
         filters_changed = False
 
-        # If only filters changed, then we don't need to
+        # If only filters changed, there is no need to
         # calculate the results again.
         if last_inputs is not None:
-            for (id, attribute, filter, _) in self.inputs:
-
+            for id, attribute, filter, _ in self.inputs:
                 if self.activate_run_selection:
                     if id == "run":
                         continue
@@ -1217,26 +1262,44 @@ 
Source code for deepcave.plugins
     @interactive
     def _process_raw_outputs(
         self, inputs: Dict[str, Dict[str, str]], raw_outputs: Dict[str, Any]
-    ) -> Union[Any, List[Any]]:
+    ) -> Any:
+        """
+        Process the raw outputs and update the layout.
+
+        Parameters
+        ----------
+        inputs : Dict[str, Dict[str, str]]
+            The inputs for the passed runs.
+        raw_outputs : Dict[str, Any]
+            The raw outputs to process.
+
+        Returns
+        -------
+        Any
+            The processed outputs.
+        """
         from deepcave import c, run_handler
 
         # Use raw outputs to update our layout
         mpl_active = c.get("matplotlib-mode")
+        passed_runs: Union[List[AbstractRun], AbstractRun]
 
         if self.activate_run_selection:
             passed_runs = run_handler.get_run(inputs["run"]["value"])
             passed_outputs = raw_outputs[passed_runs.id]
         else:
-            passed_runs = self.all_runs  # type: ignore
+            passed_runs = self.all_runs
             passed_outputs = raw_outputs
 
         # Clean inputs
         cleaned_inputs = self._clean_inputs(inputs)
 
+        # passed runs could be a list, but load mpl outputs and load outputs do not
+        # accept lists, but expect single runs
         if mpl_active:
-            outputs = self.__class__.load_mpl_outputs(passed_runs, cleaned_inputs, passed_outputs)
+            outputs = self.__class__.load_mpl_outputs(passed_runs, cleaned_inputs, passed_outputs)  # type: ignore # noqa: E501
         else:
-            outputs = self.__class__.load_outputs(passed_runs, cleaned_inputs, passed_outputs)
+            outputs = self.__class__.load_outputs(passed_runs, cleaned_inputs, passed_outputs)  # type: ignore # noqa: E501
 
         logger.debug("Raw outputs processed successfully.")
 
@@ -1251,10 +1314,10 @@ 
Source code for deepcave.plugins
             if not isinstance(outputs, list):
                 outputs = [outputs]
 
-        # We have to add no_updates here for the mode we don't want
+        # no_updates has to be added here for the mode that is not wanted
         count_outputs = 0
         count_mpl_outputs = 0
-        for (_, _, mpl_mode) in self.outputs:
+        for _, _, mpl_mode in self.outputs:
             if mpl_mode:
                 count_mpl_outputs += 1
             else:
@@ -1273,8 +1336,9 @@ 
Source code for deepcave.plugins
     @interactive
     def _list_to_dict(self, values: List[str], input: bool = True) -> Dict[str, Dict[str, str]]:
         """
-        Maps the given values to a dict, regarding the sorting from
-        either self.inputs or self.outputs.
+        Map the given values to a dict.
+
+        Regarding the sorting from either self.inputs or self.outputs.
 
         Parameters
         ----------
@@ -1286,12 +1350,15 @@ 
Source code for deepcave.plugins
         Returns
         -------
         Dict[str, Dict[str, str]]
-            _description_
+            Dictionary containing the mapping information.
         """
+        # This is necessary, because of the conditional type of order
+        order: Union[List[Tuple[str, str, bool]], List[Tuple[str, str, bool, Any]]]
+
         if input:
             order = self.inputs
         else:
-            order = self.outputs  # type: ignore
+            order = self.outputs
 
         mapping: Dict[str, Any] = {}
         for value, (id, attribute, *_) in zip(values, order):
@@ -1307,8 +1374,9 @@ 
Source code for deepcave.plugins
         self, d: Dict[str, Dict[str, str]], input: bool = False
     ) -> List[Optional[str]]:
         """
-        Maps the given dict to a list, respecting the sorting from either
-        self.inputs or self.outputs.
+        Map the given dict to a list.
+
+        Respecting the sorting from either self.inputs or self.outputs.
 
         Parameters
         ----------
@@ -1324,13 +1392,16 @@ 
Source code for deepcave.plugins
         """
         from deepcave import c
 
+        # This is necessary, because of the conditional type of order
+        order: Union[List[Tuple[str, str, bool]], List[Tuple[str, str, bool, Any]]]
+
         if input:
             order = self.inputs
         else:
-            order = self.outputs  # type: ignore
+            order = self.outputs
 
         result: List[Optional[str]] = []
-        for (id, attribute, instance, *_) in order:
+        for id, attribute, instance, *_ in order:
             if not input:
                 # Instance is mlp_mode in case of outputs
                 # Simply ignore other outputs.
@@ -1346,28 +1417,33 @@ 
Source code for deepcave.plugins
         return result
 
     @interactive
-    def _dict_as_key(self, d: Dict[str, Any], remove_filters: bool = False) -> Optional[str]:
+    def _dict_as_key(self, d: Dict[str, Any], remove_filters: bool = False) -> str:
         """
-        Converts a dictionary to a key. Only ids from self.inputs are considered.
+        Convert a dictionary to a key. Only ids from self.inputs are considered.
 
         Parameters
         ----------
         d : Dict[str, Any]
             Dictionary to get the key from.
         remove_filters : bool, optional
-            Option wheather the filters should be included or not. By default False.
+            Option whether the filters should be included or not. By default False.
 
         Returns
         -------
         Optional[str]
             Key as string from the given dictionary. Returns none if `d` is not a dictionary.
+
+        Raises
+        ------
+        TypeError
+            If `d` is not a dictionary.
         """
         if not isinstance(d, dict):
-            return None
+            raise TypeError("d must be a dictionary.")
 
         new_d = copy.deepcopy(d)
         if remove_filters:
-            for (id, _, filter, _) in self.inputs:
+            for id, _, filter, _ in self.inputs:
                 if filter:
                     if id in new_d:
                         del new_d[id]
@@ -1376,8 +1452,10 @@ 
Source code for deepcave.plugins
 
     def _cast_inputs(self, inputs: Dict[str, Dict[str, str]]) -> Dict[str, Dict[str, str]]:
         """
-        Casts the inputs based on `self.inputs`. Background is that dash always casts integers/
-        booleans to strings. This method ensured that the correct types are returned.
+        Cast the inputs based on `self.inputs`.
+
+        Background is that dash always casts integers/booleans to strings.
+        This method ensures that the correct types are returned.
 
         Parameters
         ----------
@@ -1392,10 +1470,9 @@ 
Source code for deepcave.plugins
         casted_inputs: Dict[str, Dict[str, str]] = defaultdict(dict)
         for id, attributes in inputs.items():
             for attribute in attributes:
-
                 # Find corresponding input
                 type = None
-                for (id_, attribute_, _, type_) in self.inputs:
+                for id_, attribute_, _, type_ in self.inputs:
                     if id == id_ and attribute == attribute_:
                         type = type_
                         break
@@ -1410,8 +1487,9 @@ 
Source code for deepcave.plugins
 
     def _clean_inputs(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         """
-        Cleans the given inputs s.t. only the first value is used.
-        Also, boolean values are casted to booleans.
+        Clean the given inputs s.t. only the first value is used.
+
+        Also, boolean values are cast to booleans.
 
         Example
         -------
@@ -1425,16 +1503,19 @@ 
Source code for deepcave.plugins
 
         Parameters
         ----------
-        inputs (dict): Inputs to clean.
+        inputs : Dict[str, Any]
+            Inputs to clean.
 
-        Returns:
-            dict: Cleaned inputs.
+        Returns
+        -------
+        Dict[str, Any]
+            Cleaned inputs.
         """
         used_ids = []
         cleaned_inputs = {}
-        for (id, attribute, *_) in self.inputs:
-            # Since self.inputs is ordered, we use the first occuring attribute and add
-            # the id so it is not used again.
+        for id, attribute, *_ in self.inputs:
+            # Since self.inputs is ordered, the first occurring attribute is used and
+            # the id is added so it is not used again.
             if id not in used_ids:
                 i = inputs[id][attribute]
 
@@ -1449,23 +1530,47 @@ 
Source code for deepcave.plugins
 
         return cleaned_inputs
 
-    @property  # type: ignore
+    @property
     @interactive
     def runs(self) -> List[AbstractRun]:
+        """
+        Get the runs as a list.
+
+        Returns
+        -------
+        List[AbstractRun]
+            The list with the runs.
+        """
         from deepcave import run_handler
 
         return run_handler.get_runs()
 
-    @property  # type: ignore
+    @property
     @interactive
     def groups(self) -> List[Group]:
+        """
+        Get the groups as a list.
+
+        Returns
+        -------
+        List[Group]
+            The list with the groups.
+        """
         from deepcave import run_handler
 
         return run_handler.get_groups()
 
-    @property  # type: ignore
+    @property
     @interactive
     def all_runs(self) -> List[AbstractRun]:
+        """
+        Get all runs and include the groups as a list.
+
+        Returns
+        -------
+        List[AbstractRun]
+            The list with all runs and included groups.
+        """
         from deepcave import run_handler
 
         return run_handler.get_runs(include_groups=True)
@@ -1475,13 +1580,26 @@ 
Source code for deepcave.plugins
     @interactive
     def __call__(self, render_button: bool = False) -> List[Component]:
         """
-        Returns the components for the plugin. Basically, all blocks and elements of the plugin
-        are stacked-up here
+        Return the components for the plugin.
+
+        Basically, all blocks and elements of the plugin are stacked-up here.
+
+        Parameters
+        ----------
+        render_button : bool, optional
+            Whether to render the button or not. By default False.
 
         Returns
         -------
         List[Component]
             Layout as list of components.
+
+        Raises
+        ------
+        NotMergeableError
+            If runs are not compatible.
+        FileNotFoundError
+            If the help file can not be found.
         """
         from deepcave import c, notification
 
@@ -1563,7 +1681,7 @@ 
Source code for deepcave.plugins
             ],
         )
 
-        # We always have to render it because of the button.
+        # It always has to be rendered, because of the button.
         # Button tells us if the page was just loaded.
         components += [
             html.Div(
@@ -1579,7 +1697,27 @@ 
Source code for deepcave.plugins
             )
         ]
 
-        def register_in(a, b):  # type: ignore
+        def register_in(a: str, b: Union[List[str], str]) -> str:
+            """
+            Register the given input.
+
+            Note
+            ----
+            For more information, see 'register_input'.
+
+            Parameters
+            ----------
+            a : str
+                Specifies the id of the input.
+            b : Union[List[str], str]
+                Attributes which should be passed to the (dash) component, by default ("value",).
+
+            Returns
+            -------
+            str
+                Unique id for the input and plugin.
+                This is necessary because ids are defined globally.
+            """
             return self.register_input(a, b, filter=True)
 
         filter_layout = self.__class__.get_filter_layout(register_in)
@@ -1603,7 +1741,27 @@ 
Source code for deepcave.plugins
                 )
             ]
 
-        def register_out(a, b):  # type: ignore
+        def register_out(a: str, b: Union[List[str], str]) -> str:
+            """
+            Register the output.
+
+            Note
+            ----
+            For more information, see 'register_output'
+
+            Parameters
+            ----------
+            a : str
+                Specifies the id of the output.
+            b : Union[List[str], str]
+                Attribute.
+
+            Returns
+            -------
+            str
+                Unique id for the output and plugin.
+                This is necessary because ids are defined globally.
+            """
             return self.register_output(a, b, mpl=True)
 
         output_layout = self.__class__.get_mpl_output_layout(register_out)
@@ -1667,15 +1825,17 @@ 
Source code for deepcave.plugins
 [docs]
     @staticmethod
     @interactive
-    def get_run_input_layout(register: Callable[[str, Union[str, List[str]]], str]) -> Component:
+    def get_run_input_layout(register: Callable) -> Component:
         """
-        Generates the run selection input.
+        Generate the run selection input.
+
         This is only the case if `activate_run_selection` is True.
 
         Parameters
         ----------
-        register : Callable[[str, Union[str, List[str]]], str]
+        register : Callable
             The register method to register (user) variables.
+            For more information, see 'register_input'.
 
         Returns
         -------
@@ -1702,14 +1862,15 @@ 
Source code for deepcave.plugins
         check_run_compatibility: Callable[[AbstractRun], bool],
     ) -> Dict[str, Any]:
         """
-        Loads the options for `get_run_input_layout`.
+        Load the options for `get_run_input_layout`.
+
         Both runs and groups are displayed.
 
         Parameters
         ----------
-        runs : Dict[str, Run]
+        runs : List[AbstractRun]
             The runs to display.
-        groups : Dict[str, Group]
+        groups : List[Group]
             The groups to display.
         check_run_compatibility : Callable[[AbstractRun], bool]
             If a single run is compatible. If not, the run is not shown.
@@ -1719,7 +1880,6 @@ 
Source code for deepcave.plugins
         Dict[str, Any]
             Both runs and groups, separated by a separator.
         """
-
         labels = []
         values = []
         disabled = []
@@ -1759,7 +1919,8 @@ 
Source code for deepcave.plugins
     @interactive
     def get_selected_runs(self, inputs: Dict[str, Any]) -> List[AbstractRun]:
         """
-        Parses selected runs from inputs.
+        Parse selected runs from inputs.
+
         If self.activate_run_selection is set, return only selected run. Otherwise, return all
         possible runs.
 
@@ -1791,7 +1952,7 @@ 
Source code for deepcave.plugins
 
             # Also:
             # Remove `run` from inputs_key because
-            # we don't want the run names included.
+            # The runs name does not need to be included
             _inputs = inputs.copy()
             del _inputs["run"]
 
@@ -1805,6 +1966,7 @@ 
Source code for deepcave.plugins
     def load_inputs(self) -> Dict[str, Any]:
         """
         Load the content for the defined inputs in `get_input_layout` and `get_filter_layout`.
+
         This method is necessary to pre-load contents for the inputs. So, if the plugin is
         called for the first time or there are no results in the cache, the plugin gets its
         content from this method.
@@ -1826,7 +1988,9 @@ 
Source code for deepcave.plugins
         inputs: Dict[str, Any],
     ) -> Dict[str, Any]:
         """
-        Same as `load_inputs` but called after inputs have changed. Provides a lot of flexibility.
+        Load the content as in 'load_inputs' but called after inputs have changed.
+
+        Provides a lot of flexibility.
 
         Note
         ----
@@ -1835,7 +1999,7 @@ 
Source code for deepcave.plugins
 
         Parameters
         ----------
-        selected_run : Optional[Union[AbstractRun, List[AbstractRun]]], optional
+        run : Optional[Union[AbstractRun, List[AbstractRun]]], optional
             The selected run from the user. In case of `activate_run_selection`, only one run
             is passed. Defaults to None.
         previous_inputs : Dict[str, Any]
@@ -1854,14 +2018,15 @@ 
Source code for deepcave.plugins
 

 [docs]
     @staticmethod
-    def get_input_layout(register: Callable[[str, Union[str, List[str]]], str]) -> List[Component]:
+    def get_input_layout(register: Callable) -> List[Component]:
         """
         Layout for the input block.
 
         Parameters
         ----------
-        register : Callable[[str, Union[str, List[str]]], str]
+        register : Callable
             The register method to register (user) variables.
+            For more information, see 'register_input'.
 
         Returns
         -------
@@ -1874,14 +2039,15 @@ 
Source code for deepcave.plugins
 

 [docs]
     @staticmethod
-    def get_filter_layout(register: Callable[[str, Union[str, List[str]]], str]) -> List[Component]:
+    def get_filter_layout(register: Callable) -> List[Component]:
         """
         Layout for the filter block.
 
         Parameters
         ----------
-        register : Callable[[str, Union[str, List[str]]], str]
+        register : Callable
             The register method to register (user) variables.
+            For more information, see 'register_input'.
 
         Returns
         -------
@@ -1894,16 +2060,15 @@ 
Source code for deepcave.plugins
 

 [docs]
     @staticmethod
-    def get_output_layout(
-        register: Callable[[str, Union[str, List[str]]], str]
-    ) -> Optional[Union[Component, List[Component]]]:
+    def get_output_layout(register: Callable) -> Optional[Union[Component, List[Component]]]:
         """
         Layout for the output block.
 
         Parameters
         ----------
-        register : Callable[[str, Union[str, List[str]]], str]
+        register : Callable
             The register method to register outputs.
+            For more information, see 'register_input'.
 
         Returns
         -------
@@ -1916,20 +2081,19 @@ 
Source code for deepcave.plugins
 

 [docs]
     @staticmethod
-    def get_mpl_output_layout(
-        register: Callable[[str, Union[str, List[str]]], str]
-    ) -> Optional[Union[Component, List[Component]]]:
+    def get_mpl_output_layout(register: Callable) -> Optional[Union[Component, List[Component]]]:
         """
         Layout for the matplotlib output block.
 
         Parameters
         ----------
-        register : Callable[[str, Union[str, List[str]]], str]
+        register : Callable
             The register method to register outputs.
+            For more information, see 'register_input'.
 
         Returns
         -------
-        Union[Component, List[Component]]
+        Optional[Union[Component, List[Component]]]
             Layout for the matplotlib output block.
         """
         return None

@@ -1944,12 +2108,14 @@ 
Source code for deepcave.plugins
         outputs: Dict[str, Union[str, Dict[str, str]]],
     ) -> Union[Component, List[Component]]:
         """
-        Reads in the raw data and prepares them for the layout.
+        Read in the raw data and prepare them for the layout.
 
         Note
         ----
         The passed `inputs` are cleaned and therefore differs compared to `load_inputs` or
-        `load_dependency_inputs`. Please see `_clean_inputs` for more information.
+        `load_dependency_inputs`.
+        Inputs are cleaned s.t. only the first value is used.
+        Also, boolean values are casted to booleans.
 
         Parameters
         ----------
@@ -1979,12 +2145,13 @@ 
Source code for deepcave.plugins
         outputs: Dict[str, Union[str, Dict[str, str]]],
     ) -> Union[Component, List[Component]]:
         """
-        Reads in the raw data and prepares them for the layout.
+        Read in the raw data and prepare them for the layout.
 
         Note
         ----
         The passed `inputs` are cleaned and therefore differs compared to `load_inputs` or
-        `load_dependency_inputs`. Please see `_clean_inputs` for more information.
+        `load_dependency_inputs`. Inputs are cleaned s.t. only the first value is used.
+        Also, boolean values are casted to booleans.
 
         Parameters
         ----------
@@ -2010,7 +2177,7 @@ 
Source code for deepcave.plugins
     @staticmethod
     def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]:
         """
-        Returns raw data based on a run and input data.
+        Return raw data based on a run and input data.
 
         Warning
         -------
@@ -2019,7 +2186,8 @@ 
Source code for deepcave.plugins
         Note
         ----
         The passed `inputs` are cleaned and therefore differs compared to `load_inputs` or
-        `load_dependency_inputs`. Please see `_clean_inputs` for more information.
+        `load_dependency_inputs`. Inputs are cleaned s.t. only the first value is used.
+        Also, boolean values are casted to booleans.
 
         Parameters
         ----------
@@ -2043,7 +2211,8 @@ 
Source code for deepcave.plugins
         cls, runs: Union[AbstractRun, List[AbstractRun]], inputs: Dict[str, Any]
     ) -> Union[Dict[str, Any], Dict[str, Dict[str, Any]]]:
         """
-        Checks whether run selection is active and accepts either one or multiple runs at once.
+        Check whether run selection is active and accepts either one or multiple runs at once.
+
         Calls `process` internally.
 
         Parameters
@@ -2088,7 +2257,8 @@ 
Source code for deepcave.plugins
 [docs]
     def generate_inputs(self, **kwargs: Any) -> Dict[str, Any]:
         """
-        Generates inputs for the `process` and `load_outputs` required for api mode.
+        Generate inputs for the `process` and `load_outputs` required for api mode.
+
         The arguments are validated against the input schema.
 
         Note
@@ -2104,11 +2274,17 @@ 
Source code for deepcave.plugins
         -------
         Dict[str, Any]
             The inputs for the run.
+
+        Raises
+        ------
+        ValueError
+            If an unknown input is passed.
+            If an input is missing.
         """
         mapping = {}
-        for (id, attribute, *_) in self.inputs:
-            # Since `self.inputs` is ordered, we use the first occuring attribute and add
-            # the id so it is not used again.
+        for id, attribute, *_ in self.inputs:
+            # Since `self.inputs` is ordered, the first occurring attribute is used and
+            # the id is added, so it is not used again.
             if id not in mapping:
                 mapping[id] = attribute
 
diff --git a/development/_modules/deepcave/plugins/dynamic.html b/development/_modules/deepcave/plugins/dynamic.html
index 32e22b4d..de67c98e 100644
--- a/development/_modules/deepcave/plugins/dynamic.html
+++ b/development/_modules/deepcave/plugins/dynamic.html
@@ -702,8 +702,20 @@
               

                 
   
Source code for deepcave.plugins.dynamic
-from abc import ABC
-from typing import List
+#  noqa: D400
+"""
+# Dynamic
+
+This module provides a plugin class for a dynamic plugin.
+
+Register and handle callbacks.
+
+## Classes
+    - DynamicPlugin: This class provides a dynamic plugin object.
+"""
+
+from abc import ABC
+from typing import Any, List
 
 from dash.dependencies import Input, Output
 from dash.development.base_component import Component
@@ -715,6 +727,27 @@ 
Source code for deepcave.plugins.dynamic
 

 [docs]
 class DynamicPlugin(Plugin, ABC):
+    """
+    Provide a dynamic plugin object.
+
+    Register and handle callbacks.
+
+    Properties
+    ----------
+    outputs : List[Tuple[str, str, bool]]
+        The registered outputs.
+    inputs : List[Tuple[str, str, bool, Any]]
+        The registered inputs.
+    id : str
+        The identifier of the Plugin.
+    logger : Logger
+        The logger for debugging information.
+    use_cache : bool
+        Defines whether to use the cache.
+    raw_outputs : Dict[str, Any]
+        A dictionary of the raw outputs.
+    """
+
     use_cache = True
 
     def __init__(self) -> None:
@@ -724,6 +757,15 @@ 
Source code for deepcave.plugins.dynamic
 [docs]
     @interactive
     def register_callbacks(self) -> None:
+        """
+        Register basic callbacks for the plugin.
+
+        Following callbacks are registered:
+        - If inputs changes, the changes are pasted back.
+          This is in particular interest if input dependencies are used.
+        - Raw data dialog to display raw data.
+        - Callback to be redirected to the config if clicked on it.
+        """
         super().register_callbacks()
         from deepcave import app, c, rc
 
@@ -736,15 +778,26 @@ 
Source code for deepcave.plugins.dynamic
             inputs.append(Input(self.get_internal_input_id(id), attribute))
 
         # Register updates from inputs
-        @app.callback(outputs, inputs)
-        def plugin_output_update(_, *inputs_list):  # type: ignore
+        @app.callback(outputs, inputs)  # type: ignore
+        def plugin_output_update(_: Any, *inputs_list: str) -> Any:
             """
-            Parameters:
-                *inputs_list: Values from user.
+            Update the outputs.
+
+            Parameters
+            ----------
+            *inputs_list
+                Input values from user.
+
+            Returns
+            -------
+            Any
+                The raw outputs.
             """
             # Map the list `inputs_list` to a dict s.t.
             # it's easier to access them.
-            inputs = self._list_to_dict(inputs_list, input=True)
+            inputs = self._list_to_dict(list(inputs_list), input=True)
+            # The Optional[str] would need tobe changed to a str as return type annotation
+            # of the function _dict_as_key.
             inputs_key = self._dict_as_key(inputs, remove_filters=True)
             cleaned_inputs = self._clean_inputs(inputs)
             runs = self.get_selected_runs(inputs)
@@ -777,6 +830,16 @@ 
Source code for deepcave.plugins.dynamic
 [docs]
     @interactive
     def __call__(self) -> List[Component]:  # type: ignore
+        """
+        Return the components for the plugin.
+
+        Basically, all blocks and elements of the plugin are stacked-up here.
+
+        Returns
+        -------
+        List[Component]
+            Layout as list of components.
+        """
         return super().__call__(False)

 

 
diff --git a/development/_modules/deepcave/plugins/hyperparameter/importances.html b/development/_modules/deepcave/plugins/hyperparameter/importances.html
index 40a2557e..fdc15420 100644
--- a/development/_modules/deepcave/plugins/hyperparameter/importances.html
+++ b/development/_modules/deepcave/plugins/hyperparameter/importances.html
@@ -702,17 +702,33 @@
               

                 
   
Source code for deepcave.plugins.hyperparameter.importances
-import dash_bootstrap_components as dbc
+#  noqa: D400
+"""
+# Importances
+
+This module provides a plugin for the visualization of the importances.
+
+Provided utilities include getting input and output layout (filtered or non-filtered),
+processing the data and loading the outputs. Also provides a matplotlib version.
+
+## Classes
+    - Importances: This class provides a plugin for the visualization of the importances.
+"""
+
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import dash_bootstrap_components as dbc
 import numpy as np
 import plotly.graph_objs as go
+from ConfigSpace import ConfigurationSpace, Constant
 from dash import dcc, html
 from dash.exceptions import PreventUpdate
-from ConfigSpace import ConfigurationSpace, Constant
 
 from deepcave.config import Config
 from deepcave.evaluators.fanova import fANOVA as GlobalEvaluator
 from deepcave.evaluators.lpi import LPI as LocalEvaluator
 from deepcave.plugins.static import StaticPlugin
+from deepcave.runs import AbstractRun
 from deepcave.utils.cast import optional_int
 from deepcave.utils.layout import get_checklist_options, get_select_options, help_button
 from deepcave.utils.styled_plot import plt
@@ -722,6 +738,13 @@ 
Source code for deepcave.plugins.hyperparameter.importances

 [docs]
 class Importances(StaticPlugin):
+    """
+    Provide a plugin for the visualization of the importances.
+
+    Provided utilities include getting input/output layout, data processing
+    and loading outputs. Also provides a matplotlib version.
+    """
+
     id = "importances"
     name = "Importances"
     icon = "far fa-star"
@@ -731,7 +754,21 @@ 
Source code for deepcave.plugins.hyperparameter.importances

 [docs]
     @staticmethod
-    def get_input_layout(register):
+    def get_input_layout(register: Callable) -> List[Any]:
+        """
+        Get the layout for the input block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            Layout for the input block.
+        """
         return [
             html.Div(
                 [
@@ -782,7 +819,21 @@ 
Source code for deepcave.plugins.hyperparameter.importances

 [docs]
     @staticmethod
-    def get_filter_layout(register):
+    def get_filter_layout(register: Callable) -> List[html.Div]:
+        """
+        Get the layout for the filter block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[html.Div]
+            Layout for the filter block.
+        """
         return [
             html.Div(
                 [
@@ -816,7 +867,19 @@ 
Source code for deepcave.plugins.hyperparameter.importances

 [docs]
-    def load_inputs(self):
+    def load_inputs(self) -> Dict[str, Dict[str, Any]]:
+        """
+        Load the content for the defined inputs in 'get_input_layout' and 'get_filter_layout'.
+
+        This method is necessary to pre-load contents for the inputs.
+        If the plugin is called for the first time, or there are no results in the cache,
+        the plugin gets its content from this method.
+
+        Returns
+        -------
+        Dict[str, Dict[str, Any]]
+            Content to be filled.
+        """
         method_labels = ["Local Parameter Importance (local)", "fANOVA (global)"]
         method_values = ["local", "global"]
 
@@ -834,8 +897,28 @@ 
Source code for deepcave.plugins.hyperparameter.importances

 [docs]
-    def load_dependency_inputs(self, run, _, inputs):
-        # Prepare objetives
+    def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[str, Any]:  # type: ignore # noqa: E501
+        """
+        Work like 'load_inputs' but called after inputs have changed.
+
+        Note
+        ----
+        Only the changes have to be returned.
+        The returned dictionary will be merged with the inputs.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs : Dict[str, Any]
+            Current content of the inputs.
+
+        Returns
+        -------
+        Dict[str, Any]
+            A dictionary with the changes.
+        """
+        # Prepare objectives
         objective_names = run.get_objective_names()
         objective_ids = run.get_objective_ids()
         objective_options = get_select_options(objective_names, objective_ids)
@@ -890,7 +973,38 @@ 
Source code for deepcave.plugins.hyperparameter.importances

 [docs]
     @staticmethod
-    def process(run, inputs):
+    def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Return raw data based on the run and input data.
+
+        Warning
+        -------
+        The returned data must be JSON serializable.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differ
+        compared to 'load_inputs' or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run : AbstractRun
+            The run to process.
+        inputs :  Dict[str, Any]
+            The input data.
+
+        Returns
+        -------
+        Dict[str, Any]
+            A serialzied dictionary.
+
+        Raises
+        ------
+        RuntimeError
+            If the number of trees is not specified.
+            If the method is not found.
+        """
         objective = run.get_objective(inputs["objective_id"])
         method = inputs["method"]
         n_trees = inputs["n_trees"]
@@ -898,14 +1012,15 @@ 
Source code for deepcave.plugins.hyperparameter.importances
if n_trees is None:
             raise RuntimeError("Please specify the number of trees.")
 
-        # Handle constant values in fANOVA: As the fANOVA implementation relies on pyrfr and pyrfr cannot be applied
-        # to constant hyperparameters (see https://github.com/automl/fanova/issues/81), as a workaround we remove
-        # constant hyperparameters before calculation.
-        # Note: This will break if there are conditions or forbiddens including constant hyperparameters.
+        # Handle constant values in fANOVA: As the fANOVA implementation relies on pyrfr
+        # and pyrfr cannot be applied to constant hyperparameters
+        # (see https://github.com/automl/fanova/issues/81), as a workaround constant
+        # hyperparameters are removed before calculation.
+        # Note: This will break if there are conditions or forbiddens including
+        # constant hyperparameters.
         hp_dict = run.configspace.get_hyperparameters_dict()
         if method == "global" and any([type(v) == Constant for v in hp_dict.values()]):
-            hp_dict_wo_const = {
-                k: v for k, v in hp_dict.items() if type(v) != Constant}
+            hp_dict_wo_const = {k: v for k, v in hp_dict.items() if type(v) != Constant}
             configspace_wo_const = ConfigurationSpace()
             for k in hp_dict_wo_const.keys():
                 configspace_wo_const.add_hyperparameter(hp_dict_wo_const[k])
@@ -915,14 +1030,17 @@ 
Source code for deepcave.plugins.hyperparameter.importances
configs_wo_const = []
             for n in range(len(run.configs)):
-                configs_wo_const.append({k: v for k,v in run.configs[n].items() if k in hp_dict_wo_const.keys()})
-            run.configs = configs_wo_const
+                configs_wo_const.append(
+                    {k: v for k, v in run.configs[n].items() if k in hp_dict_wo_const.keys()}
+                )
+            run.configs = dict(enumerate(configs_wo_const))
 
         hp_names = run.configspace.get_hyperparameter_names()
         budgets = run.get_budgets(include_combined=True)
 
+        evaluator: Optional[Union[LocalEvaluator, GlobalEvaluator]] = None
         if method == "local":
-            # Intiatize the evaluator
+            # Initialize the evaluator
             evaluator = LocalEvaluator(run)
         elif method == "global":
             evaluator = GlobalEvaluator(run)
@@ -932,25 +1050,63 @@ 
Source code for deepcave.plugins.hyperparameter.importances
# Collect data
         data = {}
         for budget_id, budget in enumerate(budgets):
+            assert isinstance(budget, (int, float))
             evaluator.calculate(objective, budget, n_trees=n_trees, seed=0)
 
             importances = evaluator.get_importances(hp_names)
             data[budget_id] = importances
 
-        return data

+        return data  # type: ignore

 
 
 

 [docs]
     @staticmethod
-    def get_output_layout(register):
+    def get_output_layout(register: Callable) -> dcc.Graph:
+        """
+        Get the layout for the output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register outputs.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        dcc.Graph
+            Layout for the output block.
+        """
         return dcc.Graph(register("graph", "figure"), style={"height": Config.FIGURE_HEIGHT})

 
 
 

 [docs]
     @staticmethod
-    def load_outputs(run, inputs, outputs):
+    def load_outputs(run, inputs, outputs) -> go.Figure:  # type: ignore
+        """
+        Read in raw data and prepare for layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differ
+        compared to 'load_inputs' or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Input and filter values from the user.
+        outputs
+            Raw output from the run.
+
+        Returns
+        -------
+        go.figure
+            The figure of the importances.
+        """
         # First selected, should always be shown first
         selected_hp_names = inputs["hyperparameter_names"]
         selected_budget_ids = inputs["budget_ids"]
@@ -1030,7 +1186,21 @@ 
Source code for deepcave.plugins.hyperparameter.importances

 [docs]
     @staticmethod
-    def get_mpl_output_layout(register):
+    def get_mpl_output_layout(register: Callable) -> html.Img:
+        """
+        Get the layout for the matplotlib output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register outputs.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        html.Img
+            The layout for the matplotlib output block.
+        """
         return html.Img(
             id=register("graph", "src"),
             className="img-fluid",
@@ -1040,7 +1210,23 @@ 
Source code for deepcave.plugins.hyperparameter.importances

 [docs]
     @staticmethod
-    def load_mpl_outputs(run, inputs, outputs):
+    def load_mpl_outputs(run, inputs: Dict[str, Any], outputs):  # type: ignore
+        """
+        Read the raw data and prepare it for the layout.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs : Dict[str, Any]
+            Input and filter values from the user.
+        outputs
+            Raw output from the run.
+
+        Returns
+        -------
+        The rendered matplotlib figure of the importances.
+        """
         # First selected, should always be shown first
         selected_hp_names = inputs["hyperparameter_names"]
         selected_budget_ids = inputs["budget_ids"]
@@ -1086,7 +1272,7 @@ 
Source code for deepcave.plugins.hyperparameter.importances
hp_name = "..." + hp_name[-18:]
 
             x_labels += [hp_name]
-        x = np.arange(len(x_labels))
+        x_values = np.arange(len(x_labels))
 
         plt.figure()
         for budget_id, values in data.items():
@@ -1098,10 +1284,10 @@ 
Source code for deepcave.plugins.hyperparameter.importances
budget = run.get_budget(budget_id, human=True)
             plt.bar(
-                x,
+                x_values,
                 y,
                 yerr=y_err,
-                color=plt.get_color(budget_id),
+                color=plt.get_color(budget_id),  # type: ignore
                 label=budget,
                 error_kw=dict(lw=1, capsize=2, capthick=1),
             )
@@ -1109,10 +1295,10 @@ 
Source code for deepcave.plugins.hyperparameter.importances
plt.legend(title="Budgets")
 
         # Rotate x ticks
-        plt.xticks(x, x_labels, rotation=90)
+        plt.xticks(x_values, x_labels, rotation=90)
         plt.ylabel("Importance")
 
-        return plt.render()

+        return plt.render()  # type: ignore

 

 
 

diff --git a/development/_modules/deepcave/plugins/hyperparameter/pdp.html b/development/_modules/deepcave/plugins/hyperparameter/pdp.html
index 948fc0bf..706ee95d 100644
--- a/development/_modules/deepcave/plugins/hyperparameter/pdp.html
+++ b/development/_modules/deepcave/plugins/hyperparameter/pdp.html
@@ -702,7 +702,28 @@
               

                 
   
Source code for deepcave.plugins.hyperparameter.pdp
-import dash_bootstrap_components as dbc
+#  noqa: D400
+"""
+# PartialDependencies
+
+This module provides utilities for generating Partial Dependency Plots (PDP).
+
+Provided utilities include getting input and output layout (filtered or non-filtered),
+processing the data and loading the outputs.
+
+## Classes
+    - PartialDependencies: Generate a Partial Dependency Plot (PDP).
+
+## Constants
+    GRID_POINTS_PER_AXIS : int
+    SAMPLES_PER_HP : int
+    MAX_SAMPLES : int
+    MAX_SHOWN_SAMPLES : int
+"""
+
+from typing import Any, Callable, Dict, List
+
+import dash_bootstrap_components as dbc
 import numpy as np
 import plotly.graph_objs as go
 from dash import dcc, html
@@ -724,6 +745,13 @@ 
Source code for deepcave.plugins.hyperparameter.pdp

 [docs]
 class PartialDependencies(StaticPlugin):
+    """
+    Generate Partial Dependency Plots (PDP).
+
+    Provided utilities include getting input and output layout (filtered or non-filtered),
+    processing the data and loading the outputs.
+    """
+
     id = "pdp"
     name = "Partial Dependencies"
     icon = "fas fa-grip-lines"
@@ -733,7 +761,21 @@ 
Source code for deepcave.plugins.hyperparameter.pdp

 [docs]
     @staticmethod
-    def get_input_layout(register):
+    def get_input_layout(register: Callable) -> List[dbc.Row]:
+        """
+        Get the layout for the input block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[dbc.Row]
+            The layout for the input block.
+        """
         return [
             dbc.Row(
                 [
@@ -798,7 +840,21 @@ 
Source code for deepcave.plugins.hyperparameter.pdp

 [docs]
     @staticmethod
-    def get_filter_layout(register):
+    def get_filter_layout(register: Callable) -> List[Any]:
+        """
+        Get the layout for the filter block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            The layout for the filter block.
+        """
         return [
             dbc.Row(
                 [
@@ -838,7 +894,19 @@ 
Source code for deepcave.plugins.hyperparameter.pdp

 [docs]
-    def load_inputs(self):
+    def load_inputs(self) -> Dict[str, Dict[str, Any]]:
+        """
+        Load the content for the defined inputs in 'get_input_layout' and 'get_filter_layout'.
+
+        This method is necessary to pre-load contents for the inputs.
+        If the plugin is called for the first time, or there are no results in the cache,
+        the plugin gets its content from this method.
+
+        Returns
+        -------
+        Dict[str, Dict[str, Any]]
+            Content to be filled.
+        """
         return {
             "show_confidence": {"options": get_select_options(binary=True), "value": "true"},
             "show_ice": {"options": get_select_options(binary=True), "value": "true"},
@@ -847,7 +915,30 @@ 
Source code for deepcave.plugins.hyperparameter.pdp

 [docs]
-    def load_dependency_inputs(self, run, previous_inputs, inputs):
+    def load_dependency_inputs(self, run, previous_inputs, inputs) -> Dict[str, Any]:  # type: ignore # noqa: E501
+        """
+        Work like 'load_inputs' but called after inputs have changed.
+
+        Note
+        ----
+        Only the changes have to be returned. The returned dictionary
+        will be merged with the inputs.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Current content of the inputs.
+        previous_inputs
+            Previous content of the inputs.
+            Not used in this specific function.
+
+        Returns
+        -------
+        Dict[str, Any]
+            Dictionary with the changes.
+        """
         objective_names = run.get_objective_names()
         objective_ids = run.get_objective_ids()
         objective_options = get_select_options(objective_names, objective_ids)
@@ -862,7 +953,6 @@ 
Source code for deepcave.plugins.hyperparameter.pdp
objective_value = inputs["objective_id"]["value"]
         budget_value = inputs["budget_id"]["value"]
         hp1_value = inputs["hyperparameter_name_1"]["value"]
-        hp2_value = inputs["hyperparameter_name_2"]["value"]
 
         if objective_value is None:
             objective_value = objective_ids[0]
@@ -885,7 +975,37 @@ 
Source code for deepcave.plugins.hyperparameter.pdp

 [docs]
     @staticmethod
-    def process(run, inputs):
+    def process(run, inputs) -> Dict[str, Any]:  # type: ignore
+        """
+        Return raw data based on a run and the input data.
+
+        Warning
+        -------
+        The returned data must be JSON serializable.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differ
+        compared to 'load_inputs' or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The run to process.
+        inputs
+            The input data.
+
+        Returns
+        -------
+        Dict[str, Any]
+            A serialized dictionary.
+
+        Raises
+        ------
+        RuntimeError
+            If the objective is None.
+        """
         # Surrogate
         hp_names = run.configspace.get_hyperparameter_names()
         objective = run.get_objective(inputs["objective_id"])
@@ -917,7 +1037,7 @@ 
Source code for deepcave.plugins.hyperparameter.pdp
selected_hyperparameters += [hp2]
 
         num_samples = SAMPLES_PER_HP * len(X)
-        # We limit the samples to max 10k
+        # The samples are limited to max 10k
         if num_samples > MAX_SAMPLES:
             num_samples = MAX_SAMPLES
 
@@ -933,7 +1053,7 @@ 
Source code for deepcave.plugins.hyperparameter.pdp
x = pdp.x_pdp.tolist()
         y = pdp.y_pdp.tolist()
 
-        # We have to cut the ICE curves because it's too much data
+        # The ICE curves have to be cut because it's too much data
         x_ice = pdp._ice.x_ice.tolist()
         y_ice = pdp._ice.y_ice.tolist()
 
@@ -953,12 +1073,53 @@ 
Source code for deepcave.plugins.hyperparameter.pdp

 [docs]
     @staticmethod
-    def get_output_layout(register):
+    def get_output_layout(register: Callable) -> dcc.Graph:
+        """
+        Get the layout for the output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register outputs.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        dcc.Graph
+            Layout for the output block.
+        """
         return dcc.Graph(register("graph", "figure"), style={"height": Config.FIGURE_HEIGHT})

 
 
+

+[docs]
     @staticmethod
-    def get_pdp_figure(run, inputs, outputs, show_confidence, show_ice, title=None):
+    def get_pdp_figure(  # type: ignore
+        run, inputs, outputs, show_confidence, show_ice, title=None
+    ) -> go.Figure:
+        """
+        Create a figure of the Partial Dependency Plot (PDP).
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Input and filter values from the user.
+        outputs
+            Raw output from the run.
+        show_confidence
+            Whether to show confidence in the plot.
+        show_ice
+            Whether to show ice curves in the plot.
+        title
+            Title of the plot.
+
+        Returns
+        -------
+        go.Figure
+            The figure of the Partial Dependency Plot (PDP).
+        """
         # Parse inputs
         hp1_name = inputs["hyperparameter_name_1"]
         hp1_idx = run.configspace.get_idx_by_hyperparameter_name(hp1_name)
@@ -1041,7 +1202,7 @@ 
Source code for deepcave.plugins.hyperparameter.pdp
"yaxis": {
                         "title": objective_name,
                     },
-                    "title": title
+                    "title": title,
                 }
             )
         else:
@@ -1068,19 +1229,43 @@ 
Source code for deepcave.plugins.hyperparameter.pdp
xaxis=dict(tickvals=x_tickvals, ticktext=x_ticktext, title=hp1_name),
                     yaxis=dict(tickvals=y_tickvals, ticktext=y_ticktext, title=hp2_name),
                     margin=Config.FIGURE_MARGIN,
-                    title=title
+                    title=title,
                 )
             )
 
         figure = go.Figure(data=traces, layout=layout)
         save_image(figure, "pdp.pdf")
 
-        return figure
+        return figure

+
 
 

 [docs]
     @staticmethod
-    def load_outputs(run, inputs, outputs):
+    def load_outputs(run, inputs, outputs):  # type: ignore
+        """
+        Read the raw data and prepare it for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differ
+        compared to 'load_inputs' or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Input and filter values from the user.
+        outputs
+            Raw output from the run.
+
+        Returns
+        -------
+        go.Figure
+            The figure of the Partial Dependency Plot (PDP).
+        """
         show_confidence = inputs["show_confidence"]
         show_ice = inputs["show_ice"]
 
diff --git a/development/_modules/deepcave/plugins/hyperparameter/symbolic_explanations.html b/development/_modules/deepcave/plugins/hyperparameter/symbolic_explanations.html
index e1b25795..c8d7cdf7 100644
--- a/development/_modules/deepcave/plugins/hyperparameter/symbolic_explanations.html
+++ b/development/_modules/deepcave/plugins/hyperparameter/symbolic_explanations.html
@@ -853,7 +853,7 @@ 
Source code for deepcave.plugins.hyperparameter.symbolic_explanations
),
                                     dcc.Slider(
                                         id=register("parsimony", "value", type=int),
-                                        marks=dict([i, str(10**i)] for i in range(-8, 1)),
+                                        marks=dict((i, str(10**i)) for i in range(-8, 1)),
                                         min=-8,
                                         max=0,
                                         step=1,
@@ -1165,10 +1165,10 @@ 
Source code for deepcave.plugins.hyperparameter.symbolic_explanations
f"{objective.name} = "
                 f"{convert_symb(symb_model, n_decimals=3, hp_names=selected_hyperparameters)}"
             )
-        except:
+        except Exception as e:
             conv_expr = (
                 "Conversion of the expression failed. Please try another seed or increase "
-                "the parsimony hyperparameter."
+                f"the parsimony hyperparameter: {e}"
             )
 
         if len(conv_expr) > 150:
diff --git a/development/_modules/deepcave/plugins/objective/configuration_cube.html b/development/_modules/deepcave/plugins/objective/configuration_cube.html
index d85943f1..589dd232 100644
--- a/development/_modules/deepcave/plugins/objective/configuration_cube.html
+++ b/development/_modules/deepcave/plugins/objective/configuration_cube.html
@@ -702,7 +702,19 @@
               

                 
   
Source code for deepcave.plugins.objective.configuration_cube
-from typing import Dict, Tuple
+#  noqa: D400
+"""
+# ConfigurationCube
+
+This module provides utilities for visualizing and creating a configuration cube.
+
+The configuration cube displays configurations and their score on the objective.
+
+## Classes
+    - ConfigurationCube: A plugin for visualizing a configuration cube.
+"""
+
+from typing import Any, Callable, Dict, List, Tuple
 
 import dash_bootstrap_components as dbc
 import numpy as np
@@ -713,7 +725,7 @@ 
Source code for deepcave.plugins.objective.configuration_cube
from deepcave.config import Config
 from deepcave.plugins.dynamic import DynamicPlugin
-from deepcave.runs import Status
+from deepcave.runs import AbstractRun, Status
 from deepcave.utils.compression import deserialize, serialize
 from deepcave.utils.layout import (
     get_checklist_options,
@@ -734,6 +746,8 @@ 
Source code for deepcave.plugins.objective.configuration_cube

 [docs]
 class ConfigurationCube(DynamicPlugin):
+    """A plugin for visualizing a configuration cube."""
+
     id = "ccube"
     name = "Configuration Cube"
     icon = "fas fa-cube"
@@ -743,7 +757,21 @@ 
Source code for deepcave.plugins.objective.configuration_cube

 [docs]
     @staticmethod
-    def get_input_layout(register):
+    def get_input_layout(register: Callable) -> List[dbc.Row]:
+        """
+        Get the layout for the input block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[dbc.Row]
+            Layouts for the input block.
+        """
         return [
             dbc.Row(
                 [
@@ -783,7 +811,21 @@ 
Source code for deepcave.plugins.objective.configuration_cube

 [docs]
     @staticmethod
-    def get_filter_layout(register):
+    def get_filter_layout(register: Callable) -> List[html.Div]:
+        """
+        Get the layout for the filter block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[html.Div]
+            Layouts for the filter block.
+        """
         return [
             html.Div(
                 [
@@ -812,7 +854,19 @@ 
Source code for deepcave.plugins.objective.configuration_cube

 [docs]
-    def load_inputs(self):
+    def load_inputs(self) -> Dict[str, Any]:
+        """
+        Load the content for the defined inputs in 'get_input_layout' and 'get_filter_layout'.
+
+        This method is necessary to pre-load contents for the inputs.
+        So, if the plugin is called for the first time or there are no results in the cache,
+        the plugin gets its content from this method.
+
+        Returns
+        -------
+        Dict[str, Any]
+            The content to be filled.
+        """
         return {
             "n_configs": {"min": 0, "max": 0, "marks": get_slider_marks(), "value": 0},
             "hyperparameter_names": {"options": get_checklist_options(), "value": []},
@@ -821,8 +875,30 @@ 
Source code for deepcave.plugins.objective.configuration_cube

 [docs]
-    def load_dependency_inputs(self, run, _, inputs):
-        # Prepare objetives
+    def load_dependency_inputs(self, run, _, inputs) -> Dict[str, Any]:  # type: ignore
+        """
+        Work like 'load_inputs' but called after inputs have changed.
+
+        It is restricted to three Hyperparameters.
+
+        Note
+        ----
+        Only the changes have to be returned.
+        The returned dictionary will be merged with the inputs.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Current content of the inputs.
+
+        Returns
+        -------
+        Dict[str, Any]
+           The dictionary with the changes.
+        """
+        # Prepare objectives
         objective_names = run.get_objective_names()
         objective_ids = run.get_objective_ids()
         objective_options = get_select_options(objective_names, objective_ids)
@@ -887,7 +963,32 @@ 
Source code for deepcave.plugins.objective.configuration_cube

 [docs]
     @staticmethod
-    def process(run, inputs):
+    def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, str]:
+        """
+        Return raw data based on the run and input data.
+
+        Warning
+        -------
+        The returned data must be JSON serializable.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run : AbstractRun
+            The selected run.
+        inputs : Dict[str, Any]
+            The input data.
+
+        Returns
+        -------
+        Dict[str, str]
+            The serialized dictionary.
+        """
         budget = run.get_budget(inputs["budget_id"])
         objective = run.get_objective(inputs["objective_id"])
 
@@ -900,14 +1001,51 @@ 
Source code for deepcave.plugins.objective.configuration_cube

 [docs]
     @staticmethod
-    def get_output_layout(register):
+    def get_output_layout(register: Callable) -> Tuple[dcc.Graph,]:
+        """
+        Get the layout for the output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register output.
+            The register_output function is located in the Plugin superclass.
+
+        Returns
+        -------
+        Tuple[dcc.Graph,]
+            Layout for the output block.
+        """
         return (dcc.Graph(register("graph", "figure"), style={"height": Config.FIGURE_HEIGHT}),)

 
 
 

 [docs]
     @staticmethod
-    def load_outputs(run, inputs, outputs):
+    def load_outputs(run, inputs, outputs) -> go.Figure:  # type: ignore
+        """
+        Read in the raw data and prepares them for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Input and filter values from the user.
+        outputs
+            Raw output from the run.
+
+        Returns
+        -------
+        go.Figure
+            The output figure.
+        """
         df = deserialize(outputs["df"], dtype=pd.DataFrame)
         hp_names = inputs["hyperparameter_names"]
         n_configs = inputs["n_configs"]
@@ -943,7 +1081,7 @@ 
Source code for deepcave.plugins.objective.configuration_cube
# Transforms data to values
         values = np.transpose(np.array(data)).tolist()
 
-        # Now we want to filter duplicates
+        # Now the duplicates are filtered
         filtered_data: Dict[Tuple[int, float], Tuple] = {}
         for config_id, cost, v in zip(config_ids, costs, values):
             v = tuple(v)  # Make hashable
@@ -961,7 +1099,7 @@ 
Source code for deepcave.plugins.objective.configuration_cube
if old_cost > cost:
                         continue
 
-                # Otherwise we have to replace
+                # Otherwise it is replaced
                 del filtered_data[old_key]
 
             filtered_data[key] = v
diff --git a/development/_modules/deepcave/plugins/objective/cost_over_time.html b/development/_modules/deepcave/plugins/objective/cost_over_time.html
index 0d2743d7..927534ea 100644
--- a/development/_modules/deepcave/plugins/objective/cost_over_time.html
+++ b/development/_modules/deepcave/plugins/objective/cost_over_time.html
@@ -702,7 +702,20 @@
               

                 
   
Source code for deepcave.plugins.objective.cost_over_time
-from typing import List
+#  noqa: D400
+"""
+# CostOverTime
+
+This module provides utilities for visualizing the cost over time.
+
+Visualized changes can be regarding to number of configurations or time.
+It includes a corresponding plugin class.
+
+## Classes
+    - CostOverTime: A plugin to provide a visualization for the cost over time.
+"""
+
+from typing import Any, Callable, Dict, List
 
 import dash_bootstrap_components as dbc
 import numpy as np
@@ -724,6 +737,17 @@ 
Source code for deepcave.plugins.objective.cost_over_time

 [docs]
 class CostOverTime(DynamicPlugin):
+    """
+    A plugin to provide a visualization for the cost over time.
+
+    Properties
+    ----------
+    objective_options : List[Dict[str, Any]]
+        A list of dictionaries of the objective options.
+    budget_options : List[Dict[str, Any]]
+        A list of dictionaries of the budget options.
+    """
+
     id = "cost_over_time"
     name = "Cost Over Time"
     icon = "fas fa-chart-line"
@@ -732,6 +756,27 @@ 
Source code for deepcave.plugins.objective.cost_over_time

 [docs]
     def check_runs_compatibility(self, runs: List[AbstractRun]) -> None:
+        """
+        Check if the runs are compatible.
+
+        This function is needed if all selected runs need something in common
+        (e.g. budget or objective).
+        Since this function is called before the layout is created,
+        it can be also used to set common values for the plugin.
+
+        Parameters
+        ----------
+        runs : List[AbstractRun]
+            A list containing the selected runs.
+
+        Raises
+        ------
+        NotMergeableError
+            If the meta data of the runs are not equal.
+            If the configuration spaces of the runs are not equal.
+            If the budgets of the runs are not equal.
+            If the objective of the runs are not equal.
+        """
         check_equality(runs, objectives=True, budgets=True)
 
         # Set some attributes here
@@ -749,7 +794,21 @@ 
Source code for deepcave.plugins.objective.cost_over_time

 [docs]
     @staticmethod
-    def get_input_layout(register):
+    def get_input_layout(register: Callable) -> List[dbc.Row]:
+        """
+        Get the layout for the input block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[dbc.Row]
+            Layouts for the input block.
+        """
         return [
             dbc.Row(
                 [
@@ -785,7 +844,21 @@ 
Source code for deepcave.plugins.objective.cost_over_time

 [docs]
     @staticmethod
-    def get_filter_layout(register):
+    def get_filter_layout(register: Callable) -> List[Any]:
+        """
+        Get the layout for the filter block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            Layouts for the filter block.
+        """
         return [
             html.Div(
                 [
@@ -826,7 +899,19 @@ 
Source code for deepcave.plugins.objective.cost_over_time

 [docs]
-    def load_inputs(self):
+    def load_inputs(self) -> Dict[str, Any]:
+        """
+        Load the content for the defined inputs in 'get_input_layout' and 'get_filter_layout'.
+
+        This method is necessary to pre-load contents for the inputs.
+        So, if the plugin is called for the first time or there are no results in the cache,
+        the plugin gets its content from this method.
+
+        Returns
+        -------
+        Dict[str, Any]
+            The content to be filled.
+        """
         return {
             "objective_id": {
                 "options": self.objective_options,
@@ -852,7 +937,32 @@ 
Source code for deepcave.plugins.objective.cost_over_time

 [docs]
     @staticmethod
-    def process(run, inputs):
+    def process(run, inputs) -> Dict[str, Any]:  # type: ignore
+        """
+        Return raw data based on a run and input data.
+
+        Warning
+        -------
+        The returned data must be JSON serializable.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run : AbstractRun
+            The selected run to process.
+        inputs : Dict[str, Any]
+            The input data.
+
+        Returns
+        -------
+        Dict[str, Any]
+            A serialized dictionary.
+        """
         budget = run.get_budget(inputs["budget_id"])
         objective = run.get_objective(inputs["objective_id"])
 
@@ -872,14 +982,51 @@ 
Source code for deepcave.plugins.objective.cost_over_time

 [docs]
     @staticmethod
-    def get_output_layout(register):
+    def get_output_layout(register: Callable) -> dcc.Graph:
+        """
+        Get the layout for the output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register outputs.
+            The register_output function is located in the Plugin superclass.
+
+        Returns
+        -------
+        dcc.Graph
+            The layouts for the output block.
+        """
         return dcc.Graph(register("graph", "figure"), style={"height": Config.FIGURE_HEIGHT})

 
 
 

 [docs]
     @staticmethod
-    def load_outputs(runs, inputs, outputs):
+    def load_outputs(runs, inputs, outputs) -> go.Figure:  # type: ignore
+        """
+        Read in the raw data and prepare them for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        runs :
+            The selected runs.
+        inputs :
+            The input and filter values from the user.
+        outputs :
+            The raw outputs from the runs.
+
+        Returns
+        -------
+        go.Figure
+            The output figure.
+        """
         show_runs = inputs["show_runs"]
         show_groups = inputs["show_groups"]
         objective = None
@@ -905,9 +1052,9 @@ 
Source code for deepcave.plugins.objective.cost_over_time
y_err = np.array(outputs[run.id]["costs_std"])
             y_upper = list(y + y_err)
             y_lower = list(y - y_err)
-            y = list(y)
+            y_list = list(y)
 
-            hovertext = ""
+            hovertext = None
             hoverinfo = "skip"
             symbol = None
             mode = "lines"
@@ -920,7 +1067,7 @@ 
Source code for deepcave.plugins.objective.cost_over_time
traces.append(
                 go.Scatter(
                     x=x,
-                    y=y,
+                    y=y_list,
                     name=run.name,
                     line_shape="hv",
                     line=dict(color=get_color(idx)),
diff --git a/development/_modules/deepcave/plugins/objective/parallel_coordinates.html b/development/_modules/deepcave/plugins/objective/parallel_coordinates.html
index ed1def79..5360003e 100644
--- a/development/_modules/deepcave/plugins/objective/parallel_coordinates.html
+++ b/development/_modules/deepcave/plugins/objective/parallel_coordinates.html
@@ -702,7 +702,20 @@
               

                 
   
Source code for deepcave.plugins.objective.parallel_coordinates
-from collections import defaultdict
+#  noqa: D400
+
+"""
+# ParallelCoordinates
+
+This module provides utilities for visualizing the parallel coordinates.
+
+## Classes
+    - ParallelCoordinates : Can be used for visualizing the parallel coordinates.
+"""
+
+from typing import Any, Callable, Dict, List
+
+from collections import defaultdict
 
 import dash_bootstrap_components as dbc
 import numpy as np
@@ -726,6 +739,8 @@ 
Source code for deepcave.plugins.objective.parallel_coordinates

 [docs]
 class ParallelCoordinates(StaticPlugin):
+    """Can be used for visualizing the parallel coordinates."""
+
     id = "parallel_coordinates"
     name = "Parallel Coordinates"
     icon = "far fa-map"
@@ -735,7 +750,21 @@ 
Source code for deepcave.plugins.objective.parallel_coordinates

 [docs]
     @staticmethod
-    def get_input_layout(register):
+    def get_input_layout(register: Callable) -> List[Any]:
+        """
+        Get the layout for the input block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to regsiter (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            The layouts for the input block.
+        """
         return [
             dbc.Row(
                 [
@@ -790,7 +819,21 @@ 
Source code for deepcave.plugins.objective.parallel_coordinates

 [docs]
     @staticmethod
-    def get_filter_layout(register):
+    def get_filter_layout(register: Callable) -> List[Any]:
+        """
+        Get the layout for the filter block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            The layouts for the filter block.
+        """
         return [
             dbc.Row(
                 [
@@ -834,7 +877,19 @@ 
Source code for deepcave.plugins.objective.parallel_coordinates

 [docs]
-    def load_inputs(self):
+    def load_inputs(self) -> Dict[str, Dict[str, Any]]:
+        """
+        Load the content for the defined inputs in 'get_input_layout' and 'get_filter_layout'.
+
+        This method is necessary to pre-load contents for the inputs.
+        So, if the plugin is called for the first time or there are no results in the cache,
+        the plugin gets its content from this method.
+
+        Returns
+        -------
+        Dict[str, Dict[str, Any]]
+            Content to be filled.
+        """
         return {
             "show_important_only": {"options": get_select_options(binary=True), "value": "true"},
             "show_unsuccessful": {"options": get_select_options(binary=True), "value": "false"},
@@ -846,8 +901,28 @@ 
Source code for deepcave.plugins.objective.parallel_coordinates

 [docs]
-    def load_dependency_inputs(self, run, _, inputs):
-        # Prepare objetives
+    def load_dependency_inputs(self, run, _, inputs) -> Dict[str, Any]:  # type: ignore
+        """
+        Work like 'load_inputs' but called after inputs have changed.
+
+        Note
+        ----
+        Only the changes have to be returned.
+        The returned dictionary will be merged with the inputs.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Current content of the inputs.
+
+        Returns
+        -------
+        Dict[str, Any]
+            The dictionary with the changes.
+        """
+        # Prepare objectives
         objective_names = run.get_objective_names()
         objective_ids = run.get_objective_ids()
         objective_options = get_select_options(objective_names, objective_ids)
@@ -906,18 +981,43 @@ 
Source code for deepcave.plugins.objective.parallel_coordinates

 [docs]
     @staticmethod
-    def process(run, inputs):
+    def process(run, inputs) -> Dict[str, Any]:  # type: ignore
+        """
+        Return raw data based on a run and input data.
+
+        Warning
+        -------
+        The returned data must be JSON serializable.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run : AbstractRun
+            The run to process.
+        inputs : Dict[str, Any]
+            The input data.
+
+        Returns
+        -------
+        Dict[str, Any]
+            The serialized dictionary.
+        """
         budget = run.get_budget(inputs["budget_id"])
         objective = run.get_objective(inputs["objective_id"])
         df = serialize(run.get_encoded_data(objective, budget))
-        result = {"df": df}
+        result: Dict[str, Any] = {"df": df}
 
         if inputs["show_important_only"]:
             # Let's run a quick fANOVA here
             evaluator = fANOVA(run)
             evaluator.calculate(objective, budget, n_trees=10, seed=0)
-            importances = evaluator.get_importances()
-            importances = {u: v[0] for u, v in importances.items()}
+            importances_dict = evaluator.get_importances()
+            importances = {u: v[0] for u, v in importances_dict.items()}
             important_hp_names = sorted(importances, key=lambda key: importances[key], reverse=True)
             result["important_hp_names"] = important_hp_names
 
@@ -927,14 +1027,51 @@ 
Source code for deepcave.plugins.objective.parallel_coordinates

 [docs]
     @staticmethod
-    def get_output_layout(register):
+    def get_output_layout(register: Callable) -> dcc.Graph:
+        """
+        Get the layout for the output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register outputs.
+            The register_output function is located in the Plugin superclass.
+
+        Returns
+        -------
+        dcc.Graph
+            The layouts for the output block.
+        """
         return dcc.Graph(register("graph", "figure"), style={"height": Config.FIGURE_HEIGHT})

 
 
 

 [docs]
     @staticmethod
-    def load_outputs(run, inputs, outputs):
+    def load_outputs(run, inputs, outputs) -> go.Figure:  # type: ignore
+        """
+        Read in the raw data and prepare them for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            The inputs and filter values fromt the user.
+        outputs
+            Raw output from the run.
+
+        Returns
+        -------
+        go.Figure
+            The output figure.
+        """
         objective = run.get_objective(inputs["objective_id"])
         objective_name = objective.name
 
@@ -964,7 +1101,7 @@ 
Source code for deepcave.plugins.objective.parallel_coordinates
if b:
                 objective_values += [value]
 
-        data = defaultdict(dict)
+        data: defaultdict = defaultdict(dict)
         for hp_name in hp_names:
             values = []
             for hp_v, objective_v in zip(df[hp_name].values, df[objective_name].values):
diff --git a/development/_modules/deepcave/plugins/objective/pareto_front.html b/development/_modules/deepcave/plugins/objective/pareto_front.html
index c9d8f3d3..73705bcd 100644
--- a/development/_modules/deepcave/plugins/objective/pareto_front.html
+++ b/development/_modules/deepcave/plugins/objective/pareto_front.html
@@ -702,7 +702,19 @@
               

                 
   
Source code for deepcave.plugins.objective.pareto_front
-from typing import List, Union
+#  noqa: D400
+"""
+# ParetoFront
+
+This module provides utilities for creating a visualization of the Pareto Front.
+
+It includes the corresponding Pareto Front plugin.
+
+## Classes
+    - ParetoFront: Generate an interactive Pareto Front visualization.
+"""
+
+from typing import Any, Callable, Dict, List, Literal, Union
 
 import dash_bootstrap_components as dbc
 import numpy as np
@@ -711,7 +723,7 @@ 
Source code for deepcave.plugins.objective.pareto_front
from deepcave.config import Config
 from deepcave.plugins.dynamic import DynamicPlugin
-from deepcave.runs import Status, check_equality
+from deepcave.runs import AbstractRun, Status, check_equality
 from deepcave.utils.layout import get_select_options, help_button
 from deepcave.utils.styled_plot import plt
 from deepcave.utils.styled_plotty import (
@@ -724,6 +736,17 @@ 
Source code for deepcave.plugins.objective.pareto_front

 [docs]
 class ParetoFront(DynamicPlugin):
+    """
+    Generate an interactive Pareto Front visualization.
+
+    Properties
+    ----------
+    objective_options : List[Dict[str, Any]]
+        A list of the objective options.
+    budget_options : List[Dict[str, Any]]
+        A list of the budget options.
+    """
+
     id = "pareto_front"
     name = "Pareto Front"
     icon = "fas fa-wind"
@@ -731,7 +754,28 @@ 
Source code for deepcave.plugins.objective.pareto_front

 [docs]
-    def check_runs_compatibility(self, runs):
+    def check_runs_compatibility(self, runs: List[AbstractRun]) -> None:
+        """
+        Check if the runs are compatible.
+
+        This function is needed if all selected runs need something in common
+        (e.g. budget or objective).
+        Since this function is called before the layout is created,
+        it can be also used to set common values for the plugin.
+
+        Parameters
+        ----------
+        runs : List[AbstractRun]
+            A list containing the selected runs.
+
+        Raises
+        ------
+        NotMergeableError
+            If the meta data of the runs are not equal.
+            If the configuration spaces of the runs are not equal.
+            If the budgets of the runs are not equal.
+            If the objective of the runs are not equal.
+        """
         check_equality(runs, objectives=True, budgets=True)
 
         # Set some attributes here
@@ -749,7 +793,21 @@ 
Source code for deepcave.plugins.objective.pareto_front

 [docs]
     @staticmethod
-    def get_input_layout(register):
+    def get_input_layout(register: Callable) -> List[Any]:
+        """
+        Get layout for the input block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            The layouts for the input block.
+        """
         return [
             dbc.Row(
                 [
@@ -799,7 +857,21 @@ 
Source code for deepcave.plugins.objective.pareto_front

 [docs]
     @staticmethod
-    def get_filter_layout(register):
+    def get_filter_layout(register: Callable) -> List[Any]:
+        """
+        Get the layout for the filter block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            The layouts for the filter block.
+        """
         return [
             html.Div(
                 [
@@ -845,7 +917,19 @@ 
Source code for deepcave.plugins.objective.pareto_front

 [docs]
-    def load_inputs(self):
+    def load_inputs(self) -> Dict[str, Dict[str, Any]]:
+        """
+        Load the content for the defined inputs in 'get_input_layout' and 'get_filter_layout'.
+
+        This method is necessary to pre-load contents for the inputs.
+        So, if the plugin is called for the first time or there are no results in the cache,
+        the plugin gets its content from this method.
+
+        Returns
+        -------
+        Dict[str, Dict[str, Any]]
+            The content to be filled.
+        """
         return {
             "objective_id_1": {
                 "options": self.objective_options,
@@ -868,7 +952,32 @@ 
Source code for deepcave.plugins.objective.pareto_front

 [docs]
     @staticmethod
-    def process(run, inputs):
+    def process(run, inputs) -> Dict[str, Any]:  # type: ignore
+        """
+        Return raw data based on a run and input data.
+
+        Warning
+        -------
+        The returned data must be JSON serializable.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run : AbstractRun
+            The run to process.
+        inputs : Dict[str, Any]
+            The input data.
+
+        Returns
+        -------
+        Dict[str, Any]
+            The serialized dictionary.
+        """
         # Get budget
         budget = run.get_budget(inputs["budget_id"])
 
@@ -892,15 +1001,14 @@ 
Source code for deepcave.plugins.objective.pareto_front
points = points[sorted_idx]
         config_ids = config_ids[sorted_idx]
 
-        is_front: Union[List, np.ndarray] = np.ones(points.shape[0], dtype=bool)
+        is_front: np.ndarray = np.ones(points.shape[0], dtype=bool)
         for point_idx, costs in enumerate(points):
-
             if is_front[point_idx]:
                 # Keep any point with a lower/upper cost
                 # This loop is a little bit complicated than
                 # is_front[is_front] = np.any(points[is_front] < c, axis=1)
                 # because objectives can be optimized in different directions.
-                # We therefore have to check for each objective separately.
+                # Therefore it has to be checked for each objective separately.
                 select = None
                 for idx, (objective, cost) in enumerate(zip([objective_1, objective_2], costs)):
                     if objective.optimize == "upper":
@@ -928,14 +1036,51 @@ 
Source code for deepcave.plugins.objective.pareto_front

 [docs]
     @staticmethod
-    def get_output_layout(register):
+    def get_output_layout(register: Callable) -> dcc.Graph:
+        """
+        Get the layout for the output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register outputs.
+            The register_output function is located in the Plugin superclass.
+
+        Returns
+        -------
+        dcc.Graph
+            The layout for the output block.
+        """
         return dcc.Graph(register("graph", "figure"), style={"height": Config.FIGURE_HEIGHT})

 
 
 

 [docs]
     @staticmethod
-    def load_outputs(runs, inputs, outputs):
+    def load_outputs(runs, inputs, outputs) -> go.Figure:  # type: ignore
+        """
+        Read in the raw data and prepare them for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        runs :
+            The selected runs.
+        inputs :
+            The input and filter values from the user.
+        outputs :
+            Raw outputs from the runs.
+
+        Returns
+        -------
+        go.Figure
+            The output figure.
+        """
         show_all = inputs["show_all"]
 
         traces = []
@@ -982,7 +1127,7 @@ 
Source code for deepcave.plugins.objective.pareto_front
)
                 )
 
-            # Check if we need hv or vh
+            # Check if hv or vh is needed
             objective_1 = run.get_objective(inputs["objective_id_1"])
             objective_2 = run.get_objective(inputs["objective_id_2"])
             optimize1 = objective_1.optimize
@@ -1031,7 +1176,21 @@ 
Source code for deepcave.plugins.objective.pareto_front

 [docs]
     @staticmethod
-    def get_mpl_output_layout(register):
+    def get_mpl_output_layout(register: Callable) -> html.Img:
+        """
+        Get the layout for the matplotlib output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register outputs.
+            The register_output function is located in the Plugin superclass.
+
+        Returns
+        -------
+        html.Img
+            The layout for the matplotlib output block.
+        """
         return html.Img(
             id=register("graph", "src"),
             className="img-fluid",
@@ -1041,7 +1200,29 @@ 
Source code for deepcave.plugins.objective.pareto_front

 [docs]
     @staticmethod
-    def load_mpl_outputs(runs, inputs, outputs):
+    def load_mpl_outputs(runs, inputs, outputs):  # type: ignore
+        """
+        Read in the raw data and prepare them for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        runs :
+            The selected runs.
+        inputs :
+            Input and filter values from the user.
+        outputs :
+            Raw outputs from the runs.
+
+        Returns
+        -------
+        The rendered matplotlib figure.
+        """
         show_all = inputs["show_all"] == "true"
 
         plt.figure()
@@ -1069,18 +1250,20 @@ 
Source code for deepcave.plugins.objective.pareto_front
x += [points[point_idx][0]]
                     y += [points[point_idx][1]]
 
-            color = plt.get_color(idx)  # , alpha=0.1)
-            color_pareto = plt.get_color(idx)
+            # , alpha=0.1)
+            color = plt.get_color(idx)  # type: ignore
+            color_pareto = plt.get_color(idx)  # type: ignore
 
             if show_all:
                 plt.scatter(x, y, color=color, marker="o", alpha=0.1, s=3)
 
-            # Check if we need hv or vh
+            # Check if hv or vh is needed
             objective_1 = run.get_objective(inputs["objective_id_1"])
             objective_2 = run.get_objective(inputs["objective_id_2"])
             optimize1 = objective_1.optimize
             optimize2 = objective_2.optimize
 
+            line_shape: Union[Literal["post"], Literal["pre"], Literal["mid"]]
             if optimize1 == optimize2:
                 if objective_1.optimize == "lower":
                     line_shape = "post"
@@ -1104,7 +1287,7 @@ 
Source code for deepcave.plugins.objective.pareto_front
plt.legend()
 
-        return plt.render()

+        return plt.render()  # type: ignore

 

 
 

diff --git a/development/_modules/deepcave/plugins/static.html b/development/_modules/deepcave/plugins/static.html
index 98be767d..cf4ff7dc 100644
--- a/development/_modules/deepcave/plugins/static.html
+++ b/development/_modules/deepcave/plugins/static.html
@@ -702,8 +702,21 @@
               

                 
   
Source code for deepcave.plugins.static
-from abc import ABC
-from typing import Any, Callable, Dict, List
+#  noqa: D400
+"""
+# Static
+
+This module provides a plugin class for a static plugin.
+
+It provides an Enum used for the plugin state.
+
+## Classes
+    - PluginState: An Enum to define the state of the Plugin.
+    - StaticPlugin: This class provides a static plugin object.
+"""
+
+from abc import ABC
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import traceback
 from enum import Enum
@@ -724,6 +737,8 @@ 
Source code for deepcave.plugins.static
 

 [docs]
 class PluginState(Enum):
+    """An Enum to define the state of the Plugin."""
+
     UNSET = -1
     READY = 0
     NEEDS_PROCESSING = 1
@@ -734,7 +749,26 @@ 
Source code for deepcave.plugins.static
 
 def _process(
     process: Callable[[AbstractRun, Any], None], run: AbstractRun, inputs: Dict[str, Any]
-) -> Any:
+) -> None:
+    """
+    Process the run with the input data if possible.
+
+    Return raw data based on a run and input data.
+
+    Parameters
+    ----------
+    process : Callable[[AbstractRun, Any], None]
+        The process function.
+    run : AbstractRun
+        The run.
+    inputs : Dict[str, Any]
+        The inputs as a dictionary.
+
+    Raises
+    ------
+    Exception
+        If the process function fails.
+    """
     try:
         return process(run, inputs)
     except Exception:
@@ -746,7 +780,29 @@ 
Source code for deepcave.plugins.static
 [docs]
 class StaticPlugin(Plugin, ABC):
     """
+    Provide a static plugin object.
+
     Calculation with queue. Made for time-consuming tasks.
+    Register and handle callbacks.
+
+    Properties
+    ----------
+    outputs : List[Tuple[str, str, bool]]
+        The plugin specific outputs.
+    inputs : List[Tuple[str, str, bool, Any]]
+        The plugin specific inputs.
+    id : str
+        The plugin id.
+    raw_outputs : dict[str, Any]
+        The raw outputs of a run.
+    logger : Logger
+        The logger for the plugin.
+    name : str
+        The name of the plugin.
+    process : Callable
+        Return raw data based on a run and input data.
+    button_caption : str
+        The caption for the button.
     """
 
     def __init__(self) -> None:
@@ -754,8 +810,9 @@ 
Source code for deepcave.plugins.static
         self._setup()
 
     def _setup(self) -> None:
+        """Set up the plugin."""
         self._state = PluginState.UNSET  # Set in the main loop to track what's going on right now
-        self._previous_state = None  # Used for updating status
+        self._previous_state: Optional[PluginState] = None  # Used for updating status
         self._refresh_required = True
         self._reset_button = False
         self._blocked = False
@@ -764,6 +821,7 @@ 
Source code for deepcave.plugins.static
 [docs]
     @interactive
     def register_callbacks(self) -> None:
+        """Register different callbacks."""
         super().register_callbacks()
         self._callback_inputs_changed()
         self._callback_loop_update_status_label()
@@ -772,6 +830,7 @@ 
Source code for deepcave.plugins.static
 
     @interactive
     def _callback_inputs_changed(self) -> None:
+        """Handle callback if the inputs changed."""
         from deepcave import app, c, queue, rc, run_handler
 
         # Plugin specific outputs
@@ -789,13 +848,13 @@ 
Source code for deepcave.plugins.static
             inputs.append(Input(self.get_internal_input_id(id), attribute))
 
         # Register updates from inputs
-        @app.callback(outputs, inputs)
-        def plugin_process(n_clicks, _, *inputs_list):  # type: ignore
+        @app.callback(outputs, inputs)  # type: ignore
+        def plugin_process(n_clicks: int, _: Any, *inputs_list: str) -> Optional[Any]:
+            """Register updates from inputs."""
             self._blocked = True
-
             # Map the list `inputs_list` to a dict s.t.
             # it's easier to access them.
-            inputs = self._list_to_dict(inputs_list, input=True)
+            inputs = self._list_to_dict(list(inputs_list), input=True)
             inputs_key = self._dict_as_key(inputs, remove_filters=True)
             cleaned_inputs = self._clean_inputs(inputs)
             last_inputs = c.get("last_inputs", self.id)
@@ -810,7 +869,7 @@ 
Source code for deepcave.plugins.static
             raw_outputs = {}
             raw_outputs_available = True
             for run in runs:
-                raw_outputs[run.id] = rc.get(run, self.id, inputs_key)
+                raw_outputs[run.id] = rc.get(run, self.id, inputs_key)  # same problem
 
                 if raw_outputs[run.id] is None:
                     raw_outputs_available = False
@@ -837,11 +896,11 @@ 
Source code for deepcave.plugins.static
                 if button_pressed and self._state != PluginState.PROCESSING:
                     self.logger.debug("Button pressed.")
 
-                    # Check if we need to process
+                    # Check if processing is needed
                     for run in runs:
-                        job_id = self._get_job_id(run.id, inputs_key)
+                        job_id = self._get_job_id(run.id, inputs_key)  # same problem
 
-                        # We already got our results or it was already processed
+                        # Results are already achieved or it was already processed
                         if raw_outputs[run.id] is not None or queue.is_processed(job_id):
                             continue
 
@@ -882,6 +941,7 @@ 
Source code for deepcave.plugins.static
                             run = run_handler.get_run(job_run_id)
 
                             # Save results in cache
+                            # Same optional string problem
                             rc.set(run, job_plugin_id, job_inputs_key, job_run_outputs)
                             self.logger.debug(f"Job {job_id} cached.")
 
@@ -933,8 +993,16 @@ 
Source code for deepcave.plugins.static
         # Interval should not always run the main callback the whole time
         # Especially not if it's blocked because PreventUpdate
         # prevent output updates from previous callback calls.
-        @app.callback(output, inputs)
-        def plugin_check_blocked(_, data):  # type: ignore
+        @app.callback(output, inputs)  # type: ignore
+        def plugin_check_blocked(_: Any, data: Any) -> Any:
+            """
+            Check if blocked.
+
+            Raises
+            ------
+            PreventUpdate
+                If '_blocked' is True.
+            """
             if self._blocked:
                 raise PreventUpdate
 
@@ -954,15 +1022,16 @@ 
Source code for deepcave.plugins.static
 
         # Update status label
         # Register updates from inputs
-        @app.callback(output, input)
-        def plugin_update_status(_):  # type: ignore
+        @app.callback(output, input)  # type: ignore
+        def plugin_update_status(_: Any) -> Tuple[List[Any], Optional[Any], bool]:
+            """Update the status of the plugin."""
             button_text = [html.Span(self.button_caption)]
 
             if self._state == PluginState.UNSET:
                 # Disable and reset button
                 return button_text, None, True
 
-            # Important so we don't update the button every time (would result in an ugly spinner)
+            # Important so the button is not updated every time (would result in an ugly spinner)
             if self._previous_state == self._state:
                 raise PreventUpdate
 
@@ -973,8 +1042,8 @@ 
Source code for deepcave.plugins.static
                 self._previous_state == PluginState.PROCESSING
                 and self._state == PluginState.NEEDS_PROCESSING
             ):
-                # However: We have to unset the previous state so if we really change the inputs
-                # the visualizes will be updated.
+                # However: The previous state has to be unset, so if the inputs are really changed
+                # the visualizer will be updated.
                 self._previous_state = PluginState.UNSET
                 raise PreventUpdate
 
@@ -1003,12 +1072,37 @@ 
Source code for deepcave.plugins.static
             return button_text, button, disabled
 
     def _get_job_id(self, run_name: str, inputs_key: str) -> str:
+        """
+        Get the job id.
+
+        Parameters
+        ----------
+        run_name : str
+            The name of the run.
+        inputs_key : str
+            The inputs key.
+
+        Returns
+        -------
+        str
+            The job id.
+        """
         return f"{run_name}-{inputs_key}"
 
 

 [docs]
     @interactive
     def __call__(self) -> List[Component]:  # type: ignore
+        """
+        Return the components for the plugin.
+
+        Basically, all blocks and elements of the plugin are stacked-up here.
+
+        Returns
+        -------
+        List[Component]
+            Layout as list of components.
+        """
         from deepcave.config import Config
 
         self._setup()
diff --git a/development/_modules/deepcave/plugins/summary/configurations.html b/development/_modules/deepcave/plugins/summary/configurations.html
index c5caf969..4465fe1d 100644
--- a/development/_modules/deepcave/plugins/summary/configurations.html
+++ b/development/_modules/deepcave/plugins/summary/configurations.html
@@ -702,7 +702,21 @@
               

                 
   
Source code for deepcave.plugins.summary.configurations
-from collections import defaultdict
+#  noqa: D400
+"""
+# Configurations
+
+This module provides utilities to visualize the characteristics of a configuration within a run.
+
+The module provides a corresponding dynamic plugin.
+
+## Classes
+    - Configurations: Visualize the characteristics of a configuration.
+"""
+
+from typing import Any, Callable, Dict, List, Union
+
+from collections import defaultdict
 
 import dash_bootstrap_components as dbc
 import pandas as pd
@@ -727,6 +741,8 @@ 
Source code for deepcave.plugins.summary.configurations

 [docs]
 class Configurations(DynamicPlugin):
+    """Visualize the characteristics of a configuration."""
+
     id = "configurations"
     name = "Configurations"
     icon = "fas fa-sliders-h"
@@ -739,14 +755,14 @@ 
Source code for deepcave.plugins.summary.configurations
@staticmethod
     def get_link(run: AbstractRun, config_id: int) -> str:
         """
-        Creates a link to a specific configuration.
+        Create a link to a specific configuration.
 
         Parameters
         ----------
         run : AbstractRun
-            Selected run.
+            The selected run.
         config_id : int
-            Configuration, which should be visited.
+            Configuration which should be visited.
 
         Returns
         -------
@@ -767,7 +783,21 @@ 
Source code for deepcave.plugins.summary.configurations

 [docs]
     @staticmethod
-    def get_input_layout(register):
+    def get_input_layout(register: Callable) -> List[html.Div]:
+        """
+        Get the layout for the input block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[html.Div]
+            The layouts for the input block.
+        """
         return [
             html.Div(
                 [
@@ -780,7 +810,19 @@ 
Source code for deepcave.plugins.summary.configurations

 [docs]
-    def load_inputs(self):
+    def load_inputs(self) -> Dict[str, Any]:
+        """
+        Load the content for the defined inputs in 'get_input_layout' and 'get_filter_layout'.
+
+        This method is necessary to pre-load contents for the inputs.
+        So, if the plugin is called for the first time or there are no results in the cache,
+        the plugin gets its content from this method.
+
+        Returns
+        -------
+        Dict[str, Any]
+            The content to be filled
+        """
         return {
             "config_id": {"min": 0, "max": 0, "marks": get_slider_marks(), "value": 0},
         }

@@ -788,7 +830,32 @@ 
Source code for deepcave.plugins.summary.configurations

 [docs]
-    def load_dependency_inputs(self, run, previous_inputs, inputs):
+    def load_dependency_inputs(  # type: ignore
+        self, run, previous_inputs: Dict[str, Any], inputs: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """
+        Work like 'load_inputs' but called after inputs have changed.
+
+        Note
+        ----
+        Only the changes have to be returned.
+        The returned dictionary will be merged with the inputs.
+
+        Parameters
+        ----------
+        run :
+            The selected run.
+        previous_inputs :
+            Previous content of the inputs.
+            Not used in this specific function
+        inputs : Dict[str, Any]
+            The current content of the inputs.
+
+        Returns
+        -------
+        Dict[str, Any]
+            A dictionary with the changes.
+        """
         # Get selected values
         config_id_value = inputs["config_id"]["value"]
         configs = run.get_configs()
@@ -808,7 +875,32 @@ 
Source code for deepcave.plugins.summary.configurations

 [docs]
     @staticmethod
-    def process(run, inputs):
+    def process(run, inputs) -> Dict[str, Any]:  # type: ignore
+        """
+        Return raw data based on a run and input data.
+
+        Warning
+        -------
+        The returned data must be JSON serializable.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run :
+            The selected run.
+        inputs :
+            The input data.
+
+        Returns
+        -------
+        Dict[str, Any]
+            A serialized dictionary.
+        """
         selected_config_id = int(inputs["config_id"])
         origin = run.get_origin(selected_config_id)
         objectives = run.get_objectives()
@@ -827,8 +919,8 @@ 
Source code for deepcave.plugins.summary.configurations
str(original_run.path) + f" (Configuration ID: {original_config_id})"
             ]
 
-        performances = {}
-        performances_table_data = {"Budget": []}
+        performances: Dict[str, Dict[Union[int, float], float]] = {}
+        performances_table_data: Dict[str, List[Any]] = {"Budget": []}
         for objective_id, objective in enumerate(objectives):
             if objective.name not in performances:
                 performances[objective.name] = {}
@@ -855,20 +947,20 @@ 
Source code for deepcave.plugins.summary.configurations
# Let's start with the configspace
         X = []
-        cs_table_data = {"Hyperparameter": [], "Value": []}
-        for config_id, config in run.get_configs().items():
-            x = run.encode_config(config)
+        cs_table_data: Dict[str, List[Any]] = {"Hyperparameter": [], "Value": []}
+        for config_id, configuration in run.get_configs().items():
+            x = run.encode_config(configuration)
 
             highlight = 0
             if config_id == selected_config_id:
                 highlight = 1
 
-                for k, v in config.items():
+                for k, v in configuration.items():
                     # Add accurate data for our table here
                     cs_table_data["Hyperparameter"] += [k]
                     cs_table_data["Value"] += [v]
 
-            # We simply add highlight as a new column
+            # Highlight is simply added as a new column
             x += [highlight]
 
             # And add it to the lists
@@ -891,7 +983,23 @@ 
Source code for deepcave.plugins.summary.configurations

 [docs]
     @staticmethod
-    def get_output_layout(register):
+    def get_output_layout(
+        register: Callable,
+    ) -> List[Any]:
+        """
+        Get the layout for the output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method used to register outputs.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            The layouts for the outputs.
+        """
         return [
             html.Div(id=register("overview_table", "children"), className="mb-3"),
             html.Hr(),
@@ -938,7 +1046,24 @@ 
Source code for deepcave.plugins.summary.configurations
@staticmethod
-    def _get_objective_figure(_, outputs, run):
+    def _get_objective_figure(
+        _: Any, outputs: Dict[str, Dict[str, Dict[Any, Any]]], run: AbstractRun
+    ) -> go.Figure:
+        """
+        Get the figure for the visualization of the objectives.
+
+        Parameters
+        ----------
+        outputs : Dict[str, Dict[str, Dict[Any, Any]]]
+            Raw outputs from the run.
+        run : AbstractRun
+            The selected run.
+
+        Returns
+        -------
+        go.Figure
+            The figure of the objectives.
+        """
         objective_data = []
         for i, (metric, values) in enumerate(outputs["performances"].items()):
             trace_kwargs = {
@@ -954,12 +1079,12 @@ 
Source code for deepcave.plugins.summary.configurations
trace = go.Scatter(**trace_kwargs)
             objective_data.append(trace)
 
-        layout_kwargs = {
+        layout_kwargs: Dict[str, Any] = {
             "margin": Config.FIGURE_MARGIN,
             "xaxis": {"title": "Budget", "domain": [0.05 * len(run.get_objectives()), 1]},
         }
 
-        # We create an axis for each objective now
+        # An axis is created for each objective now
         for id, objective in enumerate(run.get_objectives()):
             yaxis = "yaxis"
             if id > 0:
@@ -990,9 +1115,25 @@ 
Source code for deepcave.plugins.summary.configurations
return objective_figure
 
     @staticmethod
-    def _get_configspace_figure(inputs, outputs, run):
-        df = outputs["cs_df"]
-        df = deserialize(df, dtype=pd.DataFrame)
+    def _get_configspace_figure(
+        inputs: Any, outputs: Dict[str, str], run: AbstractRun
+    ) -> go.Figure:
+        """
+        Get the figure for the visualization of the configuration space.
+
+        Parameters
+        ----------
+        outputs : Dict[str, str]
+            Raw outputs from the run.
+        run : AbstractRun
+            The selected run.
+
+        Returns
+        -------
+        go.Figure
+            The figure of the configuration space.
+        """
+        df = deserialize(outputs["cs_df"], dtype=pd.DataFrame)
 
         highlighted = df["highlighted"].values
         hp_names = run.configspace.get_hyperparameter_names()
@@ -1000,7 +1141,7 @@ 
Source code for deepcave.plugins.summary.configurations
# Get highlighted column
         highlighted_df = df[df["highlighted"] == 1]
 
-        data = defaultdict(dict)
+        data: defaultdict = defaultdict(dict)
         for hp_name in hp_names:
             data[hp_name]["values"] = df[hp_name].values
             data[hp_name]["label"] = hp_name
@@ -1038,7 +1179,30 @@ 
Source code for deepcave.plugins.summary.configurations

 [docs]
     @staticmethod
-    def load_outputs(run, inputs, outputs):
+    def load_outputs(run, inputs, outputs) -> List[Any]:  # type: ignore
+        """
+        Read in the raw data and prepare them for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Input and filter values from the user.
+        outputs
+            Raw output from the run.
+
+        Returns
+        -------
+        List[Any]
+            A list of the created tables containing output information.
+        """
         config_id = inputs["config_id"]
         config = run.get_config(config_id)
 
diff --git a/development/_modules/deepcave/plugins/summary/footprint.html b/development/_modules/deepcave/plugins/summary/footprint.html
index a16f46d7..1bfa604a 100644
--- a/development/_modules/deepcave/plugins/summary/footprint.html
+++ b/development/_modules/deepcave/plugins/summary/footprint.html
@@ -702,7 +702,21 @@
               

                 
   
Source code for deepcave.plugins.summary.footprint
-import dash_bootstrap_components as dbc
+# noqa: D400
+"""
+# FootPrint
+
+This module provides utilities to visualize a configuration footprint.
+
+The module contains a static plugin class for defining the footprint.
+
+## Classes
+    - FootPrint: A static plugin for the footprint of a configuration.
+"""
+
+from typing import Any, Callable, Dict, List
+
+import dash_bootstrap_components as dbc
 import plotly.graph_objs as go
 from dash import dcc, html
 
@@ -721,6 +735,12 @@ 
Source code for deepcave.plugins.summary.footprint

 [docs]
 class FootPrint(StaticPlugin):
+    """
+    Visualize the footprint of a configuration.
+
+    A static plugin for the footprint.
+    """
+
     id = "footprint"
     name = "Configuration Footprint"
     icon = "fas fa-shoe-prints"
@@ -730,7 +750,21 @@ 
Source code for deepcave.plugins.summary.footprint

 [docs]
     @staticmethod
-    def get_input_layout(register):
+    def get_input_layout(register: Callable) -> List[Any]:
+        """
+        Get the layout for the input block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            The layouts for the input block.
+        """
         return [
             dbc.Row(
                 [
@@ -786,8 +820,22 @@ 
Source code for deepcave.plugins.summary.footprint

 [docs]
     @staticmethod
-    def get_filter_layout(register):
-        return (
+    def get_filter_layout(register: Callable) -> List[dbc.Row]:
+        """
+        Get layout for the filter block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register (user) variables.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[dbc.Row]
+            The layouts for the filter block.
+        """
+        return [
             dbc.Row(
                 [
                     dbc.Col(
@@ -810,14 +858,26 @@ 
Source code for deepcave.plugins.summary.footprint
],
                         md=6,
                     ),
-                ],
-            ),
-        )

+                ]
+            )
+        ]

 
 
 

 [docs]
-    def load_inputs(self):
+    def load_inputs(self) -> Dict[str, Dict[str, Any]]:
+        """
+        Load the content for the defined inputs in 'get_input_layout' and 'get_filter_layout'.
+
+        This method is necessary to pre-load contents for the inputs.
+        So, if the plugin is called for the first time or there are no results in the cache,
+        the plugin gets its content from this method.
+
+        Returns
+        -------
+        Dict[str, Dict[str, Any]]
+            The content to be filled.
+        """
         return {
             "details": {"value": 0.5},
             "show_borders": {"options": get_select_options(binary=True), "value": "true"},
@@ -827,8 +887,31 @@ 
Source code for deepcave.plugins.summary.footprint

 [docs]
-    def load_dependency_inputs(self, run, previous_inputs, inputs):
-        # Prepare objetives
+    def load_dependency_inputs(self, run, previous_inputs, inputs) -> Dict[str, Any]:  # type: ignore # noqa: E501
+        """
+        Work like 'load_inputs' but called after inputs have changed.
+
+        Note
+        ----
+        Only the changes have to be returned.
+        The returned dictionary will be merged with the inputs.
+
+        Parameters
+        ----------
+        run :
+            The selected run.
+        previous_inputs :
+            Previous content of the inputs.
+            Not used in this specific function.
+        inputs :
+            Current content of the inputs.
+
+        Returns
+        -------
+        Dict[str, Any]
+            The dictionary with the changes.
+        """
+        # Prepare objectives
         objective_names = run.get_objective_names()
         objective_ids = run.get_objective_ids()
         objective_options = get_select_options(objective_names, objective_ids)
@@ -862,7 +945,32 @@ 
Source code for deepcave.plugins.summary.footprint

 [docs]
     @staticmethod
-    def process(run, inputs):
+    def process(run, inputs) -> Dict[str, Any]:  # type: ignore
+        """
+        Return raw data based on a run and input data.
+
+        Warning
+        -------
+        The returned data must be JSON serializable.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            The input data.
+
+        Returns
+        -------
+        Dict[str, Any]
+            A serialized dictionary.
+        """
         budget = run.get_budget(inputs["budget_id"])
         objective = run.get_objective(inputs["objective_id"])
         details = inputs["details"]
@@ -891,7 +999,21 @@ 
Source code for deepcave.plugins.summary.footprint

 [docs]
     @staticmethod
-    def get_output_layout(register):
+    def get_output_layout(register: Callable) -> dbc.Tabs:
+        """
+        Get the layout for the output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method for registering outputs.
+            The register_output function is located in the Plugin superclass.
+
+        Returns
+        -------
+        dbc.Tabs
+            The layout for the output block.
+        """
         return dbc.Tabs(
             [
                 dbc.Tab(
@@ -913,7 +1035,30 @@ 
Source code for deepcave.plugins.summary.footprint

 [docs]
     @staticmethod
-    def load_outputs(run, inputs, outputs):
+    def load_outputs(run, inputs, outputs) -> List[Any]:  # type: ignore
+        """
+        Read in the raw data and prepare them for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Input and filter values from the user.
+        outputs
+            Raw output from the run.
+
+        Returns
+        -------
+        List[Any]
+            The plotly figure of the footprint performance and area.
+        """
         objective = run.get_objective(inputs["objective_id"])
         show_borders = inputs["show_borders"]
         show_supports = inputs["show_supports"]
@@ -1005,7 +1150,21 @@ 
Source code for deepcave.plugins.summary.footprint

 [docs]
     @staticmethod
-    def get_mpl_output_layout(register):
+    def get_mpl_output_layout(register: Callable) -> List[dbc.Tabs]:
+        """
+        Get the layout for the matplotlib output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register the outputs.
+            The register_output function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[dbc.Tabs]
+            The layout for the output block.
+        """
         return [
             dbc.Tabs(
                 [
@@ -1025,7 +1184,29 @@ 
Source code for deepcave.plugins.summary.footprint

 [docs]
     @staticmethod
-    def load_mpl_outputs(run, inputs, outputs):
+    def load_mpl_outputs(run, inputs, outputs):  # type: ignore
+        """
+        Read in the raw data and prepare them for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+        inputs
+            Input and filter values from the user.
+        outputs
+            Raw outputs from the run.
+
+        Returns
+        -------
+        The rendered matplotlib figure of the footprint
+        """
         objective = run.get_objective(inputs["objective_id"])
         show_borders = inputs["show_borders"]
         show_supports = inputs["show_supports"]
@@ -1070,13 +1251,13 @@ 
Source code for deepcave.plugins.summary.footprint
size = 10
                     marker_symbol = "^"
 
-                color = plt.get_color(color_id)
+                color = plt.get_color(color_id)  # type: ignore
                 plt.scatter(x, y, marker=marker_symbol, s=size, label=name, c=color)
 
             plt.axis("off")
             plt.legend(loc="lower right")
 
-            images += [plt.render()]
+            images += [plt.render()]  # type: ignore
 
         return images

 

diff --git a/development/_modules/deepcave/plugins/summary/overview.html b/development/_modules/deepcave/plugins/summary/overview.html
index 2f6c2e6f..0daf5e5c 100644
--- a/development/_modules/deepcave/plugins/summary/overview.html
+++ b/development/_modules/deepcave/plugins/summary/overview.html
@@ -702,7 +702,23 @@
               

                 
   
Source code for deepcave.plugins.summary.overview
-import dash_bootstrap_components as dbc
+# noqa: D400
+"""
+# Overview
+
+This module provides utilities for visualizing an overview of the selected runs.
+
+It holds the most important information, e.g. meta data, objectives and statistics.
+
+The module includes a dynamic plugin for the overview.
+
+## Classes
+    - Overview: Visualize an overall overview of the selected run.
+"""
+
+from typing import Any, Callable, Dict, List
+
+import dash_bootstrap_components as dbc
 import numpy as np
 import plotly.graph_objs as go
 from ConfigSpace.hyperparameters import (
@@ -729,6 +745,8 @@ 
Source code for deepcave.plugins.summary.overview

 [docs]
 class Overview(DynamicPlugin):
+    """Visualize an overall overview of the selected run."""
+
     id = "overview"
     name = "Overview"
     icon = "fas fa-search"
@@ -739,7 +757,21 @@ 
Source code for deepcave.plugins.summary.overview

 [docs]
     @staticmethod
-    def get_output_layout(register):
+    def get_output_layout(register: Callable) -> List[Any]:
+        """
+        Get the layout for the output block.
+
+        Parameters
+        ----------
+        register : Callable
+            Method to register the outputs.
+            The register_input function is located in the Plugin superclass.
+
+        Returns
+        -------
+        List[Any]
+            The layouts for the output block.
+        """
         return [
             html.Div(
                 id=register("card", "children"),
@@ -782,7 +814,26 @@ 
Source code for deepcave.plugins.summary.overview

 [docs]
     @staticmethod
-    def load_outputs(run, *_):
+    def load_outputs(run, *_: Any) -> List[Any]:  # type: ignore
+        """
+        Read in the raw data and prepare them for the layout.
+
+        Note
+        ----
+        The passed inputs are cleaned and therefore differs compared to 'load_inputs'
+        or 'load_dependency_inputs'.
+        Please see '_clean_inputs' for more information.
+
+        Parameters
+        ----------
+        run
+            The selected run.
+
+        Returns
+        -------
+        List[Any]
+            A list of the created tables of the overview.
+        """
         # Get best cost across all objectives, highest budget
         incumbent, _ = run.get_incumbent()
         config_id = run.get_config_id(incumbent)
@@ -853,7 +904,7 @@ 
Source code for deepcave.plugins.summary.overview
)
 
         # Meta
-        meta = {"Attribute": [], "Value": []}
+        meta: Dict[str, List[str]] = {"Attribute": [], "Value": []}
         for k, v in run.get_meta().items():
             if k == "objectives":
                 continue
@@ -865,7 +916,7 @@ 
Source code for deepcave.plugins.summary.overview
meta["Value"].append(str(v))
 
         # Objectives
-        objectives = {"Name": [], "Bounds": []}
+        objectives: Dict[str, List[str]] = {"Name": [], "Bounds": []}
         for objective in run.get_objectives():
             objectives["Name"].append(objective.name)
             objectives["Bounds"].append(f"[{objective.lower}, {objective.upper}]")
@@ -874,8 +925,13 @@ 
Source code for deepcave.plugins.summary.overview
budgets = run.get_budgets(include_combined=False)
 
         # Statistics
-        status_statistics = {}
-        status_details = {"Configuration ID": [], "Budget": [], "Status": [], "Error": []}
+        status_statistics: Dict[float, Dict[Status, int]] = {}
+        status_details: Dict[str, List[Any]] = {
+            "Configuration ID": [],
+            "Budget": [],
+            "Status": [],
+            "Error": [],
+        }
         for budget in budgets:
             budget = round(budget, 2)
             if budget not in status_statistics:
@@ -952,8 +1008,8 @@ 
Source code for deepcave.plugins.summary.overview
str(round(count / len_trials * 100, 2)) + "%" for count in status_budget.values()
         ]
         status_budget_values_text = "/".join(status_budget_values)
-        status_budget_keys_text = [str(key) for key in status_budget.keys()]
-        status_budget_keys_text = "/".join(status_budget_keys_text)
+        status_budget_keys_text_list = [str(key) for key in status_budget.keys()]
+        status_budget_keys_text = "/".join(status_budget_keys_text_list)
 
         status_text = f"""
         Taking all evaluated trials into account, {successful_trials_rate}% have been successful.
@@ -994,7 +1050,7 @@ 
Source code for deepcave.plugins.summary.overview
config_statistics["Z_labels"] = z_labels
 
         # Prepare configspace table
-        configspace = {
+        configspace: Dict[str, List] = {
             "Hyperparameter": [],
             "Possible Values": [],
             "Default": [],
@@ -1002,7 +1058,6 @@ 
Source code for deepcave.plugins.summary.overview
}
 
         for hp_name, hp in run.configspace.get_hyperparameters_dict().items():
-
             log = False
             value = None
             if (
@@ -1021,12 +1076,12 @@ 
Source code for deepcave.plugins.summary.overview
value = str(hp.value)
 
             default = str(hp.default_value)
-            log = str(log)
+            log_str = str(log)
 
             configspace["Hyperparameter"].append(hp_name)
             configspace["Possible Values"].append(value)
             configspace["Default"].append(default)
-            configspace["Log"].append(log)
+            configspace["Log"].append(log_str)
 
         stats_data = []
         for budget, stats in status_statistics.items():
diff --git a/development/_modules/deepcave/runs.html b/development/_modules/deepcave/runs.html
index 82493f6f..1b6a8481 100644
--- a/development/_modules/deepcave/runs.html
+++ b/development/_modules/deepcave/runs.html
@@ -702,7 +702,19 @@
               

                 
   
Source code for deepcave.runs
-from abc import ABC, abstractmethod
+#  noqa: D400
+"""
+# AbstractRun
+
+This module provides utilities to create and handle an abstract run.
+
+It provides functions to get information of the run, as well as the used objectives.
+
+## Classes
+    - AbstractRun: Create a new run.
+"""
+
+from abc import ABC, abstractmethod
 from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 from pathlib import Path
@@ -734,6 +746,37 @@ 
Source code for deepcave.runs
 

 [docs]
 class AbstractRun(ABC):
+    """
+    Create a new run.
+
+    Provide functions to get information of the run, as well as the used objectives.
+
+    Properties
+    ----------
+    name : str
+        The name of the run.
+    path : Optional[Path]
+        The path to the run.
+    logger : Logger
+        The logger for the run.
+    meta: Dict[str, Any]
+        Contains the run's meta information.
+    configspace: ConfigSpace.ConfigurationSpace
+        The configuration space of the run.
+    configs: Dict[int, Configuration]
+        Contains the configurations.
+    origins: Dict[int, str]
+        The origin of the configuration.
+    models: Dict[int, Optional[Union[str, "torch.nn.Module"]]]
+        Contains the modules.
+    history: List[Trial]
+        The history of Trials.
+    trial_keys: Dict[Tuple[str, int], int]
+        Contains config_id, budget and the corresponding trial_id.
+    models_dir : Path
+        The directory of the model.
+    """
+
     prefix: str
 
     def __init__(self, name: str) -> None:
@@ -744,22 +787,46 @@ 
Source code for deepcave.runs
         # objects created by reset
         self.reset()
 
+

+[docs]
     def reset(self) -> None:
+        """
+        Reset the run to default values / empties.
+
+        Clear the initial data and configurations of the object.
+        """
         self.meta: Dict[str, Any] = {}
         self.configspace: ConfigSpace.ConfigurationSpace
-        self.configs: Dict[int, Configuration] = {}
-        self.origins: Dict[int, str] = {}
-        self.models: Dict[int, Optional[Union[str, "torch.nn.Module"]]] = {}
+        self.configs: Dict[int, Union[Configuration, Dict[Any, Any]]] = {}
+        self.origins: Dict[int, Optional[str]] = {}
+        self.models: Dict[  # type: ignore
+            int, Optional[Union[str, "torch.nn.Module"]]  # noqa: F821
+        ] = {}
 
         self.history: List[Trial] = []
-        self.trial_keys: Dict[Tuple[str, int], int] = {}  # (config_id, budget) -> trial_id
+        self.trial_keys: Dict[
+            Tuple[int, Union[int, float, None]], int
+        ] = {}  # (config_id, budget) -> trial_id
 
         # Cached data
-        self._highest_budget: Dict[int, Union[int, float]] = {}  # config_id -> budget
+        self._highest_budget: Dict[int, Union[int, float]] = {}  # config_id -> budget

+
 
     def _update_highest_budget(
         self, config_id: int, budget: Union[int, float], status: Status
     ) -> None:
+        """
+        Update the highest budget.
+
+        Parameters
+        ----------
+        config_id : int
+            The identificator of the configuration.
+        budget : Union[int, float]
+            The new highest budget.
+        status : Status
+            The status of the run.
+        """
         if status == Status.SUCCESS:
             # Update highest budget
             if config_id not in self._highest_budget:
@@ -772,7 +839,9 @@ 
Source code for deepcave.runs
     @abstractmethod
     def hash(self) -> str:
         """
-        Hash of the current run. If hash changes, cache has to be cleared. This ensures that
+        Hash of the current run.
+
+        If hash changes, cache has to be cleared. This ensures that
         the cache always holds the latest results of the run.
 
         Returns
@@ -786,7 +855,9 @@ 
Source code for deepcave.runs
     @abstractmethod
     def id(self) -> str:
         """
-        Hash of the file. This is used to identify the file.
+        Hash of the file.
+
+        This is used to identify the file.
         In contrast to `hash`, this hash should not be changed throughout the run.
 
         Returns
@@ -798,42 +869,149 @@ 
Source code for deepcave.runs
 
     @property
     def latest_change(self) -> float:
+        """
+        Get the latest change.
+
+        Returns
+        -------
+        float
+            The latest change.
+        """
         return 0
 
+

+[docs]
     @staticmethod
-    def get_trial_key(config_id: int, budget: Union[int, float]):
-        return (config_id, budget)
+    def get_trial_key(
+        config_id: int, budget: Union[int, float, None]
+    ) -> Tuple[int, Union[int, float, None]]:
+        """
+        Get the trial key.
+
+        It is obtained through the assembly of configuration and budget.
+
+        Parameters
+        ----------
+        config_id : int
+            The identificator of the configuration.
+        budget : Union[int, float, None]
+            The budget of the Trial.
+
+        Returns
+        -------
+        Tuple[int, Union[int, float, None]]
+            Tuple representing the trial key, consisting of configuration id and the budget.
+        """
+        return (config_id, budget)

+
 
-    def get_trial(self, trial_key) -> Optional[Trial]:
+

+[docs]
+    def get_trial(self, trial_key: Tuple[int, int]) -> Optional[Trial]:
+        """
+        Get the trial with the responding key if existing.
+
+        Parameters
+        ----------
+        trial_key : Tuple[int, int]
+            The key for the desired trial.
+
+        Returns
+        -------
+        Optional[Trial]
+            The trial object.
+        """
         if trial_key not in self.trial_keys:
             return None
 
-        return self.history[self.trial_keys[trial_key]]
+        return self.history[self.trial_keys[trial_key]]

+
 
+

+[docs]
     def get_trials(self) -> Iterator[Trial]:
-        yield from self.history
+        """
+        Get an iterator of all stored trials.
+
+        Returns
+        -------
+        Iterator[Trial]
+            An iterator over all stored trials.
+        """
+        yield from self.history

+
 
+

+[docs]
     def get_meta(self) -> Dict[str, Any]:
-        return self.meta.copy()
+        """
+        Get a shallow copy of the meta information.
+
+        Returns
+        -------
+        Dict[str, Any]
+            A shallow copy of the meta information dictionary.
+        """
+        return self.meta.copy()

 
+
+

+[docs]
     def empty(self) -> bool:
-        return len(self.history) == 0
+        """
+        Check if the run's history is empty.
+
+        Returns
+        -------
+        bool
+            True if run history is empty, False otherwise.
+        """
+        return len(self.history) == 0

 
-    def get_origin(self, config_id: int) -> str:
-        return self.origins[config_id]
 
+

+[docs]
+    def get_origin(self, config_id: int) -> Optional[str]:
+        """
+        Get the origin, given a config id.
+
+        Parameters
+        ----------
+        config_id : int
+            The identificator of the configuration.
+
+        Returns
+        -------
+        Optional[str]
+            An origin string corresponding to the given configuration id.
+        """
+        return self.origins[config_id]

+
+
+

+[docs]
     def get_objectives(self) -> List[Objective]:
+        """
+        Get a list of all objectives corresponding to the run.
+
+        Returns
+        -------
+        List[Objective]
+            A list containing all objectives associated with the run.
+        """
         objectives = []
         for d in self.meta["objectives"].copy():
             objective = Objective.from_json(d)
             objectives += [objective]
 
-        return objectives
+        return objectives

+
 
 

 [docs]
     def get_objective(self, id: Union[str, int]) -> Optional[Objective]:
-        """Returns the objective based on the id or the name.
+        """
+        Return the objective based on the id or the name.
 
         Parameters
         ----------
@@ -842,10 +1020,9 @@ 
Source code for deepcave.runs
 
         Returns
         -------
-        Objective
-            The objective object.
+        Optional[Objective]
+            The objective.
         """
-
         objectives = self.get_objectives()
         if type(id) == int:
             return objectives[id]
@@ -862,7 +1039,7 @@ 
Source code for deepcave.runs
 [docs]
     def get_objective_id(self, objective: Union[Objective, str]) -> int:
         """
-        Returns the id of the objective if it is found.
+        Return the id of the objective if it is found.
 
         Parameters
         ----------
@@ -891,17 +1068,40 @@ 
Source code for deepcave.runs
         raise RuntimeError("Objective was not found.")

 
 
+

+[docs]
     def get_objective_ids(self) -> List[int]:
-        return list(range(len(self.get_objectives())))
+        """
+        Get the ids of the objectives.
+
+        Returns
+        -------
+        List[int]
+            A list of the ids of the objectives.
+        """
+        return list(range(len(self.get_objectives())))

+
 
 

 [docs]
     def get_objective_name(self, objectives: Optional[List[Objective]] = None) -> str:
         """
         Get the cost name of given objective names.
+
         Returns "Combined Cost" if multiple objective names were involved.
-        """
 
+        Parameters
+        ----------
+        objectives : Optional[List[Objective]]
+            A list of the objectives.
+            By default, None.
+
+        Returns
+        -------
+        str
+            The name of the objective.
+            Returns "Combined Cost" if multiple objective names were involved.
+        """
         available_objective_names = self.get_objective_names()
 
         if objectives is None:
@@ -914,27 +1114,41 @@ 
Source code for deepcave.runs
         return COMBINED_COST_NAME

 
 
+

+[docs]
     def get_objective_names(self) -> List[str]:
-        return [obj.name for obj in self.get_objectives()]
+        """
+        Get the names of the objectives.
+
+        Returns
+        -------
+        List[str]
+            A list containing the names of the objectives.
+        """
+        return [obj.name for obj in self.get_objectives()]

+
 
 

 [docs]
-    def get_configs(self, budget: Union[int, float] = None) -> Dict[int, Configuration]:
+    def get_configs(self, budget: Optional[Union[int, float]] = None) -> Dict[int, Configuration]:
         """
-        Get configurations of the run. Optionally, only configurations which were evaluated
+        Get configurations of the run.
+
+        Optionally, only configurations which were evaluated
         on the passed budget are considered.
 
         Parameters
         ----------
-        budget : Union[int, float], optional
-            Considered budget. By default None (all configurations are included).
+        budget : Optiona[Union[int, float]]
+            Considered budget.
+            By default, None (all configurations are included).
 
         Returns
         -------
         Dict[int, Configuration]
             Configuration id and the configuration.
         """
-        # Include all configs if we have combined budget
+        # Include all configs if budget is a combined budget
         if budget == COMBINED_BUDGET:
             budget = None
 
@@ -954,11 +1168,42 @@ 
Source code for deepcave.runs
         return configs

 
 
+

+[docs]
     def get_config(self, id: int) -> Configuration:
+        """
+        Retrieve the configuration with the corresponding id.
+
+        Parameters
+        ----------
+        id : int
+            The id of the configuration.
+
+        Returns
+        -------
+        Configuration
+            The corresponding Configuration.
+        """
         config = Configuration(self.configspace, self.configs[id])
-        return config
+        return config

 
+
+

+[docs]
     def get_config_id(self, config: Union[Configuration, Dict]) -> Optional[int]:
+        """
+        Get the id of the configuration.
+
+        Parameters
+        ----------
+        config : Union[Configuration, Dict]
+            The configuration for which to find the id.
+
+        Returns
+        -------
+        Optional[int]
+            The configuration id.
+        """
         if isinstance(config, Configuration):
             config = config.get_dictionary()
 
@@ -967,62 +1212,112 @@ 
Source code for deepcave.runs
             if c == config:
                 return id
 
-        return None
+        return None

+
+
+

+[docs]
+    def get_num_configs(self, budget: Optional[Union[int, float]] = None) -> int:
+        """
+        Count the number of configurations stored in this run with a specific budget.
+
+        Parameters
+        ----------
+        budget : Optional[Union[int, float]]
+            The budget for which to count the configurations.
+            If not provided, counts all configurations.
+            Default is None.
+
+        Returns
+        -------
+        int
+            The number of all configurations with a given budget.
+            If budget is None, counts all configurations.
+        """
+        return len(self.get_configs(budget=budget))

 
-    def get_num_configs(self, budget: Union[int, float] = None) -> int:
-        return len(self.get_configs(budget=budget))
 
 

 [docs]
-    def get_budget(self, id: Union[int, str], human=False) -> float:
+    def get_budget(self, id: Union[int, str], human: bool = False) -> Union[int, float]:
         """
-        Gets the budget given an id.
+        Get the budget given an id.
 
         Parameters
         ----------
         id : Union[int, str]
-            Id of the wanted budget. If id is a string, it is converted to an integer.
+            The id of the wanted budget.
+            If id is a string, it is converted to an integer.
+        human : bool, optional
+            Make the output more readable.
+            By default False.
 
         Returns
         -------
-        float
-            Budget.
+        float, int
+            The budget.
+
+        Raises
+        ------
+        TypeError
+            If the budget with this id is invalid.
         """
         budgets = self.get_budgets(human=human)
-        return budgets[int(id)]

+        return budgets[int(id)]  # type: ignore

 
 
+

+[docs]
     def get_budget_ids(self, include_combined: bool = True) -> List[int]:
+        """
+        Get the corresponding ids for the budgets.
+
+        Parameters
+        ----------
+        include_combined : bool, optional
+            If False, cut last id of budget ids.
+            By default True.
+
+        Returns
+        -------
+        List[int]
+            A list of the budget ids.
+        """
         budget_ids = list(range(len(self.get_budgets())))
         if not include_combined:
             budget_ids = budget_ids[:-1]
 
-        return budget_ids
+        return budget_ids

+
 
 

 [docs]
     def get_budgets(
         self, human: bool = False, include_combined: bool = True
-    ) -> List[Union[int, float]]:
+    ) -> List[Union[int, float, str]]:
         """
-        Returns the budgets from the meta data.
+        Return the budgets from the meta data.
 
         Parameters
         ----------
         human : bool, optional
-            Make the output better readable. By default False.
+            Make the output more readable.
+            By default False.
+        include_combined : bool, optional
+            If True, include the combined budget.
+            By default True.
 
         Returns
         -------
-        List[Union[int, float]]
-            List of budgets.
+        List[Union[int, float, str]]
+            List of budgets. In a readable form, if human is True.
         """
         budgets = self.meta["budgets"].copy()
         if include_combined and len(budgets) > 1 and COMBINED_BUDGET not in budgets:
             budgets += [COMBINED_BUDGET]
 
         if human:
-            readable_budgets = []
+            readable_budgets: List[Union[str, float]] = []
             for b in budgets:
                 if b == COMBINED_BUDGET:
                     readable_budgets += ["Combined"]
@@ -1038,10 +1333,17 @@ 
Source code for deepcave.runs
 [docs]
     def get_highest_budget(self, config_id: Optional[int] = None) -> Optional[Union[int, float]]:
         """
-        Returns the highest found budget for a config id. If no config id is specified then
+        Return the highest found budget for a config id.
+
+        If no config id is specified then
         the highest available budget is returned.
         Moreover, if no budget is available None is returned.
 
+        Parameters
+        ----------
+        config_id : Optional[int]
+            The config id for which the highest budget is returned.
+
         Returns
         -------
         Optional[Union[int, float]]
@@ -1059,13 +1361,16 @@ 
Source code for deepcave.runs
 
     def _process_costs(self, costs: List[float]) -> List[float]:
         """
-        Processes the costs to get rid of NaNs. NaNs are replaced by the worst value of the
+        Process the costs to get rid of NaNs.
+
+        NaNs are replaced by the worst value of the
         objective.
 
         Parameters
         ----------
         costs : List[float]
-            Costs, which should be processed. Must be the same length as the number of objectives.
+            Costs, which should be processed.
+            Must be the same length as the number of objectives.
 
         Returns
         -------
@@ -1085,28 +1390,30 @@ 
Source code for deepcave.runs
 [docs]
     def get_costs(self, config_id: int, budget: Optional[Union[int, float]] = None) -> List[float]:
         """
-        Returns the costs of a configuration. In case of multi-objective, multiple costs are
+        Return the costs of a configuration.
+
+        In case of multi-objective, multiple costs are
         returned.
 
         Parameters
         ----------
         config_id : int
             Configuration id to get the costs for.
-        budget : Optional[Union[int, float]], optional
-            Budget to get the costs from the configuration id for. By default None. If budget is
+        budget : Optional[Union[int, float]]
+            Budget to get the costs from the configuration id for. By default, None. If budget is
             None, the highest budget is chosen.
 
+        Returns
+        -------
+        List[float]
+            List of costs from the associated configuration.
+
         Raises
         ------
         ValueError
             If the configuration id is not found.
         RuntimeError
             If the budget was not evaluated for the passed config id.
-
-        Returns
-        -------
-        List[float]
-            List of costs from the associated configuration.
         """
         if budget is None:
             budget = self.get_highest_budget()
@@ -1129,15 +1436,17 @@ 
Source code for deepcave.runs
         statuses: Optional[Union[Status, List[Status]]] = None,
     ) -> Dict[int, List[float]]:
         """
-        Get all costs in the history with their config ids. Only configs from the given budget
+        Get all costs in the history with their config ids.
+
+        Only configs from the given budget
         and statuses are returned.
 
         Parameters
         ----------
-        budget : Optional[Union[int, float]], optional
+        budget : Optional[Union[int, float]]
             Budget to select the costs. If no budget is given, the highest budget is chosen.
-            By default None.
-        statuses : Optional[Union[Status, List[Status]]], optional
+            By default, None.
+        statuses : Optional[Union[Status, List[Status]]]
             Only selected stati are considered. If no status is given, all stati are considered.
             By default None.
 
@@ -1149,7 +1458,7 @@ 
Source code for deepcave.runs
         if budget is None:
             budget = self.get_highest_budget()
 
-        # In case of COMBINED_BUDGET, we only keep the costs of the highest found budget
+        # In case of COMBINED_BUDGET, only the costs of the highest found budget are kept
         highest_evaluated_budget = {}
 
         results = {}
@@ -1166,7 +1475,7 @@ 
Source code for deepcave.runs
                     highest_evaluated_budget[trial.config_id] = trial.budget
 
                 latest_budget = highest_evaluated_budget[trial.config_id]
-                # We only keep the highest budget
+                # Only the highest budget is kept
                 if trial.budget >= latest_budget:
                     results[trial.config_id] = trial.costs
             else:
@@ -1183,25 +1492,25 @@ 
Source code for deepcave.runs
 [docs]
     def get_status(self, config_id: int, budget: Optional[Union[int, float]] = None) -> Status:
         """
-        Returns the status of a configuration.
+        Return the status of a configuration.
 
         Parameters
         ----------
         config_id : int
             Configuration id to get the status for.
-        budget : Optional[Union[int, float]], optional
-            Budget to get the status from the configuration id for. By default None. If budget is
+        budget : Optional[Union[int, float]]
+            Budget to get the status from the configuration id for. By default, None. If budget is
             None, the highest budget is chosen.
 
-        Raises
-        ------
-        ValueError
-            If the configuration id is not found.
-
         Returns
         -------
         Status
             Status of the configuration.
+
+        Raises
+        ------
+        ValueError
+            If the configuration id is not found.
         """
         if budget == COMBINED_BUDGET:
             return Status.NOT_EVALUATED
@@ -1214,7 +1523,7 @@ 
Source code for deepcave.runs
 
         trial_key = self.get_trial_key(config_id, budget)
 
-        # Unfortunately, we have to iterate through the history to find the status
+        # Unfortunately, it is necessary to iterate through the history to find the status
         # TODO: Cache the stati
         for trial in self.history:
             if trial_key == trial.get_key():
@@ -1232,16 +1541,16 @@ 
Source code for deepcave.runs
         statuses: Optional[Union[Status, List[Status]]] = None,
     ) -> Tuple[Configuration, float]:
         """
-        Returns the incumbent with its normalized cost.
+        Return the incumbent with its normalized cost.
 
         Parameters
         ----------
-        objectives : Optional[Union[Objective, List[Objective]]], optional
-            Considerd objectives. By default None. If None, all objectives are considered.
-        budget : Optional[Union[int, float]], optional
-            Considered budget. By default None. If None, the highest budget is chosen.
-        statuses : Optional[Union[Status, List[Status]]], optional
-            Considered statuses. By default None. If None, all stati are considered.
+        objectives : Optional[Union[Objective, List[Objective]]]
+            Considered objectives. By default, None. If None, all objectives are considered.
+        budget : Optional[Union[int, float]]
+            Considered budget. By default, None. If None, the highest budget is chosen.
+        statuses : Optional[Union[Status, List[Status]]]
+            Considered statuses. By default, None. If None, all stati are considered.
 
         Returns
         -------
@@ -1280,16 +1589,18 @@ 
Source code for deepcave.runs
         self, costs: List[float], objectives: Optional[Union[Objective, List[Objective]]] = None
     ) -> float:
         """
-        Calculates one cost value from multiple costs.
-        Normalizes the costs first and weight every cost the same.
+        Calculate one cost value from multiple costs.
+
+        Normalizes the costs first and weigh every cost the same.
         The lower the normalized cost, the better.
 
         Parameters
         ----------
         costs : List[float]
-            The costs, which should be merged. Must be the same length as the original number of objectives.
-        objectives : Optional[List[Objective]], optional
-            The considered objectives to the costs. By default None.
+            The costs, which should be merged. Must be the same length as the original number of
+            objectives.
+        objectives : Optional[List[Objective]]
+            The considered objectives to the costs. By default, None.
             If None, all objectives are considered. The passed objectives can differ from the
             original number objectives.
 
@@ -1297,6 +1608,12 @@ 
Source code for deepcave.runs
         -------
         float
             Merged costs.
+
+        Raises
+        ------
+        RuntimeError
+            If the number of costs is different from the original number of objectives.
+            If the objective was not found.
         """
         # Get rid of NaN values
         costs = self._process_costs(costs)
@@ -1334,8 +1651,8 @@ 
Source code for deepcave.runs
             b = objective.upper - objective.lower
             normalized_cost = a / b
 
-            # We optimize the lower
-            # So we need to flip the normalized cost
+            # The lower is optimized
+            # So the normalized cost needs to be flipped
             if objective.optimize == "upper":
                 normalized_cost = 1 - normalized_cost
 
@@ -1351,14 +1668,30 @@ 
Source code for deepcave.runs
         return cost

 
 
-    def get_model(self, config_id: int) -> Optional["torch.nn.Module"]:
+

+[docs]
+    def get_model(self, config_id: int) -> Optional["torch.nn.Module"]:  # type: ignore # noqa: F821
+        """
+        Get a model associated with the configuration id.
+
+        Parameters
+        ----------
+        config_id : int
+            The configuration id.
+
+        Returns
+        -------
+        Optional["torch.nn.Module]
+            A model for the provided configuration id.
+        """
         import torch
 
-        filename = self.models_dir / f"{str(config_id)}.pth"
+        filename = self.models_dir / f"{str(config_id)}.pth"  # type: ignore
         if not filename.exists():
             return None
 
-        return torch.load(filename)
+        return torch.load(filename)

+
 
 

 [docs]
@@ -1366,29 +1699,30 @@ 
Source code for deepcave.runs
         self, objective: Objective, budget: Optional[Union[int, float]] = None
     ) -> Tuple[List[float], List[float], List[float], List[int], List[int]]:
         """
-        Calculates the trajectory of the given objective and budget.
+        Calculate the trajectory of the given objective and budget.
 
         Parameters
         ----------
         objective : Objective
             Objective to calculate the trajectory for.
-        budget : Optional[Union[int, float]], optional
+        budget : Optional[Union[int, float]]
             Budget to calculate the trajectory for. If no budget is given, then the highest budget
             is chosen. By default None.
 
         Returns
         -------
-        times : List[float]
-            Times of the trajectory.
-        costs_mean : List[float]
-            Costs of the trajectory.
-        costs_std : List[float]
-            Standard deviation of the costs of the trajectory. This is particularly useful for
-            grouped runs.
-        ids : List[int]
-            The "global" ids of the selected trials.
-        config_ids : List[int]
-            Config ids of the selected trials.
+        Tuple[List[float], List[float], List[float], List[int], List[int]]
+            times : List[float]
+                Times of the trajectory.
+            costs_mean : List[float]
+                Costs of the trajectory.
+            costs_std : List[float]
+                Standard deviation of the costs of the trajectory. This is particularly useful for
+                grouped runs.
+            ids : List[int]
+                The "global" ids of the selected trials.
+            config_ids : List[int]
+                Config ids of the selected trials.
         """
         if budget is None:
             budget = self.get_highest_budget()
@@ -1417,7 +1751,7 @@ 
Source code for deepcave.runs
         for id, _ in order:
             trial = self.history[id]
 
-            # We want to use all budgets
+            # All budgets should should be used
             if budget != COMBINED_BUDGET:
                 # Only consider selected/last budget
                 if trial.budget != budget:
@@ -1437,7 +1771,7 @@ 
Source code for deepcave.runs
                 current_cost = cost
 
                 costs_mean.append(cost)
-                costs_std.append(0)
+                costs_std.append(0.0)
                 times.append(trial.end_time)
                 ids.append(id)
                 config_ids.append(trial.config_id)
@@ -1451,14 +1785,16 @@ 
Source code for deepcave.runs
         self, config: Union[int, Dict[Any, Any], Configuration], specific: bool = False
     ) -> List:
         """
-        Encodes a given config (id) to a normalized list.
-        If a config is passed, no look-up has to be done.
+        Encode a given configuration (id) to a normalized list.
+
+        If a configuration is passed, no look-up has to be done.
 
         Parameters
         ----------
         config : Union[int, Dict[Any, Any], Configuration]
-            Either the config id, config as dict, or Configuration itself.
-        specific : bool, optional
+            Either the configuration id, as configuration as dict,
+            or a Configuration itself.
+        specific : bool
             Use specific encoding for fanova tree, by default False.
 
         Returns
@@ -1495,13 +1831,29 @@ 
Source code for deepcave.runs
         return x

 
 
+

+[docs]
     def encode_configs(self, configs: List[Configuration]) -> np.ndarray:
-        X = []
+        """
+        Encode a list of configurations into a corresponding numpy array.
+
+        Parameters
+        ----------
+        configs : List[Configuration]
+            A list containing the configurations to be encoded.
+
+        Returns
+        -------
+        np.ndarray
+            A numpy array with the encoded configurations.
+        """
+        x_set = []
         for config in configs:
             x = self.encode_config(config)
-            X.append(x)
+            x_set.append(x)
+
+        return np.array(x_set)

 
-        return np.array(X)
 
 

 [docs]
@@ -1513,30 +1865,30 @@ 
Source code for deepcave.runs
         specific: bool = False,
         include_config_ids: bool = False,
         include_combined_cost: bool = False,
-    ) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    ) -> pd.DataFrame:
         """
-        Encodes configurations to process them further. After the configurations are encoded,
+        Encode configurations to process them further.
+
+        After the configurations are encoded,
         they can be used in model prediction.
 
         Parameters
         ----------
-        objectives : Optional[Union[Objective, List[Objective]]], optional
+        objectives : Optional[Union[Objective, List[Objective]]]
             Which objectives should be considered. If None, all objectives are
-            considered. By default None.
-        budget : Optional[List[Status]], optional
-            Which budget should be considered. By default None. If None, only the highest budget
+            considered. By default, None.
+        budget : Optional[List[Status]]
+            Which budget should be considered. By default, None. If None, only the highest budget
             is considered.
-        statuses : Optional[Union[Status, List[Status]]], optional
-            Which statuses should be considered. By default None. If None, all statuses are
+        statuses : Optional[Union[Status, List[Status]]]
+            Which statuses should be considered. By default, None. If None, all statuses are
             considered.
-        encode_y : bool, optional
-            Whether y should be normalized too. By default False.
-        specific : bool, optional
+        specific : bool
             Whether a specific encoding should be used. This encoding is compatible with pyrfr.
             A wrapper for pyrfr is implemented in ``deepcave.evaluators.epm``.
             By default False.
-        include_config_ids : bool, optional
-            Whether to include config ids. By default False.
+        include_config_ids : bool
+            Whether to include configuration ids. By default, False.
         include_combined_cost : bool, optional
             Whether to include combined cost. Note that the combined cost is calculated by the
             passed objectives only. By default False.
@@ -1550,16 +1902,15 @@ 
Source code for deepcave.runs
         Raises
         ------
         ValueError
-            If a hyperparameter is not supported.
+            If a hyperparameter (HP) is not supported.
         """
-
         if objectives is None:
             objectives = self.get_objectives()
 
         if isinstance(objectives, Objective):
             objectives = [objectives]
 
-        X, Y = [], []
+        x_set, y_set = [], []
         config_ids = []
 
         results = self.get_all_costs(budget, statuses)
@@ -1577,17 +1928,17 @@ 
Source code for deepcave.runs
             if include_combined_cost:
                 y += [self.merge_costs(costs, objectives)]
 
-            X.append(x)
-            Y.append(y)
+            x_set.append(x)
+            y_set.append(y)
             config_ids.append(config_id)
 
-        X = np.array(X)  # type: ignore
-        Y = np.array(Y)  # type: ignore
-        config_ids = np.array(config_ids).reshape(-1, 1)  # type: ignore
+        x_set_array = np.array(x_set)
+        y_set_array = np.array(y_set)
+        config_ids_array = np.array(config_ids).reshape(-1, 1)
 
         # Imputation: Easiest case is to replace all nans with -1
-        # However, since Stefan used different values for inactives
-        # we also have to use different inactives to be compatible
+        # However, since Stefan used different values for inactive hyperparameters,
+        # Also different inactive hyperparameters have to be used, to be compatible
         # with the random forests.
         # https://github.com/automl/SMAC3/blob/a0c89502f240c1205f83983c8f7c904902ba416d/smac/epm/base_rf.py#L45
         if specific:
@@ -1614,10 +1965,10 @@ 
Source code for deepcave.runs
                             raise ValueError("Hyperparameter not supported.")
 
                 if conditional[idx] is True:
-                    nonfinite_mask = ~np.isfinite(X[:, idx])
-                    X[nonfinite_mask, idx] = impute_values[idx]
+                    non_finite_mask = ~np.isfinite(x_set_array[:, idx])
+                    x_set_array[non_finite_mask, idx] = impute_values[idx]
 
-        # Now we create dataframes for both values and labels
+        # Now dataframes are created for both values and labels
         # [CONFIG_ID, HP1, HP2, ..., HPn, OBJ1, OBJ2, ..., OBJm, COMBINED_COST]
         if include_config_ids:
             columns = ["config_id"]
@@ -1631,9 +1982,9 @@ 
Source code for deepcave.runs
             columns += [COMBINED_COST_NAME]
 
         if include_config_ids:
-            data = np.concatenate((config_ids, X, Y), axis=1)
+            data: np.ndarray = np.concatenate((config_ids_array, x_set_array, y_set_array), axis=1)
         else:
-            data = np.concatenate((X, Y), axis=1)
+            data = np.concatenate((x_set_array, y_set_array), axis=1)
 
         data = pd.DataFrame(data=data, columns=columns)
 
@@ -1652,28 +2003,37 @@ 
Source code for deepcave.runs
     budgets: bool = True,
 ) -> Dict[str, Any]:
     """
-    Checks the passed runs on equality based on the selected runs and returns the requested
-    attributes.
+    Check the passed runs on equality based on the selected runs.
+
+    Return the requested attributes.
 
     Parameters
     ----------
     runs : list[AbstractRun]
         Runs to check for equality.
     meta : bool, optional
-        Meta-Data excluding objectives and budgets, by default True
+        Meta-Data excluding objectives and budgets, by default True.
     configspace : bool, optional
-        ConfigSpace, by default True
+        Wheter to include the configuration space, by default True.
     objectives : bool, optional
-        Objectives, by default True
+        Wheter to include the objectives, by default True.
     budgets : bool, optional
-        Budgets, by default True
+        Whether to include the budgets, by default True.
 
     Returns
     -------
     Dict[str, Any]
         Dictionary containing the checked attributes.
+
+    Raises
+    ------
+    NotMergeableError
+        If the meta data of the runs are not equal.
+        If the configuration spaces of the runs are not equal.
+        If the budgets of the runs are not equal.
+        If the objective of the runs are not equal.
     """
-    result = {}
+    result: Dict[str, Any] = {}
 
     if len(runs) == 0:
         return result
@@ -1736,6 +2096,7 @@ 
Source code for deepcave.runs
             for o1_, o2_ in zip(o1, o2):
                 o1_.merge(o2_)
 
+        assert o1 is not None
         serialized_objectives = [o.to_json() for o in o1]
         result["objectives"] = serialized_objectives
         if meta:
diff --git a/development/_modules/deepcave/runs/converters/bohb.html b/development/_modules/deepcave/runs/converters/bohb.html
index b7e23567..4a2db247 100644
--- a/development/_modules/deepcave/runs/converters/bohb.html
+++ b/development/_modules/deepcave/runs/converters/bohb.html
@@ -702,7 +702,20 @@
               

                 
   
Source code for deepcave.runs.converters.bohb
-from pathlib import Path
+# noqa: D400
+"""
+# BOHBRun
+
+This module provides utilities for managing and processing data concerning a BOHB
+(Bayesian Optimization and Hyperparameter Bandits) run.
+
+## Classes
+    - BOHBRun: Create a BOHB Run.
+"""
+
+from typing import Any, Dict, Union
+
+from pathlib import Path
 
 from deepcave.runs import Status
 from deepcave.runs.objective import Objective
@@ -713,11 +726,31 @@ 
Source code for deepcave.runs.converters.bohb

 [docs]
 class BOHBRun(Run):
+    """
+    Create a BOHB (Bayesian Optimization and Hyperparameter Bandits) run.
+
+    Properties
+    ----------
+    path : Path
+        The path to the run.
+    """
+
     prefix = "BOHB"
     _initial_order = 2
 
     @property
-    def hash(self):
+    def hash(self) -> str:
+        """
+        Get the hash of the current run.
+
+        If the hash changes, the cache has to be cleared.
+        This ensures that the cache always holds the latest results of the run.
+
+        Returns
+        -------
+        str
+            The hash of the run.
+        """
         if self.path is None:
             return ""
 
@@ -725,7 +758,15 @@ 
Source code for deepcave.runs.converters.bohb
return file_to_hash(self.path / "results.json")
 
     @property
-    def latest_change(self):
+    def latest_change(self) -> Union[float, int]:
+        """
+        Get the timestamp of the latest change.
+
+        Returns
+        -------
+        Union[float, int]
+            The latest change.
+        """
         if self.path is None:
             return 0
 
@@ -734,7 +775,19 @@ 
Source code for deepcave.runs.converters.bohb

 [docs]
     @classmethod
-    def from_path(cls, path):
+    def from_path(cls, path: Union[Path, str]) -> "BOHBRun":
+        """
+        Create a new BOHB run from a given path and add a new trial to it.
+
+        Parameters
+        ----------
+        path : Union[Path, str]
+            The pathname to base the run on.
+
+        Returns
+        -------
+        The BOHB run
+        """
         path = Path(path)
 
         # Read configspace
@@ -743,7 +796,7 @@ 
Source code for deepcave.runs.converters.bohb
configspace = cs_json.read((path / "configspace.json").read_text())
 
         # Read objectives
-        # We have to define it ourselves, because we don't know the type of the objective
+        # It has to be defined here, because the type of the objective is not known
         # Only lock lower
         objective = Objective("Cost", lower=0)
 
@@ -771,25 +824,31 @@ 
Source code for deepcave.runs.converters.bohb
budget = bohb_run.budget
 
             if bohb_run.info is None:
-                status = "CRASHED"
+                status_string = "CRASHED"
             else:
-                status = bohb_run.info["state"]
+                status_string = bohb_run.info["state"]
 
             config = config_mapping[bohb_run.config_id]["config"]
 
-            origin = None
-            additional = {}
+            additional: Dict[str, Any] = {}
+            status: Status
 
             # QUEUED, RUNNING, CRASHED, REVIEW, TERMINATED, COMPLETED, SUCCESS
-            if "SUCCESS" in status or "TERMINATED" in status or "COMPLETED" in status:
+            if (
+                "SUCCESS" in status_string
+                or "TERMINATED" in status_string
+                or "COMPLETED" in status_string
+            ):
                 status = Status.SUCCESS
-            elif "RUNNING" in status or "QUEUED" in status or "REVIEW" in status:
-                continue
+            elif (
+                "RUNNING" in status_string or "QUEUED" in status_string or "REVIEW" in status_string
+            ):
+                status = status_string  # type: ignore
             else:
                 status = Status.CRASHED
 
             if status != Status.SUCCESS:
-                # We don't want cost included which are failed
+                # Costs which failed, should not be included
                 cost = None
 
             run.add(
@@ -799,7 +858,6 @@ 
Source code for deepcave.runs.converters.bohb
start_time=starttime,
                 end_time=endtime,
                 status=status,
-                origin=origin,
                 additional=additional,
             )
 
diff --git a/development/_modules/deepcave/runs/converters/deepcave.html b/development/_modules/deepcave/runs/converters/deepcave.html
index e7a68475..8ac8a477 100644
--- a/development/_modules/deepcave/runs/converters/deepcave.html
+++ b/development/_modules/deepcave/runs/converters/deepcave.html
@@ -702,7 +702,19 @@
               

                 
   
Source code for deepcave.runs.converters.deepcave
-from pathlib import Path
+#  noqa: D400
+"""
+# DeepCAVE
+
+This module defines the DeepCAVE run object.
+
+## Classes
+    - DeepCAVERun: Create a DeepCAVE run and provide handling utilities.
+"""
+
+from typing import Union
+
+from pathlib import Path
 
 from deepcave.runs.run import Run
 from deepcave.utils.hash import file_to_hash
@@ -711,11 +723,31 @@ 
Source code for deepcave.runs.converters.deepcave

 [docs]
 class DeepCAVERun(Run):
+    """
+    Create a DeepCAVE run and provide handling utilities.
+
+    Properties
+    ----------
+    path : Path
+        The path the run.
+    """
+
     prefix = "DeepCAVE"
     _initial_order = 1
 
     @property
-    def hash(self):
+    def hash(self) -> str:
+        """
+        Hash of the current run.
+
+        If the hash changes, the cache has to be cleared.
+        This ensures that the cache always holds the latest results of the run.
+
+        Returns
+        -------
+        str
+            The hash of the run.
+        """
         if self.path is None:
             return ""
 
@@ -723,7 +755,15 @@ 
Source code for deepcave.runs.converters.deepcave
return file_to_hash(self.path / "history.jsonl")
 
     @property
-    def latest_change(self):
+    def latest_change(self) -> Union[float, int]:
+        """
+        Get the timestamp of the latest change.
+
+        Returns
+        -------
+        Union[float, int]
+            The latest change.
+        """
         if self.path is None:
             return 0
 
@@ -732,7 +772,19 @@ 
Source code for deepcave.runs.converters.deepcave

 [docs]
     @classmethod
-    def from_path(cls, path):
+    def from_path(cls, path: Path) -> "DeepCAVERun":
+        """
+        Get a DeepCAVE run from a given path.
+
+        Parameters
+        ----------
+        path : Path
+            The path to base the run on.
+
+        Returns
+        -------
+        The DeepCAVE run.
+        """
         return DeepCAVERun(path.stem, path=Path(path))

 

 
diff --git a/development/_modules/deepcave/runs/converters/smac3v1.html b/development/_modules/deepcave/runs/converters/smac3v1.html
index 41da4921..028fcef1 100644
--- a/development/_modules/deepcave/runs/converters/smac3v1.html
+++ b/development/_modules/deepcave/runs/converters/smac3v1.html
@@ -702,7 +702,21 @@
               

                 
   
Source code for deepcave.runs.converters.smac3v1
-import json
+#  noqa: D400
+"""
+# SMAC3v1Run
+
+This module provides utilities to create a SMAC3v1
+(Sequential Model-based Algorithm Configuration) run.
+
+Version 1.4 is used.
+
+## Classes
+    - SMAC3v1Run: Define a SMAC3v1 run object.
+"""
+from typing import Optional, Union
+
+import json
 from pathlib import Path
 
 import numpy as np
@@ -716,11 +730,33 @@ 
Source code for deepcave.runs.converters.smac3v1

 

 [docs]
 class SMAC3v1Run(Run):
+    """
+    Define a SMAC3v1 (Sequential Model-based Algorithm Configuration) run object.
+
+    Version 1.4 is used.
+
+    Properties
+    ----------
+    path : Path
+        The path to the run.
+    """
+
     prefix = "SMAC3v1"
     _initial_order = 2
 
     @property
-    def hash(self):
+    def hash(self) -> str:
+        """
+        Hash of the current run.
+
+        If the hash changes, the cache has to be cleared.
+        This ensures that the cache always holds the latest results of the run.
+
+        Returns
+        -------
+        str
+            The hash of the run.
+        """
         if self.path is None:
             return ""
 
@@ -728,7 +764,15 @@ 
Source code for deepcave.runs.converters.smac3v1

         return file_to_hash(self.path / "runhistory.json")
 
     @property
-    def latest_change(self):
+    def latest_change(self) -> Union[float, int]:
+        """
+        Get the timestamp of the latest change.
+
+        Returns
+        -------
+        Union[float, int]
+            The latest change.
+        """
         if self.path is None:
             return 0
 
@@ -737,9 +781,25 @@ 
Source code for deepcave.runs.converters.smac3v1

 

 [docs]
     @classmethod
-    def from_path(cls, path):
+    def from_path(cls, path: Union[Path, str]) -> "SMAC3v1Run":
         """
         Based on working_dir/run_name/*, return a new trials object.
+
+        Parameters
+        ----------
+        path : Union[Path, str]
+            The path to base the run on.
+
+        Returns
+        -------
+        A SMAC3v1 run.
+
+        Raises
+        ------
+        RuntimeError
+            Instances are not supported.
+        RuntimeError
+            Multiple Seeds are not supported.
         """
         path = Path(path)
 
@@ -750,7 +810,7 @@ 
Source code for deepcave.runs.converters.smac3v1

             configspace = cs_json.read(f.read())
 
         # Read objectives
-        # We have to define it ourselves, because we don't know the type of the objective
+        # It has to be defined here, because the type of the objective is not known
         # Only lock lower
         objective1 = Objective("Cost", lower=0)
         objective2 = Objective("Time", lower=0)
@@ -777,7 +837,7 @@ 
Source code for deepcave.runs.converters.smac3v1

             name=path.stem, configspace=configspace, objectives=[objective1, objective2], meta=meta
         )
 
-        # We have to set the path manually
+        # The path has to be set manually
         run._path = path
 
         # Iterate over the runhistory
@@ -836,7 +896,7 @@ 
Source code for deepcave.runs.converters.smac3v1

                 status = Status.CRASHED
 
             if status != Status.SUCCESS:
-                # We don't want cost included which are failed
+                # Costs which failed, should not be included
                 cost = None
                 time = None
             else:
@@ -845,7 +905,7 @@ 
Source code for deepcave.runs.converters.smac3v1

             # Round budget
             budget = np.round(budget, 2)
 
-            origin = None
+            origin: Optional[str] = None
             if config_id in config_origins:
                 origin = config_origins[config_id]
 
diff --git a/development/_modules/deepcave/runs/converters/smac3v2.html b/development/_modules/deepcave/runs/converters/smac3v2.html
index 5641bda9..48f49659 100644
--- a/development/_modules/deepcave/runs/converters/smac3v2.html
+++ b/development/_modules/deepcave/runs/converters/smac3v2.html
@@ -702,7 +702,22 @@
               

                 
   
Source code for deepcave.runs.converters.smac3v2
-import json
+#  noqa: D400
+"""
+# SMAC3v2Run
+
+This module provides utilities to create a SMAC3v2
+(Sequential Model-based Algorithm Configuration) run.
+
+Version 2.0.0 is used.
+
+## Classes
+    - SMAC3v2Run: Define a SMAC3v2 run object.
+"""
+
+from typing import Union
+
+import json
 from pathlib import Path
 
 import numpy as np
@@ -716,11 +731,33 @@ 
Source code for deepcave.runs.converters.smac3v2

 

 [docs]
 class SMAC3v2Run(Run):
+    """
+    Define a SMAC3v2 (Sequential Model-based Algorithm Configuration) run object.
+
+    Version 2.0.0 is used.
+
+    Properties
+    ----------
+    path : Path
+        The path to the run.
+    """
+
     prefix = "SMAC3v2"
     _initial_order = 2
 
     @property
-    def hash(self):
+    def hash(self) -> str:
+        """
+        Hash of the current run.
+
+        If the hash changes, the cache has to be cleared.
+        This ensures that the cache always holds the latest results of the run.
+
+        Returns
+        -------
+        str
+            The hash of the run.
+        """
         if self.path is None:
             return ""
 
@@ -728,7 +765,15 @@ 
Source code for deepcave.runs.converters.smac3v2

         return file_to_hash(self.path / "runhistory.json")
 
     @property
-    def latest_change(self):
+    def latest_change(self) -> Union[float, int]:
+        """
+        Get the timestamp of the latest change.
+
+        Returns
+        -------
+        Union[float, int]
+            The latest change.
+        """
         if self.path is None:
             return 0
 
@@ -737,9 +782,25 @@ 
Source code for deepcave.runs.converters.smac3v2

 

 [docs]
     @classmethod
-    def from_path(cls, path):
+    def from_path(cls, path: Union[Path, str]) -> "SMAC3v2Run":
         """
         Based on working_dir/run_name/*, return a new trials object.
+
+        Parameters
+        ----------
+        path : Union[Path, str]
+            The path to base the trial object on.
+
+        Returns
+        -------
+        The SMAC3v2 run.
+
+        Raises
+        ------
+        RuntimeError
+            Instances are not supported.
+        RuntimeError
+            Multiple Seeds are not supported.
         """
         path = Path(path)
 
@@ -768,11 +829,9 @@ 
Source code for deepcave.runs.converters.smac3v2

             meta["run_objectives"] = meta.pop("objectives")
 
         # Let's create a new run object
-        run = SMAC3v2Run(
-            name=path.stem, configspace=configspace, objectives=obj_list, meta=meta
-        )
+        run = SMAC3v2Run(name=path.stem, configspace=configspace, objectives=obj_list, meta=meta)
 
-        # We have to set the path manually
+        # The path has to be set manually
         run._path = path
 
         # Iterate over the runhistory
@@ -832,7 +891,7 @@ 
Source code for deepcave.runs.converters.smac3v2

                 status = Status.CRASHED
 
             if status != Status.SUCCESS:
-                # We don't want cost included which are failed
+                # Costs which failed, should not be included
                 cost = [None] * len(cost) if isinstance(cost, list) else None
                 time = None
             else:
diff --git a/development/_modules/deepcave/runs/exceptions.html b/development/_modules/deepcave/runs/exceptions.html
index f8775ad5..56f0538e 100644
--- a/development/_modules/deepcave/runs/exceptions.html
+++ b/development/_modules/deepcave/runs/exceptions.html
@@ -702,9 +702,24 @@
               

                 
   
Source code for deepcave.runs.exceptions
+# noqa: D400
+"""
+# Exceptions
+
+This module provides utilities for different errors concerning the runs.
+
+Exceptions will be raised, if a directory is not a valid run,
+as well as if runs are not mergeable.
+
+## Classes
+    - NotValidRunError: Raised if directory is not a valid run.
+    - NotMergeableError: Raised if two or more runs are not mergeable.
+"""
+
+
 

 [docs]
-class NotValidRunError(Exception):
+class NotValidRunError(Exception):
     """Raised if directory is not a valid run."""
 
     pass

@@ -714,7 +729,7 @@ 
Source code for deepcave.runs.exceptions
 

 [docs]
 class NotMergeableError(Exception):
-    """Raised if two or more runs are not mergeable"""
+    """Raised if two or more runs are not mergeable."""
 
     pass

 
diff --git a/development/_modules/deepcave/runs/group.html b/development/_modules/deepcave/runs/group.html
index 8e513b23..a013e982 100644
--- a/development/_modules/deepcave/runs/group.html
+++ b/development/_modules/deepcave/runs/group.html
@@ -702,7 +702,18 @@
               

                 
   
Source code for deepcave.runs.group
-from typing import Dict, List, Tuple
+#  noqa: D400
+"""
+# Group
+
+This module provides utilities for grouping and managing a group of runs.
+Utilities include getting attributes of the grouped runs, as well as the group itself.
+
+## Classes
+    - Group: Can group and manage a group of runs.
+"""
+
+from typing import Any, Dict, Iterator, List, Optional, Tuple
 
 from copy import deepcopy
 
@@ -715,6 +726,37 @@ 
Source code for deepcave.runs.group
 

 [docs]
 class Group(AbstractRun):
+    """
+    Can group and manage a group of runs.
+
+    Utilities include getting attributes of the grouped runs, as well as the group itself.
+
+    Properties
+    ----------
+    runs : List[AbstractRun]
+        A list of the runs.
+    meta : Dict[str, Any]
+        Contains budgets, objectives and their attributes.
+    configspace : ConfigurationSpace
+        The configuration space of the runs.
+    objectives : Objective
+        The objectives of the runs.
+    budgets : List[Union[int, float]]
+        The budgets of the runs.
+    configs : Dict[int, Any]
+        A dictionary of the configurations and their ids as key.
+    origins : Dict[int, str]
+        The origins of the configurations and their ids as key.
+    trial_keys : Dict[Tuple[str, int], int]
+        The keys of the trial.
+    history : List[Trial]
+        The trial history.
+    prefix : str
+        The prefix for the id of the group.
+    name : str
+        The name for the id of the group.
+    """
+
     prefix = "group"
 
     def __init__(self, name: str, runs: List[AbstractRun]):
@@ -733,7 +775,7 @@ 
Source code for deepcave.runs.group
             self.objectives = attributes["objectives"]
             self.budgets = attributes["budgets"]
 
-            # We need new config ids
+            # New config ids are needed
             current_config_id = 0
 
             # Key: new_config_id; Value: (run_id, config_id)
@@ -775,7 +817,7 @@ 
Source code for deepcave.runs.group
                     # Update config id
                     trial.config_id = new_config_id
 
-                    # Now we add it to the history
+                    # Now it is added to the history
                     trial_key = trial.get_key()
                     if trial_key not in self.trial_keys:
                         self.trial_keys[trial_key] = len(self.history)
@@ -792,28 +834,58 @@ 
Source code for deepcave.runs.group
         except Exception as e:
             raise NotMergeableError(f"Runs can not be merged: {e}")
 
-    def __iter__(self):
+

+[docs]
+    def __iter__(self: "Group") -> Iterator[str]:
+        """Allow to iterate over the object."""
         for run in self.runs:
-            yield run.name
+            yield run.name

+
 
     @property
     def hash(self) -> str:
+        """
+        Sorted hashes of the group.
+
+        Returns
+        -------
+        str
+            The sorted hash of the group.
+        """
         hashes = []
         for run in self.runs:
             hashes += [run.hash]
 
-        # We sort hashes now because we don't want to be dependent on the order
+        # Hashes are sorted now, so there is no dependence on the order
         hashes = sorted(hashes)
         return string_to_hash("-".join(hashes))
 
     @property
     def id(self) -> str:
-        # Groups do not have a path, therefore we use the name.
+        """
+        Get the hash as id of the group.
+
+        In contrast to hash, this hash should not be changed throughout the run.
+
+        Returns
+        -------
+        str
+            The hash of the group.
+        """
+        # Groups do not have a path, therefore the name is used.
         return string_to_hash(f"{self.prefix}:{self.name}")
 
     @property
-    def latest_change(self) -> int:
-        latest_change = 0
+    def latest_change(self) -> float:
+        """
+        Get the latest change made to the grouped runs.
+
+        Returns
+        -------
+        float
+            The latest change.
+        """
+        latest_change = 0.0
         for run in self.runs:
             if run.latest_change > latest_change:
                 latest_change = run.latest_change
@@ -822,48 +894,160 @@ 
Source code for deepcave.runs.group
 
     @property
     def run_paths(self) -> List[str]:
+        """Get the path of the runs in the group."""
         return [str(run.path) for run in self.runs]
 
     @property
     def run_names(self) -> List[str]:
+        """
+        Get the names of the runs in the group.
+
+        Returns
+        -------
+        List[str]
+            A list of the names of the runs in the group.
+        """
         return [run.name for run in self.runs]
 
+

+[docs]
     def get_runs(self) -> List[AbstractRun]:
-        return self.runs
+        """
+        Get the runs in the group.
 
-    def get_new_config_id(self, run_id: int, original_config_id: int) -> int:
-        return self._new_config_mapping[(run_id, original_config_id)]
+        Returns
+        -------
+        List[AbstractRun]
+            A list of the grouped runs.
+        """
+        return self.runs

 
-    def get_original_config_id(self, config_id: int) -> id:
-        return self._original_config_mapping[config_id][1]
 
+

+[docs]
+    def get_new_config_id(self, run_id: int, original_config_id: int) -> int:
+        """
+        Get a new identificator for a configuration.
+
+        Parameters
+        ----------
+        run_id : int
+            The id of the run.
+        original_config_id : int
+            The original identificator of a configuration.
+
+        Returns
+        -------
+        int
+            The new identificator of a configuration.
+        """
+        return self._new_config_mapping[(run_id, original_config_id)]

+
+
+

+[docs]
+    def get_original_config_id(self, config_id: int) -> int:
+        """
+        Get the original identificator of a configuration.
+
+        Parameters
+        ----------
+        config_id : int
+            The identificator of a configuration.
+
+        Returns
+        -------
+        int
+            The original identificator of a configuration.
+        """
+        return self._original_config_mapping[config_id][1]

+
+
+

+[docs]
     def get_original_run(self, config_id: int) -> AbstractRun:
+        """
+        Get the original run.
+
+        Parameters
+        ----------
+        config_id : int
+            The identificator of the configuration.
+
+        Returns
+        -------
+        AbstractRun
+            The original run.
+        """
         run_id = self._original_config_mapping[config_id][0]
-        return self.runs[run_id]
+        return self.runs[run_id]

+
 
-    def get_model(self, config_id):
+

+[docs]
+    def get_model(self, config_id: int) -> Optional[Any]:
+        """
+        Get the model given the configuration id.
+
+        Parameters
+        ----------
+        config_id : int
+            The identificator of the configuration.
+
+        Returns
+        -------
+        Optional[Any]
+            The model.
+        """
         run_id, config_id = self._original_config_mapping[config_id]
-        return self.runs[run_id].get_model(config_id)
+        return self.runs[run_id].get_model(config_id)

+
 
+    # Types dont match superclass
 

 [docs]
-    def get_trajectory(self, *args, **kwargs):
+    def get_trajectory(self, *args, **kwargs):  # type: ignore
+        """
+        Calculate the trajectory of the given objective and budget.
+
+        This includes the times, the mean costs, and the standard deviation of the costs.
+
+        Parameters
+        ----------
+        *args
+            Should be the objective to calculate the trajectory from.
+        **kwargs
+            Should be the budget to calculate the trajectory for.
+
+        Returns
+        -------
+        times : List[float]
+            Times of the trajectory.
+        costs_mean : List[float]
+            Costs of the trajectory.
+        costs_std : List[float]
+            Standard deviation of the costs of the trajectory.
+        ids : List[int]
+            The "global" ids of the selected trial.
+        config_ids : List[int]
+            The configuration ids of the selected trials.
+        """
         # Cache costs
         run_costs = []
         run_times = []
 
-        # All x values on which we need y values
+        # All x values on which y values are needed
         all_times = []
 
         for _, run in enumerate(self.runs):
             times, costs_mean, _, _, _ = run.get_trajectory(*args, **kwargs)
 
-            # Cache s.t. we don't calculate it multiple times
+            # Cache s.t. calculate it is not calculated multiple times
             run_costs.append(costs_mean)
             run_times.append(times)
 
             # Add all times
-            # We want to calculate standard deviation on all times
+            # Standard deviation needs to be calculated on all times
             for time in times:
                 if time not in all_times:
                     all_times.append(time)
@@ -885,11 +1069,11 @@ 
Source code for deepcave.runs.group
             all_costs.append(y)
 
         # Make numpy arrays
-        all_costs = np.array(all_costs)
+        all_costs_array = np.array(all_costs)
 
         times = all_times
-        costs_mean = np.mean(all_costs, axis=1)
-        costs_std = np.std(all_costs, axis=1)
+        costs_mean = np.mean(all_costs_array, axis=1)
+        costs_std = np.std(all_costs_array, axis=1)
 
         return times, list(costs_mean), list(costs_std), [], []

 

diff --git a/development/_modules/deepcave/runs/handler.html b/development/_modules/deepcave/runs/handler.html
index 8ff34184..6a6d10f3 100644
--- a/development/_modules/deepcave/runs/handler.html
+++ b/development/_modules/deepcave/runs/handler.html
@@ -702,9 +702,21 @@
               

                 
   
Source code for deepcave.runs.handler
-from typing import Dict, List, Optional, Type, Union
+#  noqa: D400
+"""
+# Handler
+
+This module provides utilities to handle a run.
+
+It can retrieve working directories, run paths, run names, as well as groups of runs.
+It provides utilities to update and remove runs as well as groups of runs.
+
+# Classes
+    - RunHandler: Handle the runs.
+"""
+
+from typing import Dict, List, Optional, Type, Union
 
-import logging
 import time
 from pathlib import Path
 
@@ -712,15 +724,39 @@ 
Source code for deepcave.runs.handler
 from deepcave.runs import AbstractRun
 from deepcave.runs.group import Group
 from deepcave.runs.run import Run
+from deepcave.utils.cache import Cache
 from deepcave.utils.logs import get_logger
+from deepcave.utils.run_caches import RunCaches
 
 
 

 [docs]
 class RunHandler:
     """
-    Handles the runs. Based on the meta data in the cache, automatically selects the right converter
+    Handle the runs.
+
+    Based on the meta data in the cache, automatically selects the right converter
     and switches to the right (plugin) cache.
+
+    Provides utilities to retrieve working directories, run paths, run names, and groups of runs.
+    Also update and remove runs as well a groups of runs.
+
+    Properties
+    ----------
+    c : Cache
+        The cache containing information about a run(s).
+    rc : RunCaches
+        The caches for the selected runs.
+    logger : Logger
+        The logger for the run handler.
+    available_run_yfes : List[Type[Run]]
+        A list of the available converters.
+    runs : Dict[str, AbstractRun]
+        A dictionary of runs with their path as key.
+    groups : Dict[str, Group]
+        A dictionary of the groups.
+    available_run_classes : List[Type[Run]]
+        Contains the available run classes.
     """
 
     def __init__(self, config: Config, cache: "Cache", run_cache: "RunCaches") -> None:
@@ -745,7 +781,7 @@ 
Source code for deepcave.runs.handler
 [docs]
     def set_working_directory(self, working_directory: Union[Path, str]) -> None:
         """
-        Sets the working directoy to the meta cache.
+        Set the working directory to the meta cache.
 
         Parameters
         ----------
@@ -759,26 +795,39 @@ 
Source code for deepcave.runs.handler
 [docs]
     def get_working_directory(self) -> Path:
         """
-        Returns the current working directory in the cache.
+        Return the current working directory in the cache.
 
         Returns
         -------
         Path
             Path of the working directory.
+
+        Raises
+        ------
+        AssertionError
+            If the working directory is not a string or a Path, an error is thrown.
         """
-        return Path(self.c.get("working_dir"))

+        working_dir = self.c.get("working_dir")
+        assert isinstance(
+            working_dir, (str, Path)
+        ), "Working directory of cache must be a string or a Path like."
+        return Path(working_dir)

 
 
 

 [docs]
     def get_available_run_paths(self) -> Dict[str, str]:
         """
-        Returns the available run paths from the current directory.
+        Return the available run paths from the current directory.
 
         Returns
         -------
         Dict[str, str]
             Run path as key and run name as value.
+
+        Exceptions
+        ----------
+        FileNotFoundError
         """
         runs = {}
         working_dir = self.get_working_directory()
@@ -806,21 +855,30 @@ 
Source code for deepcave.runs.handler
 [docs]
     def get_selected_run_paths(self) -> List[str]:
         """
-        Returns the selected run paths from the cache.
+        Return the selected run paths from the cache.
 
         Returns
         -------
-        Dict[str, str]
+        List[str]
             Run paths as a list.
+
+        Raises
+        ------
+        AssertionError.
+            If the selected run paths are not a list, an error is thrown.
         """
-        return self.c.get("selected_run_paths")

+        selected_run_paths = self.c.get("selected_run_paths")
+        assert isinstance(
+            selected_run_paths, list
+        ), "The selected run paths of the cache must be a list."
+        return selected_run_paths

 
 
 

 [docs]
     def get_selected_run_names(self) -> List[str]:
         """
-        Returns the run names of the selected runs.
+        Return the run names of the selected runs.
 
         Returns
         -------
@@ -834,7 +892,7 @@ 
Source code for deepcave.runs.handler
 [docs]
     def get_run_name(self, run_path: Union[Path, str]) -> str:
         """
-        Returns the stem of the path.
+        Return the stem of the path.
 
         Parameters
         ----------
@@ -849,14 +907,36 @@ 
Source code for deepcave.runs.handler
         return Path(run_path).stem

 
 
+

+[docs]
     def get_selected_groups(self) -> Dict[str, List[str]]:
-        return self.c.get("groups")
+        """
+        Get the selected groups.
+
+        Returns
+        -------
+        Dict[str, List[str]]
+            Dictionary with the selected groups.
+
+        Raises
+        ------
+        AssertionError
+            If groups in cache is not a dictionary, an error is thrown.
+        """
+        selected_groups = self.c.get("groups")
+        assert isinstance(
+            selected_groups, dict
+        ), "The groups aquired from the cache must be a dictionary."
+        return selected_groups

+
 
 

 [docs]
     def add_run(self, run_path: str) -> bool:
         """
-        Adds a run path to the cache. If run path is already in cache, do nothing.
+        Add a run path to the cache.
+
+        If run path is already in cache, do nothing.
 
         Parameters
         ----------
@@ -882,30 +962,44 @@ 
Source code for deepcave.runs.handler
 

 [docs]
     def remove_run(self, run_path: str) -> None:
-        """Removes a run path from the cache. If run path is not in cache, do nothing.
+        """
+        Remove a run path from the cache.
+
+        If run path is not in cache, do nothing.
 
         Parameters
         ----------
         run_path : str
             Path of a run.
+
+        Raises
+        ------
+        TypeError
+            If `selected_run_paths` or `groups` is None, an error is thrown.
         """
         selected_run_paths = self.c.get("selected_run_paths")
 
+        if selected_run_paths is None:
+            raise TypeError("Selected run paths can not be None.")
+
         if run_path in selected_run_paths:
             selected_run_paths.remove(run_path)
             self.c.set("selected_run_paths", value=selected_run_paths)
 
-            # We have to check the groups here because the removed run_path may
+            # The groups have to be checked here because the removed run_path may
             # still be included
             groups = {}
-            for group_name, run_paths in self.c.get("groups").items():
+            group_it = self.c.get("groups")
+            if group_it is None:
+                raise TypeError("Groups can not be None.")
+            for group_name, run_paths in group_it.items():
                 if run_path in run_paths:
                     run_paths.remove(run_path)
                 groups[group_name] = run_paths
 
             self.c.set("groups", value=groups)
 
-            # We also remove last inputs here
+            # Last inputs are also removed here
             self.c.set("last_inputs", value={})
             self.update_runs()

 
@@ -913,8 +1007,7 @@ 
Source code for deepcave.runs.handler
 

 [docs]
     def update(self) -> None:
-        """Updates the internal run and group instances but only if a hash changed."""
-
+        """Update the internal run and group instances but only if a hash changed."""
         update_required = False
         for run_path in list(self.runs.keys()):
             run = self.runs[run_path]
@@ -936,17 +1029,17 @@ 
Source code for deepcave.runs.handler
 [docs]
     def update_runs(self) -> bool:
         """
-        Loads selected runs and update cache if files changed.
-
-        Raises
-        ------
-        NotValidRunError
-            If directory can not be transformed into a run, an error is thrown.
+        Load selected runs and update cache if files changed.
 
         Returns
         -------
         bool
             True if all selected runs could be loaded, False otherwise.
+
+        Raises
+        ------
+        NotValidRunError
+            If directory can not be transformed into a run, an error is thrown.
         """
         runs: Dict[str, AbstractRun] = {}  # run_path: Run
         success = True
@@ -978,15 +1071,26 @@ 
Source code for deepcave.runs.handler
         self, run_path: str, class_hint: Optional[Type[Run]] = None
     ) -> Optional[AbstractRun]:
         """
-        Loads the run from `self.runs` or creates a new one.
+        Load the run from `self.runs` or create a new one.
+
+        Parameters
+        ----------
+        run_path : str
+            The path of the run.
+        class_hint : Optional[Type[Run]], optional
+            A hint/suggestion of what the Type of the Run is.
+            Default is None.
+
+        Returns
+        -------
+        Optional[AbstractRun]
+            The Run added to the cache.
 
         Raises
         ------
         NotValidRunError
             If directory can not be transformed into a run, an error is thrown.
-
         """
-
         # Try to get run from current runs
         if run_path in self.runs:
             run = self.runs[run_path]
@@ -1012,7 +1116,8 @@ 
Source code for deepcave.runs.handler
                 run = run_class.from_path(Path(run_path))
                 t2 = time.perf_counter()
                 self.logger.debug(
-                    f'Run "{Path(run_path).stem}" was successfully loaded (took {round(t2 - t1, 2)} seconds).'
+                    f'Run "{Path(run_path).stem}" was successfully loaded (took {round(t2 - t1, 2)}'
+                    f" seconds)."
                 )
             except KeyboardInterrupt:
                 # Pass KeyboardInterrupt through try-except, so it can actually interrupt.
@@ -1036,21 +1141,32 @@ 
Source code for deepcave.runs.handler
 
 

 [docs]
-    def update_groups(self, groups: Optional[Dict[str, str]] = None) -> None:
+    def update_groups(self, groups: Optional[Dict[str, List[str]]] = None) -> None:
         """
-        Loads chosen groups. If `groups` is passed, it is used to instantiate the groups and
+        Load chosen groups.
+
+        If `groups` is passed, it is used to instantiate the groups and
         saved to the cache. Otherwise, `groups` is loaded from the cache.
 
+        Parameters
+        ----------
+        groups : Optional[Dict[str, str]], optional
+            A dictionary with the groups.
+            Default is None.
+
         Raises
         ------
         NotMergeableError
             If runs can not be merged, an error is thrown.
-
+        TypeError
+            If `groups` is None, an error is thrown.
         """
         instantiated_groups = {}
         if groups is None:
             groups = self.c.get("groups")
-
+        # This check is necessary because groups could still be None
+        if groups is None:
+            raise TypeError("Groups can not be None.")
         # Add grouped runs
         for group_name, run_paths in groups.items():
             runs = []
@@ -1080,7 +1196,7 @@ 
Source code for deepcave.runs.handler
 [docs]
     def get_run(self, run_id: str) -> AbstractRun:
         """
-        Looks inside `self.runs` and `self.groups` and if the run id is found, returns the run.
+        Look inside `self.runs` and `self.groups` and if the run id is found, returns the run.
 
         Parameters
         ----------
@@ -1090,7 +1206,7 @@ 
Source code for deepcave.runs.handler
         Returns
         -------
         AbstractRun
-            Run
+            Run.
 
         Raises
         ------
@@ -1109,7 +1225,7 @@ 
Source code for deepcave.runs.handler
 [docs]
     def get_groups(self) -> List[Group]:
         """
-        Returns instantiated grouped runs.
+        Return instantiated grouped runs.
 
         Returns
         -------
@@ -1122,15 +1238,17 @@ 
Source code for deepcave.runs.handler
 
 

 [docs]
-    def get_runs(self, include_groups=False) -> List[AbstractRun]:
+    def get_runs(self, include_groups: bool = False) -> List[AbstractRun]:
         """
-        Returns the runs from the internal cache. The runs are already loaded and ready to use.
+        Return the runs from the internal cache.
+
+        The runs are already loaded and ready to use.
         Optional, if `include_groups` is set to True, the groups are also included.
 
         Parameters
         ----------
         include_groups : bool, optional
-            Includes the groups, by default False
+            Includes the groups, by default False.
 
         Returns
         -------
diff --git a/development/_modules/deepcave/runs/objective.html b/development/_modules/deepcave/runs/objective.html
index f7c537dc..a96c0617 100644
--- a/development/_modules/deepcave/runs/objective.html
+++ b/development/_modules/deepcave/runs/objective.html
@@ -702,7 +702,18 @@
               

                 
   
Source code for deepcave.runs.objective
-from typing import Any, Dict, Optional, Union
+#  noqa: D400
+"""
+# Objective
+
+This module provides utilities to convert and create objectives.
+It also provides functions for merging and comparing objectives.
+
+## Classes
+    - Objective: Convert and create objectives.
+"""
+
+from typing import Any, Dict, Optional, Union
 
 from dataclasses import dataclass
 
@@ -715,6 +726,25 @@ 
Source code for deepcave.runs.objective
 [docs]
 @dataclass
 class Objective:
+    """
+    Convert, create and merge objectives.
+
+    Properties
+    ----------
+    lower : Optional[Union[int, float]]
+        The lower bound of the objective.
+    upper : Optional[Union[int, float]]
+        The upper bound of the objective.
+    optimize : str
+        Define whether to optimize lower or upper.
+    lock_lower : bool
+        Whether to lock the lower bound.
+    lock_upper : bool
+        Whether to lock the upper bound.
+    name : str
+        The name of the objective.
+    """
+
     name: str
     lower: Optional[Union[int, float]] = None
     upper: Optional[Union[int, float]] = None
@@ -724,6 +754,8 @@ 
Source code for deepcave.runs.objective
 [docs]
     def __post_init__(self) -> None:
         """
+        Check if bounds should be locked.
+
         Lock the lower bound if lower is not None.
         Lock the upper bound if upper is not None.
 
@@ -751,7 +783,17 @@ 
Source code for deepcave.runs.objective
         self.lock_upper = lock_upper

 
 
+

+[docs]
     def to_json(self) -> Dict[str, Any]:
+        """
+        Convert objectives attributes to a dictionary in a JSON friendly format.
+
+        Returns
+        -------
+        Dict[str, Any]
+            A dictionary in a JSON friendly format with the objects attributes.
+        """
         return {
             "name": self.name,
             "lower": self.lower,
@@ -759,10 +801,26 @@ 
Source code for deepcave.runs.objective
             "lock_lower": self.lock_lower,
             "lock_upper": self.lock_upper,
             "optimize": self.optimize,
-        }
+        }

 
+
+

+[docs]
     @staticmethod
     def from_json(d: Dict[str, Any]) -> "Objective":
+        """
+        Create an objective from a JSON friendly dictionary format.
+
+        Parameters
+        ----------
+        d : Dict[str, Any]
+            A dictionary in a JSON friendly format containing the attributes.
+
+        Returns
+        -------
+        Objective
+            An objective created from the provided data.
+        """
         objective = Objective(
             name=d["name"],
             lower=d["lower"],
@@ -773,44 +831,96 @@ 
Source code for deepcave.runs.objective
         objective.lock_lower = d["lock_lower"]
         objective.lock_upper = d["lock_upper"]
 
-        return objective
+        return objective

+
 
+

+[docs]
     def __eq__(self, other: Any) -> bool:
+        """
+        Compare if two instances are equal based on their attributes.
+
+        Parameters
+        ----------
+        other : Any
+            The other instance to compare.
+
+        Returns
+        -------
+        bool
+            True if equal, else False.
+        """
         attributes = ["name", "lock_lower", "lock_upper", "optimize"]
         for a in attributes:
             if getattr(self, a) != getattr(other, a):
                 return False
 
-        return True
+        return True

 
+
+

+[docs]
     def merge(self, other: Any) -> None:
+        """
+        Merge two Objectives over their attributes.
+
+        Parameters
+        ----------
+        other : Any
+            The other Objective to merge.
+
+        Raises
+        ------
+        NotMergeableError
+            If parts of the two Objectives are not mergeable.
+        ValueError
+            If the lower bound of one Objective is None.
+            If the upper bound of one Objective is None.
+        """
         if not isinstance(other, Objective):
             raise NotMergeableError("Objective can only be merged with another Objective.")
 
         attributes = ["name", "lock_lower", "lock_upper", "optimize"]
         for attribute in attributes:
             if getattr(self, attribute) != getattr(other, attribute):
-                raise NotMergeableError(f"Objective {attribute} is not mergeable.")
+                raise NotMergeableError(f"Objective {attribute} can not be merged.")
+
+        if self.lower is None or other.lower is None:
+            raise ValueError("The lower bound of one Objective is None.")
+        if self.upper is None or other.upper is None:
+            raise ValueError("The upper bound of one Objective is None.")
 
         if self.lock_lower and self.lock_lower == other.lock_lower:
             if self.lower != other.lower:
-                raise NotMergeableError(f"Objective {other.name}'s lower bound is not mergeable.")
+                raise NotMergeableError(f"Objective {other.name}'s lower bound can not be merged.")
         else:
-            if self.lower > other.lower:  # type: ignore
+            if self.lower > other.lower:
                 self.lower = other.lower
 
         if self.lock_upper and self.lock_upper == other.lock_upper:
             if self.upper != other.upper:
-                raise NotMergeableError(f"Objective {other.name}'s upper bound is not mergeable.")
+                raise NotMergeableError(f"Objective {other.name}'s upper bound can not be merged.")
         else:
-            if self.upper < other.upper:  # type: ignore
-                self.upper = other.upper
+            if self.upper < other.upper:
+                self.upper = other.upper

 
-    def get_worst_value(self) -> float:
+
+

+[docs]
+    def get_worst_value(self) -> Optional[Union[int, float]]:
+        """
+        Get the worst value based on the optimization setting.
+
+        Returns
+        -------
+        float
+            The worst value based on the optimization setting.
+        """
         if self.optimize == "lower":
-            return self.upper  # type: ignore
+            return self.upper
         else:
-            return self.lower  # type: ignore

+            return self.lower

+

 
 

 
diff --git a/development/_modules/deepcave/runs/recorder.html b/development/_modules/deepcave/runs/recorder.html
new file mode 100644
index 00000000..de31904f
--- /dev/null
+++ b/development/_modules/deepcave/runs/recorder.html
@@ -0,0 +1,1032 @@
+
+
+
+  
+    
+    
+    deepcave.runs.recorder — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.runs.recorder
+#  noqa: D400
+"""
+# Recorder
+
+This module provides utilities to record information.
+
+## Classes
+    - Recorder: Define a Recorder for recording information.
+"""
+
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import time
+from pathlib import Path
+
+import ConfigSpace
+import numpy as np
+from ConfigSpace import Configuration
+from typing_extensions import Self
+
+from deepcave.runs import Status
+from deepcave.runs.converters.deepcave import DeepCAVERun
+from deepcave.runs.objective import Objective
+
+
+

+[docs]
+class Recorder:
+    """
+    Define a Recorder for recording information.
+
+    Properties
+    ----------
+    path : Path
+        The path to the recorded information.
+    last_trial_id : tuple[Any, Optional[float]]
+        The last id containing the configuration and the budget.
+    start_time : float
+        The current time in seconds since the epoch.
+    start_times : Dict[Any, Any]
+        A dictionary containing the start times with their id as key.
+    models : Dict[Any, Any|
+        The models used with their id as key.
+    origins : Dict[Any, Any]
+        The origins with their id as key.
+    additionals : Dict[Any, Any]
+        Additional information with the id as key.
+    run : DeepCAVERun
+        The deepcave run container.
+    """
+
+    def __init__(
+        self,
+        configspace: ConfigSpace.ConfigurationSpace,
+        objectives: Optional[List[Objective]] = None,
+        meta: Optional[Dict[str, Any]] = None,
+        save_path: str = "logs",
+        prefix: str = "run",
+        overwrite: bool = False,
+    ):
+        """
+        All objectives follow the scheme the lower the better.
+
+        Parameters
+        ----------
+        save_path : str, otpional
+            The path to the recording.
+            Default is "logs".
+        configspace : ConfigSpace.ConfigurationSpace
+            The configuration space.
+        objectives Optional[List[Objective]], optional
+            The objectives of the run.
+            Default is None.
+        prefix ; str, optional
+            Name of the trial. If not given, trial_x will be used.
+            Default is "run".
+        overwrite : bool, optional
+            Uses the prefix as name and overwrites the file.
+            Default is False.
+        """
+        if objectives is None:
+            objectives = []
+        if meta is None:
+            meta = {}
+
+        self.path: Path
+        self._set_path(save_path, prefix, overwrite)
+
+        # Set variables
+        self.last_trial_id: Optional[
+            Tuple[Union[Dict[Any, Any], Configuration], Optional[float]]
+        ] = None
+        self.start_time = time.time()
+        self.start_times: Dict[
+            Tuple[Union[Dict[Any, Any], Configuration], Optional[float]], float
+        ] = {}
+        self.models: Dict[
+            Tuple[Union[Dict[Any, Any], Configuration], Optional[float]], Optional[Any]
+        ] = {}
+        self.origins: Dict[
+            Tuple[Union[Dict[Any, Any], Configuration], Optional[float]], Optional[str]
+        ] = {}
+        self.additionals: Dict[
+            Tuple[Union[Dict[Any, Any], Configuration], Optional[float]], Dict[Any, Any]
+        ] = {}
+
+        # Define trials container
+        self.run = DeepCAVERun(
+            self.path.stem, configspace=configspace, objectives=objectives, meta=meta
+        )
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(self, type, value, traceback) -> None:  # type: ignore
+        pass
+
+    def _set_path(
+        self, path: Union[str, Path], prefix: str = "run", overwrite: bool = False
+    ) -> None:
+        """
+        Set the path.
+
+        Parameters
+        ----------
+        path : Union[str, Path]
+            The path to set.
+        prefix, optional
+            The prefix for the path.
+            Default is "run".
+        overwrite, optional
+            To determine whether to overwrite an existing folder.
+            Default is False.
+        """
+        # Make sure the word is interpreted as folder
+        path = Path(path)
+        path.mkdir(parents=True, exist_ok=True)
+
+        if not overwrite:
+            new_idx = 0
+            for file in path.iterdir():
+                if not file.name.startswith(f"{prefix}_"):
+                    continue
+                idx = file.name.split("_")[-1]
+                if idx.isnumeric():
+                    idx_int = int(idx)
+                    if idx_int > new_idx:
+                        new_idx = idx_int
+
+            # And increase the id
+            new_idx += 1
+            self.path = path / f"{prefix}_{new_idx}"
+        else:
+            self.path = path / f"{prefix}"
+
+

+[docs]
+    def start(
+        self,
+        config: Configuration,
+        budget: Optional[float] = None,
+        model: Optional[Any] = None,
+        origin: Optional[str] = None,
+        additional: Optional[dict] = None,
+        start_time: Optional[float] = None,
+    ) -> None:
+        """
+        Start recording the information.
+
+        Parameters
+        ----------
+        config : Configuration
+            Holds the configuration settings.
+        budget : Optional[float], optional
+            The budget.
+            Default is None.
+        model : Optional[Any], optional
+            The model used.
+            Default is None.
+        origin : Optional[str], optional
+            The origin.
+            Default is None.
+        additional : Optional[dict], optional
+            Additional information.
+            Default is None.
+        start_time : Optional[float], optional
+            The start time.
+            Default is None.
+        """
+        if additional is None:
+            additional = {}
+
+        id: Tuple[Union[Dict[Any, Any], Configuration], Optional[float]] = (config, budget)
+
+        if start_time is None:
+            start_time = time.time() - self.start_time
+
+        # Start timer
+        self.start_times[id] = start_time
+        self.models[id] = model
+        self.origins[id] = origin
+        self.additionals[id] = additional
+
+        self.last_trial_id = id

+
+
+

+[docs]
+    def end(
+        self,
+        costs: float = np.inf,
+        status: Status = Status.SUCCESS,
+        config: Optional[Union[dict, Configuration]] = None,
+        budget: Optional[float] = np.inf,
+        additional: Optional[dict] = None,
+        end_time: Optional[float] = None,
+    ) -> None:
+        """
+        End the recording and add it to the trial history.
+
+        In case of multi-processing, config+budget should be passed.
+        If it can't be passed, it can't be matched correctly.
+
+        Parameters
+        ----------
+        costs : float, optional
+            The costs.
+            Default is np.inf.
+        status : Status, optional
+            The status.
+            Default is Status.Success.
+        config : Union[dict, Configuration], optional
+            The configuration.
+            Default is None.
+        budget : float, optional
+            The budget.
+            Default is np.inf.
+        additional : Optional[dict], optional
+            Additional information.
+            Default is None.
+        end_time : Optional[float], optional
+            The end time.
+            Default is None.
+
+        Raises
+        ------
+        AssertionError
+            If no trial was started yet.
+        """
+        if additional is None:
+            additional = {}
+
+        if config is not None:
+            id = (config, budget)
+        else:
+            assert self.last_trial_id is not None, "No trial started yet."
+            id = self.last_trial_id
+            config, budget = id[0], id[1]
+
+        model = self.models[id]
+        start_additional = self.additionals[id].copy()
+        start_additional.update(additional)
+        start_time = self.start_times[id]
+
+        if end_time is None:
+            end_time = time.time() - self.start_time
+
+        assert budget is not None
+
+        # Add to trial history
+        self.run.add(
+            costs=costs,
+            config=config,
+            budget=budget,
+            start_time=start_time,
+            end_time=end_time,
+            status=status,
+            model=model,
+            additional=start_additional,
+        )
+
+        # Clean the dicts
+        self.start_times.pop(id)
+        self.models.pop(id)
+        self.origins.pop(id)
+        self.additionals.pop(id)
+
+        # And save the results
+        self.run.save(self.path)

+

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/runs/run.html b/development/_modules/deepcave/runs/run.html
index d9b3c4f2..85b28c11 100644
--- a/development/_modules/deepcave/runs/run.html
+++ b/development/_modules/deepcave/runs/run.html
@@ -702,7 +702,17 @@
               

                 
   
Source code for deepcave.runs.run
-from abc import ABC, abstractmethod
+#  noqa: D400
+"""
+# Run
+
+This module provides utilities to create a new run and get its attributes.
+
+## Classes
+    - Run: Create a new run.
+"""
+
+from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional, Union
 
 import json
@@ -724,26 +734,36 @@ 
Source code for deepcave.runs.run
 [docs]
 class Run(AbstractRun, ABC):
     """
-    Creates a new run.
+    Create a new run and get its attributes.
+
     If path is given, runs are loaded from the path.
 
-    Parameters
+    Properties
     ----------
-    name : str
-        Name of the run.
-    configspace : ConfigSpace, optional
-        Configuration space of the run. Should be None if `path` is used. By default None.
-    objectives : Union[Objective, List[Objective]], optional
-        Objectives of the run. Should be None if `path` is used. By default None
-    meta : Dict[str, Any], optional
-        Meta data of the run. Should be None if `path` is used. By default None.
-    path : Optional[Union[str, Path]], optional
-        If a path is specified, the run is loaded from there. By default None.
-
-    Raises
-    ------
-    RuntimeError
-        If no configuration space is provided or found.
+    configspace : ConfigurationSpace
+        The configuration space of the run.
+    path : Optional[Union[str, Path]]
+        The path of a run to be loaded.
+    meta : Dict[str, Any]
+        Contains serialized objectives and budgets.
+    prefix : str
+        The prefix for the id.
+    meta_fn : Path
+        The path to the meta data.
+    configspace_fn : Path
+        The path to the configuration space file.
+    configs_fn : Path
+        The path to the configurations file.
+    origins_fn : Path
+        The path to the origins file.
+    history_fn : Path
+        The path to the history file.
+    models_dir : Path
+        The path to the models directory.
+    configs : Dict[int, Configuration]
+        Containing the configurations.
+    models : Dict[int, Optional[Union[str, "torch.nn.Module"]]]
+        Contains the models.
     """
 
     prefix = "run"
@@ -752,10 +772,10 @@ 
Source code for deepcave.runs.run
     def __init__(
         self,
         name: str,
-        configspace: ConfigSpace = None,
-        objectives: Union[Objective, List[Objective]] = None,
-        meta: Dict[str, Any] = None,
-        path: Optional[Union[str, Path]] = None,
+        configspace: Optional[ConfigSpace.ConfigurationSpace] = None,
+        objectives: Optional[Union[Objective, List[Objective]]] = None,
+        meta: Optional[Dict[str, Any]] = None,
+        path: Optional[Path] = None,
     ) -> None:
         super(Run, self).__init__(name)
 
@@ -766,7 +786,8 @@ 
Source code for deepcave.runs.run
 
         # Reset and load configspace/path
         self.reset()
-        self.configspace = configspace
+        if configspace is not None:
+            self.configspace = configspace
         self.path = path
         if self.path is not None:
             self.load()
@@ -797,24 +818,54 @@ 
Source code for deepcave.runs.run
     def from_path(cls, path: Path) -> "Run":
         """
         Based on a path, return a new Run object.
+
+        Parameters
+        ----------
+        path : Path
+            The path to get the run from.
+
+        Returns
+        -------
+        "Run"
+            The run loaded from the path.
         """
         pass

 
 
     @property
     def id(self) -> str:
+        """
+        Get a hash as id.
+
+        Returns
+        -------
+        str
+            The hashed id.
+        """
         return string_to_hash(f"{self.prefix}:{self.path}")
 
     @property
     def path(self) -> Optional[Path]:
+        """
+        Return the path of the run if it exists.
+
+        Returns
+        -------
+        Optional[Path]
+            The path of the run.
+        """
         return self._path
 
     @path.setter
-    def path(self, value: Optional[Union[str, Path]]):
+    def path(self, value: Optional[Union[str, Path]]) -> None:
         """
-        If path is changed, also change the filenames of all created files.
-        """
+        Set the paths of the run and the JSON files.
 
+        Parameters
+        ----------
+        value : Optional[Union[str, Path]]
+            The path for the directory.
+        """
         if value is None:
             self._path = None
             return
@@ -833,7 +884,7 @@ 
Source code for deepcave.runs.run
 [docs]
     def exists(self) -> bool:
         """
-        Checks if the run exists based on the internal path.
+        Check if the run exists based on the internal path.
 
         Returns
         -------
@@ -865,12 +916,13 @@ 
Source code for deepcave.runs.run
         start_time: float = 0.0,
         end_time: float = 0.0,
         status: Status = Status.SUCCESS,
-        origin: str = None,
-        model: Union[str, "torch.nn.Module"] = None,  # type: ignore
+        origin: Optional[str] = None,
+        model: Union[str, "torch.nn.Module"] = None,  # type: ignore # noqa: F821
         additional: Optional[Dict] = None,
     ) -> None:
         """
-        Adds a trial to the run.
+        Add a trial to the run.
+
         If combination of config and budget already exists, it will be overwritten.
         Not successful runs are added with `None` costs.
 
@@ -881,17 +933,17 @@ 
Source code for deepcave.runs.run
         config : Union[Dict, Configuration]
             The corresponding configuration.
         start_time : float, optional
-            Start time. By default 0.0
+            Start time. By default, 0.0
         end_time : float, optional
-            End time. By default 0.0
+            End time. By default, 0.0
         status : Status, optional
-            Status of the trial. By default Status.SUCCESS
+            Status of the trial. By default, Status.SUCCESS
         origin : str, optional
-            Origin of the trial. By default None
-        model : Union[str, &quot;torch.nn.Module&quot;], optional
-            Model of the trial. By default None
+            Origin of the trial. By default, None
+        model : Union[str, "torch.nn.Module"], optional
+            Model of the trial. By default, None
         additional : Optional[Dict], optional
-            Additional information of the trial. By default None.
+            Additional information of the trial. By default, None.
             Following information is used by DeepCAVE:
             * traceback
 
@@ -899,6 +951,8 @@ 
Source code for deepcave.runs.run
         ------
         RuntimeError
             If number of costs does not match number of objectives.
+        ValueError
+            If config id is None.
         """
         if additional is None:
             additional = {}
@@ -922,12 +976,12 @@ 
Source code for deepcave.runs.run
             # If cost is none, replace it later with the highest cost
             if cost is not None:
                 # Update bounds here
-                if not objective.lock_lower:
-                    if cost < objective.lower:  # type: ignore
+                if not objective.lock_lower and objective.lower is not None:
+                    if cost < objective.lower:
                         objective.lower = cost
 
-                if not objective.lock_upper:
-                    if cost > objective.upper:  # type: ignore
+                if not objective.lock_upper and objective.upper is not None:
+                    if cost > objective.upper:
                         objective.upper = cost
 
             updated_objectives += [objective.to_json()]
@@ -938,11 +992,14 @@ 
Source code for deepcave.runs.run
             config = config.get_dictionary()
 
         if config not in self.configs.values():
-            config_id = len(self.configs)
-            self.configs[config_id] = config
-            self.origins[config_id] = origin
+            config_id_len = len(self.configs)
+            self.configs[config_id_len] = config
+            self.origins[config_id_len] = origin
 
         config_id = self.get_config_id(config)
+        if config_id is None:
+            raise ValueError("Config id is None.")
+
         trial = Trial(
             config_id=config_id,
             budget=budget,
@@ -969,14 +1026,29 @@ 
Source code for deepcave.runs.run
         self._update_highest_budget(config_id, budget, status)
 
         # Update models
-        # Problem: We don't want to have the model in the cache.
-        # Therefore, we first keep the model as it is,
+        # Problem: The model should not be in the cache.
+        # Therefore, first the model is kept as it is,
         # but remove it from the dict and save it to the disk later on.
         if model is not None:
             self.models[config_id] = model

 
 
-    def save(self, path: Optional[Union[str, Path]] = None):
+

+[docs]
+    def save(self, path: Union[str, Path]) -> None:
+        """
+        Save the run and its information.
+
+        Parameters
+        ----------
+        path : Optional[Union[str, Path]]
+            The path in which to save the trials.
+
+        Raises
+        ------
+        RuntimeError
+            If the path is not specified.
+        """
         if path is None:
             raise RuntimeError("Please specify a path to save the trials.")
 
@@ -1001,7 +1073,7 @@ 
Source code for deepcave.runs.run
 
         # Models
         if len(self.models) > 0:
-            # We import torch here because we don't want to have it as requirement.
+            # torch is imported here, because it is not wanted as requirement.
             import torch
 
             # Iterate over models and save them if they are a module.
@@ -1017,9 +1089,27 @@ 
Source code for deepcave.runs.run
                         raise RuntimeError("Unknown model type.")
 
                 # Remove from dict
-                del self.models[config_id]
+                del self.models[config_id]

 
+
+

+[docs]
     def load(self, path: Optional[Union[str, Path]] = None) -> None:
+        """
+        Load the run.
+
+        Parameters
+        ----------
+        path : Optional[Union[str, Path]], optional
+            The path where to load the run from.
+            Default is None.
+
+        Raises
+        ------
+        RuntimeError
+            If the path is None.
+            If the trials were not found.
+        """
         self.reset()
 
         if path is None and self.path is None:
@@ -1059,6 +1149,7 @@ 
Source code for deepcave.runs.run
 
                 # Update highest budget
                 self._update_highest_budget(trial.config_id, trial.budget, trial.status)

+

 
 

 
diff --git a/development/_modules/deepcave/runs/status.html b/development/_modules/deepcave/runs/status.html
index de46b8c3..bce65080 100644
--- a/development/_modules/deepcave/runs/status.html
+++ b/development/_modules/deepcave/runs/status.html
@@ -702,12 +702,43 @@
               

                 
   
Source code for deepcave.runs.status
-from enum import IntEnum
+# noqa: D400
+"""
+# Status
+
+This module provides the information about the status of a run.
+
+A utility to convert a string text to a simpler, lower case text format is provided.
+
+## Classes
+    - Status: Represent the status of a run as an Enum.
+
+## Constants
+    SUCCESS: int
+    TIMEOUT: int
+    MEMORYOUT: int
+    CRASHED: int
+    ABORTED: int
+    NOT_EVALUATED: int
+"""
+
+from enum import IntEnum
 
 
 

 [docs]
 class Status(IntEnum):
+    """
+    Represent the status of a run as an Enum.
+
+    A utility to convert a string text to a simpler, lower case text format is provided.
+
+    Properties
+    ----------
+    name : str
+        The status name.
+    """
+
     SUCCESS = 1
     TIMEOUT = 2
     MEMORYOUT = 3
@@ -715,8 +746,19 @@ 
Source code for deepcave.runs.status
     ABORTED = 5
     NOT_EVALUATED = 6
 
+

+[docs]
     def to_text(self) -> str:
+        """
+        Convert name to simpler, lower case text format.
+
+        Returns
+        -------
+        str
+            The converted name in lower case with spaces added
+        """
         return self.name.lower().replace("_", " ")

+

 
 

 
diff --git a/development/_modules/deepcave/runs/trial.html b/development/_modules/deepcave/runs/trial.html
index 9334c87c..e5468bda 100644
--- a/development/_modules/deepcave/runs/trial.html
+++ b/development/_modules/deepcave/runs/trial.html
@@ -702,7 +702,18 @@
               

                 
   
Source code for deepcave.runs.trial
-from typing import Any, Dict, List, Tuple, Union
+#  noqa: D400
+"""
+# Trial
+
+This module provides the trial object.
+Utilities for handling the trial are provided.
+
+## Classes
+    - Trial: This class provides the trial object itself and multiple handling utilities.
+"""
+
+from typing import Any, Dict, List, Tuple, Union
 
 from dataclasses import dataclass
 
@@ -713,6 +724,27 @@ 
Source code for deepcave.runs.trial
 [docs]
 @dataclass
 class Trial:
+    """
+    Provide the trial object itself and multiple handling utilities.
+
+    Properties
+    ----------
+    status : Status
+        The status of the trial.
+    config_id : int
+        The identificator of the configuration.
+    budget : Union[int, float]
+        The budget for the trial.
+    costs : List[float]
+        A list of the costs of the trial.
+    start_time : float
+        The start time of the trial.
+    end_time : float
+        The end time of the trial.
+    additional : Dict[str, Any]
+        A dictionary of additional information of the trial.
+    """
+
     config_id: int
     budget: Union[int, float]
     costs: List[float]
@@ -721,18 +753,43 @@ 
Source code for deepcave.runs.trial
     status: Status
     additional: Dict[str, Any]
 
+

+[docs]
     def __post_init__(self) -> None:
+        """Set the status."""
         if isinstance(self.status, int):
             self.status = Status(self.status)
 
-        assert isinstance(self.status, Status)
+        assert isinstance(self.status, Status)

 
-    def get_key(self) -> Tuple[int, int]:
+
+

+[docs]
+    def get_key(self) -> Tuple[int, Union[int, float, None]]:
+        """
+        Generate a key based on the configuration id and the budget.
+
+        Returns
+        -------
+        Tupel[int, int]
+            A Tuple representing a unique key based on the configuration id and the budget.
+        """
         from deepcave.runs import AbstractRun
 
-        return AbstractRun.get_trial_key(self.config_id, self.budget)
+        return AbstractRun.get_trial_key(self.config_id, self.budget)

+
 
+

+[docs]
     def to_json(self) -> List[Any]:
+        """
+        Convert trial object to JSON-compatible representation.
+
+        Returns
+        -------
+        List[Any]
+            A JSON-compatible list with the Trials attributes.
+        """
         return [
             self.config_id,
             self.budget,
@@ -742,6 +799,7 @@ 
Source code for deepcave.runs.trial
             self.status,
             self.additional,
         ]

+

 
 

 
diff --git a/development/_modules/deepcave/utils/cache.html b/development/_modules/deepcave/utils/cache.html
index 2cd841b1..ea62fce7 100644
--- a/development/_modules/deepcave/utils/cache.html
+++ b/development/_modules/deepcave/utils/cache.html
@@ -702,10 +702,21 @@
               

                 
   
Source code for deepcave.utils.cache
-from typing import Any, Dict, Optional
+#  noqa: D400
+"""
+# Cache
+
+This module provides utilities to handle the cache.
+
+This includes reading, writing, set and get utilities, as well as clearing the cache.
+The cache handles a json file.
+
+## Classes
+    - Cache: Cache handles a json file.
+"""
+from typing import Any, Dict, Optional
 
 import json
-import logging
 from copy import deepcopy
 from pathlib import Path
 
@@ -720,14 +731,15 @@ 
Source code for deepcave.utils.cache
 [docs]
 class Cache:
     """
-    Cache handles a json file. Decided not to use flask_caching
-    since code is easier to change to our needs.
+    Cache handles a json file.
+
+    Decided not to use flask_caching since code is easier to change to our needs.
     """
 
     def __init__(
         self,
         filename: Optional[Path] = None,
-        defaults: Dict = None,
+        defaults: Optional[Dict] = None,
         debug: bool = False,
         write_file: bool = True,
     ) -> None:
@@ -742,6 +754,17 @@ 
Source code for deepcave.utils.cache
         self._setup(filename, write_file)
 
     def _setup(self, filename: Optional[Path], write_file: bool = True) -> None:
+        """
+        Initialize the setup.
+
+        Parameters
+        ----------
+        filename : Optional[Path]
+            The filename to be set.
+        write_file : bool, optional
+            Define whether do write the content of cache into a file.
+            Default is True.
+        """
         self._data = {}
         self._filename = filename
 
@@ -753,7 +776,7 @@ 
Source code for deepcave.utils.cache
 

 [docs]
     def read(self) -> None:
-        """Reads content from a file and load into cache as dictionary."""
+        """Read content from a file and load into cache as dictionary."""
         if self._filename is None or not self._filename.exists():
             return
 
@@ -779,11 +802,27 @@ 
Source code for deepcave.utils.cache
 
 

 [docs]
-    def set(self, *keys, value: Any, write_file: bool = True) -> None:
+    def set(self, *keys: str, value: Any, write_file: bool = True) -> None:
         """
         Set a value from a chain of keys.
+
         E.g. set("a", "b", "c", value=4) creates following dictionary:
         {"a": {"b": {"c": 4}}}
+
+        Parameters
+        ----------
+        *keys : str
+            The keys to set the value from.
+        value : Any
+            The value to be set.
+        write_file : bool, optional
+            Whether to write the constant of the cache into a file.
+            Default is True.
+
+        Raises
+        ------
+        RuntimeError
+            If the type of the key is not a string.
         """
         name = "(empty)"
         if self._filename is not None:
@@ -810,7 +849,17 @@ 
Source code for deepcave.utils.cache
 

 [docs]
     def set_dict(self, d: Dict, write_file: bool = True) -> None:
-        """Updates cache to a specific value"""
+        """
+        Update cache to a specific value.
+
+        Parameters
+        ----------
+        d : Dict
+            The dictionary to be set.
+        write_file : bool, optional
+            Whether to write the constant of the cache into a file.
+            Default is True.
+        """
         self._data.update(d)
 
         if write_file:
@@ -819,8 +868,20 @@ 
Source code for deepcave.utils.cache
 
 

 [docs]
-    def get(self, *keys) -> Optional[Any]:
-        """Retrieve value for a specific key"""
+    def get(self, *keys: str) -> Optional[Any]:
+        """
+        Retrieve value for a specific key.
+
+        Parameters
+        ----------
+        *keys : str
+            The key to retrieve the value from.
+
+        Returns
+        -------
+        Optional[Any]
+            The value of the key.
+        """
         d = deepcopy(self._data)
         for key in keys:
             if key not in d:
@@ -833,8 +894,20 @@ 
Source code for deepcave.utils.cache
 
 

 [docs]
-    def has(self, *keys) -> bool:
-        """Check whether cache has specific key"""
+    def has(self, *keys: str) -> bool:
+        """
+        Check whether cache has specific key.
+
+        Parameters
+        ----------
+        *keys : str
+            The key to check for.
+
+        Returns
+        -------
+        bool
+            Whether cache has specific key.
+        """
         d = self._data
         for key in keys:
             if key not in d:
@@ -847,11 +920,20 @@ 
Source code for deepcave.utils.cache
 

 [docs]
     def clear(self, write_file: bool = True) -> None:
-        """Clear all cache and reset to defaults"""
+        """
+        Clear all cache and reset to defaults.
+
+        Parameters
+        ----------
+        write_file : bool, optional
+            Whether to write the constant of the cache into a file.
+            Default is True.
+        """
         filename = self._filename
 
-        if filename is not None and filename.exists():
-            self._filename.unlink()
+        if self._filename is not None:
+            if self._filename.exists():
+                self._filename.unlink()
 
         self._setup(filename, write_file=write_file)

 

diff --git a/development/_modules/deepcave/utils/cast.html b/development/_modules/deepcave/utils/cast.html
new file mode 100644
index 00000000..ac1575ef
--- /dev/null
+++ b/development/_modules/deepcave/utils/cast.html
@@ -0,0 +1,772 @@
+
+
+
+  
+    
+    
+    deepcave.utils.cast — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.utils.cast
+#  noqa: D400
+"""
+# Cast
+
+This module provides a utility to convert any value to an int if possible.
+"""
+from typing import Any, Optional
+
+
+

+[docs]
+def optional_int(value: Any) -> Optional[int]:
+    """
+    Convert a value to an int if possible.
+
+    Parameters
+    ----------
+    value : Any
+        The value to be turned into an int.
+
+    Returns
+    -------
+    Optional[int]
+        The converted int value.
+        If not possible, return None.
+    """
+    if value is None:
+        return None
+
+    return int(value)

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/utils/compression.html b/development/_modules/deepcave/utils/compression.html
index 5b651b9f..392d19d7 100644
--- a/development/_modules/deepcave/utils/compression.html
+++ b/development/_modules/deepcave/utils/compression.html
@@ -702,7 +702,22 @@
               

                 
   
Source code for deepcave.utils.compression
-from typing import Dict, List, TypeVar, Union
+#  noqa: D400
+"""
+# Compression
+
+This module provides utilities for serializing and deserializing a dataframe from/to a string.
+
+## Classes
+    - Encoder: This class defines a custom JSON Encoder.
+
+## Constants
+    - JSON_DENSE_SEPARATORS: Tuple(str, str)
+    - JSON_DEFAULT_SEPARATORS: Tuple(str, str)
+    - TYPE: TypeVar
+"""
+
+from typing import Any, Dict, List, TypeVar, Union
 
 import json
 
@@ -719,10 +734,35 @@ 
Source code for deepcave.utils.compression
 def serialize(data: Union[Dict, List, pd.DataFrame]) -> str:
     """
     Serialize a dataframe to a string.
+
+    Parameters
+    ----------
+    data : Union[Dict, List, pd.DataFrame]
+        The dataframe to be serialized.
+
+    Returns
+    -------
+    str
+        The serialized object as a JSON formatted string.
     """
 
     class Encoder(json.JSONEncoder):
-        def default(self, obj):
+        """Define a custom JSON Encoder."""
+
+        def default(self, obj: Any) -> Any:
+            """
+            Return the object as list if np.ndarray.
+
+            Parameters
+            ----------
+            obj : Any
+                The object to be converted.
+
+            Returns
+            -------
+            Any
+                The converted object.
+            """
             if isinstance(obj, np.ndarray):
                 return obj.tolist()
             return json.JSONEncoder.default(self, obj)
@@ -740,6 +780,19 @@ 
Source code for deepcave.utils.compression
 def deserialize(string: str, dtype: TYPE = pd.DataFrame) -> TYPE:
     """
     Deserialize a dataframe from a string.
+
+    Parameters
+    ----------
+    string : str
+        The string to be deserialized.
+    dtype : TYPE, optional
+        The type of the object.
+        Default is pd.DataFrame.
+
+    Returns
+    -------
+    TYPE
+        The deserialized object.
     """
     if dtype == pd.DataFrame:
         return pd.DataFrame.from_dict(json.loads(string))
diff --git a/development/_modules/deepcave/utils/configs.html b/development/_modules/deepcave/utils/configs.html
index 0a57d50f..403e4dfb 100644
--- a/development/_modules/deepcave/utils/configs.html
+++ b/development/_modules/deepcave/utils/configs.html
@@ -702,7 +702,14 @@
               

                 
   
Source code for deepcave.utils.configs
-from typing import Optional
+#  noqa: D400
+"""
+# Configs
+
+This module provides a utility for parsing the configurations from a filename.
+"""
+
+from typing import Optional
 
 import importlib
 import os
@@ -716,7 +723,9 @@ 
Source code for deepcave.utils.configs
 [docs]
 def parse_config(filename: Optional[str] = None) -> Config:
     """
-    Parses the config given the filename. Both relative and absolute paths are possible.
+    Parse the config given the filename.
+
+    Both relative and absolute paths are possible.
 
     Parameters
     ----------
@@ -755,7 +764,7 @@ 
Source code for deepcave.utils.configs
             script_dir = path.stem  # That's the path without the script name
             module_name = p.stem  # That's the script name without the extension
 
-            # Now we add to sys path
+            # Now it is added to sys path
             sys.path.append(str(path))
 
             module = importlib.import_module(f"{script_dir}.{module_name}")
diff --git a/development/_modules/deepcave/utils/configspace.html b/development/_modules/deepcave/utils/configspace.html
index 71b61bb4..791e188a 100644
--- a/development/_modules/deepcave/utils/configspace.html
+++ b/development/_modules/deepcave/utils/configspace.html
@@ -702,10 +702,14 @@
               

                 
   
Source code for deepcave.utils.configspace
-from typing import Generator, Iterator, List, Optional
+#  noqa: D400
+"""
+# ConfigSpace
 
-import random
-from itertools import islice, product
+This module samples random as well as border configurations.
+"""
+
+from typing import Iterator, Optional
 
 import numpy as np
 from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
@@ -722,12 +726,12 @@ 
Source code for deepcave.utils.configspace
 [docs]
 def sample_border_config(configspace: ConfigurationSpace) -> Iterator[Configuration]:
     """
-    Generates border configurations from the configuration space.
+    Generate border configurations from the configuration space.
 
     Parameters
     ----------
     configspace : ConfigurationSpace
-        The configspace from which the hyperparameters are drawn from.
+        The configuration space from which the hyperparameters are drawn from.
 
     Yields
     ------
@@ -756,12 +760,12 @@ 
Source code for deepcave.utils.configspace
             config[hp_name] = value
 
         try:
-            config = deactivate_inactive_hyperparameters(config, configspace)
-            config.is_valid_configuration()
+            configuration = deactivate_inactive_hyperparameters(config, configspace)
+            configuration.is_valid_configuration()
         except Exception:
             continue
 
-        yield config

+        yield configuration

 
 
 
@@ -771,7 +775,7 @@ 
Source code for deepcave.utils.configspace
     configspace: ConfigurationSpace, d: Optional[int] = None
 ) -> Iterator[Configuration]:
     """
-    Generates random configurations from the configuration space.
+    Generate random configurations from the configuration space.
 
     Parameters
     ----------
@@ -779,8 +783,8 @@ 
Source code for deepcave.utils.configspace
         The configspace from which the hyperparameters are drawn from.
     d : Optional[int], optional
         The possible hyperparameter values can be reduced by this argument as the range gets
-        discretized. For example, an integer or float hyperparameter has only four possible values
-        if d=4. By default None (no discretization is done).
+        discretized. For example, an integer or float hyperparameter has only four possible
+        values if d=4. By default, None (no discretization is done).
 
     Yields
     ------
@@ -796,7 +800,7 @@ 
Source code for deepcave.utils.configspace
     rng = np.random.RandomState(0)
 
     while True:
-        config = {}
+        config_dict = {}
 
         # Iterates over the hyperparameters to get considered values
         for hp_name, hp in zip(
@@ -826,15 +830,15 @@ 
Source code for deepcave.utils.configspace
 
             # Get a random choice
             value = rng.choice(values)
-            config[hp_name] = value
+            config_dict[hp_name] = value
 
         try:
-            config = deactivate_inactive_hyperparameters(config, configspace)
-            config.is_valid_configuration()
+            configuration = deactivate_inactive_hyperparameters(config_dict, configspace)
+            configuration.is_valid_configuration()
         except Exception:
             continue
 
-        yield config

+        yield configuration

 
 

 
diff --git a/development/_modules/deepcave/utils/dash.html b/development/_modules/deepcave/utils/dash.html
index 3f403fb2..819e6dcf 100644
--- a/development/_modules/deepcave/utils/dash.html
+++ b/development/_modules/deepcave/utils/dash.html
@@ -702,16 +702,35 @@
               

                 
   
Source code for deepcave.utils.dash
-import dash_bootstrap_components as dbc
+#  noqa: D400
+"""
+# Dash
+
+This module provides utilities to return and flash alerts.
+"""
+
+import dash_bootstrap_components as dbc
 
 
 

 [docs]
-def flash(message: str, category: str = "info"):
+def flash(message: str, category: str = "info") -> dbc.Alert:
     """
     Flask style flash-message with Alerts.
-    """
 
+    Parameters
+    ----------
+    message : str
+        The message to be displayed.
+    category : str, optional
+        The category of the alert.
+        Default is "info".
+
+    Returns
+    -------
+    dbc.Alert
+        The alert object.
+    """
     return dbc.Alert(
         message,
         id=f"alert_{hash(message)}",
@@ -723,8 +742,24 @@ 
Source code for deepcave.utils.dash
 
 
 
-def alert(message: str):
-    return flash(message, "danger")
+

+[docs]
+def alert(message: str) -> dbc.Alert:
+    """
+    Return an alert message.
+
+    Parameters
+    ----------
+    message : str
+        The message of the alert.
+
+    Returns
+    -------
+    dbc.Alert
+        The alert object.
+    """
+    return flash(message, "danger")

+
 

 
               

diff --git a/development/_modules/deepcave/utils/data_structures.html b/development/_modules/deepcave/utils/data_structures.html
index 3dfb289d..6662093b 100644
--- a/development/_modules/deepcave/utils/data_structures.html
+++ b/development/_modules/deepcave/utils/data_structures.html
@@ -702,14 +702,21 @@
               

                 
   
Source code for deepcave.utils.data_structures
-from typing import Dict
+#  noqa: D400
+"""
+# Data Structures
+
+This module can be used for updating one dictionary with another dictionary inplace.
+"""
+
+from typing import Dict
 
 
 

 [docs]
 def update_dict(a: Dict[str, Dict], b: Dict[str, Dict]) -> None:
     """
-    Updates dictionary a with dictionary b inplace.
+    Update dictionary a with dictionary b inplace.
 
     Parameters
     ----------
diff --git a/development/_modules/deepcave/utils/docs.html b/development/_modules/deepcave/utils/docs.html
new file mode 100644
index 00000000..70a89463
--- /dev/null
+++ b/development/_modules/deepcave/utils/docs.html
@@ -0,0 +1,817 @@
+
+
+
+  
+    
+    
+    deepcave.utils.docs — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
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+    
+    
+    
+  
+
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+    
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+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.utils.docs
+#  noqa: D400
+"""
+# Docs
+
+This module provides a function to convert reStructuredText (RST) to MarkDown (MD).
+"""
+
+from __future__ import annotations
+
+import re
+from pathlib import Path
+
+
+

+[docs]
+def rst_to_md(filename: str | Path) -> str:
+    """
+    Convert a subset of reStructuredText (RST) to MarkDown (MD).
+
+    Parameters
+    ----------
+    filename : str | Path
+        The path to the reStructuredText (RST) file.
+
+    Returns
+    -------
+    str
+        The converted data in MarkDown (MD) format.
+    """
+    if isinstance(filename, Path):
+        filename = str(filename)
+
+    with open(filename, "r") as file:
+        data = file.read()
+
+    # Remove reference
+    result = re.finditer(r":ref:`(.*?)<(.*?)>`", data)
+    for match in result:
+        a = match.group(0)
+        b = f"``{match.group(1)}``"
+        data = data.replace(a, b)
+
+    # Remove terms
+    result = re.finditer(r":term:`(.*?) <(.*?)>`", data)
+    for match in result:
+        a = match.group(0)
+        b = f"``{match.group(1)}``"
+        data = data.replace(a, b)
+
+    # Changing links
+    result = re.finditer(r"`(.*?) <(.*?)>`_", data)
+    for match in result:
+        a = match.group(0)
+        b = f"[{match.group(1)}]({match.group(1)})"
+        data = data.replace(a, b)
+
+    # Remove images
+    result = re.finditer(r".. image::(.*?)\n", data)
+    for match in result:
+        a = match.group(0)
+        data = data.replace(a, "")
+
+    # Remove notes/warnings (not the best implementation but sufficient for now)
+    data = data.replace(".. note::", "#### Note\n-----")
+    data = data.replace(".. note ::", "#### Note\n-----")
+    data = data.replace(".. warning::", "#### Warning\n-----")
+    data = data.replace(".. warning ::", "#### Warning\n-----")
+    data = data.replace(".. code::", "#### Code\n-----")
+    data = data.replace(".. code ::", "#### Code\n-----")
+
+    # Remove last \n
+    if data.endswith("\n"):
+        data = data[:-2]
+
+    return data

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/utils/files.html b/development/_modules/deepcave/utils/files.html
new file mode 100644
index 00000000..8606d973
--- /dev/null
+++ b/development/_modules/deepcave/utils/files.html
@@ -0,0 +1,773 @@
+
+
+
+  
+    
+    
+    deepcave.utils.files — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
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+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.utils.files
+#  noqa: D400
+"""
+# Files
+
+This module provides a utility to create directories from a filename.
+"""
+
+from typing import Union
+
+from pathlib import Path
+
+
+

+[docs]
+def make_dirs(filename: Union[str, Path], parents: bool = True) -> None:
+    """
+    Create a directory.
+
+    Parameters
+    ----------
+    filename : Union[str, Path]
+        The name and path of the file.
+    parents : bool, optional
+        Whether intermediate directories should be created.
+        Default is True.
+    """
+    path = Path(filename)
+    if path.suffix != "":  # Is file
+        path = path.parent
+
+    path.mkdir(exist_ok=True, parents=parents)

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/utils/hash.html b/development/_modules/deepcave/utils/hash.html
new file mode 100644
index 00000000..24b80a8d
--- /dev/null
+++ b/development/_modules/deepcave/utils/hash.html
@@ -0,0 +1,796 @@
+
+
+
+  
+    
+    
+    deepcave.utils.hash — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.utils.hash
+#  noqa: D400
+"""
+# Hash
+
+This module provides utilities to convert strings and files to hash.
+"""
+
+import hashlib
+from pathlib import Path
+
+
+

+[docs]
+def string_to_hash(string: str) -> str:
+    """
+    Convert a string to a hash.
+
+    Parameters
+    ----------
+    string : str
+        The string to be converted to hash.
+
+    Returns
+    -------
+    str
+        The hash object.
+    """
+    hash_object = hashlib.md5(string.encode())
+    return hash_object.hexdigest()

+
+
+
+

+[docs]
+def file_to_hash(filename: Path) -> str:
+    """
+    Convert a file to a hash.
+
+    Parameters
+    ----------
+    filename : Path
+        The path to the file to be converted.
+
+    Returns
+    -------
+    str
+        The hash object.
+    """
+    hash = hashlib.md5()
+    with Path(filename).open("rb") as f:
+        while chunk := f.read(4082):
+            hash.update(chunk)
+
+    return hash.hexdigest()

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/utils/layout.html b/development/_modules/deepcave/utils/layout.html
index 58692229..97891012 100644
--- a/development/_modules/deepcave/utils/layout.html
+++ b/development/_modules/deepcave/utils/layout.html
@@ -702,7 +702,15 @@
               

                 
   
Source code for deepcave.utils.layout
-from typing import Any, Dict, List, Optional
+#  noqa: D400
+"""
+# Layout
+
+This module provides utilities to customize the layout.
+
+This includes a help button, slider marks, select/checklist/radio options.
+"""
+from typing import Any, Dict, List, Optional, Union
 
 import uuid
 
@@ -713,7 +721,26 @@ 
Source code for deepcave.utils.layout
 from deepcave.utils.hash import string_to_hash
 
 
-def help_button(text: str, placement="top"):
+

+[docs]
+def help_button(text: str, placement: str = "top") -> html.Span:
+    """
+    Generate button with help icon.
+
+    Displays popover when hovered over that contains the provided text.
+
+    Parameters
+    ----------
+    text : str
+        The text that will be displayed in the popover.
+    placement : str, optional
+        The placement of the button, default is Top.
+
+    Returns
+    -------
+    html.Span
+        An html structure, that wraps the icon and the popover.
+    """
     id = "help-button-" + string_to_hash(text)
     id += str(uuid.uuid1())
 
@@ -727,19 +754,46 @@ 
Source code for deepcave.utils.layout
                 placement=placement,
             ),
         ]
-    )
+    )

+
 
 
-def render_table(df):
+def render_table(df: pd.DataFrame) -> None:  # noqa: D103
     pass
 
 
+

+[docs]
 def get_slider_marks(
-    strings: Optional[List[Dict[str, Any]]] = None, steps=10, access_all=False
-) -> Dict[int, str]:
+    strings: Optional[List[Any]] = None,
+    steps: int = 10,
+    access_all: bool = False,
+) -> Dict[int, Dict[str, str]]:
+    """
+    Generate a dictionary containing slider marks.
+
+    The slider marks are based on the provided list of dictionaries.
+
+    Parameters
+    ----------
+    strings : Optional[List[Dict[str, Any]]], optional
+        List of dictionaries containing information about the marks.
+        Default value is None.
+    steps : int, optional
+        Number of steps or marks on the slider.
+        Default is 10.
+    access_all : bool, optional
+        Indicates whether to create marks for all items.
+        Default is False.
+
+    Returns
+    -------
+    Dict[int, Dict[str, str]]
+        Contains information about the positions and labels of the marks.
+    """
     marks = {}
     if strings is None:
-        marks[0] = {"label": str("None")}
+        marks[0] = {"label": "None"}
         return marks
 
     if len(strings) < steps:
@@ -755,19 +809,51 @@ 
Source code for deepcave.utils.layout
     # Also include the last mark
     marks[len(strings) - 1] = {"label": str(strings[-1])}
 
-    return marks
+    return marks

+
 
 
 

 [docs]
 def get_select_options(
-    labels=None, values=None, disabled=None, binary=False
+    labels: Any = None,
+    values: Any = None,
+    disabled: Union[List[bool], None] = None,
+    binary: bool = False,
 ) -> List[Dict[str, Any]]:
     """
-    If values are none use labels as values.
-    If both are none return empty list.
-    """
+    Get dictionaries of the selected options.
 
+    If values are none use labels as values. If both are none return empty list.
+
+    Parameters
+    ----------
+    labels : Any, optional
+        To specify the labels of the options.
+        If labels are None, use values als labels.
+        Default is None.
+    values : Any, optional
+        To specify the values corresponding to the options.
+        If values are None, use labels as values.
+        Default is None
+    disabled : Union[List[Bool], None], optional
+        A list containing information whether each option should be disabled or not.
+        Default is None.
+    binary : bool, optional
+        Define whether to return two dictionaries with binary choices.
+        Only when both values and labels are None.
+        Default is False.
+
+    Returns
+    -------
+    List[Dict[str, Any]]
+        A list of dictionaries of the selected options.
+
+    Raises
+    ------
+    ValueError
+        If the labels and values do not have the same length.
+    """
     if labels is None and values is None:
         if binary:
             return [{"label": "Yes", "value": True}, {"label": "No", "value": False}]
@@ -794,17 +880,114 @@ 
Source code for deepcave.utils.layout
 
 
 
-def get_checklist_options(labels=None, values=None, binary=False):
-    return get_select_options(labels=labels, values=values, binary=binary)
+

+[docs]
+def get_checklist_options(
+    labels: Any = None, values: Any = None, binary: bool = False
+) -> List[Dict[str, Any]]:
+    """
+    Get a list of dictionaries of checklist options.
+
+    Parameters
+    ----------
+    labels : Any, optional
+        To specify the labels of the options.
+        If labels are None, use values als labels.
+        Default is None.
+    values : Any, optional
+        To specify the values corresponding to the options.
+        If values are None, use labels as values.
+        Default is None
+    binary : bool, optional
+        Define whether to return two dictionaries with binary choices.
+        Only when both values and labels are None.
+        Default is False.
+
+    Returns
+    -------
+    List[Dict[str, Any]]
+        A list of dictionaries of the checklist options.
+
+    Raises
+    ------
+    ValueError
+        If the labels and values do not have the same length.
+    """
+    return get_select_options(labels=labels, values=values, binary=binary)

+
+
 
+

+[docs]
+def get_radio_options(
+    labels: Any = None, values: Any = None, binary: Any = False
+) -> List[Dict[str, Any]]:
+    """
+    Get a list of dictionaries of radio options.
 
-def get_radio_options(labels=None, values=None, binary=False):
-    return get_select_options(labels=labels, values=values, binary=binary)
+    Parameters
+    ----------
+    labels : Any, optional
+        To specify the labels of the options.
+        If labels are None, use values als labels.
+        Default is None.
+    values : Any, optional
+        To specify the values corresponding to the options.
+        If values are None, use labels as values.
+        Default is None
+    binary : bool, optional
+        Define whether to return two dictionaries with binary choices.
+        Only when both values and labels are None.
+        Default is False.
 
+    Returns
+    -------
+    List[Dict[str, Any]]
+        A list of dictionaries of the radio options.
 
+    Raises
+    ------
+    ValueError
+        If the labels and values do not have the same length.
+    """
+    return get_select_options(labels=labels, values=values, binary=binary)

+
+
+
+

+[docs]
 def create_table(
-    output: Dict[str, str], fixed=False, head=True, striped=True, mb=True
+    output: Dict[str, List[str]],
+    fixed: bool = False,
+    head: bool = True,
+    striped: bool = True,
+    mb: bool = True,
 ) -> dbc.Table:
+    """
+    Create a table from the given data.
+
+    Parameters
+    ----------
+    output : Dict[str, List[str]]
+        Containing the information for the table.
+    fixed : bool, optional
+        Indicates whether the table has a fixed layout.
+        Default is False.
+    head : bool, optional
+        Indicates whether the head will be included.
+        Default is True.
+    striped : bool, optional
+        Indicates whether the rows will be striped.
+        Default is True.
+    mb : bool, optional
+        Indicates whether the table has a margin.
+        Default is True.
+
+    Returns
+    -------
+    dbc.Table
+        The created dash bootstrap component (DBC) table.
+    """
     className = ""
     if not head:
         className += "exclude-head "
@@ -813,7 +996,8 @@ 
Source code for deepcave.utils.layout
     className += "mb-0" if not mb else ""
     df = pd.DataFrame(output)
 
-    return dbc.Table.from_dataframe(df, striped=striped, bordered=True, className=className)
+    return dbc.Table.from_dataframe(df, striped=striped, bordered=True, className=className)

+
 

 
               

diff --git a/development/_modules/deepcave/utils/logs.html b/development/_modules/deepcave/utils/logs.html
new file mode 100644
index 00000000..020c3608
--- /dev/null
+++ b/development/_modules/deepcave/utils/logs.html
@@ -0,0 +1,781 @@
+
+
+
+  
+    
+    
+    deepcave.utils.logs — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.utils.logs
+#  noqa: D400
+"""
+# Logs
+
+This module sets up and gets the logging configuration.
+"""
+
+import logging
+import logging.config
+from pathlib import Path
+
+import yaml
+
+import deepcave
+
+path = Path() / deepcave.__file__
+with (path.parent / "utils" / "logging.yml").open("r") as stream:
+    config = yaml.load(stream, Loader=yaml.FullLoader)
+
+logging.config.dictConfig(config)
+
+
+

+[docs]
+def get_logger(logger_name: str) -> logging.Logger:
+    """
+    Get the logger corresponding to the logger name.
+
+    Parameters
+    ----------
+    logger_name : str
+        The name of the logger.
+
+    Returns
+    -------
+    logging.Logger
+        The logger corresponding to the logger name.
+    """
+    return logging.getLogger(logger_name)

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/utils/notification.html b/development/_modules/deepcave/utils/notification.html
new file mode 100644
index 00000000..f4caecc9
--- /dev/null
+++ b/development/_modules/deepcave/utils/notification.html
@@ -0,0 +1,811 @@
+
+
+
+  
+    
+    
+    deepcave.utils.notification — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.utils.notification
+#  noqa: D400
+"""
+# Notification
+
+This module provides utilities for creating, updating and resetting notifications.
+
+## Classes
+    - Notification: Can create, update and reset a notification.
+"""
+from typing import Optional, Tuple
+
+
+

+[docs]
+class Notification:
+    """Can create, update and reset a notification."""
+
+    def __init__(self) -> None:
+        self._update_required = False
+        self._message: Optional[str] = None
+        self._color: Optional[str] = None
+
+

+[docs]
+    def reset(self) -> None:
+        """Reset the notification."""
+        self._update_required = False
+        self._message = None
+        self._color = None

+
+
+

+[docs]
+    def update(self, message: str, color: str = "danger") -> None:
+        """
+        Update the notification.
+
+        Parameters
+        ----------
+        message : str
+            The message of the notification.
+        color : str, optional
+            The color of the notification.
+            Default is "danger".
+        """
+        self._update_required = True
+        self._message = message
+        self._color = color

+
+
+

+[docs]
+    def get_latest(self) -> Optional[Tuple[str, str]]:
+        """
+        Retrieve the latest notification and reset.
+
+        Returns
+        -------
+        Optional[Tuple[str, str]]
+            The latest notification
+        """
+        if self._update_required and self._message is not None and self._color is not None:
+            result = (self._message, self._color)
+            self.reset()
+
+            return result
+
+        return None

+

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/utils/run_caches.html b/development/_modules/deepcave/utils/run_caches.html
index 8e24feca..73edf6e9 100644
--- a/development/_modules/deepcave/utils/run_caches.html
+++ b/development/_modules/deepcave/utils/run_caches.html
@@ -702,13 +702,25 @@
               

                 
   
Source code for deepcave.utils.run_caches
-from typing import Any, Dict, Iterator, Optional, Union
+#  noqa: D400
+"""
+# RunCaches
+
+This module defines a class for holding the caches for selected runs.
+
+Utilities provided include updating, getting, setting and clearing.
+
+## Classes
+    - RunCaches: Hold the caches for the selected runs.
+"""
+
+from typing import Any, Dict, Optional
 
 import shutil
 
+from deepcave.config import Config
 from deepcave.runs import AbstractRun
 from deepcave.utils.cache import Cache
-from deepcave.utils.hash import string_to_hash
 from deepcave.utils.logs import get_logger
 
 
@@ -716,11 +728,20 @@ 
Source code for deepcave.utils.run_caches
 [docs]
 class RunCaches:
     """
-    Holds the caches for the selected runs. The caches are used for the plugins to store the
+    Hold the caches for the selected runs.
+
+    The caches are used for the plugins to store the
     raw outputs so that raw outputs must not be calculated again.
 
     Each input has its own cache. This change was necessary because it ensures that not all data
-    are loaded if they are not needed.
+    is loaded if not needed.
+
+    Properties
+    ----------
+    cache_dir : Path
+        The path to the cache directory of the run.
+    logger : Logger
+        The logger for the run cache.
     """
 
     def __init__(self, config: "Config"):
@@ -732,7 +753,8 @@ 
Source code for deepcave.utils.run_caches
 [docs]
     def update(self, run: AbstractRun) -> bool:
         """
-        Updates the cache for the given run. If the cache does not exists it will be created.
+        Update the cache for the given run. If the cache does not exists it will be created.
+
         If the run hash is different from the saved variant the cache will be reset.
 
         Parameters
@@ -797,9 +819,9 @@ 
Source code for deepcave.utils.run_caches
 
 

 [docs]
-    def get(self, run: AbstractRun, plugin_id: str, inputs_key: str) -> Dict[str, Any]:
+    def get(self, run: AbstractRun, plugin_id: str, inputs_key: str) -> Optional[Dict[str, Any]]:
         """
-        Returns the raw outputs for the given run, plugin and inputs key.
+        Return the raw outputs for the given run, plugin and inputs key.
 
         Parameters
         ----------
@@ -812,8 +834,13 @@ 
Source code for deepcave.utils.run_caches
 
         Returns
         -------
-        Dict[str, Any]
+        Optional[Dict[str, Any]]
             Raw outputs for the given run, plugin and inputs key.
+
+        Raises
+        ------
+        AssertionError
+            If the outputs of the cache are not a dict.
         """
         filename = self.cache_dir / run.id / plugin_id / f"{inputs_key}.json"
 
@@ -821,14 +848,17 @@ 
Source code for deepcave.utils.run_caches
             return None
 
         cache = Cache(filename, debug=self._debug, write_file=False)
-        return cache.get("outputs")

+        outputs = cache.get("outputs")
+        assert isinstance(outputs, dict), "Outputs of cache must be a dict."
+        return outputs

 
 
 

 [docs]
     def set(self, run: AbstractRun, plugin_id: str, inputs_key: str, value: Any) -> None:
         """
-        Sets the value for the given run, plugin and inputs key.
+        Set the value for the given run, plugin and inputs key.
+
         Since each input key has it's own cache, only necessary data are loaded.
 
         Parameters
@@ -850,18 +880,14 @@ 
Source code for deepcave.utils.run_caches
 

 [docs]
     def clear_run(self, run: AbstractRun) -> None:
-        """
-        Removes all caches for the given run.
-        """
+        """Remove all caches for the given run."""
         shutil.rmtree(self.cache_dir / run.id)

 
 
 

 [docs]
     def clear(self) -> None:
-        """
-        Removes all caches.
-        """
+        """Remove all caches."""
         try:
             shutil.rmtree(self.cache_dir)
         except Exception:
diff --git a/development/_modules/deepcave/utils/styled_plot.html b/development/_modules/deepcave/utils/styled_plot.html
index 4b023b7a..06fcbf18 100644
--- a/development/_modules/deepcave/utils/styled_plot.html
+++ b/development/_modules/deepcave/utils/styled_plot.html
@@ -702,18 +702,38 @@
               

                 
   
Source code for deepcave.utils.styled_plot
-from distutils.spawn import find_executable
+#  noqa: D400
+"""
+# StyledPlot
 
-import matplotlib
+This module provides utilities to customize functions from the matplotlib.
+Plots can be created and different parameters of the plots can be defined.
+
+## Classes
+    - StyledPlot: Overwrites default settings from matplotlib.pyplot.
+
+## Constants
+    FIG_WIDTH: float
+    FIG_HEIGHT: float
+"""
+
+from typing import Any, Dict, Optional, Union
 
-matplotlib.use("Agg")
 import base64
 import io
+from distutils.spawn import find_executable
+from pathlib import Path
 
+import matplotlib
 import matplotlib.pyplot as plt
+import numpy as np
+from numpy.typing import ArrayLike
 
 from deepcave.utils.logs import get_logger
 
+matplotlib.use("Agg")
+
+
 # IEEETrans double column standard
 FIG_WIDTH = 252.0 / 72.27  # 1pt is 1/72.27 inches
 FIG_HEIGHT = FIG_WIDTH / 1.618  # golden ratio
@@ -727,10 +747,17 @@ 
Source code for deepcave.utils.styled_plot
 class StyledPlot:
     """
     Overwrites default settings from matplotlib.pyplot.
+
     If a function is not overwritten, the default function will be used.
+
+    Properties
+    ----------
+    plt : Module("matplotlib.pyplot")
+        The matplotlib plot.
+        The style used is seaborn.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         plt.style.use("seaborn-v0_8")
 
         # Set MatPlotLib defaults
@@ -753,7 +780,29 @@ 
Source code for deepcave.utils.styled_plot
 
         self.plt = plt
 
-    def figure(self, cols=1, rows=1, dpi=200):
+

+[docs]
+    def figure(self, cols: int = 1, rows: int = 1, dpi: int = 200) -> plt.Figure:
+        """
+        Create a new figure using the input values.
+
+        Parameters
+        ----------
+        cols : int, optional
+            The number of the columns.
+            Default is 1.
+        rows : int, optional
+            The number of the rows.
+            Default is 1.
+        dpi : int, optional
+            The dots per inches (dpi).
+            Default is 200.
+
+        Returns
+        -------
+        plt.Figure
+            The figure created with the input information.
+        """
         # Clean all
         self.plt.cla()
         self.plt.clf()
@@ -761,14 +810,36 @@ 
Source code for deepcave.utils.styled_plot
         f = self.plt.figure(figsize=(FIG_WIDTH * cols, FIG_HEIGHT * rows), dpi=dpi)
         f.tight_layout()
 
-        return f
+        return f

+
 
-    def save_figure(self, filename):
+

+[docs]
+    def save_figure(self, filename: Union[str, Path]) -> None:
+        """
+        Save the figure/plot at the given filename.
+
+        Parameters
+        ----------
+        filename : Union[str, Path]
+            The name of the file the plot will be saved at.
+        """
         self.plt.savefig(filename, dpi=400, bbox_inches="tight")
-        self.plt.close()
+        self.plt.close()

 
-    def render(self):
-        # Ccreate a virtual file which matplotlib can use to save the figure
+
+

+[docs]
+    def render(self) -> str:
+        """
+        Render the Styled Plot for displaying.
+
+        Returns
+        -------
+        str
+            The rendered plot.
+        """
+        # Create a virtual file which matplotlib can use to save the figure
         buffer = io.BytesIO()
         self.plt.savefig(buffer, dpi=400, bbox_inches="tight")
         buffer.seek(0)
@@ -776,26 +847,76 @@ 
Source code for deepcave.utils.styled_plot
         # Display any kind of image taken from
         # https://github.com/plotly/dash/issues/71
         encoded_image = base64.b64encode(buffer.read())
-        return "data:image/png;base64,{}".format(encoded_image.decode())
+        return "data:image/png;base64,{}".format(encoded_image.decode())

+
 
-    def xlim(self, xmin, xmax):
+

+[docs]
+    def xlim(self, xmin: Union[float, int], xmax: Union[float, int]) -> None:
+        """
+        Set the x-axis limits with a margin of a matplotlib plot.
+
+        Parameters
+        ----------
+        xmin : Union[float, int]
+            The lower x-axis limit.
+        xmax : Union[float, int]
+            The upper x-axis limit.
+        """
         xmin_with_margin = xmin - 0.05 * (xmax - xmin)
         xmax_with_margin = xmax + 0.05 * (xmax - xmin)
-        self.plt.xlim(xmin_with_margin, xmax_with_margin)
+        self.plt.xlim(xmin_with_margin, xmax_with_margin)

+
 
-    def ylim(self, ymin, ymax, margin=True):
+

+[docs]
+    def ylim(self, ymin: Union[float, int], ymax: Union[float, int], margin: bool = True) -> None:
+        """
+        Set the y-axis limit of a matplotlib plot.
+
+        Parameters
+        ----------
+        ymin : Union[float, int]
+            The lower y-axis limit.
+        ymax : Union[float, int]
+            The upper y-axis limit.
+        margin : bool, optional
+            Determines whether a margin should be added to the limits.
+            Default is True.
+        """
         if margin:
             ymin_with_margin = ymin - 0.05 * (ymax - ymin)
             ymax_with_margin = ymax + 0.05 * (ymax - ymin)
             self.plt.ylim(ymin_with_margin, ymax_with_margin)
         else:
-            self.plt.ylim(ymin, ymax)
+            self.plt.ylim(ymin, ymax)

+
 
     # def grid(self):
     #    pass
-    #    #self.plt.grid(b=True, color='black', linestyle='--', linewidth=0.5, axis='y', zorder=0, alpha=0.5)
+    #    # self.plt.grid(b=True, color='black', linestyle='--', linewidth=0.5, axis='y', zorder=0,
+    #    # alpha=0.5)
+
+

+[docs]
+    def boxplot(
+        self, values: np.ndarray, positions: ArrayLike, color: str, widths: float = 0.5
+    ) -> None:
+        """
+        Create a boxplot on a matplotlib plot.
 
-    def boxplot(self, values, positions, color, widths=0.5):
+        Parameters
+        ----------
+        values : np.ndarray
+            Values to create the boxplot.
+        positions : ArrayLike
+            The position of the boxplot.
+        color : str
+            The color of the boxes as well as other elements in the plot.
+        widths : float, optional
+            The width of the boxes.
+            Default is 0.5.
+        """
         bp = self.plt.boxplot(values, positions=positions, patch_artist=True, widths=widths)
 
         for box in bp["boxes"]:
@@ -820,10 +941,39 @@ 
Source code for deepcave.utils.styled_plot
                 markeredgecolor="none",
                 color=color,
                 alpha=0.5,
-            )
+            )

+
 
-    def legend(self, cols=1, loc=None, title=None, outside=False):
-        kwargs = {
+

+[docs]
+    def legend(
+        self,
+        cols: int = 1,
+        loc: Optional[str] = None,
+        title: Optional[str] = None,
+        outside: bool = False,
+    ) -> None:
+        """
+        Customize and add a legend to a matplot plot.
+
+        Customize the placement and appearance of the legend.
+
+        Parameters
+        ----------
+        cols : int, optional
+            The number of the columns.
+            Default is 1
+        loc : Optional[str], optional
+            The location of the legend.
+            Default is None.
+        title : Optional[str], optional
+            The title for the legend.
+            Default is None.
+        outside : bool, optional
+            Determines if a legend is placed outside of plot area.
+            Default is False.
+        """
+        kwargs: Dict[str, Any] = {
             "ncol": cols,
             "columnspacing": 0.8,
             "labelspacing": 0,
@@ -850,32 +1000,45 @@ 
Source code for deepcave.utils.styled_plot
             legend.get_frame().set_edgecolor("white")
         else:
             legend.get_frame().set_linewidth(0.5)
-            legend.get_frame().set_edgecolor("gray")
+            legend.get_frame().set_edgecolor("gray")

+
+
+

+[docs]
+    def get_color(self, id: int) -> str:
+        """
+        Get the color from color palette based on the given id.
+
+        Parameters
+        ----------
+        id : int
+            id for retrieving a specific color.
 
-    def get_color(self, id):
+        Returns
+        -------
+        str
+            The color from the color palette.
+        """
         import seaborn as sns
 
         pal = sns.color_palette()
         hex_codes = pal.as_hex()
+        return hex_codes[id % len(hex_codes)]

 
-        return hex_codes[id % len(hex_codes)]
 
 

 [docs]
-    def __getattr__(self, name):
-        """
-        Make sure we access self.plt directly.
-        """
-
+    def __getattr__(self, name: str) -> Any:
+        """Make sure self.plt is accessed directly."""
         try:
             return self.__getattribute__(name)
-        except:
+        except AttributeError:
             return self.plt.__getattribute__(name)

 

 
 
 
-plt = StyledPlot()
+plot = StyledPlot()
 

 
               

diff --git a/development/_modules/deepcave/utils/styled_plotty.html b/development/_modules/deepcave/utils/styled_plotty.html
index 65279bdb..f1c27799 100644
--- a/development/_modules/deepcave/utils/styled_plotty.html
+++ b/development/_modules/deepcave/utils/styled_plotty.html
@@ -702,7 +702,15 @@
               

                 
   
Source code for deepcave.utils.styled_plotty
-from typing import Any, Callable, List, Optional, Tuple, Union
+# noqa: D400
+"""
+# Styled Plotty
+
+This module provides utilities for styling and customizing different plots with plotly.
+For this, it uses plotly as well as dash.
+"""
+
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import itertools
 import re
@@ -721,6 +729,7 @@ 
Source code for deepcave.utils.styled_plotty
from deepcave import interactive
 from deepcave.constants import CONSTANT_VALUE, NAN_LABEL, NAN_VALUE
+from deepcave.runs import AbstractRun
 from deepcave.utils.logs import get_logger
 
 logger = get_logger(__name__)
@@ -731,24 +740,25 @@ 
Source code for deepcave.utils.styled_plotty
@interactive
 def save_image(figure: go.Figure, name: str) -> None:
     """
-    Saves a plotly figure as an image.
+    Save a plotly figure as an image.
 
     Parameters
     ----------
-    fig : go.Figure
+    figure : go.Figure
         Plotly figure.
     name : str
-        Name of the image with extension. Will be automatically saved to the cache.
+        Name of the image with extension.
+        Will be automatically saved to the cache.
     """
-    from deepcave.config import Config
+    from deepcave import config
 
-    if not Config.SAVE_IMAGES:
+    if not config.SAVE_IMAGES:
         return
 
     ratio = 16 / 9
     width = 500
     height = int(width / ratio)
-    path = Config.CACHE_DIR / "figures" / name
+    path = config.CACHE_DIR / "figures" / name
 
     figure.write_image(path, width=width, height=height)
     logger.info(f"Saved figure {name} to {path}.")

@@ -759,10 +769,27 @@ 
Source code for deepcave.utils.styled_plotty
[docs]
 def hex_to_rgb(hex_string: str) -> Tuple[int, int, int]:
     """
-    Converts a hex_string to a tuple of rgb values.
+    Convert a hex_string to a tuple of rgb values.
+
     Requires format including #, e.g.:
     #000000
     #ff00ff
+
+    Parameters
+    ----------
+    hex_string : str
+        The hex string to be converted.
+
+    Returns
+    -------
+    Tuple[int, int, int]
+        A Tuple of the converted RGB values
+
+    Raises
+    ------
+    ValueError
+        If the hex string is longer than 7.
+        If there are invalid characters in the hex string.
     """
     if len(hex_string) != 7:
         raise ValueError(f"Invalid length for #{hex_string}")
@@ -781,7 +808,19 @@ 
Source code for deepcave.utils.styled_plotty
[docs]
 def get_color(id_: int, alpha: float = 1) -> Union[str, Tuple[float, float, float, float]]:
     """
-    Using Plotly palette for the first 10 ids and Alphabet palette for the next 26, currently 36 colors are possible.
+    Get an RGBA Color, currently (Plotly version 5.3.1) there are 10 possible colors.
+
+    Parameters
+    ----------
+    id_ : int
+        ID for retrieving a specific color.
+    alpha : float, optional
+        Alpha value for the color, by default 1.
+
+    Returns
+    -------
+    Union[str, Tuple[float, float, float, float]]
+        The color from the color palette.
     """
     if id_ < 10:
         color = px.colors.qualitative.Plotly[id_]
@@ -795,19 +834,27 @@ 
Source code for deepcave.utils.styled_plotty
 [docs]
-def get_discrete_heatmap(x, y, values: List[Any], labels: List[Any]):
+def get_discrete_heatmap(
+    x: List[Union[float, int]], y: List[int], values: List[Any], labels: List[Any]
+) -> go.Heatmap:
     """
     Generate a discrete colorscale from a (nested) list or numpy array of values.
 
     Parameters
     ----------
-    values : _type_
-        _description_
+    x : List[Union[float, int]]
+        List of values that present the x-axis of the heatmap.
+    y : List[int]
+         List of values that present the y-axis of the heatmap.
+    values : List[Any]
+        Contains the data values for the heatmap.
+    labels : List[Any]
+        Contains the labels corresponding to the values.
 
     Returns
     -------
-    _type_
-        _description_
+    go.Heatmap
+        A Plotly Heatmap object corresponding to the input.
     """
     flattened_values = list(itertools.chain(*values))
     flattened_labels = list(itertools.chain(*labels))
@@ -827,8 +874,8 @@ 
Source code for deepcave.utils.styled_plotty
unique_sorted_values += [unique_values[idx]]
         unique_sorted_labels += [unique_labels[idx]]
 
-    # Now we give them new ids and we want to create new z values
-    # For that we need a mapping from old to new
+    # Now they are given new ids, and new z values should be created
+    # For that a mapping from old to new is needed
     mapping = {}
     v = []
     for new, old in enumerate(unique_sorted_values):
@@ -840,8 +887,11 @@ 
Source code for deepcave.utils.styled_plotty
for i2, v2 in enumerate(v1):
             z[i1][i2] = mapping[v2]
 
-    n_intervals = v + [len(v)]
-    n_intervals = [(i - n_intervals[0]) / (n_intervals[-1] - n_intervals[0]) for i in n_intervals]
+    n_intervals_int = v + [len(v)]
+    n_intervals = [
+        (i - n_intervals_int[0]) / (n_intervals_int[-1] - n_intervals_int[0])
+        for i in n_intervals_int
+    ]
     colors = [get_color(i) for i in range(len(n_intervals))]
 
     discrete_colorscale = []
@@ -851,12 +901,12 @@ 
Source code for deepcave.utils.styled_plotty
tickvals = [np.mean(n_intervals[k : k + 2]) for k in range(len(n_intervals) - 1)]
     ticktext = unique_sorted_labels
 
-    x = [str(i) for i in x]
-    y = [str(i) for i in y]
+    x_str = [str(i) for i in x]
+    y_str = [str(i) for i in y]
 
     return go.Heatmap(
-        x=x,
-        y=y,
+        x=x_str,
+        y=y_str,
         z=z,
         showscale=True,
         colorscale=discrete_colorscale,
@@ -872,7 +922,9 @@ 
Source code for deepcave.utils.styled_plotty
[docs]
 def prettify_label(label: Union[str, float, int]) -> str:
     """
-    Takes a label and prettifies it. E.g. floats are shortened.
+    Take a label and prettifies it.
+
+    E.g. floats are shortened.
 
     Parameters
     ----------
@@ -912,9 +964,11 @@ 
Source code for deepcave.utils.styled_plotty
include_nan: bool = True,
 ) -> Tuple[List, List]:
     """
-    Generates tick data for both tickvals and ticktext. The background is that
-    you might have encoded data but you don't want to show all of them.
-    With this function, only 6 (default) values are shown. This behaviour is
+    Generate tick data for both tickvals and ticktext.
+
+    The background is that
+    you might have encoded data, but you don't want to show all of them.
+    With this function, only 6 (default) values are shown. This behavior is
     ignored if `hp` is categorical.
 
     Parameters
@@ -922,9 +976,9 @@ 
Source code for deepcave.utils.styled_plotty
    hp : Hyperparameter
         Hyperparameter to generate ticks from.
     additional_values : Optional[List], optional
-        Additional values, which are forced in addition. By default None.
+        Additional values, which are forced in addition. By default, None.
     ticks : int, optional
-        Number of ticks, by default 6
+        Number of ticks, by default 4
     include_nan : bool, optional
         Whether "nan" as tick should be included. By default True.
 
@@ -933,8 +987,8 @@ 
Source code for deepcave.utils.styled_plotty
    Tuple[List, List]
         tickvals and ticktext.
     """
-
     # This is basically the inverse of `encode_config`.
+    tickvals: List[Union[float, int]]
     if isinstance(hp, CategoricalHyperparameter):
         ticktext = hp.choices
         if len(ticktext) == 1:
@@ -951,7 +1005,7 @@ 
Source code for deepcave.utils.styled_plotty
min_v = 0
         max_v = 1
 
-        values = [min_v]
+        values: List[Union[float, int]] = [min_v]
 
         # Get values for each tick
         factors = [i / (ticks - 1) for i in range(1, ticks - 1)]
@@ -969,7 +1023,7 @@ 
Source code for deepcave.utils.styled_plotty
for value in values:
             inverse_values += [hp._transform_scalar(value)]
 
-        # Integers are rounded, so we map then back
+        # Integers are rounded, they are mapped
         if isinstance(hp, IntegerHyperparameter):
             for label in inverse_values:
                 value = hp._inverse_transform(label)
@@ -979,7 +1033,7 @@ 
Source code for deepcave.utils.styled_plotty
ticktext += [label]
 
             if additional_values is not None:
-                # Now we add additional values
+                # Now add additional values are added
                 for value in additional_values:
                     if not (value is None or np.isnan(value) or value == NAN_VALUE):
                         label = hp._transform_scalar(value)
@@ -994,7 +1048,7 @@ 
Source code for deepcave.utils.styled_plotty
ticktext += [label]
 
             if additional_values is not None:
-                # Now we add additional values
+                # Now additional values are added
                 for value in additional_values:
                     if (
                         not (value is None or np.isnan(value) or value == NAN_VALUE)
@@ -1019,9 +1073,11 @@ 
Source code for deepcave.utils.styled_plotty
values: List, labels: List, forced: Optional[List[bool]] = None, ticks: int = 6
 ) -> Tuple[List, List]:
     """
-    Generates tick data for both values and labels. The background is that
-    you might have encoded data but you don't want to show all of them.
-    With this function, only 6 (default) values are shown. This behaviour is
+    Generate tick data for both values and labels.
+
+    The background is that
+    you might have encoded data, but you don't want to show all of them.
+    With this function, only 6 (default) values are shown. This behavior is
     ignored if `values` is a list of strings.
 
     Parameters
@@ -1030,9 +1086,9 @@ 
Source code for deepcave.utils.styled_plotty
        List of values.
     labels : List
         List of labels. Must be the same size as `values`.
-    forced : List[bool], optional
+    forced : Optional[List[bool]], optional
         List of booleans. If True, displaying the particular tick is enforced.
-        Independent from `ticks`.
+        Independent of `ticks`.
     ticks : int, optional
         Number of ticks and labels to show. By default 6.
 
@@ -1040,6 +1096,11 @@ 
Source code for deepcave.utils.styled_plotty
    -------
     Tuple[List, List]
         Returns tickvals and ticktext as list.
+
+    Raises
+    ------
+    RuntimeError
+        If values contain both strings and non-strings.
     """
     assert len(values) == len(labels)
 
@@ -1061,9 +1122,9 @@ 
Source code for deepcave.utils.styled_plotty
tickvals = []
     ticktext = []
 
-    # If we have less than x values, we also show them
+    # If there are less than x values, they are also shown
     if return_all or len(unique_values) <= ticks:
-        # Make sure we don't have multiple (same) labels for the same value
+        # Make sure there are no multiple (same) labels for the same value
         for value, label in zip(unique_values, unique_labels):
             tickvals.append(value)
             ticktext.append(label)
@@ -1073,8 +1134,8 @@ 
Source code for deepcave.utils.styled_plotty
tickvals.append(values[idx])
             ticktext.append(labels[idx])
 
-        # After we added min and max values, we want to add
-        # intermediate values too
+        # After min and max values are added,
+        # intermediate values should be added too
         min_v = np.min(values)
         max_v = np.max(values)
 
@@ -1104,7 +1165,27 @@ 
Source code for deepcave.utils.styled_plotty
def get_hovertext_from_config(run: "AbstractRun", config_id: int) -> str:
+

+[docs]
+def get_hovertext_from_config(run: AbstractRun, config_id: int) -> str:
+    """
+    Generate hover text with metrics for a configuration.
+
+    The method gets information about a given configuration, including a link, its objectives,
+    budget, costs and hyperparameters.
+
+    Parameters
+    ----------
+    run : AbstractRun
+        The run instance
+    config_id : int
+        The id of the configuration
+
+    Returns
+    -------
+    str
+        The hover text string containing the configuration information.
+    """
     if config_id < 0:
         return ""
 
@@ -1121,6 +1202,7 @@ 
Source code for deepcave.utils.styled_plotty
budget = run.get_highest_budget(config_id)
     costs = run.get_costs(config_id, budget)
 
+    assert budget is not None
     string += f"<b>Objectives</b> (on highest found budget {round(budget, 2)})<br>"
     for objective, cost in zip(objectives, costs):
         string += f"{objective.name}: {cost}<br>"
@@ -1131,10 +1213,29 @@ 
Source code for deepcave.utils.styled_plotty
for k, v in config.items():
         string += f"{k}: {v}<br>"
 
-    return string
+    return string

 
 
+
+

+[docs]
 def generate_config_code(register: Callable, variables: List[str]) -> List[Component]:
+    """
+    Generate HTML components to display code.
+
+    Parameters
+    ----------
+    register : Callable
+        A Callable for registering Dash components.
+        The register_input function is located in the Plugin class.
+    variables : List[str]
+        A List of variable names.
+
+    Returns
+    -------
+    List[Component]
+        A List of Dash components.
+    """
     code = """
     from ConfigSpace.configuration_space import ConfigurationSpace, Configuration
     from ConfigSpace.read_and_write import cs_json
@@ -1193,7 +1294,8 @@ 
Source code for deepcave.utils.styled_plotty
]
 
     components = components[1 : len(components) - 1]
-    return components
+    return components

+
 

 
               

diff --git a/development/_modules/deepcave/utils/symbolic_regression.html b/development/_modules/deepcave/utils/symbolic_regression.html
index 663ca5c6..2cce83c8 100644
--- a/development/_modules/deepcave/utils/symbolic_regression.html
+++ b/development/_modules/deepcave/utils/symbolic_regression.html
@@ -709,7 +709,7 @@ 
Source code for deepcave.utils.symbolic_regression
This module provides utilities for running symbolic regression with gplearn.
 """
 
-from typing import List, Union
+from typing import List, Optional, Union
 
 import numpy as np
 import sympy
@@ -724,22 +724,26 @@ 
Source code for deepcave.utils.symbolic_regression

 [docs]
-def exp(x):
+def exp(x: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
     """
     Get a safe exp function with a maximum value of 100000 to avoid overflow.
 
     Parameters
     ----------
-    x : float
+    x : Union[float, np.ndarray]
         The value to calculate the exponential of.
 
     Returns
     -------
-    float
+    Union[float, np.ndarray]
         The safe exponential of x.
     """
     with np.errstate(all="ignore"):
-        max_value = np.full(shape=x.shape, fill_value=100000)
+        max_value: Union[float, np.ndarray]
+        if isinstance(x, np.ndarray):
+            max_value = np.full(shape=x.shape, fill_value=100000)
+        else:
+            max_value = 100000.0
         return np.minimum(np.exp(x), max_value)

 
 
@@ -766,11 +770,10 @@ 
Source code for deepcave.utils.symbolic_regression

 [docs]
 def convert_symb(
-    symb: SymbolicRegressor, n_decimals: int = None, hp_names: list = None
-) -> sympy.core.expr:
+    symb: SymbolicRegressor, n_decimals: Optional[int] = None, hp_names: Optional[List[str]] = None
+) -> str:
     """
-    Convert a fitted symbolic regression to a simplified and potentially rounded mathematical
-    expression.
+    Convert a fitted symbolic regression to a simplified and rounded mathematical expression.
 
     Warning: eval is used in this function, thus it should not be used on unsanitized input (see
     https://docs.sympy.org/latest/modules/core.html?highlight=eval#module-sympy.core.sympify).
@@ -786,7 +789,7 @@ 
Source code for deepcave.utils.symbolic_regression
    Returns
     -------
-    SymbolicRegressor
+    str
         Converted mathematical expression.
     """
     # sqrt is protected function in gplearn, always returning sqrt(abs(x))
@@ -838,8 +841,8 @@ 
Source code for deepcave.utils.symbolic_regression
symb_conv = symb_conv.subs(X1, hp1)
         else:
             raise ValueError(
-                f"Numer of hyperparameters to be explained by symbolic explanations must not "
-                f"be larger than 2"
+                "Numer of hyperparameters to be explained by symbolic explanations must not "
+                "be larger than 2"
             )
 
     # Simplify the formula
@@ -860,7 +863,7 @@ 
Source code for deepcave.utils.symbolic_regression
if isinstance(a, sympy.core.numbers.Float):
                 symb_simpl = symb_simpl.subs(a, round(a, n_decimals))
 
-    return symb_simpl

+    return str(symb_simpl)

 
 

 
diff --git a/development/_modules/deepcave/utils/url.html b/development/_modules/deepcave/utils/url.html
new file mode 100644
index 00000000..b3b84630
--- /dev/null
+++ b/development/_modules/deepcave/utils/url.html
@@ -0,0 +1,807 @@
+
+
+
+  
+    
+    
+    deepcave.utils.url — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.utils.url
+#  noqa: D400
+"""
+# URL
+
+This module creates and parses an URL according to its input information.
+"""
+
+from typing import Any, Dict, Optional
+
+import json
+import urllib
+
+
+

+[docs]
+def create_url(pathname: str, inputs: Dict[str, Any]) -> str:
+    """
+    Create an URL with the given pathname and inputs.
+
+    Parameters
+    ----------
+    pathname : str
+        The name of the path for the URL.
+    inputs : Dict[str, Any]
+        A dictionary containing the input parameters for the URL.
+
+    Returns
+    -------
+    str
+        The URL.
+
+    """
+    string = json.dumps(inputs)
+    converted_string = urllib.parse.quote(string)
+
+    return f"{pathname}/?inputs={converted_string}"

+
+
+
+

+[docs]
+def parse_url(pathname: str) -> Optional[Dict[str, Any]]:
+    """
+    Parse the URL and extract input information if possible.
+
+    Parameters
+    ----------
+    pathname : str
+        The name of the URL.
+
+    Returns
+    -------
+    Optional[Dict[str, Any]]
+        A dictionary with the extracted inputs if available.
+        Otherwise return None.
+    """
+    url = urllib.parse.urlparse(pathname)
+    query = urllib.parse.parse_qs(url.query)
+
+    if "inputs" in query:
+        string = query["inputs"][0]
+
+        return json.loads(string)
+
+    return None

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/deepcave/utils/util.html b/development/_modules/deepcave/utils/util.html
new file mode 100644
index 00000000..ee9d4092
--- /dev/null
+++ b/development/_modules/deepcave/utils/util.html
@@ -0,0 +1,856 @@
+
+
+
+  
+    
+    
+    deepcave.utils.util — DeepCAVE Documentation 1.2 documentation
+    
+  
+  
+
+    
+  
+  
+  
+
+    
+      
+
+    
+    
+    
+    
+    
+    
+    
+    
+  
+
+    
+    
+    
+    
+    
+    
+    
+    
+
+    
+    
+  
+  
+    
+    
+
+    
+    
+    
+
+    

+      

+          
+            
+            
+            
DeepCAVE Documentation

+
v1.2

+            

+            
+          
+
+          
+          

+            
+          

+          
+
+          
+          
+            
+          
+          

+              
+              

+                
+  
Source code for deepcave.utils.util
+#  noqa: D400
+"""
+# Util
+
+This module provides utilities for string generation and shortening.
+It also provides a function to get the difference between now and a given timestamp.
+"""
+from typing import Any
+
+import datetime
+import random
+import string
+
+
+

+[docs]
+def get_random_string(length: int) -> str:
+    """
+    Get a random string with a specific length.
+
+    Parameters
+    ----------
+    length : int
+        The length of the string.
+
+    Returns
+    -------
+    str
+        The random string with the given length.
+
+    Raises
+    ------
+    ValueError
+        If the length is smaller 0.
+    """
+    if length < 0:
+        raise ValueError("Length has to be greater than 0")
+    letters = string.ascii_lowercase
+    return "".join(random.choice(letters) for _ in range(length))

+
+
+
+

+[docs]
+def short_string(value: Any, length: int = 30, *, mode: str = "prefix") -> str:
+    """
+    Shorten the given string.
+
+    Cut either at prefix or at suffix if necessary.
+
+    Parameters
+    ----------
+    value : Any
+        The value or string to shorten.
+    length : int, optional
+        The length of the returned string.
+        Default is 30.
+    mode : str, optional
+        Define how to shorten the string.
+        Default is "prefix".
+
+    Returns
+    -------
+    str
+        The shortened string.
+
+    Raises
+    ------
+    ValueError
+        If the given mode is unknown.
+    """
+    value = str(value)
+    if len(value) <= length:
+        return value
+
+    cut_length = length - 3  # For 3 dots (...)
+    if mode == "prefix":
+        return f"...{value[-cut_length:]}"
+    elif mode == "suffix":
+        return f"{value[:cut_length]}..."
+    raise ValueError(f"Unknown mode '{mode}'")

+
+
+
+

+[docs]
+def get_latest_change(st_mtime: int) -> str:
+    """
+    Get the difference between now and a given timestamp.
+
+    Parameters
+    ----------
+    st_mtime : int
+        A timestamp to calculate the difference from.
+
+    Returns
+    -------
+    str
+        A string containing the passed time.
+    """
+    t = datetime.datetime.fromtimestamp(st_mtime)
+    s_diff = (datetime.datetime.now() - t).seconds
+    d_diff = (datetime.datetime.now() - t).days
+
+    if s_diff < 60:
+        return "Some seconds ago"
+    elif s_diff < 3600:
+        return f"{int(s_diff / 60)} minutes ago"
+    elif s_diff < 86400:
+        return f"{int(s_diff / 60 / 60)} hours ago"
+    elif d_diff < 7:
+        return f"{d_diff} days ago"
+    else:
+        return t.strftime("%Y/%m/%d")

+
+

+
+              

+              
+              
+              
+

+

+              
+          

+          
+
+      

+    

+  
+  
+

+  

+    
+    

+      

+    © Copyright Copyright 2024, R. Sass and E. Bergman and A. Biedenkapp and F. Hutter and M. Lindauer.

+

+    

+    
+    

+      

+Created using Sphinx
+7.2.6. Template is modified version of PyData Sphinx Theme. 

+

+    

+    
+  

+

+  
+
\ No newline at end of file
diff --git a/development/_modules/index.html b/development/_modules/index.html
index bd6a16ac..5b0bf8ae 100644
--- a/development/_modules/index.html
+++ b/development/_modules/index.html
@@ -707,6 +707,8 @@ 
All modules for which code is available

 
  • deepcave.evaluators.epm.random_forest_surrogate
    • 
 
  • deepcave.evaluators.epm.utils
    • 
 
  • deepcave.evaluators.fanova
    • 
+
  • deepcave.evaluators.footprint
    • 
+
  • deepcave.evaluators.lpi
    • 
 
  • deepcave.layouts
    • 
 
  • deepcave.utils.cache
    • 
+
  • deepcave.utils.cast
    • 
 
  • deepcave.utils.compression
    • 
 
  • deepcave.utils.configs
    • 
 
  • deepcave.utils.configspace
    • 
 
  • deepcave.utils.dash
    • 
 
  • deepcave.utils.data_structures
    • 
+
  • deepcave.utils.docs
    • 
+
  • deepcave.utils.files
    • 
+
  • deepcave.utils.hash
    • 
 
  • deepcave.utils.layout
    • 
+
  • deepcave.utils.logs
    • 
+
  • deepcave.utils.notification
    • 
 
  • deepcave.utils.run_caches
    • 
 
  • deepcave.utils.styled_plot
    • 
 
  • deepcave.utils.styled_plotty
    • 
 
  • deepcave.utils.symbolic_regression
    • 
+
  • deepcave.utils.url
    • 
+
  • deepcave.utils.util
    • 
 
 
               
    
diff --git a/development/_sources/examples/api/importances.rst.txt b/development/_sources/examples/api/importances.rst.txt
index 99bf5b43..9188f987 100644
--- a/development/_sources/examples/api/importances.rst.txt
+++ b/development/_sources/examples/api/importances.rst.txt
@@ -24,7 +24,7 @@ Importances
 This example shows how to use the plugin Importances.
 Note that other plugins use the same interfaces and can be used in the same fashion.
 
-.. GENERATED FROM PYTHON SOURCE LINES 8-38
+.. GENERATED FROM PYTHON SOURCE LINES 8-39
 
 .. code-block:: Python
 
@@ -49,7 +49,7 @@ Note that other plugins use the same interfaces and can be used in the same fash
             budget_ids=budget_ids,
             method="global",
             n_hps=3,
-            n_trees=10
+            n_trees=10,
         )
         # Note: Filter variables are not considered.
         outputs = plugin.generate_outputs(run, inputs)
@@ -57,9 +57,10 @@ Note that other plugins use the same interfaces and can be used in the same fash
         # Finally, you can load the figure. Here, the filter variables play a role.
         # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available.
         figure = plugin.load_outputs(run, inputs, outputs)  # plotly.go figure
-        figure.write_image("examples/api/importances.png", scale=2.)
+        figure.write_image("examples/api/importances.png", scale=2.0)
         # figure.show()
 
+
 .. _sphx_glr_download_examples_api_importances.py:
 
 .. only:: html
diff --git a/development/_sources/examples/api/parallel_coordinates.rst.txt b/development/_sources/examples/api/parallel_coordinates.rst.txt
index b355f295..5c3f7fd6 100644
--- a/development/_sources/examples/api/parallel_coordinates.rst.txt
+++ b/development/_sources/examples/api/parallel_coordinates.rst.txt
@@ -57,7 +57,7 @@ Note that other plugins use the same interfaces and can be used in the same fash
         # Finally, you can load the figure. Here, the filter variables play a role.
         # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available.
         figure = plugin.load_outputs(run, inputs, outputs)  # plotly.go figure
-        figure.write_image("examples/api/parallel_coordinates.png", scale=2.)
+        figure.write_image("examples/api/parallel_coordinates.png", scale=2.0)
         # figure.show()
 
 
diff --git a/development/_sources/examples/api/pdp.rst.txt b/development/_sources/examples/api/pdp.rst.txt
index 4e8ab866..1adc5ca4 100644
--- a/development/_sources/examples/api/pdp.rst.txt
+++ b/development/_sources/examples/api/pdp.rst.txt
@@ -57,7 +57,7 @@ Note that other plugins use the same interfaces and can be used in the same fash
         # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available.
         figure = plugin.load_outputs(run, inputs, outputs)  # plotly.go figure
         figure.write_image("examples/api/pdp.png")
-        #figure.show()
+        # figure.show()
 
 
 .. _sphx_glr_download_examples_api_pdp.py:
diff --git a/development/api.html b/development/api.html
index b30466ab..2d124ad3 100644
--- a/development/api.html
+++ b/development/api.html
@@ -723,19 +723,19 @@ 
    API References
 
 
    deepcave.runs
    
-
    
+
    # AbstractRun
    
 
 
    deepcave.plugins
    
-
    
+
    # Plugins
    
 
 
    deepcave.layouts
    
-
    
+
    # Layout
    
 
 
    deepcave.evaluators
    
-
    
+
    # evaluators
    
 
 
    deepcave.utils
    
-
    
+
    # utils
    
 
 
 
diff --git a/development/api/deepcave.evaluators.epm.fanova_forest.html b/development/api/deepcave.evaluators.epm.fanova_forest.html
index 141f01fb..b79de81d 100644
--- a/development/api/deepcave.evaluators.epm.fanova_forest.html
+++ b/development/api/deepcave.evaluators.epm.fanova_forest.html
@@ -745,11 +745,21 @@
                 
   
    
 
    deepcave.evaluators.epm.fanova_forest
    
+
    # FanovaForest
    
+
    The module provides utilities for creating a fANOVA forest.
    
+
    It includes a FanovaForest wrapper for pyrfr.
+fANOVA can be used for analyzing the importances of Hyperparameters.
    
+
    
+
    ## Classes
      
+
    • FanovaForest: A fANOVA forest wrapper for pyrfr.
      • 
+
    
+
    
+
    
 
    Classes
    
 
-
+
    
 
 
    FanovaForest(configspace[, n_trees, ...])
    A fanova forest wrapper for pyrfr.
    A fANOVA forest wrapper for pyrfr.
    		
 
    
     
 
    
@@ -757,18 +767,60 @@
 
    
 class deepcave.evaluators.epm.fanova_forest.FanovaForest(configspace, n_trees=10, ratio_features=1.0, min_samples_split=0, min_samples_leaf=0, max_depth=64, max_nodes=1048576, eps_purity=1e-08, bootstrapping=True, instance_features=None, pca_components=2, cutoffs=(-inf, inf), seed=0)[source]
    
 
    Bases: RandomForest
    
-
    A fanova forest wrapper for pyrfr.
    
+
    A fANOVA forest wrapper for pyrfr.
    
+
    
+
    Properties
    
+
    
+
    cutoffsTuple[float, float]
    The cutoffs of the model.
    
+
    
+
    percentilesNDArray[floating]
    The percentiles of the data points Y.
    
+
    
+
    all_midpointsList
    All midpoints tree wise for the whole forest.
    
+
    
+
    all_sizesList
    All interval sizes tree wise for the whole forest.
    
+
    
+
    boundsList[Tuple[float, float]
    Stores feature bounds.
    
+
    
+
    trees_total_variancesList
    The total variances of the trees.
    
+
    
+
    trees_total_varianceAny
    The total variance of a tree.
    
+
    
+
    trees_variance_fractionsDict
    The variance fractions of the trees.
    
+
    
+
    V_U_totalDict[Tuple[int, …], List[Any]]
    Store variance-related information across all trees.
    
+
    
+
    V_U_individualDict[Tuple[int, …], List[Any]]
    Store variance-related information for individual subsets.
    
+
    
+
    n_paramsint
    The number of Hyperparameters to sample.
    
+
    
+
    
 
    
 
    
 compute_marginals(hp_ids, depth=1)[source]
    
-
    Returns the marginal of selected parameters.
    
+
    Return the marginal of selected Hyperparameters.
    
 
    
 
    Parameters:
    
-
    hp_ids (List[int]) – Contains the indices of the configspace for the selected parameters (starts with 0).
    
+
      
+
    • hp_ids (Union[List[int], Tuple[int, ...]]) – Contains the indices of the configspace for the selected Hyperparameters
+(starts with 0).
      • 
+
    • depth (int) – The depth of the marginalization.
+Default value is 1.
      • 
+
    
+
    
+
    Return type:
    
+
    Tuple[Dict[Tuple[int, ...], List[Any]], Dict[Tuple[int, ...], List[Any]]]
    
+
    
+
    Returns:
    
+
      
+
    • Tuple[Dict[Tuple[int, …], List[Any]],
      • 
+
    • Dict[Tuple[int, …], List[Any]], – The marginal of selected Hyperparameters.
      • 
+
    
+
    
 
    
 
    
 
    
 
+

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    diff --git a/development/api/deepcave.evaluators.epm.html b/development/api/deepcave.evaluators.epm.html index 2fed9609..58122df3 100644 --- a/development/api/deepcave.evaluators.epm.html +++ b/development/api/deepcave.evaluators.epm.html @@ -696,9 +696,19 @@
    +
    + On this page +
    @@ -720,20 +730,35 @@

    deepcave.evaluators.epm

    +

    # epm

    +
    +

    ## Modules

    +
    +
    fanova_forest

    The module provides utilities for creating a fANOVA forest.

    +
    +
    random_forest

    This module can be used for training and using a Random Forest Regression model.

    +
    +
    random_forest_surrogate

    This module provides a RandomForest Surrogate model.

    +
    +
    utils

    This module provides a utility to get the types +as well as the bounds of the Hyperparameters.

    +
    +
    +

    Modules

    - + - + - + - +

    deepcave.evaluators.epm.fanova_forest

    # FanovaForest

    deepcave.evaluators.epm.random_forest

    # RandomForest

    deepcave.evaluators.epm.random_forest_surrogate

    # RandomForest Surrogate

    deepcave.evaluators.epm.utils

    # Utils
    diff --git a/development/api/deepcave.evaluators.epm.random_forest.html b/development/api/deepcave.evaluators.epm.random_forest.html index e0563cad..eedf7f45 100644 --- a/development/api/deepcave.evaluators.epm.random_forest.html +++ b/development/api/deepcave.evaluators.epm.random_forest.html @@ -711,6 +711,15 @@ -
  • - - - - random() - - - -
    • @@ -772,6 +772,18 @@

    deepcave.evaluators.epm.random_forest

    +

    # RandomForest

    +

    This module can be used for training and using a Random Forest Regression model.

    +

    A pyrfr wrapper is used for simplification.

    +
    +
    ## Classes
      +

    • RandomForest: A random forest wrapper for pyrfr.

      • +
    +
    +
    ## Constants

    VERY_SMALL_NUMBER : float +PYRFR_MAPPING : Dict[str, str]

    +
    +

    Classes

    @@ -782,29 +794,71 @@
    -class deepcave.evaluators.epm.random_forest.RandomForest(configspace, n_trees=16, ratio_features=0.8333333333333334, min_samples_split=3, min_samples_leaf=3, max_depth=1048576, max_nodes=1048576, eps_purity=1e-08, bootstrapping=True, instance_features=None, pca_components=2, log_y=False, seed=0)
    +class deepcave.evaluators.epm.random_forest.RandomForest(configspace, n_trees=16, ratio_features=0.8333333333333334, min_samples_split=3, min_samples_leaf=3, max_depth=1048576, max_nodes=1048576, eps_purity=1e-08, bootstrapping=True, instance_features=None, pca_components=2, log_y=False, seed=0)[source]

    Bases: object

    -

    A random forest wrapper for pyrfr. This is handy because we only need to pass the configspace +

    A random forest wrapper for pyrfr.

    +

    This is handy because only the configuration space needs to be passed. and have a working version without specifying e.g. types and bounds.

    Note

    This wrapper also supports instances.

    +
    +
    csConfigurationSpace

    The configuration space.

    +
    +
    log_ybool

    Whether y should be treated as a logarithmic transformation.

    +
    +
    seedint

    The seed. If not provided, it is random.

    +
    +
    typesList[int]

    The types of the Hyperparameters.

    +
    +
    boundsList[Tuple[float, float]]

    The bounds of the Hyperparameters.

    +
    +
    n_paramsint

    The number of Hyperparameters in the configuration space.

    +
    +
    n_featuresint

    The number of features.

    +
    +
    pca_componentsint

    The number of components to keep for the principal component analysis (PCA).

    +
    +
    pcaPCA

    The principal component analysis (PCA) object.

    +
    +
    scalerMinMaxScaler

    A MinMaxScaler to scale the features.

    +
    +
    instance_featuresndarray

    The instance features.

    +
    +
    +
    +
    +get_leaf_values(x)[source]
    +

    Get the leaf values of the model.

    +
    +
    Parameters:
    +

    x (np.ndarray) – Input data array.

    +
    +
    Returns:
    +

    The leaf values of the model.

    +
    +
    Return type:
    +

    regression.binary_rss_forest

    +
    +
    +
    +
    -predict(X)
    +predict(X)[source]

    Predict means and variances for a given X.

    Parameters:

    X (np.ndarray [n_samples, n_features (config + instance features)]) – Training samples.

    Return type:
    -

    Tuple[ndarray, Optional[ndarray]]

    +

    Tuple[ndarray, ndarray]

    Returns:

    • means (np.ndarray [n_samples, n_objectives]) – Predictive mean.

      • -

    • vars (Optional[np.ndarray] [n_samples, n_objectives] or [n_samples, n_samples]) – Predictive variance or standard deviation.

      • +

    • vars (np.ndarray [n_samples, n_objectives] or [n_samples, n_samples]) – Predictive variance or standard deviation.

    @@ -813,9 +867,9 @@
    -predict_marginalized(X)
    -

    Predict mean and variance marginalized over all instances. -Returns the predictive mean and variance marginalised over all +predict_marginalized(X)[source] +

    Predict mean and variance marginalized over all instances.

    +

    Return the predictive mean and variance marginalized over all instances for a set of configurations.

    Parameters:
    @@ -836,8 +890,9 @@
    -train(X, Y)
    -

    Trains the random forest on X and Y. Transforms X if PCA is applied. +train(X, Y)[source] +

    Train the random forest on X and Y.

    +

    Transform X if principal component analysis (PCA) is applied. Afterwards, _train is called.

    Parameters:
    @@ -855,11 +910,6 @@
    -
    -
    -deepcave.evaluators.epm.random_forest.random() → x in the interval [0, 1).
    -
    -
    diff --git a/development/api/deepcave.evaluators.epm.random_forest_surrogate.html b/development/api/deepcave.evaluators.epm.random_forest_surrogate.html index 1bd5a912..036e6a03 100644 --- a/development/api/deepcave.evaluators.epm.random_forest_surrogate.html +++ b/development/api/deepcave.evaluators.epm.random_forest_surrogate.html @@ -745,6 +745,15 @@

    deepcave.evaluators.epm.random_forest_surrogate

    +

    # RandomForest Surrogate

    +

    This module provides a RandomForest Surrogate model.

    +

    Mean and standard deviation values can be predicted for a given input with this module.

    +
    +
    ## Classes
      +

    • RandomForestSurrogate: Random forest surrogate for the pyPDP package.

      • +
    +
    +

    Classes

    @@ -758,19 +767,20 @@ class deepcave.evaluators.epm.random_forest_surrogate.RandomForestSurrogate(configspace, seed=None)[source]

    Bases: SurrogateModel

    Random forest surrogate for the pyPDP package.

    +

    Predict deviations and fit the model.

    predict(X)[source]
    -

    Calculate mean and sigma of predictions

    +

    Predict the deviations.

    Parameters:
    -

    X (ndarray) – input data shaped [NxK] for N samples and K parameters

    +

    X (np.ndarray) – The data points on which to predict.

    -
    Return type:
    -

    Tuple[ndarray, ndarray]

    +
    Returns:
    +

    The means and standard deviation.

    -
    Returns:
    -

    means, stds

    +
    Return type:
    +

    Tuple[np.ndarray, np.ndarray]

    diff --git a/development/api/deepcave.evaluators.epm.utils.html b/development/api/deepcave.evaluators.epm.utils.html index 8efd58a2..bc5f1c4e 100644 --- a/development/api/deepcave.evaluators.epm.utils.html +++ b/development/api/deepcave.evaluators.epm.utils.html @@ -734,22 +734,41 @@

    deepcave.evaluators.epm.utils

    +

    # Utils

    +

    This module provides a utility to get the types +as well as the bounds of the Hyperparameters.

    Functions

    - +

    get_types(config_space[, instance_features])

    Return the types of the hyperparameters and the bounds of the hyperparameters and instance features.

    Return the types of the Hyperparameters.
    deepcave.evaluators.epm.utils.get_types(config_space, instance_features=None)[source]
    -

    Return the types of the hyperparameters and the bounds of the -hyperparameters and instance features.

    +

    Return the types of the Hyperparameters.

    +

    Also return the bounds of the Hyperparameters and instance features.

    +
    Parameters:
    +
      +

    • config_space (ConfigurationSpace) – The configuration space.

      • +

    • instance_features (Optional[np.ndarray], optional) – The instance features. +Default is None.

      • +
    +
    +
    Returns:
    +

    The types of the Hyperparameters, as well as the bounds and instance features.

    +
    Return type:
    -

    Tuple[List[int], List[Tuple[float, float]]]

    +

    Tuple[List[int], List[Tuple[float, float]]]

    +
    +
    Raises:
    +
      +

    • ValueError – Inactive parameters not supported for Beta and Normal Hyperparameters.

      • +

    • TypeError – If the Hyperparameter Type is unknown.

      • +
    diff --git a/development/api/deepcave.evaluators.fanova.html b/development/api/deepcave.evaluators.fanova.html index 3d65c715..44b8ac81 100644 --- a/development/api/deepcave.evaluators.fanova.html +++ b/development/api/deepcave.evaluators.fanova.html @@ -754,11 +754,20 @@

    deepcave.evaluators.fanova

    +

    # fANOVA

    +

    This module provides a tool for assessing the importance of an algorithms Hyperparameters.

    +

    Utilities provide calculation of the data wrt the budget and train the forest on the encoded data.

    +
    +
    ## Classes
      +

    • fANOVA: Calculate and provide midpoints and sizes.

      • +
    +
    +

    Classes

    - +

    fANOVA(run)

    Calculate and provide midpoints and sizes from the forest's split values in order to get the marginals.

    Calculate and provide midpoints and sizes.
    @@ -766,12 +775,26 @@
    class deepcave.evaluators.fanova.fANOVA(run)[source]

    Bases: object

    -

    Calculate and provide midpoints and sizes from the forest’s -split values in order to get the marginals.

    +

    Calculate and provide midpoints and sizes.

    +

    They are generated from the forest’s split values in order to get the marginals.

    +
    +

    Properties

    +
    +
    runAbstractRun

    The Abstract Run used for the calculation.

    +
    +
    csConfigurationSpace

    The configuration space of the run.

    +
    +
    hpsList[Hyperparameters]

    The Hyperparameters of the configuration space.

    +
    +
    hp_namesList[str]

    The corresponding names of the Hyperparameters.

    +
    +
    n_treesint

    The number of trees.

    +
    +
    calculate(objectives=None, budget=None, n_trees=16, seed=0)[source]
    -

    Get the data with respect to budget and trains the forest on the encoded data.

    +

    Get the data with respect to budget and train the forest on the encoded data.

    Note

    Right now, only n_trees is used. It can be further specified if needed.

    @@ -779,7 +802,7 @@
    Parameters:
      -

    • objectives (Optional[Union[Objective, List[Objective]]], optional) – Considerd objectives. By default None. If None, all objectives are considered.

      • +

    • objectives (Optional[Union[Objective, List[Objective]]], optional) – Considered objectives. By default None. If None, all objectives are considered.

    • budget (Optional[Union[int, float]], optional) – Considered budget. By default None. If None, the highest budget is chosen.

    • n_trees (int, optional) – How many trees should be used. By default 16.

    • seed (int) – Random seed. By default 0.

      • @@ -794,7 +817,7 @@
      get_importances(hp_names=None, depth=1, sort=True)[source]
      -

      Returns the importance scores from the passed hyperparameter names.

      +

      Return the importance scores from the passed Hyperparameter names.

      Warning

      Using a depth higher than 1 might take much longer.

      @@ -802,14 +825,14 @@
      Parameters:
        -

      • hp_names (Optional[List[str]]) – Selected hyperparameter names to get the importance scores from. If None, all -hyperparameters of the configspace are used.

        • +

      • hp_names (Optional[List[str]]) – Selected Hyperparameter names to get the importance scores from. If None, all +Hyperparameters of the configuration space are used.

      • depth (int, optional) – How often dimensions should be combined. By default 1.

        • -

      • sort (bool, optional) – Whether the hyperparameters should be sorted by importance. By default True.

        • +

      • sort (bool, optional) – Whether the Hyperparameters should be sorted by importance. By default True.

      Returns:
      -

      Dictionary with hyperparameter names and the corresbonding importance scores. +

      Dictionary with Hyperparameter names and the corresponding importance scores. The values are tuples of the form (mean individual, var individual, mean total, var total). Note that individual and total are the same if depth is 1.

      @@ -822,6 +845,7 @@
      +
    diff --git a/development/api/deepcave.evaluators.footprint.html b/development/api/deepcave.evaluators.footprint.html index 16d89d42..bce48925 100644 --- a/development/api/deepcave.evaluators.footprint.html +++ b/development/api/deepcave.evaluators.footprint.html @@ -696,9 +696,52 @@
    +
    + On this page +
    @@ -720,14 +763,122 @@

    deepcave.evaluators.footprint

    +

    # Footprint

    +

    This module provides utilities for creating a footprint of a run. +It uses multidimensional scaling (MDS). +It also provides utilities to get the surface and the points of the plot.

    +
    +
    ## Classes
      +

    • Footprint: Can train and create a footprint of a run.

      • +
    +
    +

    Classes

    - - + +

    Footprint(run)

    Footprint(run)

    Can train and create a footprint of a run.
    +
    +
    +class deepcave.evaluators.footprint.Footprint(run)[source]
    +

    Bases: object

    +

    Can train and create a footprint of a run.

    +

    It uses multidimensional scaling (MDS). +Provides utilities to get the surface and the points of the plot.

    +
    +

    Properties

    +
    +
    runAbstractRun

    The AbstractRun used for the calculation of the footprint.

    +
    +
    csConfigurationSpace

    The configuration space of the run.

    +
    +
    +
    +
    +calculate(objective, budget, support_discretization=10, rejection_rate=0.01, retries=3, exclude_configs=False)[source]
    +

    Calculate the distances and train the model.

    +
    +
    Parameters:
    +
      +

    • objective (Objective) – Objective and color to show.

      • +

    • budget (Union[int, float]) – All configurations with this budget are considered.

      • +

    • support_discretization (Optional[int], optional) – Discretization steps for integer and float hyperparameter (HP) values. +Default is set to 10.

      • +

    • rejection_rate (float, optional) – Rejection rate whether a configuration should be rejected or not. Internally, +the max distance is calculated and if a configuration has a distance smaller than +max distance * rejection_rate, the configuration is rejected. +Default is set to 0.01.

      • +

    • retries (int, optional) – How many times to retry adding a new configuration. +Default is set to 3.

      • +

    • exclude_configs (bool, optional) – Whether the configurations from the run should be excluded +in the multidimensional scaling (MDS). +This is particularly interesting if only the search space should be plotted. +Default is set to False.

      • +
    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    +
    +get_points(category='configs')[source]
    +

    Return the points of the multidimensional scaling (MDS) plot.

    +
    +
    Parameters:
    +

    category (str, optional) – Points of a specific category. Chose between configs, borders, supports +or incumbents. By default configs.

    +
    +
    Returns:
    +

    X, Y and config_ids as lists.

    +
    +
    Return type:
    +

    Tuple[List[float], List[float], List[int]]

    +
    +
    Raises:
    +
      +

    • RuntimeError – If category is not supported.

      • +

    • RuntimeError – If calculated wasn’t called before.

      • +
    +
    +
    +
    + +
    +
    +get_surface(details=0.5, performance=True)[source]
    +

    Get surface of the multidimensional scaling (MDS) plot.

    +
    +
    Parameters:
    +
      +

    • details (float, optional) – Steps to create the meshgrid. By default 0.5.

      • +

    • performance (bool, optional) – Whether to get the surface from the performance or the valid areas. +Default is set to True.

      • +
    +
    +
    Returns:
    +

    x (1D), y (1D) and z (2D) arrays for heatmap.

    +
    +
    Return type:
    +

    Tuple[List, List, List]

    +
    +
    Raises:
    +
      +

    • RuntimeError – If calculate was not called before.

      • +

    • RuntimeError – If evaluated configs weren’t included.

      • +
    +
    +
    +
    + +
    +
    +
    diff --git a/development/api/deepcave.evaluators.html b/development/api/deepcave.evaluators.html index f7cdad23..c1ba5de7 100644 --- a/development/api/deepcave.evaluators.html +++ b/development/api/deepcave.evaluators.html @@ -696,9 +696,19 @@
    +
    + On this page +
    @@ -720,20 +730,32 @@

    deepcave.evaluators

    +

    # evaluators

    +
    +

    ## Modules

    +
    +
    fanova

    This module provides a tool for assessing the importance of an algorithms Hyperparameters.

    +
    +
    footprint

    This module provides utilities for creating a footprint of a run.

    +
    +
    lpi

    This module provides utilities to calculate the local parameter importance (LPI).

    +
    +
    +

    Modules

    - + - + - + - +

    deepcave.evaluators.epm

    # epm

    deepcave.evaluators.fanova

    # fANOVA

    deepcave.evaluators.footprint

    # Footprint

    deepcave.evaluators.lpi

    # LPI
    diff --git a/development/api/deepcave.evaluators.lpi.html b/development/api/deepcave.evaluators.lpi.html index fd7dcfd9..0cdfd207 100644 --- a/development/api/deepcave.evaluators.lpi.html +++ b/development/api/deepcave.evaluators.lpi.html @@ -703,13 +703,33 @@
    diff --git a/development/api/deepcave.layouts.sidebar.html b/development/api/deepcave.layouts.sidebar.html index f809311a..f2e22d00 100644 --- a/development/api/deepcave.layouts.sidebar.html +++ b/development/api/deepcave.layouts.sidebar.html @@ -720,6 +720,15 @@     +
  • + + + + SidebarLayout.register_callbacks() + + + +
    • @@ -745,11 +754,20 @@

    deepcave.layouts.sidebar

    +

    # SidebarLayout

    +

    This module defines layout for the sidebar.

    +

    Callbacks are registered and handled.

    +
    +
    ## Classes
      +

    • SidebarLayout: Customize the Sidebar Layout.

      • +
    +
    +

    Classes

    - +

    SidebarLayout(categorized_plugins)

    Customize the Sidebar Layout.
    @@ -757,6 +775,16 @@
    class deepcave.layouts.sidebar.SidebarLayout(categorized_plugins)[source]

    Bases: Layout

    +

    Customize the Sidebar Layout.

    +

    Callbacks are registered and handled.

    +
    +

    Properties

    +
    +
    pluginsDict[str, List[Plugin]]

    A dictionary of all categorized plugins.

    +
    +
    nav_pointsDict[str, List[Tuple[str, str, str]]]

    A dictionary with plugins attributes corresponding to their category.

    +
    +
    __call__()[source]
    @@ -768,6 +796,18 @@
    +
    +
    +register_callbacks()[source]
    +

    Register the callbacks for the sidebar layout.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    diff --git a/development/api/deepcave.plugins.budget.html b/development/api/deepcave.plugins.budget.html index 63bbaccf..0dfcb238 100644 --- a/development/api/deepcave.plugins.budget.html +++ b/development/api/deepcave.plugins.budget.html @@ -696,9 +696,19 @@
    +
    + On this page +
    @@ -720,6 +730,14 @@

    deepcave.plugins.budget

    +

    # budget

    +
    +

    ## Modules

    +
    +
    budget_correlation

    This module provides utilities to visualize budget correlations.

    +
    +
    +

    Modules

    diff --git a/development/api/deepcave.plugins.dynamic.html b/development/api/deepcave.plugins.dynamic.html index b8eb2028..1dc42133 100644 --- a/development/api/deepcave.plugins.dynamic.html +++ b/development/api/deepcave.plugins.dynamic.html @@ -754,11 +754,20 @@

    deepcave.plugins.dynamic

    +

    # Dynamic

    +

    This module provides a plugin class for a dynamic plugin.

    +

    Register and handle callbacks.

    +
    +
    ## Classes
      +

    • DynamicPlugin: This class provides a dynamic plugin object.

      • +
    +
    +

    Classes

    - +

    DynamicPlugin()

    Provide a dynamic plugin object.
    @@ -766,11 +775,29 @@
    class deepcave.plugins.dynamic.DynamicPlugin[source]

    Bases: Plugin, ABC

    +

    Provide a dynamic plugin object.

    +

    Register and handle callbacks.

    +
    +

    Properties

    +
    +
    outputsList[Tuple[str, str, bool]]

    The registered outputs.

    +
    +
    inputsList[Tuple[str, str, bool, Any]]

    The registered inputs.

    +
    +
    idstr

    The identifier of the Plugin.

    +
    +
    loggerLogger

    The logger for debugging information.

    +
    +
    use_cachebool

    Defines whether to use the cache.

    +
    +
    raw_outputsDict[str, Any]

    A dictionary of the raw outputs.

    +
    +
    __call__()[source]
    -

    Returns the components for the plugin. Basically, all blocks and elements of the plugin -are stacked-up here

    +

    Return the components for the plugin.

    +

    Basically, all blocks and elements of the plugin are stacked-up here.

    Returns:

    Layout as list of components.

    @@ -784,21 +811,24 @@
    register_callbacks()[source]
    -

    Registers basic callbacks for the plugin. Following callbacks are registered: -- If inputs changes, the changes are pasted back. This is in particular -interest if input dependencies are used. -- Raw data dialog to display raw data. -- Callback to be redirected to the config if clicked on it.

    +

    Register basic callbacks for the plugin.

    -
    Raises:
    -

    RuntimeError – _description_

    -
    -
    Return type:
    -

    None

    +
    Return type:
    +

    None

    +

    Following callbacks are registered: +- If inputs changes, the changes are pasted back.

    +
    +

    This is in particular interest if input dependencies are used.

    +
    +
      +

    • Raw data dialog to display raw data.

      • +

    • Callback to be redirected to the config if clicked on it.

      • +
    +
    diff --git a/development/api/deepcave.plugins.html b/development/api/deepcave.plugins.html index 4e756769..f6509de8 100644 --- a/development/api/deepcave.plugins.html +++ b/development/api/deepcave.plugins.html @@ -712,136 +712,109 @@ @@ -988,6 +979,15 @@

    deepcave.plugins

    +

    # Plugins

    +

    This module provides a base class for all the available plugins. +It provides different utilities to handle the plugins and check for compatibility in the runs.

    +
    +
    ## Classes
      +

    • Plugin: Base class for all plugins.

      • +
    +
    +

    Classes

    @@ -1001,96 +1001,70 @@ class deepcave.plugins.Plugin[source]

    Bases: Layout, ABC

    Base class for all plugins.

    -
    -
    -id
    -

    Unique identifier for the plugin.

    -
    -
    Type:
    -

    int

    +

    Provides different utilities to handle the plugins and check for compatibility in the runs.

    +
    +

    Properties

    +
    +
    inputsList[Tuple[str, str, bool, Any]]

    The registered inputs.

    -
    -
    - -
    -
    -name
    -

    Name of the plugin. It is shown in the navigation and in the title.

    -
    -
    Type:
    -

    str

    +
    outputsList[Tuple[str, str, bool]]

    The registered outputs.

    -
    -
    - -
    -
    -description
    -

    Description of the plugin. Displayed below the title.

    -
    -
    Type:
    -

    str, optional

    +
    previous_inputsDict[str, Dict[str, str]]

    The previous inputs.

    -
    -
    - -
    -
    -icon
    -

    FontAwesome icon. Shown in the navigation.

    -
    -
    Type:
    -

    str, optional

    +
    raw_outputsOptional[Dict[str, Any]]

    The raw outputs.

    -
    -
    - -
    -
    -help
    -

    Path to the help file.

    -
    -
    Type:
    -

    str, optional

    +
    activate_run_selectionbool

    Shows a dropdown to select a run in the inputs layout. +This feature is useful if only one run could be viewed at a time. +Moreover, it prevents the plugin to calculate results across all runs.

    -
    -
    - -
    -
    -button_caption
    -

    Caption of the button. Shown only, if StaticPlugin is used.

    -
    -
    Type:
    -

    str, optional

    +
    idstr

    The unique identifier for the plugin.

    +
    +
    runsList[AbstractRun]

    A list of the abstract runs.

    +
    +
    groupsList[Group]

    A list of the groups.

    +
    +
    helpstr

    The path to the documentation.

    +
    +
    namestr

    The name of the plugin. +It is shown in the navigation and title.

    +
    +
    button_captionstr

    Caption of the button. Shown only, if StaticPlugin is used.

    -
    - -
    -
    -activate_run_selection
    -

    Shows a dropdown to select a run in the inputs layout. -This feature is useful if only one run could be viewed at a time. -Moreover, it prevents the plugin to calculate results across all runs.

    +
    +
    +__call__(render_button=False)[source]
    +

    Return the components for the plugin.

    +

    Basically, all blocks and elements of the plugin are stacked-up here.

    -
    Type:
    -

    bool, optional

    +
    Parameters:
    +

    render_button (bool, optional) – Whether to render the button or not. By default False.

    +
    +
    Returns:
    +

    Layout as list of components.

    +
    +
    Return type:
    +

    List[Component]

    +
    +
    Raises:
    +
      +

    • NotMergeableError – If runs are not compatible.

      • +

    • FileNotFoundError – If the help file can not be found.

      • +
    -
    -
    -__call__(render_button=False)[source]
    -

    Returns the components for the plugin. Basically, all blocks and elements of the plugin -are stacked-up here

    +
    +
    +property all_runs: List[AbstractRun]
    +

    Get all runs and include the groups as a list.

    Returns:
    -

    Layout as list of components.

    +

    The list with all runs and included groups.

    Return type:
    -

    List[Component]

    +

    List[AbstractRun]

    @@ -1098,8 +1072,8 @@
    static check_run_compatibility(run)[source]
    -

    Checks if a run is compatible with this plugin. If a plugin is not compatible, -you can not select the run.

    +

    Check if a run is compatible with this plugin.

    +

    If a plugin is not compatible, you can not select the run.

    Note

    This function is only called if activate_run_selection is True.

    @@ -1120,8 +1094,8 @@
    check_runs_compatibility(runs)[source]
    -

    This function is needed if all selected runs need something in common -(e.g. budget or objective). Since this function is called before the layout is created, +

    Needed if all selected runs need something in common.

    +

    (e.g. budget or objective). Since this function is called before the layout is created, it can be also used to set common values for the plugin.

    Parameters:
    @@ -1139,8 +1113,8 @@
    generate_inputs(**kwargs)[source]
    -

    Generates inputs for the process and load_outputs required for api mode. -The arguments are validated against the input schema.

    +

    Generate inputs for the process and load_outputs required for api mode.

    +

    The arguments are validated against the input schema.

    Note

    Arguments are only available at runtime. Therefore, no api can be shown beforehand.

    @@ -1155,14 +1129,18 @@
    Return type:

    Dict[str, Any]

    +
    Raises:
    +

    ValueError – If an unknown input is passed. + If an input is missing.

    +
    classmethod generate_outputs(runs, inputs)[source]
    -

    Checks whether run selection is active and accepts either one or multiple runs at once. -Calls process internally.

    +

    Check whether run selection is active and accepts either one or multiple runs at once.

    +

    Calls process internally.

    Parameters:
      @@ -1184,7 +1162,7 @@
      classmethod get_base_url(cls)[source]
      -

      Generates the url for the plugin.

      +

      Generate the url for the plugin.

      Returns:

      Url for the plugin as string.

      @@ -1201,7 +1179,8 @@

      Layout for the filter block.

      Parameters:
      -

      register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

      +

      register (Callable) – The register method to register (user) variables. +For more information, see ‘register_input’.

      Returns:

      Layouts for the filter block.

      @@ -1218,7 +1197,8 @@

      Layout for the input block.

      Parameters:
      -

      register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

      +

      register (Callable) – The register method to register (user) variables. +For more information, see ‘register_input’.

      Returns:

      Layouts for the input block.

      @@ -1229,19 +1209,53 @@
      +
      +
      +get_internal_id(id)[source]
      +

      Get the internal id.

      +
      +
      Return type:
      +

      str

      +
      +
      +
      + +
      +
      +get_internal_input_id(id)[source]
      +

      Get the internal input id.

      +
      +
      Return type:
      +

      str

      +
      +
      +
      + +
      +
      +get_internal_output_id(id)[source]
      +

      Get the internal output id.

      +
      +
      Return type:
      +

      str

      +
      +
      +
      +
      static get_mpl_output_layout(register)[source]

      Layout for the matplotlib output block.

      Parameters:
      -

      register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

      +

      register (Callable) – The register method to register outputs. +For more information, see ‘register_input’.

      Returns:

      Layout for the matplotlib output block.

      Return type:
      -

      Union[Component, List[Component]]

      +

      Optional[Union[Component, List[Component]]]

      @@ -1252,7 +1266,8 @@

      Layout for the output block.

      Parameters:
      -

      register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

      +

      register (Callable) – The register method to register outputs. +For more information, see ‘register_input’.

      Returns:

      Layouts for the output block.

      @@ -1266,11 +1281,12 @@
      static get_run_input_layout(register)[source]
      -

      Generates the run selection input. -This is only the case if activate_run_selection is True.

      +

      Generate the run selection input.

      +

      This is only the case if activate_run_selection is True.

      Parameters:
      -

      register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

      +

      register (Callable) – The register method to register (user) variables. +For more information, see ‘register_input’.

      Returns:

      The layout of the run selection input.

      @@ -1284,8 +1300,8 @@
      get_selected_runs(inputs)[source]
      -

      Parses selected runs from inputs. -If self.activate_run_selection is set, return only selected run. Otherwise, return all +

      Parse selected runs from inputs.

      +

      If self.activate_run_selection is set, return only selected run. Otherwise, return all possible runs.

      Parameters:
      @@ -1303,10 +1319,25 @@
      +
      +
      +property groups: List[Group]
      +

      Get the groups as a list.

      +
      +
      Returns:
      +

      The list with the groups.

      +
      +
      Return type:
      +

      List[Group]

      +
      +
      +
      +
      load_dependency_inputs(run, previous_inputs, inputs)[source]
      -

      Same as load_inputs but called after inputs have changed. Provides a lot of flexibility.

      +

      Load the content as in ‘load_inputs’ but called after inputs have changed.

      +

      Provides a lot of flexibility.

      Note

      Only the changes have to be returned. The returned dictionary will be merged with the @@ -1315,7 +1346,7 @@

      Parameters:
        -

      • selected_run (Optional[Union[AbstractRun, List[AbstractRun]]], optional) – The selected run from the user. In case of activate_run_selection, only one run +

      • run (Optional[Union[AbstractRun, List[AbstractRun]]], optional) – The selected run from the user. In case of activate_run_selection, only one run is passed. Defaults to None.

      • previous_inputs (Dict[str, Any]) – Previous content of the inputs.

      • inputs (Dict[str, Any]) – Current content of the inputs.

        • @@ -1333,8 +1364,8 @@
        load_inputs()[source]
        -

        Load the content for the defined inputs in get_input_layout and get_filter_layout. -This method is necessary to pre-load contents for the inputs. So, if the plugin is +

        Load the content for the defined inputs in get_input_layout and get_filter_layout.

        +

        This method is necessary to pre-load contents for the inputs. So, if the plugin is called for the first time or there are no results in the cache, the plugin gets its content from this method.

        @@ -1350,11 +1381,12 @@
        static load_mpl_outputs(runs, inputs, outputs)[source]
        -

        Reads in the raw data and prepares them for the layout.

        +

        Read in the raw data and prepare them for the layout.

        Note

        The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

        +load_dependency_inputs. Inputs are cleaned s.t. only the first value is used. +Also, boolean values are casted to booleans.

        Parameters:
        @@ -1378,11 +1410,13 @@
        static load_outputs(runs, inputs, outputs)[source]
        -

        Reads in the raw data and prepares them for the layout.

        +

        Read in the raw data and prepare them for the layout.

        Note

        The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

        +load_dependency_inputs. +Inputs are cleaned s.t. only the first value is used. +Also, boolean values are casted to booleans.

        Parameters:
        @@ -1406,13 +1440,13 @@
        static load_run_inputs(runs, groups, check_run_compatibility)[source]
        -

        Loads the options for get_run_input_layout. -Both runs and groups are displayed.

        +

        Load the options for get_run_input_layout.

        +

        Both runs and groups are displayed.

        Parameters:
          -

        • runs (Dict[str, Run]) – The runs to display.

          • -

        • groups (Dict[str, Group]) – The groups to display.

          • +

        • runs (List[AbstractRun]) – The runs to display.

          • +

        • groups (List[Group]) – The groups to display.

        • check_run_compatibility (Callable[[AbstractRun], bool]) – If a single run is compatible. If not, the run is not shown.

        @@ -1428,7 +1462,7 @@
        static process(run, inputs)[source]
        -

        Returns raw data based on a run and input data.

        +

        Return raw data based on a run and input data.

        Warning

        The returned data must be JSON serializable.

        @@ -1436,7 +1470,8 @@

        Note

        The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

        +load_dependency_inputs. Inputs are cleaned s.t. only the first value is used. +Also, boolean values are casted to booleans.

        Parameters:
        @@ -1457,14 +1492,15 @@
        register_callbacks()[source]
        -

        Registers basic callbacks for the plugin. Following callbacks are registered: +

        Register basic callbacks for the plugin.

        +

        Following callbacks are registered: - If inputs changes, the changes are pasted back. This is in particular interest if input dependencies are used. - Raw data dialog to display raw data. - Callback to be redirected to the config if clicked on it.

        Raises:
        -

        RuntimeError – _description_

        +

        RuntimeError – If no run id is found.

        Return type:

        None

        @@ -1475,14 +1511,15 @@
        register_input(id, attributes='value', filter=False, type=None)[source]
        -

        Registers an input variable for the plugin. It is important to register the inputs -because callbacks have to be defined before the server is started. +

        Register an input variable for the plugin.

        +

        It is important to register the inputs. +This is, because callbacks have to be defined before the server is started. After registering all inputs, an internal mapping is created.

        Parameters:

        • id (str) – Specifies the id of the input.

          • -

        • attributes (Union[str, List[str]], optional) – Attributes which should be passed to the (dash) component, by default (“value”,)

          • +

        • attributes (Union[str, List[str]], optional) – Attributes which should be passed to the (dash) component, by default (“value”,).

        • filter (bool, optional) – Specifies if the input is a filter. By default False.

        • type (Any, optional) – Type to which the first attribute should be casted to. By default str.

        @@ -1499,7 +1536,7 @@
        register_output(id, attributes='value', mpl=False)[source]
        -

        Registers an output variable for the plugin.

        +

        Register an output variable for the plugin.

        Parameters:
          @@ -1517,28 +1554,43 @@
        +
        +
        +property runs: List[AbstractRun]
        +

        Get the runs as a list.

        +
        +
        Returns:
        +

        The list with the runs.

        +
        +
        Return type:
        +

        List[AbstractRun]

        +
        +
        +
        + +

        Modules

    - + - + - + - + - + - +

    deepcave.plugins.budget

    # budget

    deepcave.plugins.dynamic

    # Dynamic

    deepcave.plugins.hyperparameter

    # hyperparameter

    deepcave.plugins.objective

    # objective

    deepcave.plugins.static

    # Static

    deepcave.plugins.summary

    # summary
    diff --git a/development/api/deepcave.plugins.hyperparameter.html b/development/api/deepcave.plugins.hyperparameter.html index ed70f6ac..2049980c 100644 --- a/development/api/deepcave.plugins.hyperparameter.html +++ b/development/api/deepcave.plugins.hyperparameter.html @@ -696,9 +696,19 @@
    +
    + On this page +
    @@ -720,14 +730,26 @@

    deepcave.plugins.hyperparameter

    +

    # hyperparameter

    +
    +

    ## Modules

    +
    +
    importances

    This module provides a plugin for the visualization of the importances.

    +
    +
    pdp

    This module provides utilities for generating Partial Dependency Plots (PDP).

    +
    +
    symbolic_explanations

    This module provides utilities for generating symbolic explanations.

    +
    +
    +

    Modules

    - + - + diff --git a/development/api/deepcave.plugins.hyperparameter.importances.html b/development/api/deepcave.plugins.hyperparameter.importances.html index 1d8b2b4c..e8cd0ca7 100644 --- a/development/api/deepcave.plugins.hyperparameter.importances.html +++ b/development/api/deepcave.plugins.hyperparameter.importances.html @@ -817,11 +817,21 @@

    deepcave.plugins.hyperparameter.importances

    +

    # Importances

    +

    This module provides a plugin for the visualization of the importances.

    +

    Provided utilities include getting input and output layout (filtered or non-filtered), +processing the data and loading the outputs. Also provides a matplotlib version.

    +
    +
    ## Classes
      +

    • Importances: This class provides a plugin for the visualization of the importances.

      • +
    +
    +

    Classes

    deepcave.plugins.hyperparameter.importances

    # Importances

    deepcave.plugins.hyperparameter.pdp

    # PartialDependencies

    deepcave.plugins.hyperparameter.symbolic_explanations

    # SymbolicExplanations

    - +

    Importances()

    Provide a plugin for the visualization of the importances.
    @@ -829,19 +839,23 @@
    class deepcave.plugins.hyperparameter.importances.Importances[source]

    Bases: StaticPlugin

    +

    Provide a plugin for the visualization of the importances.

    +

    Provided utilities include getting input/output layout, data processing +and loading outputs. Also provides a matplotlib version.

    static get_filter_layout(register)[source]
    -

    Layout for the filter block.

    +

    Get the layout for the filter block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the filter block.

    +

    Layout for the filter block.

    Return type:
    -

    List[Component]

    +

    List[html.Div]

    @@ -849,16 +863,17 @@
    static get_input_layout(register)[source]
    -

    Layout for the input block.

    +

    Get the layout for the input block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the input block.

    +

    Layout for the input block.

    Return type:
    -

    List[Component]

    +

    List[Any]

    @@ -866,16 +881,17 @@
    static get_mpl_output_layout(register)[source]
    -

    Layout for the matplotlib output block.

    +

    Get the layout for the matplotlib output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register outputs. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layout for the matplotlib output block.

    +

    The layout for the matplotlib output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    html.Img

    @@ -883,16 +899,17 @@
    static get_output_layout(register)[source]
    -

    Layout for the output block.

    +

    Get the layout for the output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register outputs. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the output block.

    +

    Layout for the output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    dcc.Graph

    @@ -900,23 +917,21 @@
    load_dependency_inputs(run, _, inputs)[source]
    -

    Same as load_inputs but called after inputs have changed. Provides a lot of flexibility.

    +

    Work like ‘load_inputs’ but called after inputs have changed.

    Note

    -

    Only the changes have to be returned. The returned dictionary will be merged with the -inputs.

    +

    Only the changes have to be returned. +The returned dictionary will be merged with the inputs.

    Parameters:
      -

    • selected_run (Optional[Union[AbstractRun, List[AbstractRun]]], optional) – The selected run from the user. In case of activate_run_selection, only one run -is passed. Defaults to None.

      • -

    • previous_inputs (Dict[str, Any]) – Previous content of the inputs.

      • +

    • run – The selected run.

    • inputs (Dict[str, Any]) – Current content of the inputs.

    Returns:
    -

    Dictionary with the changes.

    +

    A dictionary with the changes.

    Return type:

    Dict[str, Any]

    @@ -927,16 +942,16 @@
    load_inputs()[source]
    -

    Load the content for the defined inputs in get_input_layout and get_filter_layout. -This method is necessary to pre-load contents for the inputs. So, if the plugin is -called for the first time or there are no results in the cache, the plugin gets its -content from this method.

    +

    Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.

    +

    This method is necessary to pre-load contents for the inputs. +If the plugin is called for the first time, or there are no results in the cache, +the plugin gets its content from this method.

    Returns:

    Content to be filled.

    Return type:
    -

    Dict[str, Any]

    +

    Dict[str, Dict[str, Any]]

    @@ -944,27 +959,17 @@
    static load_mpl_outputs(run, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    -
    -

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    -
    +

    Read the raw data and prepare it for the layout.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • run – The selected run.

      • +

    • inputs (Dict[str, Any]) – Input and filter values from the user.

      • +

    • outputs – Raw output from the run.

    -
    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    -
    -
    Return type:
    -

    Union[Component, List[Component]]

    +
    Return type:
    +

    The rendered matplotlib figure of the importances.

    @@ -972,27 +977,26 @@
    static load_outputs(run, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in raw data and prepare for layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differ +compared to ‘load_inputs’ or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • run – The selected run.

      • +

    • inputs – Input and filter values from the user.

      • +

    • outputs – Raw output from the run.

    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    +

    The figure of the importances.

    Return type:
    -

    Union[Component, List[Component]]

    +

    go.figure

    @@ -1000,29 +1004,34 @@
    static process(run, inputs)[source]
    -

    Returns raw data based on a run and input data.

    +

    Return raw data based on the run and input data.

    Warning

    The returned data must be JSON serializable.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differ +compared to ‘load_inputs’ or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:

    • run (AbstractRun) – The run to process.

      • -

    • inputs (Dict[str, Any]) – Input data.

      • +

    • inputs (Dict[str, Any]) – The input data.

    Returns:
    -

    Serialized dictionary.

    +

    A serialzied dictionary.

    Return type:

    Dict[str, Any]

    +
    Raises:
    +

    RuntimeError – If the number of trees is not specified. + If the method is not found.

    +
    diff --git a/development/api/deepcave.plugins.hyperparameter.pdp.html b/development/api/deepcave.plugins.hyperparameter.pdp.html index f15a9916..2bec353b 100644 --- a/development/api/deepcave.plugins.hyperparameter.pdp.html +++ b/development/api/deepcave.plugins.hyperparameter.pdp.html @@ -738,6 +738,15 @@ +
  • + + + + PartialDependencies.get_pdp_figure() + + + +
  • @@ -799,11 +808,26 @@

    deepcave.plugins.hyperparameter.pdp

    +

    # PartialDependencies

    +

    This module provides utilities for generating Partial Dependency Plots (PDP).

    +

    Provided utilities include getting input and output layout (filtered or non-filtered), +processing the data and loading the outputs.

    +
    +
    ## Classes
      +

    • PartialDependencies: Generate a Partial Dependency Plot (PDP).

      • +
    +
    +
    ## Constants

    GRID_POINTS_PER_AXIS : int +SAMPLES_PER_HP : int +MAX_SAMPLES : int +MAX_SHOWN_SAMPLES : int

    +
    +

    Classes

    - +

    PartialDependencies()

    Generate Partial Dependency Plots (PDP).
    @@ -811,19 +835,23 @@
    class deepcave.plugins.hyperparameter.pdp.PartialDependencies[source]

    Bases: StaticPlugin

    +

    Generate Partial Dependency Plots (PDP).

    +

    Provided utilities include getting input and output layout (filtered or non-filtered), +processing the data and loading the outputs.

    static get_filter_layout(register)[source]
    -

    Layout for the filter block.

    +

    Get the layout for the filter block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the filter block.

    +

    The layout for the filter block.

    Return type:
    -

    List[Component]

    +

    List[Any]

    @@ -831,16 +859,17 @@
    static get_input_layout(register)[source]
    -

    Layout for the input block.

    +

    Get the layout for the input block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the input block.

    +

    The layout for the input block.

    Return type:
    -

    List[Component]

    +

    List[dbc.Row]

    @@ -848,16 +877,41 @@
    static get_output_layout(register)[source]
    -

    Layout for the output block.

    +

    Get the layout for the output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register outputs. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the output block.

    +

    Layout for the output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    dcc.Graph

    +
    +
    +
    + +
    +
    +static get_pdp_figure(run, inputs, outputs, show_confidence, show_ice, title=None)[source]
    +

    Create a figure of the Partial Dependency Plot (PDP).

    +
    +
    Parameters:
    +
      +

    • run – The selected run.

      • +

    • inputs – Input and filter values from the user.

      • +

    • outputs – Raw output from the run.

      • +

    • show_confidence – Whether to show confidence in the plot.

      • +

    • show_ice – Whether to show ice curves in the plot.

      • +

    • title – Title of the plot.

      • +
    +
    +
    Returns:
    +

    The figure of the Partial Dependency Plot (PDP).

    +
    +
    Return type:
    +

    go.Figure

    @@ -865,19 +919,19 @@
    load_dependency_inputs(run, previous_inputs, inputs)[source]
    -

    Same as load_inputs but called after inputs have changed. Provides a lot of flexibility.

    +

    Work like ‘load_inputs’ but called after inputs have changed.

    Note

    -

    Only the changes have to be returned. The returned dictionary will be merged with the -inputs.

    +

    Only the changes have to be returned. The returned dictionary +will be merged with the inputs.

    Parameters:
      -

    • selected_run (Optional[Union[AbstractRun, List[AbstractRun]]], optional) – The selected run from the user. In case of activate_run_selection, only one run -is passed. Defaults to None.

      • -

    • previous_inputs (Dict[str, Any]) – Previous content of the inputs.

      • -

    • inputs (Dict[str, Any]) – Current content of the inputs.

      • +

    • run – The selected run.

      • +

    • inputs – Current content of the inputs.

      • +

    • previous_inputs – Previous content of the inputs. +Not used in this specific function.

    Returns:
    @@ -892,16 +946,16 @@
    load_inputs()[source]
    -

    Load the content for the defined inputs in get_input_layout and get_filter_layout. -This method is necessary to pre-load contents for the inputs. So, if the plugin is -called for the first time or there are no results in the cache, the plugin gets its -content from this method.

    +

    Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.

    +

    This method is necessary to pre-load contents for the inputs. +If the plugin is called for the first time, or there are no results in the cache, +the plugin gets its content from this method.

    Returns:

    Content to be filled.

    Return type:
    -

    Dict[str, Any]

    +

    Dict[str, Dict[str, Any]]

    @@ -909,27 +963,26 @@
    static load_outputs(run, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read the raw data and prepare it for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differ +compared to ‘load_inputs’ or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • run – The selected run.

      • +

    • inputs – Input and filter values from the user.

      • +

    • outputs – Raw output from the run.

    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    +

    The figure of the Partial Dependency Plot (PDP).

    Return type:
    -

    Union[Component, List[Component]]

    +

    go.Figure

    @@ -937,29 +990,33 @@
    static process(run, inputs)[source]
    -

    Returns raw data based on a run and input data.

    +

    Return raw data based on a run and the input data.

    Warning

    The returned data must be JSON serializable.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differ +compared to ‘load_inputs’ or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • run (AbstractRun) – The run to process.

      • -

    • inputs (Dict[str, Any]) – Input data.

      • +

    • run – The run to process.

      • +

    • inputs – The input data.

    Returns:
    -

    Serialized dictionary.

    +

    A serialized dictionary.

    Return type:

    Dict[str, Any]

    +
    Raises:
    +

    RuntimeError – If the objective is None.

    +
    diff --git a/development/api/deepcave.plugins.objective.configuration_cube.html b/development/api/deepcave.plugins.objective.configuration_cube.html index 33f24cfa..46beb662 100644 --- a/development/api/deepcave.plugins.objective.configuration_cube.html +++ b/development/api/deepcave.plugins.objective.configuration_cube.html @@ -799,11 +799,20 @@

    deepcave.plugins.objective.configuration_cube

    +

    # ConfigurationCube

    +

    This module provides utilities for visualizing and creating a configuration cube.

    +

    The configuration cube displays configurations and their score on the objective.

    +
    +
    ## Classes
      +

    • ConfigurationCube: A plugin for visualizing a configuration cube.

      • +
    +
    +

    Classes

    - +

    ConfigurationCube()

    A plugin for visualizing a configuration cube.
    @@ -811,19 +820,21 @@
    class deepcave.plugins.objective.configuration_cube.ConfigurationCube[source]

    Bases: DynamicPlugin

    +

    A plugin for visualizing a configuration cube.

    static get_filter_layout(register)[source]
    -

    Layout for the filter block.

    +

    Get the layout for the filter block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:

    Layouts for the filter block.

    Return type:
    -

    List[Component]

    +

    List[html.Div]

    @@ -831,16 +842,17 @@
    static get_input_layout(register)[source]
    -

    Layout for the input block.

    +

    Get the layout for the input block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:

    Layouts for the input block.

    Return type:
    -

    List[Component]

    +

    List[dbc.Row]

    @@ -848,16 +860,17 @@
    static get_output_layout(register)[source]
    -

    Layout for the output block.

    +

    Get the layout for the output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register output. +The register_output function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the output block.

    +

    Layout for the output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    Tuple[dcc.Graph,]

    @@ -865,23 +878,22 @@
    load_dependency_inputs(run, _, inputs)[source]
    -

    Same as load_inputs but called after inputs have changed. Provides a lot of flexibility.

    +

    Work like ‘load_inputs’ but called after inputs have changed.

    +

    It is restricted to three Hyperparameters.

    Note

    -

    Only the changes have to be returned. The returned dictionary will be merged with the -inputs.

    +

    Only the changes have to be returned. +The returned dictionary will be merged with the inputs.

    Parameters:
      -

    • selected_run (Optional[Union[AbstractRun, List[AbstractRun]]], optional) – The selected run from the user. In case of activate_run_selection, only one run -is passed. Defaults to None.

      • -

    • previous_inputs (Dict[str, Any]) – Previous content of the inputs.

      • -

    • inputs (Dict[str, Any]) – Current content of the inputs.

      • +

    • run – The selected run.

      • +

    • inputs – Current content of the inputs.

    Returns:
    -

    Dictionary with the changes.

    +

    The dictionary with the changes.

    Return type:

    Dict[str, Any]

    @@ -892,13 +904,13 @@
    load_inputs()[source]
    -

    Load the content for the defined inputs in get_input_layout and get_filter_layout. -This method is necessary to pre-load contents for the inputs. So, if the plugin is -called for the first time or there are no results in the cache, the plugin gets its -content from this method.

    +

    Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.

    +

    This method is necessary to pre-load contents for the inputs. +So, if the plugin is called for the first time or there are no results in the cache, +the plugin gets its content from this method.

    Returns:
    -

    Content to be filled.

    +

    The content to be filled.

    Return type:

    Dict[str, Any]

    @@ -909,27 +921,26 @@
    static load_outputs(run, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in the raw data and prepares them for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • run – The selected run.

      • +

    • inputs – Input and filter values from the user.

      • +

    • outputs – Raw output from the run.

    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    +

    The output figure.

    Return type:
    -

    Union[Component, List[Component]]

    +

    go.Figure

    @@ -937,28 +948,29 @@
    static process(run, inputs)[source]
    -

    Returns raw data based on a run and input data.

    +

    Return raw data based on the run and input data.

    Warning

    The returned data must be JSON serializable.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • run (AbstractRun) – The run to process.

      • -

    • inputs (Dict[str, Any]) – Input data.

      • +

    • run (AbstractRun) – The selected run.

      • +

    • inputs (Dict[str, Any]) – The input data.

    Returns:
    -

    Serialized dictionary.

    +

    The serialized dictionary.

    Return type:
    -

    Dict[str, Any]

    +

    Dict[str, str]

    diff --git a/development/api/deepcave.plugins.objective.cost_over_time.html b/development/api/deepcave.plugins.objective.cost_over_time.html index 56a1d543..173f46fc 100644 --- a/development/api/deepcave.plugins.objective.cost_over_time.html +++ b/development/api/deepcave.plugins.objective.cost_over_time.html @@ -799,11 +799,21 @@

    deepcave.plugins.objective.cost_over_time

    +

    # CostOverTime

    +

    This module provides utilities for visualizing the cost over time.

    +

    Visualized changes can be regarding to number of configurations or time. +It includes a corresponding plugin class.

    +
    +
    ## Classes
      +

    • CostOverTime: A plugin to provide a visualization for the cost over time.

      • +
    +
    +

    Classes

    - +

    CostOverTime()

    A plugin to provide a visualization for the cost over time.
    @@ -811,18 +821,32 @@
    class deepcave.plugins.objective.cost_over_time.CostOverTime[source]

    Bases: DynamicPlugin

    +

    A plugin to provide a visualization for the cost over time.

    +
    +

    Properties

    +
    +
    objective_optionsList[Dict[str, Any]]

    A list of dictionaries of the objective options.

    +
    +
    budget_optionsList[Dict[str, Any]]

    A list of dictionaries of the budget options.

    +
    +
    check_runs_compatibility(runs)[source]
    -

    This function is needed if all selected runs need something in common -(e.g. budget or objective). Since this function is called before the layout is created, +

    Check if the runs are compatible.

    +

    This function is needed if all selected runs need something in common +(e.g. budget or objective). +Since this function is called before the layout is created, it can be also used to set common values for the plugin.

    Parameters:
    -

    runs (List[AbstractRun]) – Selected runs.

    +

    runs (List[AbstractRun]) – A list containing the selected runs.

    Raises:
    -

    NotMergeableError – If runs are not compatible, an error is thrown.

    +

    NotMergeableError – If the meta data of the runs are not equal. + If the configuration spaces of the runs are not equal. + If the budgets of the runs are not equal. + If the objective of the runs are not equal.

    Return type:

    None

    @@ -833,16 +857,17 @@
    static get_filter_layout(register)[source]
    -

    Layout for the filter block.

    +

    Get the layout for the filter block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:

    Layouts for the filter block.

    Return type:
    -

    List[Component]

    +

    List[Any]

    @@ -850,16 +875,17 @@
    static get_input_layout(register)[source]
    -

    Layout for the input block.

    +

    Get the layout for the input block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:

    Layouts for the input block.

    Return type:
    -

    List[Component]

    +

    List[dbc.Row]

    @@ -867,16 +893,17 @@
    static get_output_layout(register)[source]
    -

    Layout for the output block.

    +

    Get the layout for the output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register outputs. +The register_output function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the output block.

    +

    The layouts for the output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    dcc.Graph

    @@ -884,13 +911,13 @@
    load_inputs()[source]
    -

    Load the content for the defined inputs in get_input_layout and get_filter_layout. -This method is necessary to pre-load contents for the inputs. So, if the plugin is -called for the first time or there are no results in the cache, the plugin gets its -content from this method.

    +

    Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.

    +

    This method is necessary to pre-load contents for the inputs. +So, if the plugin is called for the first time or there are no results in the cache, +the plugin gets its content from this method.

    Returns:
    -

    Content to be filled.

    +

    The content to be filled.

    Return type:

    Dict[str, Any]

    @@ -901,27 +928,26 @@
    static load_outputs(runs, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in the raw data and prepare them for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • runs – The selected runs.

      • +

    • inputs – The input and filter values from the user.

      • +

    • outputs – The raw outputs from the runs.

    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    +

    The output figure.

    Return type:
    -

    Union[Component, List[Component]]

    +

    go.Figure

    @@ -929,25 +955,26 @@
    static process(run, inputs)[source]
    -

    Returns raw data based on a run and input data.

    +

    Return raw data based on a run and input data.

    Warning

    The returned data must be JSON serializable.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • run (AbstractRun) – The run to process.

      • -

    • inputs (Dict[str, Any]) – Input data.

      • +

    • run (AbstractRun) – The selected run to process.

      • +

    • inputs (Dict[str, Any]) – The input data.

    Returns:
    -

    Serialized dictionary.

    +

    A serialized dictionary.

    Return type:

    Dict[str, Any]

    @@ -955,6 +982,7 @@
    +
    diff --git a/development/api/deepcave.plugins.objective.html b/development/api/deepcave.plugins.objective.html index 7d8fcb56..ce4e1e96 100644 --- a/development/api/deepcave.plugins.objective.html +++ b/development/api/deepcave.plugins.objective.html @@ -696,9 +696,19 @@
    +
    + On this page +
    @@ -720,20 +730,34 @@

    deepcave.plugins.objective

    +

    # objective

    +
    +

    ## Modules

    +
    +
    configuration_cube

    This module provides utilities for visualizing and creating a configruation cube.

    +
    +
    cost_over_time

    This module provides utilities for visualizing the cost over time.

    +
    +
    parallel_coordinates

    This module provides utilities for visualizing the parallel coordinates.

    +
    +
    pareto_front

    This module provides utilities for creating a visualization of the Pareto Front.

    +
    +
    +

    Modules

    - + - + - + - +

    deepcave.plugins.objective.configuration_cube

    # ConfigurationCube

    deepcave.plugins.objective.cost_over_time

    # CostOverTime

    deepcave.plugins.objective.parallel_coordinates

    # ParallelCoordinates

    deepcave.plugins.objective.pareto_front

    # ParetoFront
    diff --git a/development/api/deepcave.plugins.objective.parallel_coordinates.html b/development/api/deepcave.plugins.objective.parallel_coordinates.html index 05f317b7..78322958 100644 --- a/development/api/deepcave.plugins.objective.parallel_coordinates.html +++ b/development/api/deepcave.plugins.objective.parallel_coordinates.html @@ -799,11 +799,19 @@

    deepcave.plugins.objective.parallel_coordinates

    +

    # ParallelCoordinates

    +

    This module provides utilities for visualizing the parallel coordinates.

    +
    +
    ## Classes
      +

    • ParallelCoordinates : Can be used for visualizing the parallel coordinates.

      • +
    +
    +

    Classes

    - +

    ParallelCoordinates()

    Can be used for visualizing the parallel coordinates.
    @@ -811,19 +819,21 @@
    class deepcave.plugins.objective.parallel_coordinates.ParallelCoordinates[source]

    Bases: StaticPlugin

    +

    Can be used for visualizing the parallel coordinates.

    static get_filter_layout(register)[source]
    -

    Layout for the filter block.

    +

    Get the layout for the filter block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the filter block.

    +

    The layouts for the filter block.

    Return type:
    -

    List[Component]

    +

    List[Any]

    @@ -831,16 +841,17 @@
    static get_input_layout(register)[source]
    -

    Layout for the input block.

    +

    Get the layout for the input block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to regsiter (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the input block.

    +

    The layouts for the input block.

    Return type:
    -

    List[Component]

    +

    List[Any]

    @@ -848,16 +859,17 @@
    static get_output_layout(register)[source]
    -

    Layout for the output block.

    +

    Get the layout for the output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register outputs. +The register_output function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the output block.

    +

    The layouts for the output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    dcc.Graph

    @@ -865,23 +877,21 @@
    load_dependency_inputs(run, _, inputs)[source]
    -

    Same as load_inputs but called after inputs have changed. Provides a lot of flexibility.

    +

    Work like ‘load_inputs’ but called after inputs have changed.

    Note

    -

    Only the changes have to be returned. The returned dictionary will be merged with the -inputs.

    +

    Only the changes have to be returned. +The returned dictionary will be merged with the inputs.

    Parameters:
      -

    • selected_run (Optional[Union[AbstractRun, List[AbstractRun]]], optional) – The selected run from the user. In case of activate_run_selection, only one run -is passed. Defaults to None.

      • -

    • previous_inputs (Dict[str, Any]) – Previous content of the inputs.

      • -

    • inputs (Dict[str, Any]) – Current content of the inputs.

      • +

    • run – The selected run.

      • +

    • inputs – Current content of the inputs.

    Returns:
    -

    Dictionary with the changes.

    +

    The dictionary with the changes.

    Return type:

    Dict[str, Any]

    @@ -892,16 +902,16 @@
    load_inputs()[source]
    -

    Load the content for the defined inputs in get_input_layout and get_filter_layout. -This method is necessary to pre-load contents for the inputs. So, if the plugin is -called for the first time or there are no results in the cache, the plugin gets its -content from this method.

    +

    Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.

    +

    This method is necessary to pre-load contents for the inputs. +So, if the plugin is called for the first time or there are no results in the cache, +the plugin gets its content from this method.

    Returns:

    Content to be filled.

    Return type:
    -

    Dict[str, Any]

    +

    Dict[str, Dict[str, Any]]

    @@ -909,27 +919,26 @@
    static load_outputs(run, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in the raw data and prepare them for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • run – The selected run.

      • +

    • inputs – The inputs and filter values fromt the user.

      • +

    • outputs – Raw output from the run.

    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    +

    The output figure.

    Return type:
    -

    Union[Component, List[Component]]

    +

    go.Figure

    @@ -937,25 +946,26 @@
    static process(run, inputs)[source]
    -

    Returns raw data based on a run and input data.

    +

    Return raw data based on a run and input data.

    Warning

    The returned data must be JSON serializable.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:

    • run (AbstractRun) – The run to process.

      • -

    • inputs (Dict[str, Any]) – Input data.

      • +

    • inputs (Dict[str, Any]) – The input data.

    Returns:
    -

    Serialized dictionary.

    +

    The serialized dictionary.

    Return type:

    Dict[str, Any]

    diff --git a/development/api/deepcave.plugins.objective.pareto_front.html b/development/api/deepcave.plugins.objective.pareto_front.html index 5542e506..09f60913 100644 --- a/development/api/deepcave.plugins.objective.pareto_front.html +++ b/development/api/deepcave.plugins.objective.pareto_front.html @@ -817,11 +817,20 @@

    deepcave.plugins.objective.pareto_front

    +

    # ParetoFront

    +

    This module provides utilities for creating a visualization of the Pareto Front.

    +

    It includes the corresponding Pareto Front plugin.

    +
    +
    ## Classes
      +

    • ParetoFront: Generate an interactive Pareto Front visualization.

      • +
    +
    +

    Classes

    - +

    ParetoFront()

    Generate an interactive Pareto Front visualization.
    @@ -829,18 +838,35 @@
    class deepcave.plugins.objective.pareto_front.ParetoFront[source]

    Bases: DynamicPlugin

    +

    Generate an interactive Pareto Front visualization.

    +
    +

    Properties

    +
    +
    objective_optionsList[Dict[str, Any]]

    A list of the objective options.

    +
    +
    budget_optionsList[Dict[str, Any]]

    A list of the budget options.

    +
    +
    check_runs_compatibility(runs)[source]
    -

    This function is needed if all selected runs need something in common -(e.g. budget or objective). Since this function is called before the layout is created, +

    Check if the runs are compatible.

    +

    This function is needed if all selected runs need something in common +(e.g. budget or objective). +Since this function is called before the layout is created, it can be also used to set common values for the plugin.

    Parameters:
    -

    runs (List[AbstractRun]) – Selected runs.

    +

    runs (List[AbstractRun]) – A list containing the selected runs.

    Raises:
    -

    NotMergeableError – If runs are not compatible, an error is thrown.

    +

    NotMergeableError – If the meta data of the runs are not equal. + If the configuration spaces of the runs are not equal. + If the budgets of the runs are not equal. + If the objective of the runs are not equal.

    +
    +
    Return type:
    +

    None

    @@ -848,16 +874,17 @@
    static get_filter_layout(register)[source]
    -

    Layout for the filter block.

    +

    Get the layout for the filter block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the filter block.

    +

    The layouts for the filter block.

    Return type:
    -

    List[Component]

    +

    List[Any]

    @@ -865,16 +892,17 @@
    static get_input_layout(register)[source]
    -

    Layout for the input block.

    +

    Get layout for the input block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the input block.

    +

    The layouts for the input block.

    Return type:
    -

    List[Component]

    +

    List[Any]

    @@ -882,16 +910,17 @@
    static get_mpl_output_layout(register)[source]
    -

    Layout for the matplotlib output block.

    +

    Get the layout for the matplotlib output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register outputs. +The register_output function is located in the Plugin superclass.

    Returns:
    -

    Layout for the matplotlib output block.

    +

    The layout for the matplotlib output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    html.Img

    @@ -899,16 +928,17 @@
    static get_output_layout(register)[source]
    -

    Layout for the output block.

    +

    Get the layout for the output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register outputs. +The register_output function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the output block.

    +

    The layout for the output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    dcc.Graph

    @@ -916,16 +946,16 @@
    load_inputs()[source]
    -

    Load the content for the defined inputs in get_input_layout and get_filter_layout. -This method is necessary to pre-load contents for the inputs. So, if the plugin is -called for the first time or there are no results in the cache, the plugin gets its -content from this method.

    +

    Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.

    +

    This method is necessary to pre-load contents for the inputs. +So, if the plugin is called for the first time or there are no results in the cache, +the plugin gets its content from this method.

    Returns:
    -

    Content to be filled.

    +

    The content to be filled.

    Return type:
    -

    Dict[str, Any]

    +

    Dict[str, Dict[str, Any]]

    @@ -933,27 +963,23 @@
    static load_mpl_outputs(runs, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in the raw data and prepare them for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • runs – The selected runs.

      • +

    • inputs – Input and filter values from the user.

      • +

    • outputs – Raw outputs from the runs.

    -
    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    -
    -
    Return type:
    -

    Union[Component, List[Component]]

    +
    Return type:
    +

    The rendered matplotlib figure.

    @@ -961,27 +987,26 @@
    static load_outputs(runs, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in the raw data and prepare them for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • runs – The selected runs.

      • +

    • inputs – The input and filter values from the user.

      • +

    • outputs – Raw outputs from the runs.

    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    +

    The output figure.

    Return type:
    -

    Union[Component, List[Component]]

    +

    go.Figure

    @@ -989,25 +1014,26 @@
    static process(run, inputs)[source]
    -

    Returns raw data based on a run and input data.

    +

    Return raw data based on a run and input data.

    Warning

    The returned data must be JSON serializable.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:

    • run (AbstractRun) – The run to process.

      • -

    • inputs (Dict[str, Any]) – Input data.

      • +

    • inputs (Dict[str, Any]) – The input data.

    Returns:
    -

    Serialized dictionary.

    +

    The serialized dictionary.

    Return type:

    Dict[str, Any]

    @@ -1015,6 +1041,7 @@
    +
    diff --git a/development/api/deepcave.plugins.static.html b/development/api/deepcave.plugins.static.html index 82295315..b5457120 100644 --- a/development/api/deepcave.plugins.static.html +++ b/development/api/deepcave.plugins.static.html @@ -763,14 +763,24 @@

    deepcave.plugins.static

    +

    # Static

    +

    This module provides a plugin class for a static plugin.

    +

    It provides an Enum used for the plugin state.

    +
    +
    ## Classes
      +

    • PluginState: An Enum to define the state of the Plugin.

      • +

    • StaticPlugin: This class provides a static plugin object.

      • +
    +
    +

    Classes

    - + - +

    PluginState(value)

    An enumeration.

    An Enum to define the state of the Plugin.

    StaticPlugin()

    Calculation with queue.

    Provide a static plugin object.
    @@ -778,19 +788,41 @@
    class deepcave.plugins.static.PluginState(value)[source]

    Bases: Enum

    -

    An enumeration.

    +

    An Enum to define the state of the Plugin.

    class deepcave.plugins.static.StaticPlugin[source]

    Bases: Plugin, ABC

    -

    Calculation with queue. Made for time-consuming tasks.

    +

    Provide a static plugin object.

    +

    Calculation with queue. Made for time-consuming tasks. +Register and handle callbacks.

    +
    +

    Properties

    +
    +
    outputsList[Tuple[str, str, bool]]

    The plugin specific outputs.

    +
    +
    inputsList[Tuple[str, str, bool, Any]]

    The plugin specific inputs.

    +
    +
    idstr

    The plugin id.

    +
    +
    raw_outputsdict[str, Any]

    The raw outputs of a run.

    +
    +
    loggerLogger

    The logger for the plugin.

    +
    +
    namestr

    The name of the plugin.

    +
    +
    processCallable

    Return raw data based on a run and input data.

    +
    +
    button_captionstr

    The caption for the button.

    +
    +
    __call__()[source]
    -

    Returns the components for the plugin. Basically, all blocks and elements of the plugin -are stacked-up here

    +

    Return the components for the plugin.

    +

    Basically, all blocks and elements of the plugin are stacked-up here.

    Returns:

    Layout as list of components.

    @@ -804,21 +836,15 @@
    register_callbacks()[source]
    -

    Registers basic callbacks for the plugin. Following callbacks are registered: -- If inputs changes, the changes are pasted back. This is in particular -interest if input dependencies are used. -- Raw data dialog to display raw data. -- Callback to be redirected to the config if clicked on it.

    +

    Register different callbacks.

    -
    Raises:
    -

    RuntimeError – _description_

    -
    -
    Return type:
    -

    None

    +
    Return type:
    +

    None

    +
    diff --git a/development/api/deepcave.plugins.summary.configurations.html b/development/api/deepcave.plugins.summary.configurations.html index cc21c3ea..bb9515ec 100644 --- a/development/api/deepcave.plugins.summary.configurations.html +++ b/development/api/deepcave.plugins.summary.configurations.html @@ -799,11 +799,20 @@

    deepcave.plugins.summary.configurations

    +

    # Configurations

    +

    This module provides utilities to visualize the characteristics of a configuration within a run.

    +

    The module provides a corresponding dynamic plugin.

    +
    +
    ## Classes
      +

    • Configurations: Visualize the characteristics of a configuration.

      • +
    +
    +

    Classes

    - +

    Configurations()

    Visualize the characteristics of a configuration.
    @@ -811,19 +820,21 @@
    class deepcave.plugins.summary.configurations.Configurations[source]

    Bases: DynamicPlugin

    +

    Visualize the characteristics of a configuration.

    static get_input_layout(register)[source]
    -

    Layout for the input block.

    +

    Get the layout for the input block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the input block.

    +

    The layouts for the input block.

    Return type:
    -

    List[Component]

    +

    List[html.Div]

    @@ -831,12 +842,12 @@
    -

    Creates a link to a specific configuration.

    +

    Create a link to a specific configuration.

    Parameters:
      -

    • run (AbstractRun) – Selected run.

      • -

    • config_id (int) – Configuration, which should be visited.

      • +

    • run (AbstractRun) – The selected run.

      • +

    • config_id (int) – Configuration which should be visited.

    Returns:
    @@ -851,16 +862,17 @@
    static get_output_layout(register)[source]
    -

    Layout for the output block.

    +

    Get the layout for the output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method used to register outputs. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the output block.

    +

    The layouts for the outputs.

    Return type:
    -

    Union[Component, List[Component]]

    +

    List[Any]

    @@ -868,23 +880,23 @@
    load_dependency_inputs(run, previous_inputs, inputs)[source]
    -

    Same as load_inputs but called after inputs have changed. Provides a lot of flexibility.

    +

    Work like ‘load_inputs’ but called after inputs have changed.

    Note

    -

    Only the changes have to be returned. The returned dictionary will be merged with the -inputs.

    +

    Only the changes have to be returned. +The returned dictionary will be merged with the inputs.

    Parameters:
      -

    • selected_run (Optional[Union[AbstractRun, List[AbstractRun]]], optional) – The selected run from the user. In case of activate_run_selection, only one run -is passed. Defaults to None.

      • -

    • previous_inputs (Dict[str, Any]) – Previous content of the inputs.

      • -

    • inputs (Dict[str, Any]) – Current content of the inputs.

      • +

    • run – The selected run.

      • +

    • previous_inputs (Dict[str, Any]) – Previous content of the inputs. +Not used in this specific function

      • +

    • inputs (Dict[str, Any]) – The current content of the inputs.

    Returns:
    -

    Dictionary with the changes.

    +

    A dictionary with the changes.

    Return type:

    Dict[str, Any]

    @@ -895,13 +907,13 @@
    load_inputs()[source]
    -

    Load the content for the defined inputs in get_input_layout and get_filter_layout. -This method is necessary to pre-load contents for the inputs. So, if the plugin is -called for the first time or there are no results in the cache, the plugin gets its -content from this method.

    +

    Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.

    +

    This method is necessary to pre-load contents for the inputs. +So, if the plugin is called for the first time or there are no results in the cache, +the plugin gets its content from this method.

    Returns:
    -

    Content to be filled.

    +

    The content to be filled

    Return type:

    Dict[str, Any]

    @@ -912,27 +924,26 @@
    static load_outputs(run, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in the raw data and prepare them for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • run – The selected run.

      • +

    • inputs – Input and filter values from the user.

      • +

    • outputs – Raw output from the run.

    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    +

    A list of the created tables containing output information.

    Return type:
    -

    Union[Component, List[Component]]

    +

    List[Any]

    @@ -940,25 +951,26 @@
    static process(run, inputs)[source]
    -

    Returns raw data based on a run and input data.

    +

    Return raw data based on a run and input data.

    Warning

    The returned data must be JSON serializable.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • run (AbstractRun) – The run to process.

      • -

    • inputs (Dict[str, Any]) – Input data.

      • +

    • run – The selected run.

      • +

    • inputs – The input data.

    Returns:
    -

    Serialized dictionary.

    +

    A serialized dictionary.

    Return type:

    Dict[str, Any]

    diff --git a/development/api/deepcave.plugins.summary.footprint.html b/development/api/deepcave.plugins.summary.footprint.html index ce3b28e9..f8dcd9c9 100644 --- a/development/api/deepcave.plugins.summary.footprint.html +++ b/development/api/deepcave.plugins.summary.footprint.html @@ -817,11 +817,20 @@

    deepcave.plugins.summary.footprint

    +

    # FootPrint

    +

    This module provides utilities to visualize a configuration footprint.

    +

    The module contains a static plugin class for defining the footprint.

    +
    +
    ## Classes
      +

    • FootPrint: A static plugin for the footprint of a configuration.

      • +
    +
    +

    Classes

    - +

    FootPrint()

    Visualize the footprint of a configuration.
    @@ -829,19 +838,22 @@
    class deepcave.plugins.summary.footprint.FootPrint[source]

    Bases: StaticPlugin

    +

    Visualize the footprint of a configuration.

    +

    A static plugin for the footprint.

    static get_filter_layout(register)[source]
    -

    Layout for the filter block.

    +

    Get layout for the filter block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the filter block.

    +

    The layouts for the filter block.

    Return type:
    -

    List[Component]

    +

    List[dbc.Row]

    @@ -849,16 +861,17 @@
    static get_input_layout(register)[source]
    -

    Layout for the input block.

    +

    Get the layout for the input block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register (user) variables.

    +

    register (Callable) – Method to register (user) variables. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the input block.

    +

    The layouts for the input block.

    Return type:
    -

    List[Component]

    +

    List[Any]

    @@ -866,16 +879,17 @@
    static get_mpl_output_layout(register)[source]
    -

    Layout for the matplotlib output block.

    +

    Get the layout for the matplotlib output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register the outputs. +The register_output function is located in the Plugin superclass.

    Returns:
    -

    Layout for the matplotlib output block.

    +

    The layout for the output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    List[dbc.Tabs]

    @@ -883,16 +897,17 @@
    static get_output_layout(register)[source]
    -

    Layout for the output block.

    +

    Get the layout for the output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method for registering outputs. +The register_output function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the output block.

    +

    The layout for the output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    dbc.Tabs

    @@ -900,23 +915,23 @@
    load_dependency_inputs(run, previous_inputs, inputs)[source]
    -

    Same as load_inputs but called after inputs have changed. Provides a lot of flexibility.

    +

    Work like ‘load_inputs’ but called after inputs have changed.

    Note

    -

    Only the changes have to be returned. The returned dictionary will be merged with the -inputs.

    +

    Only the changes have to be returned. +The returned dictionary will be merged with the inputs.

    Parameters:
      -

    • selected_run (Optional[Union[AbstractRun, List[AbstractRun]]], optional) – The selected run from the user. In case of activate_run_selection, only one run -is passed. Defaults to None.

      • -

    • previous_inputs (Dict[str, Any]) – Previous content of the inputs.

      • -

    • inputs (Dict[str, Any]) – Current content of the inputs.

      • +

    • run – The selected run.

      • +

    • previous_inputs – Previous content of the inputs. +Not used in this specific function.

      • +

    • inputs – Current content of the inputs.

    Returns:
    -

    Dictionary with the changes.

    +

    The dictionary with the changes.

    Return type:

    Dict[str, Any]

    @@ -927,16 +942,16 @@
    load_inputs()[source]
    -

    Load the content for the defined inputs in get_input_layout and get_filter_layout. -This method is necessary to pre-load contents for the inputs. So, if the plugin is -called for the first time or there are no results in the cache, the plugin gets its -content from this method.

    +

    Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.

    +

    This method is necessary to pre-load contents for the inputs. +So, if the plugin is called for the first time or there are no results in the cache, +the plugin gets its content from this method.

    Returns:
    -

    Content to be filled.

    +

    The content to be filled.

    Return type:
    -

    Dict[str, Any]

    +

    Dict[str, Dict[str, Any]]

    @@ -944,27 +959,23 @@
    static load_mpl_outputs(run, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in the raw data and prepare them for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • run – The selected run.

      • +

    • inputs – Input and filter values from the user.

      • +

    • outputs – Raw outputs from the run.

    -
    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    -
    -
    Return type:
    -

    Union[Component, List[Component]]

    +
    Return type:
    +

    The rendered matplotlib figure of the footprint

    @@ -972,27 +983,26 @@
    static load_outputs(run, inputs, outputs)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in the raw data and prepare them for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • +

    • run – The selected run.

      • +

    • inputs – Input and filter values from the user.

      • +

    • outputs – Raw output from the run.

    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    +

    The plotly figure of the footprint performance and area.

    Return type:
    -

    Union[Component, List[Component]]

    +

    List[Any]

    @@ -1000,25 +1010,26 @@
    static process(run, inputs)[source]
    -

    Returns raw data based on a run and input data.

    +

    Return raw data based on a run and input data.

    Warning

    The returned data must be JSON serializable.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
      -

    • run (AbstractRun) – The run to process.

      • -

    • inputs (Dict[str, Any]) – Input data.

      • +

    • run – The selected run.

      • +

    • inputs – The input data.

    Returns:
    -

    Serialized dictionary.

    +

    A serialized dictionary.

    Return type:

    Dict[str, Any]

    diff --git a/development/api/deepcave.plugins.summary.html b/development/api/deepcave.plugins.summary.html index d94b7b5f..7766c67b 100644 --- a/development/api/deepcave.plugins.summary.html +++ b/development/api/deepcave.plugins.summary.html @@ -696,9 +696,19 @@
    +
    + On this page +
    @@ -720,17 +730,29 @@

    deepcave.plugins.summary

    +

    # summary

    +
    +

    ## Modules

    +
    +
    configurations

    This module provides utilities to visualize the characteristics of a configuration within a run.

    +
    +
    footprint

    This module provides utilities to visualize a configuration footprint.

    +
    +
    overview

    This module provides utilities for visualizing an overview of the selected run.

    +
    +
    +

    Modules

    - + - + - +

    deepcave.plugins.summary.configurations

    # Configurations

    deepcave.plugins.summary.footprint

    # FootPrint

    deepcave.plugins.summary.overview

    # Overview
    diff --git a/development/api/deepcave.plugins.summary.overview.html b/development/api/deepcave.plugins.summary.overview.html index 595a1ad1..7d2d30dd 100644 --- a/development/api/deepcave.plugins.summary.overview.html +++ b/development/api/deepcave.plugins.summary.overview.html @@ -754,11 +754,21 @@

    deepcave.plugins.summary.overview

    +

    # Overview

    +

    This module provides utilities for visualizing an overview of the selected runs.

    +

    It holds the most important information, e.g. meta data, objectives and statistics.

    +

    The module includes a dynamic plugin for the overview.

    +
    +
    ## Classes
      +

    • Overview: Visualize an overall overview of the selected run.

      • +
    +
    +

    Classes

    - +

    Overview()

    Visualize an overall overview of the selected run.
    @@ -766,19 +776,21 @@
    class deepcave.plugins.summary.overview.Overview[source]

    Bases: DynamicPlugin

    +

    Visualize an overall overview of the selected run.

    static get_output_layout(register)[source]
    -

    Layout for the output block.

    +

    Get the layout for the output block.

    Parameters:
    -

    register (Callable[[str, Union[str, List[str]]], str]) – The register method to register outputs.

    +

    register (Callable) – Method to register the outputs. +The register_input function is located in the Plugin superclass.

    Returns:
    -

    Layouts for the output block.

    +

    The layouts for the output block.

    Return type:
    -

    Union[Component, List[Component]]

    +

    List[Any]

    @@ -786,27 +798,22 @@
    static load_outputs(run, *_)[source]
    -

    Reads in the raw data and prepares them for the layout.

    +

    Read in the raw data and prepare them for the layout.

    Note

    -

    The passed inputs are cleaned and therefore differs compared to load_inputs or -load_dependency_inputs. Please see _clean_inputs for more information.

    +

    The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ +or ‘load_dependency_inputs’. +Please see ‘_clean_inputs’ for more information.

    Parameters:
    -
      -

    • runs (Union[AbstractRun, Dict[str, AbstractRun]]) – All selected runs. If activate_run_selection is set, only the selected run is -returned.

      • -

    • inputs (Dict[str, Dict[str, str]]) – Input and filter values from the user.

      • -

    • outputs (Dict[str, Union[str, Dict[str, str]]]) – Raw outputs from the runs. If activate_run_selection is set, -a Dict[str, str] is returned.

      • -
    +

    run – The selected run.

    Returns:
    -

    The components must be in the same position as defined in get_output_layout.

    +

    A list of the created tables of the overview.

    Return type:
    -

    Union[Component, List[Component]]

    +

    List[Any]

    diff --git a/development/api/deepcave.runs.converters.bohb.html b/development/api/deepcave.runs.converters.bohb.html index 7203a5aa..071affee 100644 --- a/development/api/deepcave.runs.converters.bohb.html +++ b/development/api/deepcave.runs.converters.bohb.html @@ -729,6 +729,15 @@
    • +
  • + + + + BOHBRun.latest_change + + + +
    • @@ -754,11 +763,20 @@

    deepcave.runs.converters.bohb

    +

    # BOHBRun

    +

    This module provides utilities for managing and processing data concerning a BOHB +(Bayesian Optimization and Hyperparameter Bandits) run.

    +
    +
    ## Classes
      +

    • BOHBRun: Create a BOHB Run.

      • +
    +
    +

    Classes

    - +

    BOHBRun(name[, configspace, objectives, ...])

    Create a BOHB (Bayesian Optimization and Hyperparameter Bandits) run.
    @@ -766,20 +784,36 @@
    class deepcave.runs.converters.bohb.BOHBRun(name, configspace=None, objectives=None, meta=None, path=None)[source]

    Bases: Run

    +

    Create a BOHB (Bayesian Optimization and Hyperparameter Bandits) run.

    +
    +

    Properties

    +
    +
    pathPath

    The path to the run.

    +
    +
    classmethod from_path(path)[source]
    -

    Based on a path, return a new Run object.

    +

    Create a new BOHB run from a given path and add a new trial to it.

    +
    +
    Parameters:
    +

    path (Union[Path, str]) – The pathname to base the run on.

    +
    +
    Return type:
    +

    The BOHB run

    +
    +
    -property hash
    -

    Hash of the current run. If hash changes, cache has to be cleared. This ensures that -the cache always holds the latest results of the run.

    +property hash: str +

    Get the hash of the current run.

    +

    If the hash changes, the cache has to be cleared. +This ensures that the cache always holds the latest results of the run.

    Returns:
    -

    hash – Hash of the run.

    +

    The hash of the run.

    Return type:

    str

    @@ -787,6 +821,21 @@
    +
    +
    +property latest_change: float | int
    +

    Get the timestamp of the latest change.

    +
    +
    Returns:
    +

    The latest change.

    +
    +
    Return type:
    +

    Union[float, int]

    +
    +
    +
    + +
    diff --git a/development/api/deepcave.runs.converters.deepcave.html b/development/api/deepcave.runs.converters.deepcave.html index d5062a8f..f0896157 100644 --- a/development/api/deepcave.runs.converters.deepcave.html +++ b/development/api/deepcave.runs.converters.deepcave.html @@ -729,6 +729,15 @@ +
  • + + + + DeepCAVERun.latest_change + + + +
    • @@ -754,11 +763,19 @@

    deepcave.runs.converters.deepcave

    +

    # DeepCAVE

    +

    This module defines the DeepCAVE run object.

    +
    +
    ## Classes
      +

    • DeepCAVERun: Create a DeepCAVE run and provide handling utilities.

      • +
    +
    +

    Classes

    - +

    DeepCAVERun(name[, configspace, objectives, ...])

    Create a DeepCAVE run and provide handling utilities.
    @@ -766,20 +783,36 @@
    class deepcave.runs.converters.deepcave.DeepCAVERun(name, configspace=None, objectives=None, meta=None, path=None)[source]

    Bases: Run

    +

    Create a DeepCAVE run and provide handling utilities.

    +
    +

    Properties

    +
    +
    pathPath

    The path the run.

    +
    +
    classmethod from_path(path)[source]
    -

    Based on a path, return a new Run object.

    +

    Get a DeepCAVE run from a given path.

    +
    +
    Parameters:
    +

    path (Path) – The path to base the run on.

    +
    +
    Return type:
    +

    The DeepCAVE run.

    +
    +
    -property hash
    -

    Hash of the current run. If hash changes, cache has to be cleared. This ensures that -the cache always holds the latest results of the run.

    +property hash: str +

    Hash of the current run.

    +

    If the hash changes, the cache has to be cleared. +This ensures that the cache always holds the latest results of the run.

    Returns:
    -

    hash – Hash of the run.

    +

    The hash of the run.

    Return type:

    str

    @@ -787,6 +820,21 @@
    +
    +
    +property latest_change: float | int
    +

    Get the timestamp of the latest change.

    +
    +
    Returns:
    +

    The latest change.

    +
    +
    Return type:
    +

    Union[float, int]

    +
    +
    +
    + +
    diff --git a/development/api/deepcave.runs.converters.html b/development/api/deepcave.runs.converters.html index 955af4b7..bdb45162 100644 --- a/development/api/deepcave.runs.converters.html +++ b/development/api/deepcave.runs.converters.html @@ -696,9 +696,19 @@
    +
    + On this page +
    @@ -720,20 +730,37 @@

    deepcave.runs.converters

    +

    # converters

    +
    +

    ## Modules

    +
    +
    bohb

    This module provides utilities for managing and processing data concerning a BOHB +(Bayesian Optimization and Hyperparameter Bandits) run.

    +
    +
    deepcave

    This module defines the DeepCAVE run object.

    +
    +
    smac3v1

    This module provides utilities to create a SMAC3v1 +(Sequential Model-based Algorithm Configuration) run.

    +
    +
    smac3v2

    This module provides utilities to create a SMAC3v2 +(Sequential Model-based Algorithm Configuration) run.

    +
    +
    +

    Modules

    - + - + - + - +

    deepcave.runs.converters.bohb

    # BOHBRun

    deepcave.runs.converters.deepcave

    # DeepCAVE

    deepcave.runs.converters.smac3v1

    # SMAC3v1Run

    deepcave.runs.converters.smac3v2

    # SMAC3v2Run
    diff --git a/development/api/deepcave.runs.converters.smac3v1.html b/development/api/deepcave.runs.converters.smac3v1.html index 9b8c3b2f..fc6a4175 100644 --- a/development/api/deepcave.runs.converters.smac3v1.html +++ b/development/api/deepcave.runs.converters.smac3v1.html @@ -729,6 +729,15 @@ +
  • + + + + SMAC3v1Run.latest_change + + + +
    • @@ -754,11 +763,21 @@

    deepcave.runs.converters.smac3v1

    +

    # SMAC3v1Run

    +

    This module provides utilities to create a SMAC3v1 +(Sequential Model-based Algorithm Configuration) run.

    +

    Version 1.4 is used.

    +
    +
    ## Classes
      +

    • SMAC3v1Run: Define a SMAC3v1 run object.

      • +
    +
    +

    Classes

    - +

    SMAC3v1Run(name[, configspace, objectives, ...])

    Define a SMAC3v1 (Sequential Model-based Algorithm Configuration) run object.
    @@ -766,20 +785,43 @@
    class deepcave.runs.converters.smac3v1.SMAC3v1Run(name, configspace=None, objectives=None, meta=None, path=None)[source]

    Bases: Run

    +

    Define a SMAC3v1 (Sequential Model-based Algorithm Configuration) run object.

    +

    Version 1.4 is used.

    +
    +

    Properties

    +
    +
    pathPath

    The path to the run.

    +
    +
    classmethod from_path(path)[source]

    Based on working_dir/run_name/*, return a new trials object.

    +
    +
    Parameters:
    +

    path (Union[Path, str]) – The path to base the run on.

    +
    +
    Return type:
    +

    A SMAC3v1 run.

    +
    +
    Raises:
    +
      +

    • RuntimeError – Instances are not supported.

      • +

    • RuntimeError – Multiple Seeds are not supported.

      • +
    +
    +
    -property hash
    -

    Hash of the current run. If hash changes, cache has to be cleared. This ensures that -the cache always holds the latest results of the run.

    +property hash: str +

    Hash of the current run.

    +

    If the hash changes, the cache has to be cleared. +This ensures that the cache always holds the latest results of the run.

    Returns:
    -

    hash – Hash of the run.

    +

    The hash of the run.

    Return type:

    str

    @@ -787,6 +829,21 @@
    +
    +
    +property latest_change: float | int
    +

    Get the timestamp of the latest change.

    +
    +
    Returns:
    +

    The latest change.

    +
    +
    Return type:
    +

    Union[float, int]

    +
    +
    +
    + +
    diff --git a/development/api/deepcave.runs.converters.smac3v2.html b/development/api/deepcave.runs.converters.smac3v2.html index d3e1a112..54943f87 100644 --- a/development/api/deepcave.runs.converters.smac3v2.html +++ b/development/api/deepcave.runs.converters.smac3v2.html @@ -729,6 +729,15 @@ +
  • + + + + SMAC3v2Run.latest_change + + + +
    • @@ -754,11 +763,21 @@

    deepcave.runs.converters.smac3v2

    +

    # SMAC3v2Run

    +

    This module provides utilities to create a SMAC3v2 +(Sequential Model-based Algorithm Configuration) run.

    +

    Version 2.0.0 is used.

    +
    +
    ## Classes
      +

    • SMAC3v2Run: Define a SMAC3v2 run object.

      • +
    +
    +

    Classes

    - +

    SMAC3v2Run(name[, configspace, objectives, ...])

    Define a SMAC3v2 (Sequential Model-based Algorithm Configuration) run object.
    @@ -766,20 +785,43 @@
    class deepcave.runs.converters.smac3v2.SMAC3v2Run(name, configspace=None, objectives=None, meta=None, path=None)[source]

    Bases: Run

    +

    Define a SMAC3v2 (Sequential Model-based Algorithm Configuration) run object.

    +

    Version 2.0.0 is used.

    +
    +

    Properties

    +
    +
    pathPath

    The path to the run.

    +
    +
    classmethod from_path(path)[source]

    Based on working_dir/run_name/*, return a new trials object.

    +
    +
    Parameters:
    +

    path (Union[Path, str]) – The path to base the trial object on.

    +
    +
    Return type:
    +

    The SMAC3v2 run.

    +
    +
    Raises:
    +
      +

    • RuntimeError – Instances are not supported.

      • +

    • RuntimeError – Multiple Seeds are not supported.

      • +
    +
    +
    -property hash
    -

    Hash of the current run. If hash changes, cache has to be cleared. This ensures that -the cache always holds the latest results of the run.

    +property hash: str +

    Hash of the current run.

    +

    If the hash changes, the cache has to be cleared. +This ensures that the cache always holds the latest results of the run.

    Returns:
    -

    hash – Hash of the run.

    +

    The hash of the run.

    Return type:

    str

    @@ -787,6 +829,21 @@
    +
    +
    +property latest_change: float | int
    +

    Get the timestamp of the latest change.

    +
    +
    Returns:
    +

    The latest change.

    +
    +
    Return type:
    +

    Union[float, int]

    +
    +
    +
    + +
    diff --git a/development/api/deepcave.runs.exceptions.html b/development/api/deepcave.runs.exceptions.html index 2e773178..db54a7d9 100644 --- a/development/api/deepcave.runs.exceptions.html +++ b/development/api/deepcave.runs.exceptions.html @@ -743,11 +743,22 @@

    deepcave.runs.exceptions

    +

    # Exceptions

    +

    This module provides utilities for different errors concerning the runs.

    +

    Exceptions will be raised, if a directory is not a valid run, +as well as if runs are not mergeable.

    +
    +
    ## Classes
      +

    • NotValidRunError: Raised if directory is not a valid run.

      • +

    • NotMergeableError: Raised if two or more runs are not mergeable.

      • +
    +
    +

    Exceptions

    - + @@ -758,7 +769,7 @@
    exception deepcave.runs.exceptions.NotMergeableError[source]

    Bases: Exception

    -

    Raised if two or more runs are not mergeable

    +

    Raised if two or more runs are not mergeable.

    diff --git a/development/api/deepcave.runs.group.html b/development/api/deepcave.runs.group.html index 101f60d9..833c0cc0 100644 --- a/development/api/deepcave.runs.group.html +++ b/development/api/deepcave.runs.group.html @@ -711,6 +711,60 @@ @@ -763,11 +844,20 @@

    deepcave.runs.group

    +

    # Group

    +

    This module provides utilities for grouping and managing a group of runs. +Utilities include getting attributes of the grouped runs, as well as the group itself.

    +
    +
    ## Classes
      +

    • Group: Can group and manage a group of runs.

      • +
    +
    +

    Classes

    NotMergeableError

    Raised if two or more runs are not mergeable

    Raised if two or more runs are not mergeable.

    NotValidRunError

    Raised if directory is not a valid run.
    - +

    Group(name, runs)

    Can group and manage a group of runs.
    @@ -775,26 +865,149 @@
    class deepcave.runs.group.Group(name, runs)[source]

    Bases: AbstractRun

    +

    Can group and manage a group of runs.

    +

    Utilities include getting attributes of the grouped runs, as well as the group itself.

    +
    +

    Properties

    +
    +
    runsList[AbstractRun]

    A list of the runs.

    +
    +
    metaDict[str, Any]

    Contains budgets, objectives and their attributes.

    +
    +
    configspaceConfigurationSpace

    The configuration space of the runs.

    +
    +
    objectivesObjective

    The objectives of the runs.

    +
    +
    budgetsList[Union[int, float]]

    The budgets of the runs.

    +
    +
    configsDict[int, Any]

    A dictionary of the configurations and their ids as key.

    +
    +
    originsDict[int, str]

    The origins of the configurations and their ids as key.

    +
    +
    trial_keysDict[Tuple[str, int], int]

    The keys of the trial.

    +
    +
    historyList[Trial]

    The trial history.

    +
    +
    prefixstr

    The prefix for the id of the group.

    +
    +
    namestr

    The name for the id of the group.

    +
    +
    +
    +
    +__iter__()[source]
    +

    Allow to iterate over the object.

    +
    +
    Return type:
    +

    Iterator[str]

    +
    +
    +
    + +
    +
    +get_model(config_id)[source]
    +

    Get the model given the configuration id.

    +
    +
    Parameters:
    +

    config_id (int) – The identificator of the configuration.

    +
    +
    Returns:
    +

    The model.

    +
    +
    Return type:
    +

    Optional[Any]

    +
    +
    +
    + +
    +
    +get_new_config_id(run_id, original_config_id)[source]
    +

    Get a new identificator for a configuration.

    +
    +
    Parameters:
    +
      +

    • run_id (int) – The id of the run.

      • +

    • original_config_id (int) – The original identificator of a configuration.

      • +
    +
    +
    Returns:
    +

    The new identificator of a configuration.

    +
    +
    Return type:
    +

    int

    +
    +
    +
    + +
    +
    +get_original_config_id(config_id)[source]
    +

    Get the original identificator of a configuration.

    +
    +
    Parameters:
    +

    config_id (int) – The identificator of a configuration.

    +
    +
    Returns:
    +

    The original identificator of a configuration.

    +
    +
    Return type:
    +

    int

    +
    +
    +
    + +
    +
    +get_original_run(config_id)[source]
    +

    Get the original run.

    +
    +
    Parameters:
    +

    config_id (int) – The identificator of the configuration.

    +
    +
    Returns:
    +

    The original run.

    +
    +
    Return type:
    +

    AbstractRun

    +
    +
    +
    + +
    +
    +get_runs()[source]
    +

    Get the runs in the group.

    +
    +
    Returns:
    +

    A list of the grouped runs.

    +
    +
    Return type:
    +

    List[AbstractRun]

    +
    +
    +
    +
    get_trajectory(*args, **kwargs)[source]
    -

    Calculates the trajectory of the given objective and budget.

    +

    Calculate the trajectory of the given objective and budget.

    +

    This includes the times, the mean costs, and the standard deviation of the costs.

    Parameters:
      -

    • objective (Objective) – Objective to calculate the trajectory for.

      • -

    • budget (Optional[Union[int, float]], optional) – Budget to calculate the trajectory for. If no budget is given, then the highest budget -is chosen. By default None.

      • +

    • *args – Should be the objective to calculate the trajectory from.

      • +

    • **kwargs – Should be the budget to calculate the trajectory for.

    Returns:

    • times (List[float]) – Times of the trajectory.

    • costs_mean (List[float]) – Costs of the trajectory.

      • -

    • costs_std (List[float]) – Standard deviation of the costs of the trajectory. This is particularly useful for -grouped runs.

      • -

    • ids (List[int]) – The “global” ids of the selected trials.

      • -

    • config_ids (List[int]) – Config ids of the selected trials.

      • +

    • costs_std (List[float]) – Standard deviation of the costs of the trajectory.

      • +

    • ids (List[int]) – The “global” ids of the selected trial.

      • +

    • config_ids (List[int]) – The configuration ids of the selected trials.

    @@ -804,11 +1017,10 @@
    property hash: str
    -

    Hash of the current run. If hash changes, cache has to be cleared. This ensures that -the cache always holds the latest results of the run.

    +

    Sorted hashes of the group.

    Returns:
    -

    hash – Hash of the run.

    +

    The sorted hash of the group.

    Return type:

    str

    @@ -819,11 +1031,11 @@
    property id: str
    -

    Hash of the file. This is used to identify the file. -In contrast to hash, this hash should not be changed throughout the run.

    +

    Get the hash as id of the group.

    +

    In contrast to hash, this hash should not be changed throughout the run.

    Returns:
    -

    Hash of the run.

    +

    The hash of the group.

    Return type:

    str

    @@ -831,6 +1043,41 @@
    +
    +
    +property latest_change: float
    +

    Get the latest change made to the grouped runs.

    +
    +
    Returns:
    +

    The latest change.

    +
    +
    Return type:
    +

    float

    +
    +
    +
    + +
    +
    +property run_names: List[str]
    +

    Get the names of the runs in the group.

    +
    +
    Returns:
    +

    A list of the names of the runs in the group.

    +
    +
    Return type:
    +

    List[str]

    +
    +
    +
    + +
    +
    +property run_paths: List[str]
    +

    Get the path of the runs in the group.

    +
    + +
    diff --git a/development/api/deepcave.runs.handler.html b/development/api/deepcave.runs.handler.html index 18088d3f..6a5c6a90 100644 --- a/development/api/deepcave.runs.handler.html +++ b/development/api/deepcave.runs.handler.html @@ -765,6 +765,15 @@ +
  • + + + + RunHandler.get_selected_groups() + + + +
  • @@ -871,11 +880,21 @@

    deepcave.runs.handler

    +

    # Handler

    +

    This module provides utilities to handle a run.

    +

    It can retrieve working directories, run paths, run names, as well as groups of runs. +It provides utilities to update and remove runs as well as groups of runs.

    +
    +
    # Classes
      +

    • RunHandler: Handle the runs.

      • +
    +
    +

    Classes

    - +

    RunHandler(config, cache, run_cache)

    Handles the runs.

    Handle the runs.
    @@ -883,12 +902,34 @@
    class deepcave.runs.handler.RunHandler(config, cache, run_cache)[source]

    Bases: object

    -

    Handles the runs. Based on the meta data in the cache, automatically selects the right converter +

    Handle the runs.

    +

    Based on the meta data in the cache, automatically selects the right converter and switches to the right (plugin) cache.

    +

    Provides utilities to retrieve working directories, run paths, run names, and groups of runs. +Also update and remove runs as well a groups of runs.

    +
    +

    Properties

    +
    +
    cCache

    The cache containing information about a run(s).

    +
    +
    rcRunCaches

    The caches for the selected runs.

    +
    +
    loggerLogger

    The logger for the run handler.

    +
    +
    available_run_yfesList[Type[Run]]

    A list of the available converters.

    +
    +
    runsDict[str, AbstractRun]

    A dictionary of runs with their path as key.

    +
    +
    groupsDict[str, Group]

    A dictionary of the groups.

    +
    +
    available_run_classesList[Type[Run]]

    Contains the available run classes.

    +
    +
    add_run(run_path)[source]
    -

    Adds a run path to the cache. If run path is already in cache, do nothing.

    +

    Add a run path to the cache.

    +

    If run path is already in cache, do nothing.

    Parameters:

    run_path (str) – Path of a run.

    @@ -905,13 +946,19 @@
    get_available_run_paths()[source]
    -

    Returns the available run paths from the current directory.

    +

    Return the available run paths from the current directory.

    -
    Returns:
    -

    Run path as key and run name as value.

    +
    Return type:
    +

    Dict[str, str]

    -
    Return type:
    -

    Dict[str, str]

    +
    Returns:
    +

      +

    • Dict[str, str] – Run path as key and run name as value.

      • +

    • Exceptions

      • +

    • ———-

      • +

    • FileNotFoundError

      • +
    +

    @@ -919,7 +966,7 @@
    get_groups()[source]
    -

    Returns instantiated grouped runs.

    +

    Return instantiated grouped runs.

    Returns:

    Instances of grouped runs.

    @@ -933,13 +980,13 @@
    get_run(run_id)[source]
    -

    Looks inside self.runs and self.groups and if the run id is found, returns the run.

    +

    Look inside self.runs and self.groups and if the run id is found, returns the run.

    Parameters:

    run_id (str) – Internal id of the run. Referred to run.id.

    Returns:
    -

    Run

    +

    Run.

    Return type:

    AbstractRun

    @@ -953,7 +1000,7 @@
    get_run_name(run_path)[source]
    -

    Returns the stem of the path.

    +

    Return the stem of the path.

    Parameters:

    run_path (Union[Path, str]) – Path, which should be converted to a name.

    @@ -970,11 +1017,12 @@
    get_runs(include_groups=False)[source]
    -

    Returns the runs from the internal cache. The runs are already loaded and ready to use. +

    Return the runs from the internal cache.

    +

    The runs are already loaded and ready to use. Optional, if include_groups is set to True, the groups are also included.

    Parameters:
    -

    include_groups (bool, optional) – Includes the groups, by default False

    +

    include_groups (bool, optional) – Includes the groups, by default False.

    Returns:

    Instances of runs.

    @@ -985,10 +1033,27 @@
    +
    +
    +get_selected_groups()[source]
    +

    Get the selected groups.

    +
    +
    Returns:
    +

    Dictionary with the selected groups.

    +
    +
    Return type:
    +

    Dict[str, List[str]]

    +
    +
    Raises:
    +

    AssertionError – If groups in cache is not a dictionary, an error is thrown.

    +
    +
    +
    +
    get_selected_run_names()[source]
    -

    Returns the run names of the selected runs.

    +

    Return the run names of the selected runs.

    Returns:

    List of run names of the selected runs.

    @@ -1002,13 +1067,16 @@
    get_selected_run_paths()[source]
    -

    Returns the selected run paths from the cache.

    +

    Return the selected run paths from the cache.

    Returns:

    Run paths as a list.

    Return type:
    -

    Dict[str, str]

    +

    List[str]

    +
    +
    Raises:
    +

    AssertionError. – If the selected run paths are not a list, an error is thrown.

    @@ -1016,7 +1084,7 @@
    get_working_directory()[source]
    -

    Returns the current working directory in the cache.

    +

    Return the current working directory in the cache.

    Returns:

    Path of the working directory.

    @@ -1024,19 +1092,26 @@
    Return type:

    Path

    +
    Raises:
    +

    AssertionError – If the working directory is not a string or a Path, an error is thrown.

    +
    remove_run(run_path)[source]
    -

    Removes a run path from the cache. If run path is not in cache, do nothing.

    +

    Remove a run path from the cache.

    +

    If run path is not in cache, do nothing.

    Parameters:

    run_path (str) – Path of a run.

    -
    Return type:
    -

    None

    +
    Raises:
    +

    TypeError – If selected_run_paths or groups is None, an error is thrown.

    +
    +
    Return type:
    +

    None

    @@ -1044,7 +1119,7 @@
    set_working_directory(working_directory)[source]
    -

    Sets the working directoy to the meta cache.

    +

    Set the working directory to the meta cache.

    Parameters:

    working_directory (Union[Path, str]) – Directory to be set.

    @@ -1058,7 +1133,7 @@
    update()[source]
    -

    Updates the internal run and group instances but only if a hash changed.

    +

    Update the internal run and group instances but only if a hash changed.

    Return type:

    None

    @@ -1069,14 +1144,22 @@
    update_groups(groups=None)[source]
    -

    Loads chosen groups. If groups is passed, it is used to instantiate the groups and +

    Load chosen groups.

    +

    If groups is passed, it is used to instantiate the groups and saved to the cache. Otherwise, groups is loaded from the cache.

    -
    Raises:
    -

    NotMergeableError – If runs can not be merged, an error is thrown.

    +
    Parameters:
    +

    groups (Optional[Dict[str, str]], optional) – A dictionary with the groups. +Default is None.

    -
    Return type:
    -

    None

    +
    Raises:
    +
      +

    • NotMergeableError – If runs can not be merged, an error is thrown.

      • +

    • TypeError – If groups is None, an error is thrown.

      • +
    +
    +
    Return type:
    +

    None

    @@ -1084,13 +1167,23 @@
    update_run(run_path, class_hint=None)[source]
    -

    Loads the run from self.runs or creates a new one.

    +

    Load the run from self.runs or create a new one.

    -
    Raises:
    -

    NotValidRunError – If directory can not be transformed into a run, an error is thrown.

    +
    Parameters:
    +
      +

    • run_path (str) – The path of the run.

      • +

    • class_hint (Optional[Type[Run]], optional) – A hint/suggestion of what the Type of the Run is. +Default is None.

      • +
    -
    Return type:
    -

    Optional[AbstractRun]

    +
    Returns:
    +

    The Run added to the cache.

    +
    +
    Return type:
    +

    Optional[AbstractRun]

    +
    +
    Raises:
    +

    NotValidRunError – If directory can not be transformed into a run, an error is thrown.

    @@ -1098,20 +1191,21 @@
    update_runs()[source]
    -

    Loads selected runs and update cache if files changed.

    +

    Load selected runs and update cache if files changed.

    -
    Raises:
    -

    NotValidRunError – If directory can not be transformed into a run, an error is thrown.

    +
    Returns:
    +

    True if all selected runs could be loaded, False otherwise.

    -
    Returns:
    -

    True if all selected runs could be loaded, False otherwise.

    +
    Return type:
    +

    bool

    -
    Return type:
    -

    bool

    +
    Raises:
    +

    NotValidRunError – If directory can not be transformed into a run, an error is thrown.

    +
    diff --git a/development/api/deepcave.runs.html b/development/api/deepcave.runs.html index 685d63bf..db9ad500 100644 --- a/development/api/deepcave.runs.html +++ b/development/api/deepcave.runs.html @@ -711,6 +711,15 @@
  • @@ -898,11 +1051,20 @@

    deepcave.runs

    +

    # AbstractRun

    +

    This module provides utilities to create and handle an abstract run.

    +

    It provides functions to get information of the run, as well as the used objectives.

    +
    +
    ## Classes
      +

    • AbstractRun: Create a new run.

      • +
    +
    +

    Functions

    - +

    check_equality(runs[, meta, configspace, ...])

    Checks the passed runs on equality based on the selected runs and returns the requested attributes.

    Check the passed runs on equality based on the selected runs.
    @@ -910,7 +1072,7 @@ - +

    AbstractRun(name)

    Create a new run.
    @@ -918,16 +1080,59 @@
    class deepcave.runs.AbstractRun(name)[source]

    Bases: ABC

    +

    Create a new run.

    +

    Provide functions to get information of the run, as well as the used objectives.

    +
    +

    Properties

    +
    +
    namestr

    The name of the run.

    +
    +
    pathOptional[Path]

    The path to the run.

    +
    +
    loggerLogger

    The logger for the run.

    +
    +
    meta: Dict[str, Any]

    Contains the run’s meta information.

    +
    +
    configspace: ConfigSpace.ConfigurationSpace

    The configuration space of the run.

    +
    +
    configs: Dict[int, Configuration]

    Contains the configurations.

    +
    +
    origins: Dict[int, str]

    The origin of the configuration.

    +
    +
    models: Dict[int, Optional[Union[str, “torch.nn.Module”]]]

    Contains the modules.

    +
    +
    history: List[Trial]

    The history of Trials.

    +
    +
    trial_keys: Dict[Tuple[str, int], int]

    Contains config_id, budget and the corresponding trial_id.

    +
    +
    models_dirPath

    The directory of the model.

    +
    +
    +
    +
    +empty()[source]
    +

    Check if the run’s history is empty.

    +
    +
    Returns:
    +

    True if run history is empty, False otherwise.

    +
    +
    Return type:
    +

    bool

    +
    +
    +
    +
    encode_config(config, specific=False)[source]
    -

    Encodes a given config (id) to a normalized list. -If a config is passed, no look-up has to be done.

    +

    Encode a given configuration (id) to a normalized list.

    +

    If a configuration is passed, no look-up has to be done.

    Parameters:
      -

    • config (Union[int, Dict[Any, Any], Configuration]) – Either the config id, config as dict, or Configuration itself.

      • -

    • specific (bool, optional) – Use specific encoding for fanova tree, by default False.

      • +

    • config (Union[int, Dict[Any, Any], Configuration]) – Either the configuration id, as configuration as dict, +or a Configuration itself.

      • +

    • specific (bool) – Use specific encoding for fanova tree, by default False.

    Returns:
    @@ -939,17 +1144,35 @@
    +
    +
    +encode_configs(configs)[source]
    +

    Encode a list of configurations into a corresponding numpy array.

    +
    +
    Parameters:
    +

    configs (List[Configuration]) – A list containing the configurations to be encoded.

    +
    +
    Returns:
    +

    A numpy array with the encoded configurations.

    +
    +
    Return type:
    +

    np.ndarray

    +
    +
    +
    +
    get_all_costs(budget=None, statuses=None)[source]
    -

    Get all costs in the history with their config ids. Only configs from the given budget +

    Get all costs in the history with their config ids.

    +

    Only configs from the given budget and statuses are returned.

    Parameters:
      -

    • budget (Optional[Union[int, float]], optional) – Budget to select the costs. If no budget is given, the highest budget is chosen. -By default None.

      • -

    • statuses (Optional[Union[Status, List[Status]]], optional) – Only selected stati are considered. If no status is given, all stati are considered. +

    • budget (Optional[Union[int, float]]) – Budget to select the costs. If no budget is given, the highest budget is chosen. +By default, None.

      • +

    • statuses (Optional[Union[Status, List[Status]]]) – Only selected stati are considered. If no status is given, all stati are considered. By default None.

    @@ -965,16 +1188,42 @@
    get_budget(id, human=False)[source]
    -

    Gets the budget given an id.

    +

    Get the budget given an id.

    +
    +
    Parameters:
    +
      +

    • id (Union[int, str]) – The id of the wanted budget. +If id is a string, it is converted to an integer.

      • +

    • human (bool, optional) – Make the output more readable. +By default False.

      • +
    +
    +
    Returns:
    +

    The budget.

    +
    +
    Return type:
    +

    float, int

    +
    +
    Raises:
    +

    TypeError – If the budget with this id is invalid.

    +
    +
    +
    + +
    +
    +get_budget_ids(include_combined=True)[source]
    +

    Get the corresponding ids for the budgets.

    Parameters:
    -

    id (Union[int, str]) – Id of the wanted budget. If id is a string, it is converted to an integer.

    +

    include_combined (bool, optional) – If False, cut last id of budget ids. +By default True.

    Returns:
    -

    Budget.

    +

    A list of the budget ids.

    Return type:
    -

    float

    +

    List[int]

    @@ -982,16 +1231,55 @@
    get_budgets(human=False, include_combined=True)[source]
    -

    Returns the budgets from the meta data.

    +

    Return the budgets from the meta data.

    +
    +
    Parameters:
    +
      +

    • human (bool, optional) – Make the output more readable. +By default False.

      • +

    • include_combined (bool, optional) – If True, include the combined budget. +By default True.

      • +
    +
    +
    Returns:
    +

    List of budgets. In a readable form, if human is True.

    +
    +
    Return type:
    +

    List[Union[int, float, str]]

    +
    +
    +
    + +
    +
    +get_config(id)[source]
    +

    Retrieve the configuration with the corresponding id.

    Parameters:
    -

    human (bool, optional) – Make the output better readable. By default False.

    +

    id (int) – The id of the configuration.

    Returns:
    -

    List of budgets.

    +

    The corresponding Configuration.

    Return type:
    -

    List[Union[int, float]]

    +

    Configuration

    +
    +
    +
    + +
    +
    +get_config_id(config)[source]
    +

    Get the id of the configuration.

    +
    +
    Parameters:
    +

    config (Union[Configuration, Dict]) – The configuration for which to find the id.

    +
    +
    Returns:
    +

    The configuration id.

    +
    +
    Return type:
    +

    Optional[int]

    @@ -999,11 +1287,13 @@
    get_configs(budget=None)[source]
    -

    Get configurations of the run. Optionally, only configurations which were evaluated +

    Get configurations of the run.

    +

    Optionally, only configurations which were evaluated on the passed budget are considered.

    Parameters:
    -

    budget (Union[int, float], optional) – Considered budget. By default None (all configurations are included).

    +

    budget (Optiona[Union[int, float]]) – Considered budget. +By default, None (all configurations are included).

    Returns:

    Configuration id and the configuration.

    @@ -1017,50 +1307,51 @@
    get_costs(config_id, budget=None)[source]
    -

    Returns the costs of a configuration. In case of multi-objective, multiple costs are +

    Return the costs of a configuration.

    +

    In case of multi-objective, multiple costs are returned.

    Parameters:

    • config_id (int) – Configuration id to get the costs for.

      • -

    • budget (Optional[Union[int, float]], optional) – Budget to get the costs from the configuration id for. By default None. If budget is +

    • budget (Optional[Union[int, float]]) – Budget to get the costs from the configuration id for. By default, None. If budget is None, the highest budget is chosen.

    +
    Returns:
    +

    List of costs from the associated configuration.

    +
    +
    Return type:
    +

    List[float]

    +
    Raises:

    • ValueError – If the configuration id is not found.

    • RuntimeError – If the budget was not evaluated for the passed config id.

    -
    Returns:
    -

    List of costs from the associated configuration.

    -
    -
    Return type:
    -

    List[float]

    -
    get_encoded_data(objectives=None, budget=None, statuses=None, specific=False, include_config_ids=False, include_combined_cost=False)[source]
    -

    Encodes configurations to process them further. After the configurations are encoded, +

    Encode configurations to process them further.

    +

    After the configurations are encoded, they can be used in model prediction.

    Parameters:
      -

    • objectives (Optional[Union[Objective, List[Objective]]], optional) – Which objectives should be considered. If None, all objectives are -considered. By default None.

      • -

    • budget (Optional[List[Status]], optional) – Which budget should be considered. By default None. If None, only the highest budget +

    • objectives (Optional[Union[Objective, List[Objective]]]) – Which objectives should be considered. If None, all objectives are +considered. By default, None.

      • +

    • budget (Optional[List[Status]]) – Which budget should be considered. By default, None. If None, only the highest budget is considered.

      • -

    • statuses (Optional[Union[Status, List[Status]]], optional) – Which statuses should be considered. By default None. If None, all statuses are +

    • statuses (Optional[Union[Status, List[Status]]]) – Which statuses should be considered. By default, None. If None, all statuses are considered.

      • -

    • encode_y (bool, optional) – Whether y should be normalized too. By default False.

      • -

    • specific (bool, optional) – Whether a specific encoding should be used. This encoding is compatible with pyrfr. +

    • specific (bool) – Whether a specific encoding should be used. This encoding is compatible with pyrfr. A wrapper for pyrfr is implemented in deepcave.evaluators.epm. By default False.

      • -

    • include_config_ids (bool, optional) – Whether to include config ids. By default False.

      • +

    • include_config_ids (bool) – Whether to include configuration ids. By default, False.

    • include_combined_cost (bool, optional) – Whether to include combined cost. Note that the combined cost is calculated by the passed objectives only. By default False.

    @@ -1073,7 +1364,7 @@

    pd.DataFrame

    Raises:
    -

    ValueError – If a hyperparameter is not supported.

    +

    ValueError – If a hyperparameter (HP) is not supported.

    @@ -1081,15 +1372,19 @@
    get_highest_budget(config_id=None)[source]
    -

    Returns the highest found budget for a config id. If no config id is specified then +

    Return the highest found budget for a config id.

    +

    If no config id is specified then the highest available budget is returned. Moreover, if no budget is available None is returned.

    -
    Returns:
    -

    The highest budget or None if no budget was specified.

    +
    Parameters:
    +

    config_id (Optional[int]) – The config id for which the highest budget is returned.

    -
    Return type:
    -

    Optional[Union[int, float]]

    +
    Returns:
    +

    The highest budget or None if no budget was specified.

    +
    +
    Return type:
    +

    Optional[Union[int, float]]

    @@ -1097,13 +1392,13 @@
    get_incumbent(objectives=None, budget=None, statuses=None)[source]
    -

    Returns the incumbent with its normalized cost.

    +

    Return the incumbent with its normalized cost.

    Parameters:
      -

    • objectives (Optional[Union[Objective, List[Objective]]], optional) – Considerd objectives. By default None. If None, all objectives are considered.

      • -

    • budget (Optional[Union[int, float]], optional) – Considered budget. By default None. If None, the highest budget is chosen.

      • -

    • statuses (Optional[Union[Status, List[Status]]], optional) – Considered statuses. By default None. If None, all stati are considered.

      • +

    • objectives (Optional[Union[Objective, List[Objective]]]) – Considered objectives. By default, None. If None, all objectives are considered.

      • +

    • budget (Optional[Union[int, float]]) – Considered budget. By default, None. If None, the highest budget is chosen.

      • +

    • statuses (Optional[Union[Status, List[Status]]]) – Considered statuses. By default, None. If None, all stati are considered.

    Returns:
    @@ -1118,19 +1413,70 @@
    +
    +
    +get_meta()[source]
    +

    Get a shallow copy of the meta information.

    +
    +
    Returns:
    +

    A shallow copy of the meta information dictionary.

    +
    +
    Return type:
    +

    Dict[str, Any]

    +
    +
    +
    + +
    +
    +get_model(config_id)[source]
    +

    Get a model associated with the configuration id.

    +
    +
    Parameters:
    +

    config_id (int) – The configuration id.

    +
    +
    Returns:
    +

    A model for the provided configuration id.

    +
    +
    Return type:
    +

    Optional[“torch.nn.Module]

    +
    +
    +
    + +
    +
    +get_num_configs(budget=None)[source]
    +

    Count the number of configurations stored in this run with a specific budget.

    +
    +
    Parameters:
    +

    budget (Optional[Union[int, float]]) – The budget for which to count the configurations. +If not provided, counts all configurations. +Default is None.

    +
    +
    Returns:
    +

    The number of all configurations with a given budget. +If budget is None, counts all configurations.

    +
    +
    Return type:
    +

    int

    +
    +
    +
    +
    get_objective(id)[source]
    -

    Returns the objective based on the id or the name.

    +

    Return the objective based on the id or the name.

    Parameters:

    id (Union[str, int]) – The id or name of the objective.

    Returns:
    -

    The objective object.

    +

    The objective.

    Return type:
    -

    Objective

    +

    Optional[Objective]

    @@ -1138,7 +1484,7 @@
    get_objective_id(objective)[source]
    -

    Returns the id of the objective if it is found.

    +

    Return the id of the objective if it is found.

    Parameters:

    objective (Union[Objective, str]) – The objective or objective name for which the id is returned.

    @@ -1155,14 +1501,81 @@
    +
    +
    +get_objective_ids()[source]
    +

    Get the ids of the objectives.

    +
    +
    Returns:
    +

    A list of the ids of the objectives.

    +
    +
    Return type:
    +

    List[int]

    +
    +
    +
    +
    get_objective_name(objectives=None)[source]
    -

    Get the cost name of given objective names. +

    Get the cost name of given objective names.

    +

    Returns “Combined Cost” if multiple objective names were involved.

    +
    +
    Parameters:
    +

    objectives (Optional[List[Objective]]) – A list of the objectives. +By default, None.

    +
    +
    Returns:
    +

    The name of the objective. Returns “Combined Cost” if multiple objective names were involved.

    +
    +
    Return type:
    +

    str

    +
    +
    +
    + +
    +
    +get_objective_names()[source]
    +

    Get the names of the objectives.

    +
    Returns:
    +

    A list containing the names of the objectives.

    +
    +
    Return type:
    +

    List[str]

    +
    +
    +
    + +
    +
    +get_objectives()[source]
    +

    Get a list of all objectives corresponding to the run.

    +
    +
    Returns:
    +

    A list containing all objectives associated with the run.

    +
    +
    Return type:
    +

    List[Objective]

    +
    +
    +
    + +
    +
    +get_origin(config_id)[source]
    +

    Get the origin, given a config id.

    +
    +
    Parameters:
    +

    config_id (int) – The identificator of the configuration.

    +
    +
    Returns:
    +

    An origin string corresponding to the given configuration id.

    +
    Return type:
    -

    str

    +

    Optional[str]

    @@ -1170,23 +1583,23 @@
    get_status(config_id, budget=None)[source]
    -

    Returns the status of a configuration.

    +

    Return the status of a configuration.

    Parameters:

    • config_id (int) – Configuration id to get the status for.

      • -

    • budget (Optional[Union[int, float]], optional) – Budget to get the status from the configuration id for. By default None. If budget is +

    • budget (Optional[Union[int, float]]) – Budget to get the status from the configuration id for. By default, None. If budget is None, the highest budget is chosen.

    -
    Raises:
    -

    ValueError – If the configuration id is not found.

    +
    Returns:
    +

    Status of the configuration.

    -
    Returns:
    -

    Status of the configuration.

    +
    Return type:
    +

    Status

    -
    Return type:
    -

    Status

    +
    Raises:
    +

    ValueError – If the configuration id is not found.

    @@ -1194,36 +1607,94 @@
    get_trajectory(objective, budget=None)[source]
    -

    Calculates the trajectory of the given objective and budget.

    +

    Calculate the trajectory of the given objective and budget.

    Parameters:

    • objective (Objective) – Objective to calculate the trajectory for.

      • -

    • budget (Optional[Union[int, float]], optional) – Budget to calculate the trajectory for. If no budget is given, then the highest budget +

    • budget (Optional[Union[int, float]]) – Budget to calculate the trajectory for. If no budget is given, then the highest budget is chosen. By default None.

    -
    Return type:
    -

    Tuple[List[float], List[float], List[float], List[int], List[int]]

    +
    Returns:
    +

    +
    timesList[float]

    Times of the trajectory.

    -
    Returns:
    -

      -

    • times (List[float]) – Times of the trajectory.

      • -

    • costs_mean (List[float]) – Costs of the trajectory.

      • -

    • costs_std (List[float]) – Standard deviation of the costs of the trajectory. This is particularly useful for -grouped runs.

      • -

    • ids (List[int]) – The “global” ids of the selected trials.

      • -

    • config_ids (List[int]) – Config ids of the selected trials.

      • -
    +
    costs_meanList[float]

    Costs of the trajectory.

    +
    +
    costs_stdList[float]

    Standard deviation of the costs of the trajectory. This is particularly useful for +grouped runs.

    +
    +
    idsList[int]

    The “global” ids of the selected trials.

    +
    +
    config_idsList[int]

    Config ids of the selected trials.

    +
    +

    +
    Return type:
    +

    Tuple[List[float], List[float], List[float], List[int], List[int]]

    +
    +
    +
    + +
    +
    +get_trial(trial_key)[source]
    +

    Get the trial with the responding key if existing.

    +
    +
    Parameters:
    +

    trial_key (Tuple[int, int]) – The key for the desired trial.

    +
    +
    Returns:
    +

    The trial object.

    +
    +
    Return type:
    +

    Optional[Trial]

    +
    +
    +
    + +
    +
    +static get_trial_key(config_id, budget)[source]
    +

    Get the trial key.

    +

    It is obtained through the assembly of configuration and budget.

    +
    +
    Parameters:
    +
      +

    • config_id (int) – The identificator of the configuration.

      • +

    • budget (Union[int, float, None]) – The budget of the Trial.

      • +
    +
    +
    Returns:
    +

    Tuple representing the trial key, consisting of configuration id and the budget.

    +
    +
    Return type:
    +

    Tuple[int, Union[int, float, None]]

    +
    +
    +
    + +
    +
    +get_trials()[source]
    +

    Get an iterator of all stored trials.

    +
    +
    Returns:
    +

    An iterator over all stored trials.

    +
    +
    Return type:
    +

    Iterator[Trial]

    +
    abstract property hash: str
    -

    Hash of the current run. If hash changes, cache has to be cleared. This ensures that +

    Hash of the current run.

    +

    If hash changes, cache has to be cleared. This ensures that the cache always holds the latest results of the run.

    Returns:
    @@ -1238,7 +1709,8 @@
    abstract property id: str
    -

    Hash of the file. This is used to identify the file. +

    Hash of the file.

    +

    This is used to identify the file. In contrast to hash, this hash should not be changed throughout the run.

    Returns:
    @@ -1250,17 +1722,32 @@
    +
    +
    +property latest_change: float
    +

    Get the latest change.

    +
    +
    Returns:
    +

    The latest change.

    +
    +
    Return type:
    +

    float

    +
    +
    +
    +
    merge_costs(costs, objectives=None)[source]
    -

    Calculates one cost value from multiple costs. -Normalizes the costs first and weight every cost the same. +

    Calculate one cost value from multiple costs.

    +

    Normalizes the costs first and weigh every cost the same. The lower the normalized cost, the better.

    Parameters:
      -

    • costs (List[float]) – The costs, which should be merged. Must be the same length as the original number of objectives.

      • -

    • objectives (Optional[List[Objective]], optional) – The considered objectives to the costs. By default None. +

    • costs (List[float]) – The costs, which should be merged. Must be the same length as the original number of +objectives.

      • +

    • objectives (Optional[List[Objective]]) – The considered objectives to the costs. By default, None. If None, all objectives are considered. The passed objectives can differ from the original number objectives.

    @@ -1271,24 +1758,41 @@
    Return type:

    float

    +
    Raises:
    +

    RuntimeError – If the number of costs is different from the original number of objectives. + If the objective was not found.

    +
    +
    +
    +reset()[source]
    +

    Reset the run to default values / empties.

    +

    Clear the initial data and configurations of the object.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    deepcave.runs.check_equality(runs, meta=False, configspace=True, objectives=True, budgets=True)[source]
    -

    Checks the passed runs on equality based on the selected runs and returns the requested -attributes.

    +

    Check the passed runs on equality based on the selected runs.

    +

    Return the requested attributes.

    Parameters:

    • runs (list[AbstractRun]) – Runs to check for equality.

      • -

    • meta (bool, optional) – Meta-Data excluding objectives and budgets, by default True

      • -

    • configspace (bool, optional) – ConfigSpace, by default True

      • -

    • objectives (bool, optional) – Objectives, by default True

      • -

    • budgets (bool, optional) – Budgets, by default True

      • +

    • meta (bool, optional) – Meta-Data excluding objectives and budgets, by default True.

      • +

    • configspace (bool, optional) – Wheter to include the configuration space, by default True.

      • +

    • objectives (bool, optional) – Wheter to include the objectives, by default True.

      • +

    • budgets (bool, optional) – Whether to include the budgets, by default True.

    Returns:
    @@ -1297,6 +1801,12 @@
    Return type:

    Dict[str, Any]

    +
    Raises:
    +

    NotMergeableError – If the meta data of the runs are not equal. + If the configuration spaces of the runs are not equal. + If the budgets of the runs are not equal. + If the objective of the runs are not equal.

    +
    @@ -1304,31 +1814,31 @@ - + - + - + - + - + - + - + - + - +

    deepcave.runs.converters

    # converters

    deepcave.runs.exceptions

    # Exceptions

    deepcave.runs.group

    # Group

    deepcave.runs.handler

    # Handler

    deepcave.runs.objective

    # Objective

    deepcave.runs.recorder

    # Recorder

    deepcave.runs.run

    # Run

    deepcave.runs.status

    # Status

    deepcave.runs.trial

    # Trial
    diff --git a/development/api/deepcave.runs.objective.html b/development/api/deepcave.runs.objective.html index e0ca6776..4b2f2257 100644 --- a/development/api/deepcave.runs.objective.html +++ b/development/api/deepcave.runs.objective.html @@ -711,6 +711,15 @@
    • @@ -745,11 +790,20 @@

    deepcave.runs.objective

    +

    # Objective

    +

    This module provides utilities to convert and create objectives. +It also provides functions for merging and comparing objectives.

    +
    +
    ## Classes
      +

    • Objective: Convert and create objectives.

      • +
    +
    +

    Classes

    - +

    Objective(name[, lower, upper, optimize])

    Convert, create and merge objectives.
    @@ -757,10 +811,45 @@
    class deepcave.runs.objective.Objective(name, lower=None, upper=None, optimize='lower')[source]

    Bases: object

    +

    Convert, create and merge objectives.

    +
    +

    Properties

    +
    +
    lowerOptional[Union[int, float]]

    The lower bound of the objective.

    +
    +
    upperOptional[Union[int, float]]

    The upper bound of the objective.

    +
    +
    optimizestr

    Define whether to optimize lower or upper.

    +
    +
    lock_lowerbool

    Whether to lock the lower bound.

    +
    +
    lock_upperbool

    Whether to lock the upper bound.

    +
    +
    namestr

    The name of the objective.

    +
    +
    +
    +
    +__eq__(other)[source]
    +

    Compare if two instances are equal based on their attributes.

    +
    +
    Parameters:
    +

    other (Any) – The other instance to compare.

    +
    +
    Returns:
    +

    True if equal, else False.

    +
    +
    Return type:
    +

    bool

    +
    +
    +
    +
    __post_init__()[source]
    -

    Lock the lower bound if lower is not None. +

    Check if bounds should be locked.

    +

    Lock the lower bound if lower is not None. Lock the upper bound if upper is not None.

    Raises:
    @@ -772,6 +861,73 @@
    +
    +
    +static from_json(d)[source]
    +

    Create an objective from a JSON friendly dictionary format.

    +
    +
    Parameters:
    +

    d (Dict[str, Any]) – A dictionary in a JSON friendly format containing the attributes.

    +
    +
    Returns:
    +

    An objective created from the provided data.

    +
    +
    Return type:
    +

    Objective

    +
    +
    +
    + +
    +
    +get_worst_value()[source]
    +

    Get the worst value based on the optimization setting.

    +
    +
    Returns:
    +

    The worst value based on the optimization setting.

    +
    +
    Return type:
    +

    float

    +
    +
    +
    + +
    +
    +merge(other)[source]
    +

    Merge two Objectives over their attributes.

    +
    +
    Parameters:
    +

    other (Any) – The other Objective to merge.

    +
    +
    Raises:
    +
      +

    • NotMergeableError – If parts of the two Objectives are not mergeable.

      • +

    • ValueError – If the lower bound of one Objective is None. + If the upper bound of one Objective is None.

      • +
    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    +
    +to_json()[source]
    +

    Convert objectives attributes to a dictionary in a JSON friendly format.

    +
    +
    Returns:
    +

    A dictionary in a JSON friendly format with the objects attributes.

    +
    +
    Return type:
    +

    Dict[str, Any]

    +
    +
    +
    + +
    diff --git a/development/api/deepcave.runs.recorder.html b/development/api/deepcave.runs.recorder.html index 8dfdced5..b70888de 100644 --- a/development/api/deepcave.runs.recorder.html +++ b/development/api/deepcave.runs.recorder.html @@ -696,9 +696,43 @@
    +
    + On this page +
    @@ -720,14 +754,108 @@

    deepcave.runs.recorder

    +

    # Recorder

    +

    This module provides utilities to record information.

    +
    +
    ## Classes
      +

    • Recorder: Define a Recorder for recording information.

      • +
    +
    +

    Classes

    - - + +

    Recorder(configspace[, objectives, meta, ...])

    Recorder(configspace[, objectives, meta, ...])

    Define a Recorder for recording information.
    +
    +
    +class deepcave.runs.recorder.Recorder(configspace, objectives=None, meta=None, save_path='logs', prefix='run', overwrite=False)[source]
    +

    Bases: object

    +

    Define a Recorder for recording information.

    +
    +

    Properties

    +
    +
    pathPath

    The path to the recorded information.

    +
    +
    last_trial_idtuple[Any, Optional[float]]

    The last id containing the configuration and the budget.

    +
    +
    start_timefloat

    The current time in seconds since the epoch.

    +
    +
    start_timesDict[Any, Any]

    A dictionary containing the start times with their id as key.

    +
    +
    modelsDict[Any, Any|

    The models used with their id as key.

    +
    +
    originsDict[Any, Any]

    The origins with their id as key.

    +
    +
    additionalsDict[Any, Any]

    Additional information with the id as key.

    +
    +
    runDeepCAVERun

    The deepcave run container.

    +
    +
    +
    +
    +end(costs=inf, status=Status.SUCCESS, config=None, budget=inf, additional=None, end_time=None)[source]
    +

    End the recording and add it to the trial history.

    +

    In case of multi-processing, config+budget should be passed. +If it can’t be passed, it can’t be matched correctly.

    +
    +
    Parameters:
    +
      +

    • costs (float, optional) – The costs. +Default is np.inf.

      • +

    • status (Status, optional) – The status. +Default is Status.Success.

      • +

    • config (Union[dict, Configuration], optional) – The configuration. +Default is None.

      • +

    • budget (float, optional) – The budget. +Default is np.inf.

      • +

    • additional (Optional[dict], optional) – Additional information. +Default is None.

      • +

    • end_time (Optional[float], optional) – The end time. +Default is None.

      • +
    +
    +
    Raises:
    +

    AssertionError – If no trial was started yet.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    +
    +start(config, budget=None, model=None, origin=None, additional=None, start_time=None)[source]
    +

    Start recording the information.

    +
    +
    Parameters:
    +
      +

    • config (Configuration) – Holds the configuration settings.

      • +

    • budget (Optional[float], optional) – The budget. +Default is None.

      • +

    • model (Optional[Any], optional) – The model used. +Default is None.

      • +

    • origin (Optional[str], optional) – The origin. +Default is None.

      • +

    • additional (Optional[dict], optional) – Additional information. +Default is None.

      • +

    • start_time (Optional[float], optional) – The start time. +Default is None.

      • +
    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    +
    +
    diff --git a/development/api/deepcave.runs.run.html b/development/api/deepcave.runs.run.html index 03a95871..c37a7f82 100644 --- a/development/api/deepcave.runs.run.html +++ b/development/api/deepcave.runs.run.html @@ -747,6 +747,33 @@ +
  • + + + + Run.load() + + + +
    • +
  • + + + + Run.path + + + +
    • +
  • + + + + Run.save() + + + +
    • @@ -772,11 +799,19 @@

    deepcave.runs.run

    +

    # Run

    +

    This module provides utilities to create a new run and get its attributes.

    +
    +
    ## Classes
      +

    • Run: Create a new run.

      • +
    +
    +

    Classes

    - +

    Run(name[, configspace, objectives, meta, path])

    Creates a new run.

    Create a new run and get its attributes.
    @@ -784,45 +819,62 @@
    class deepcave.runs.run.Run(name, configspace=None, objectives=None, meta=None, path=None)[source]

    Bases: AbstractRun, ABC

    -

    Creates a new run. -If path is given, runs are loaded from the path.

    -
    -
    Parameters:
    -
      -

    • name (str) – Name of the run.

      • -

    • configspace (ConfigSpace, optional) – Configuration space of the run. Should be None if path is used. By default None.

      • -

    • objectives (Union[Objective, List[Objective]], optional) – Objectives of the run. Should be None if path is used. By default None

      • -

    • meta (Dict[str, Any], optional) – Meta data of the run. Should be None if path is used. By default None.

      • -

    • path (Optional[Union[str, Path]], optional) – If a path is specified, the run is loaded from there. By default None.

      • -
    +

    Create a new run and get its attributes.

    +

    If path is given, runs are loaded from the path.

    +
    +

    Properties

    +
    +
    configspaceConfigurationSpace

    The configuration space of the run.

    -
    Raises:
    -

    RuntimeError – If no configuration space is provided or found.

    +
    pathOptional[Union[str, Path]]

    The path of a run to be loaded.

    +
    +
    metaDict[str, Any]

    Contains serialized objectives and budgets.

    +
    +
    prefixstr

    The prefix for the id.

    +
    +
    meta_fnPath

    The path to the meta data.

    +
    +
    configspace_fnPath

    The path to the configuration space file.

    +
    +
    configs_fnPath

    The path to the configurations file.

    +
    +
    origins_fnPath

    The path to the origins file.

    +
    +
    history_fnPath

    The path to the history file.

    +
    +
    models_dirPath

    The path to the models directory.

    +
    +
    configsDict[int, Configuration]

    Containing the configurations.

    +
    +
    modelsDict[int, Optional[Union[str, “torch.nn.Module”]]]

    Contains the models.

    add(costs, config, budget=inf, start_time=0.0, end_time=0.0, status=Status.SUCCESS, origin=None, model=None, additional=None)[source]
    -

    Adds a trial to the run. -If combination of config and budget already exists, it will be overwritten. +

    Add a trial to the run.

    +

    If combination of config and budget already exists, it will be overwritten. Not successful runs are added with None costs.

    Parameters:

    • costs (Union[List[float], float]) – Costs of the run. In case of multi-objective, a list of costs is expected.

    • config (Union[Dict, Configuration]) – The corresponding configuration.

      • -

    • start_time (float, optional) – Start time. By default 0.0

      • -

    • end_time (float, optional) – End time. By default 0.0

      • -

    • status (Status, optional) – Status of the trial. By default Status.SUCCESS

      • -

    • origin (str, optional) – Origin of the trial. By default None

      • -

    • model (Union[str, &quot;torch.nn.Module&quot;], optional) – Model of the trial. By default None

      • -

    • additional (Optional[Dict], optional) – Additional information of the trial. By default None. +

    • start_time (float, optional) – Start time. By default, 0.0

      • +

    • end_time (float, optional) – End time. By default, 0.0

      • +

    • status (Status, optional) – Status of the trial. By default, Status.SUCCESS

      • +

    • origin (str, optional) – Origin of the trial. By default, None

      • +

    • model (Union[str, "torch.nn.Module"], optional) – Model of the trial. By default, None

      • +

    • additional (Optional[Dict], optional) – Additional information of the trial. By default, None. Following information is used by DeepCAVE: * traceback

    Raises:
    -

    RuntimeError – If number of costs does not match number of objectives.

    +
      +

    • RuntimeError – If number of costs does not match number of objectives.

      • +

    • ValueError – If config id is None.

      • +
    Return type:

    None

    @@ -833,7 +885,7 @@
    exists()[source]
    -

    Checks if the run exists based on the internal path.

    +

    Check if the run exists based on the internal path.

    Returns:

    If run exists.

    @@ -849,8 +901,14 @@ abstract classmethod from_path(path)[source]

    Based on a path, return a new Run object.

    +
    Parameters:
    +

    path (Path) – The path to get the run from.

    +
    +
    Returns:
    +

    The run loaded from the path.

    +
    Return type:
    -

    Run

    +

    “Run”

    @@ -858,11 +916,10 @@
    property id: str
    -

    Hash of the file. This is used to identify the file. -In contrast to hash, this hash should not be changed throughout the run.

    +

    Get a hash as id.

    Returns:
    -

    Hash of the run.

    +

    The hashed id.

    Return type:

    str

    @@ -870,6 +927,57 @@
    +
    +
    +load(path=None)[source]
    +

    Load the run.

    +
    +
    Parameters:
    +

    path (Optional[Union[str, Path]], optional) – The path where to load the run from. +Default is None.

    +
    +
    Raises:
    +

    RuntimeError – If the path is None. + If the trials were not found.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    +
    +property path: Path | None
    +

    Return the path of the run if it exists.

    +
    +
    Returns:
    +

    The path of the run.

    +
    +
    Return type:
    +

    Optional[Path]

    +
    +
    +
    + +
    +
    +save(path)[source]
    +

    Save the run and its information.

    +
    +
    Parameters:
    +

    path (Optional[Union[str, Path]]) – The path in which to save the trials.

    +
    +
    Raises:
    +

    RuntimeError – If the path is not specified.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    diff --git a/development/api/deepcave.runs.status.html b/development/api/deepcave.runs.status.html index 9ae597d3..630c6523 100644 --- a/development/api/deepcave.runs.status.html +++ b/development/api/deepcave.runs.status.html @@ -710,6 +710,17 @@ + @@ -734,11 +745,27 @@

    deepcave.runs.status

    +

    # Status

    +

    This module provides the information about the status of a run.

    +

    A utility to convert a string text to a simpler, lower case text format is provided.

    +
    +
    ## Classes
      +

    • Status: Represent the status of a run as an Enum.

      • +
    +
    +
    ## Constants

    SUCCESS: int +TIMEOUT: int +MEMORYOUT: int +CRASHED: int +ABORTED: int +NOT_EVALUATED: int

    +
    +

    Classes

    - +

    Status(value)

    An enumeration.

    Represent the status of a run as an Enum.
    @@ -746,7 +773,29 @@
    class deepcave.runs.status.Status(value)[source]

    Bases: IntEnum

    -

    An enumeration.

    +

    Represent the status of a run as an Enum.

    +

    A utility to convert a string text to a simpler, lower case text format is provided.

    +
    +

    Properties

    +
    +
    namestr

    The status name.

    +
    +
    +
    +
    +to_text()[source]
    +

    Convert name to simpler, lower case text format.

    +
    +
    Returns:
    +

    The converted name in lower case with spaces added

    +
    +
    Return type:
    +

    str

    +
    +
    +
    + +
    diff --git a/development/api/deepcave.runs.trial.html b/development/api/deepcave.runs.trial.html index b8a76758..33884494 100644 --- a/development/api/deepcave.runs.trial.html +++ b/development/api/deepcave.runs.trial.html @@ -710,6 +710,35 @@ + @@ -734,11 +763,20 @@

    deepcave.runs.trial

    +

    # Trial

    +

    This module provides the trial object. +Utilities for handling the trial are provided.

    +
    +
    ## Classes
      +

    • Trial: This class provides the trial object itself and multiple handling utilities.

      • +
    +
    +

    Classes

    - +

    Trial(config_id, budget, costs, start_time, ...)

    Provide the trial object itself and multiple handling utilities.
    @@ -746,6 +784,65 @@
    class deepcave.runs.trial.Trial(config_id, budget, costs, start_time, end_time, status, additional)[source]

    Bases: object

    +

    Provide the trial object itself and multiple handling utilities.

    +
    +

    Properties

    +
    +
    statusStatus

    The status of the trial.

    +
    +
    config_idint

    The identificator of the configuration.

    +
    +
    budgetUnion[int, float]

    The budget for the trial.

    +
    +
    costsList[float]

    A list of the costs of the trial.

    +
    +
    start_timefloat

    The start time of the trial.

    +
    +
    end_timefloat

    The end time of the trial.

    +
    +
    additionalDict[str, Any]

    A dictionary of additional information of the trial.

    +
    +
    +
    +
    +__post_init__()[source]
    +

    Set the status.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    +
    +get_key()[source]
    +

    Generate a key based on the configuration id and the budget.

    +
    +
    Returns:
    +

    A Tuple representing a unique key based on the configuration id and the budget.

    +
    +
    Return type:
    +

    Tupel[int, int]

    +
    +
    +
    + +
    +
    +to_json()[source]
    +

    Convert trial object to JSON-compatible representation.

    +
    +
    Returns:
    +

    A JSON-compatible list with the Trials attributes.

    +
    +
    Return type:
    +

    List[Any]

    +
    +
    +
    + +
    diff --git a/development/api/deepcave.utils.cache.html b/development/api/deepcave.utils.cache.html index 1b9c0170..c1487706 100644 --- a/development/api/deepcave.utils.cache.html +++ b/development/api/deepcave.utils.cache.html @@ -799,6 +799,16 @@

    deepcave.utils.cache

    +

    # Cache

    +

    This module provides utilities to handle the cache.

    +

    This includes reading, writing, set and get utilities, as well as clearing the cache. +The cache handles a json file.

    +
    +
    ## Classes
      +

    • Cache: Cache handles a json file.

      • +
    +
    +

    Classes

    @@ -811,15 +821,19 @@
    class deepcave.utils.cache.Cache(filename=None, defaults=None, debug=False, write_file=True)[source]

    Bases: object

    -

    Cache handles a json file. Decided not to use flask_caching -since code is easier to change to our needs.

    +

    Cache handles a json file.

    +

    Decided not to use flask_caching since code is easier to change to our needs.

    clear(write_file=True)[source]
    -

    Clear all cache and reset to defaults

    +

    Clear all cache and reset to defaults.

    -
    Return type:
    -

    None

    +
    Parameters:
    +

    write_file (bool, optional) – Whether to write the constant of the cache into a file. +Default is True.

    +
    +
    Return type:
    +

    None

    @@ -827,10 +841,16 @@
    get(*keys)[source]
    -

    Retrieve value for a specific key

    +

    Retrieve value for a specific key.

    +
    Parameters:
    +

    *keys (str) – The key to retrieve the value from.

    +
    +
    Returns:
    +

    The value of the key.

    +
    Return type:
    -

    Optional[Any]

    +

    Optional[Any]

    @@ -838,10 +858,16 @@
    has(*keys)[source]
    -

    Check whether cache has specific key

    +

    Check whether cache has specific key.

    +
    Parameters:
    +

    *keys (str) – The key to check for.

    +
    +
    Returns:
    +

    Whether cache has specific key.

    +
    Return type:
    -

    bool

    +

    bool

    @@ -849,7 +875,7 @@
    read()[source]
    -

    Reads content from a file and load into cache as dictionary.

    +

    Read content from a file and load into cache as dictionary.

    Return type:

    None

    @@ -860,10 +886,21 @@
    set(*keys, value, write_file=True)[source]
    -

    Set a value from a chain of keys. -E.g. set(“a”, “b”, “c”, value=4) creates following dictionary: +

    Set a value from a chain of keys.

    +

    E.g. set(“a”, “b”, “c”, value=4) creates following dictionary: {“a”: {“b”: {“c”: 4}}}

    +
    Parameters:
    +
      +

    • *keys (str) – The keys to set the value from.

      • +

    • value (Any) – The value to be set.

      • +

    • write_file (bool, optional) – Whether to write the constant of the cache into a file. +Default is True.

      • +
    +
    +
    Raises:
    +

    RuntimeError – If the type of the key is not a string.

    +
    Return type:

    None

    @@ -873,10 +910,17 @@
    set_dict(d, write_file=True)[source]
    -

    Updates cache to a specific value

    +

    Update cache to a specific value.

    -
    Return type:
    -

    None

    +
    Parameters:
    +
      +

    • d (Dict) – The dictionary to be set.

      • +

    • write_file (bool, optional) – Whether to write the constant of the cache into a file. +Default is True.

      • +
    +
    +
    Return type:
    +

    None

    diff --git a/development/api/deepcave.utils.cast.html b/development/api/deepcave.utils.cast.html index 0d4969a9..aefb94c3 100644 --- a/development/api/deepcave.utils.cast.html +++ b/development/api/deepcave.utils.cast.html @@ -696,9 +696,23 @@
    +
    + On this page +
    @@ -720,14 +734,34 @@

    deepcave.utils.cast

    +

    # Cast

    +

    This module provides a utility to convert any value to an int if possible.

    Functions

    - - + +

    optional_int(value)

    optional_int(value)

    Convert a value to an int if possible.
    +
    +
    +deepcave.utils.cast.optional_int(value)[source]
    +

    Convert a value to an int if possible.

    +
    +
    Parameters:
    +

    value (Any) – The value to be turned into an int.

    +
    +
    Returns:
    +

    The converted int value. +If not possible, return None.

    +
    +
    Return type:
    +

    Optional[int]

    +
    +
    +
    +
    diff --git a/development/api/deepcave.utils.compression.html b/development/api/deepcave.utils.compression.html index 3a4c972e..55e26f00 100644 --- a/development/api/deepcave.utils.compression.html +++ b/development/api/deepcave.utils.compression.html @@ -743,6 +743,20 @@

    deepcave.utils.compression

    +

    # Compression

    +

    This module provides utilities for serializing and deserializing a dataframe from/to a string.

    +
    +
    ## Classes
      +

    • Encoder: This class defines a custom JSON Encoder.

      • +
    +
    +
    ## Constants
      +

    • JSON_DENSE_SEPARATORS: Tuple(str, str)

      • +

    • JSON_DEFAULT_SEPARATORS: Tuple(str, str)

      • +

    • TYPE: TypeVar

      • +
    +
    +

    Functions

    @@ -759,8 +773,18 @@ deepcave.utils.compression.deserialize(string, dtype=<class 'pandas.core.frame.DataFrame'>)[source]

    Deserialize a dataframe from a string.

    +
    Parameters:
    +
      +

    • string (str) – The string to be deserialized.

      • +

    • dtype (TYPE, optional) – The type of the object. +Default is pd.DataFrame.

      • +
    +
    +
    Returns:
    +

    The deserialized object.

    +
    Return type:
    -

    TypeVar(TYPE)

    +

    TYPE

    @@ -770,8 +794,14 @@ deepcave.utils.compression.serialize(data)[source]

    Serialize a dataframe to a string.

    +
    Parameters:
    +

    data (Union[Dict, List, pd.DataFrame]) – The dataframe to be serialized.

    +
    +
    Returns:
    +

    The serialized object as a JSON formatted string.

    +
    Return type:
    -

    str

    +

    str

    diff --git a/development/api/deepcave.utils.configs.html b/development/api/deepcave.utils.configs.html index 63b98250..3404b404 100644 --- a/development/api/deepcave.utils.configs.html +++ b/development/api/deepcave.utils.configs.html @@ -734,18 +734,21 @@

    deepcave.utils.configs

    +

    # Configs

    +

    This module provides a utility for parsing the configurations from a filename.

    Functions

    - +

    parse_config([filename])

    Parses the config given the filename.

    Parse the config given the filename.
    deepcave.utils.configs.parse_config(filename=None)[source]
    -

    Parses the config given the filename. Both relative and absolute paths are possible.

    +

    Parse the config given the filename.

    +

    Both relative and absolute paths are possible.

    Parameters:

    filename (Optional[str], optional) – Location of the config. Must be a python file. diff --git a/development/api/deepcave.utils.configspace.html b/development/api/deepcave.utils.configspace.html index 098c84f5..3e892d0a 100644 --- a/development/api/deepcave.utils.configspace.html +++ b/development/api/deepcave.utils.configspace.html @@ -743,24 +743,26 @@

    deepcave.utils.configspace

    +

    # ConfigSpace

    +

    This module samples random as well as border configurations.

    Functions

    - + - +

    sample_border_config(configspace)

    Generates border configurations from the configuration space.

    Generate border configurations from the configuration space.

    sample_random_config(configspace[, d])

    Generates random configurations from the configuration space.

    Generate random configurations from the configuration space.
    deepcave.utils.configspace.sample_border_config(configspace)[source]
    -

    Generates border configurations from the configuration space.

    +

    Generate border configurations from the configuration space.

    Parameters:
    -

    configspace (ConfigurationSpace) – The configspace from which the hyperparameters are drawn from.

    +

    configspace (ConfigurationSpace) – The configuration space from which the hyperparameters are drawn from.

    Yields:

    Iterator[Configuration] – Border configurations.

    @@ -774,14 +776,14 @@
    deepcave.utils.configspace.sample_random_config(configspace, d=None)[source]
    -

    Generates random configurations from the configuration space.

    +

    Generate random configurations from the configuration space.

    Parameters:

    • configspace (ConfigurationSpace) – The configspace from which the hyperparameters are drawn from.

    • d (Optional[int], optional) – The possible hyperparameter values can be reduced by this argument as the range gets -discretized. For example, an integer or float hyperparameter has only four possible values -if d=4. By default None (no discretization is done).

      • +discretized. For example, an integer or float hyperparameter has only four possible +values if d=4. By default, None (no discretization is done).

    Yields:
    diff --git a/development/api/deepcave.utils.dash.html b/development/api/deepcave.utils.dash.html index f587731e..e280b183 100644 --- a/development/api/deepcave.utils.dash.html +++ b/development/api/deepcave.utils.dash.html @@ -702,6 +702,15 @@
    + +
    +
    +deepcave.utils.styled_plotty.get_hovertext_from_config(run, config_id)[source]
    +

    Generate hover text with metrics for a configuration.

    +

    The method gets information about a given configuration, including a link, its objectives, +budget, costs and hyperparameters.

    +
    +
    Parameters:
    +
      +

    • run (AbstractRun) – The run instance

      • +

    • config_id (int) – The id of the configuration

      • +
    +
    +
    Returns:
    +

    The hover text string containing the configuration information.

    +
    +
    Return type:
    +

    str

    @@ -861,16 +929,17 @@
    deepcave.utils.styled_plotty.get_hyperparameter_ticks(hp, additional_values=None, ticks=4, include_nan=True)[source]
    -

    Generates tick data for both tickvals and ticktext. The background is that -you might have encoded data but you don’t want to show all of them. -With this function, only 6 (default) values are shown. This behaviour is +

    Generate tick data for both tickvals and ticktext.

    +

    The background is that +you might have encoded data, but you don’t want to show all of them. +With this function, only 6 (default) values are shown. This behavior is ignored if hp is categorical.

    Parameters:

    • hp (Hyperparameter) – Hyperparameter to generate ticks from.

      • -

    • additional_values (Optional[List], optional) – Additional values, which are forced in addition. By default None.

      • -

    • ticks (int, optional) – Number of ticks, by default 6

      • +

    • additional_values (Optional[List], optional) – Additional values, which are forced in addition. By default, None.

      • +

    • ticks (int, optional) – Number of ticks, by default 4

    • include_nan (bool, optional) – Whether “nan” as tick should be included. By default True.

    @@ -886,17 +955,18 @@
    deepcave.utils.styled_plotty.get_hyperparameter_ticks_from_values(values, labels, forced=None, ticks=6)[source]
    -

    Generates tick data for both values and labels. The background is that -you might have encoded data but you don’t want to show all of them. -With this function, only 6 (default) values are shown. This behaviour is +

    Generate tick data for both values and labels.

    +

    The background is that +you might have encoded data, but you don’t want to show all of them. +With this function, only 6 (default) values are shown. This behavior is ignored if values is a list of strings.

    Parameters:

    • values (List) – List of values.

    • labels (List) – List of labels. Must be the same size as values.

      • -

    • forced (List[bool], optional) – List of booleans. If True, displaying the particular tick is enforced. -Independent from ticks.

      • +

    • forced (Optional[List[bool]], optional) – List of booleans. If True, displaying the particular tick is enforced. +Independent of ticks.

    • ticks (int, optional) – Number of ticks and labels to show. By default 6.

    @@ -906,19 +976,32 @@
    Return type:

    Tuple[List, List]

    +
    Raises:
    +

    RuntimeError – If values contain both strings and non-strings.

    +
    deepcave.utils.styled_plotty.hex_to_rgb(hex_string)[source]
    -

    Converts a hex_string to a tuple of rgb values. -Requires format including #, e.g.: +

    Convert a hex_string to a tuple of rgb values.

    +

    Requires format including #, e.g.: #000000 #ff00ff

    +
    Parameters:
    +

    hex_string (str) – The hex string to be converted.

    +
    +
    Returns:
    +

    A Tuple of the converted RGB values

    +
    Return type:
    -

    Tuple[int, int, int]

    +

    Tuple[int, int, int]

    +
    +
    Raises:
    +

    ValueError – If the hex string is longer than 7. + If there are invalid characters in the hex string.

    @@ -926,7 +1009,8 @@
    deepcave.utils.styled_plotty.prettify_label(label)[source]
    -

    Takes a label and prettifies it. E.g. floats are shortened.

    +

    Take a label and prettifies it.

    +

    E.g. floats are shortened.

    Parameters:

    label (Union[str, float, int]) – Label, which should be prettified.

    @@ -943,12 +1027,13 @@
    deepcave.utils.styled_plotty.save_image(figure, name)[source]
    -

    Saves a plotly figure as an image.

    +

    Save a plotly figure as an image.

    Parameters:
      -

    • fig (go.Figure) – Plotly figure.

      • -

    • name (str) – Name of the image with extension. Will be automatically saved to the cache.

      • +

    • figure (go.Figure) – Plotly figure.

      • +

    • name (str) – Name of the image with extension. +Will be automatically saved to the cache.

    Return type:
    diff --git a/development/api/deepcave.utils.symbolic_regression.html b/development/api/deepcave.utils.symbolic_regression.html index 05832de9..31558bd0 100644 --- a/development/api/deepcave.utils.symbolic_regression.html +++ b/development/api/deepcave.utils.symbolic_regression.html @@ -758,7 +758,7 @@ - + @@ -771,8 +771,7 @@
    deepcave.utils.symbolic_regression.convert_symb(symb, n_decimals=None, hp_names=None)[source]
    -

    Convert a fitted symbolic regression to a simplified and potentially rounded mathematical -expression.

    +

    Convert a fitted symbolic regression to a simplified and rounded mathematical expression.

    Warning: eval is used in this function, thus it should not be used on unsanitized input (see https://docs.sympy.org/latest/modules/core.html?highlight=eval#module-sympy.core.sympify).

    @@ -787,7 +786,7 @@

    Converted mathematical expression.

    Return type:
    -

    SymbolicRegressor

    +

    str

    @@ -798,13 +797,13 @@

    Get a safe exp function with a maximum value of 100000 to avoid overflow.

    Parameters:
    -

    x (float) – The value to calculate the exponential of.

    +

    x (Union[float, np.ndarray]) – The value to calculate the exponential of.

    Returns:

    The safe exponential of x.

    Return type:
    -

    float

    +

    Union[float, np.ndarray]

    diff --git a/development/api/deepcave.utils.url.html b/development/api/deepcave.utils.url.html index e2879331..30bacdf6 100644 --- a/development/api/deepcave.utils.url.html +++ b/development/api/deepcave.utils.url.html @@ -696,9 +696,32 @@
    +
    + On this page +
    @@ -720,27 +743,57 @@

    deepcave.utils.url

    +

    # URL

    +

    This module creates and parses an URL according to its input information.

    Functions

    convert_symb(symb[, n_decimals, hp_names])

    Convert a fitted symbolic regression to a simplified and potentially rounded mathematical expression.

    Convert a fitted symbolic regression to a simplified and rounded mathematical expression.

    exp(x)

    Get a safe exp function with a maximum value of 100000 to avoid overflow.

    - - + + - - + +

    create_url(pathname, inputs)

    -
    rtype:
    -

    str

    -
    -
    -

    create_url(pathname, inputs)

    Create an URL with the given pathname and inputs.

    parse_url(pathname)

    -
    rtype:
    -

    Optional[Dict[str, Any]]

    -
    -
    -

    parse_url(pathname)

    Parse the URL and extract input information if possible.
    +
    +
    +deepcave.utils.url.create_url(pathname, inputs)[source]
    +

    Create an URL with the given pathname and inputs.

    +
    +
    Parameters:
    +
      +

    • pathname (str) – The name of the path for the URL.

      • +

    • inputs (Dict[str, Any]) – A dictionary containing the input parameters for the URL.

      • +
    +
    +
    Returns:
    +

    The URL.

    +
    +
    Return type:
    +

    str

    +
    +
    +
    + +
    +
    +deepcave.utils.url.parse_url(pathname)[source]
    +

    Parse the URL and extract input information if possible.

    +
    +
    Parameters:
    +

    pathname (str) – The name of the URL.

    +
    +
    Returns:
    +

    A dictionary with the extracted inputs if available. +Otherwise return None.

    +
    +
    Return type:
    +

    Optional[Dict[str, Any]]

    +
    +
    +
    +
    diff --git a/development/api/deepcave.utils.util.html b/development/api/deepcave.utils.util.html index 7026b4ac..b630fad2 100644 --- a/development/api/deepcave.utils.util.html +++ b/development/api/deepcave.utils.util.html @@ -696,9 +696,41 @@
    +
    + On this page +
    @@ -720,35 +752,87 @@

    deepcave.utils.util

    +

    # Util

    +

    This module provides utilities for string generation and shortening. +It also provides a function to get the difference between now and a given timestamp.

    Functions

    - - + + + + + + + + + +

    get_latest_change(st_mtime)

    -
    rtype:
    -

    str

    +

    get_latest_change(st_mtime)

    Get the difference between now and a given timestamp.

    get_random_string(length)

    Get a random string with a specific length.

    short_string(value[, length, mode])

    Shorten the given string.

    +
    +
    +deepcave.utils.util.get_latest_change(st_mtime)[source]
    +

    Get the difference between now and a given timestamp.

    +
    +
    Parameters:
    +

    st_mtime (int) – A timestamp to calculate the difference from.

    +
    +
    Returns:
    +

    A string containing the passed time.

    +
    +
    Return type:
    +

    str

    -

    - -

    get_random_string(length)

    -

    -
    rtype:
    -

    str

    +
    + +
    +
    +deepcave.utils.util.get_random_string(length)[source]
    +

    Get a random string with a specific length.

    +
    +
    Parameters:
    +

    length (int) – The length of the string.

    +
    +
    Returns:
    +

    The random string with the given length.

    +
    +
    Return type:
    +

    str

    +
    +
    Raises:
    +

    ValueError – If the length is smaller 0.

    -

    - -

    short_string(value[, length, mode])

    -

    -
    rtype:
    -

    str

    +
    + +
    +
    +deepcave.utils.util.short_string(value, length=30, *, mode='prefix')[source]
    +

    Shorten the given string.

    +

    Cut either at prefix or at suffix if necessary.

    +
    +
    Parameters:
    +
      +

    • value (Any) – The value or string to shorten.

      • +

    • length (int, optional) – The length of the returned string. +Default is 30.

      • +

    • mode (str, optional) – Define how to shorten the string. +Default is “prefix”.

      • +
    +
    +
    Returns:
    +

    The shortened string.

    +
    +
    Return type:
    +

    str

    +
    +
    Raises:
    +

    ValueError – If the given mode is unknown.

    -

    - - - +
    +
    diff --git a/development/examples/api/importances.html b/development/examples/api/importances.html index a9d93654..1cb95eef 100644 --- a/development/examples/api/importances.html +++ b/development/examples/api/importances.html @@ -747,7 +747,7 @@ budget_ids=budget_ids, method="global", n_hps=3, - n_trees=10 + n_trees=10, ) # Note: Filter variables are not considered. outputs = plugin.generate_outputs(run, inputs) @@ -755,7 +755,7 @@ # Finally, you can load the figure. Here, the filter variables play a role. # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available. figure = plugin.load_outputs(run, inputs, outputs) # plotly.go figure - figure.write_image("examples/api/importances.png", scale=2.) + figure.write_image("examples/api/importances.png", scale=2.0) # figure.show() diff --git a/development/examples/api/parallel_coordinates.html b/development/examples/api/parallel_coordinates.html index 9ca83768..560c91c2 100644 --- a/development/examples/api/parallel_coordinates.html +++ b/development/examples/api/parallel_coordinates.html @@ -755,7 +755,7 @@ # Finally, you can load the figure. Here, the filter variables play a role. # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available. figure = plugin.load_outputs(run, inputs, outputs) # plotly.go figure - figure.write_image("examples/api/parallel_coordinates.png", scale=2.) + figure.write_image("examples/api/parallel_coordinates.png", scale=2.0) # figure.show() diff --git a/development/examples/api/pdp.html b/development/examples/api/pdp.html index 82be54ff..fe4328fa 100644 --- a/development/examples/api/pdp.html +++ b/development/examples/api/pdp.html @@ -755,7 +755,7 @@ # Alternatively: Use the matplotlib output (`load_mpl_outputs`) if available. figure = plugin.load_outputs(run, inputs, outputs) # plotly.go figure figure.write_image("examples/api/pdp.png") - #figure.show() + # figure.show()

    _

    @@ -746,10 +748,18 @@

    _

    @@ -758,13 +768,15 @@

    A

    @@ -778,9 +790,9 @@

    B

    @@ -791,7 +803,13 @@

    C

  • Cache (class in deepcave.utils.cache)
  • calculate() (deepcave.evaluators.fanova.fANOVA method) + +
  • check_equality() (in module deepcave.runs)
  • check_run_compatibility() (deepcave.plugins.Plugin static method) @@ -823,6 +841,10 @@

    C

  • convert_symb() (in module deepcave.utils.symbolic_regression)
  • CostOverTime (class in deepcave.plugins.objective.cost_over_time) +
    • +
  • create_table() (in module deepcave.utils.layout) +
    • +
  • create_url() (in module deepcave.utils.url)
    • @@ -1054,6 +1076,8 @@

    D

  • module
    • + +
    • deepcave.runs.converters.deepcave @@ -1061,8 +1085,6 @@

      D

    • module
    - -
    • deepcave.runs.converters.smac3v1 @@ -1274,8 +1296,6 @@

      D

  • DeepCAVERun (class in deepcave.runs.converters.deepcave) -
    • -
  • description (deepcave.plugins.Plugin attribute)
  • deserialize() (in module deepcave.utils.compression)
    • @@ -1287,10 +1307,16 @@

    D

    E

      -
    • help (deepcave.plugins.Plugin attribute) +
    • help_button() (in module deepcave.utils.layout)
    • hex_to_rgb() (in module deepcave.utils.styled_plotty)
      • @@ -1521,13 +1643,9 @@

      H

      I

        -
      • icon (deepcave.plugins.Plugin attribute) -
        • -
      • id (deepcave.plugins.Plugin attribute) +
      • id (deepcave.runs.AbstractRun property)
          -
        • (deepcave.runs.AbstractRun property) -
        • (deepcave.runs.group.Group property)
        • (deepcave.runs.run.Run property) @@ -1543,7 +1661,25 @@

          I

          L

        • load_run_inputs() (deepcave.plugins.Plugin static method) +
          • +
        • LPI (class in deepcave.evaluators.lpi)
          • @@ -1629,6 +1767,10 @@

          L

          M

            +
          • make_dirs() (in module deepcave.utils.files) +
            • +
          • merge() (deepcave.runs.objective.Objective method) +
          • merge_costs() (deepcave.runs.AbstractRun method)
          • @@ -1768,9 +1910,9 @@

            M

            N

            @@ -1805,8 +1949,12 @@

            P

          • ParetoFront (class in deepcave.plugins.objective.pareto_front)
          • parse_config() (in module deepcave.utils.configs) +
            • +
          • parse_url() (in module deepcave.utils.url)
          • PartialDependencies (class in deepcave.plugins.hyperparameter.pdp) +
            • +
          • path (deepcave.runs.run.Run property)
          • Plugin (class in deepcave.plugins)
            • @@ -1819,11 +1967,11 @@

            P

          • predict_marginalized() (deepcave.evaluators.epm.random_forest.RandomForest method) -
            • -
          • prettify_label() (in module deepcave.utils.styled_plotty)
              • @@ -1792,6 +1934,8 @@

              O
              +
            • short_string() (in module deepcave.utils.util) +
            • SidebarLayout (class in deepcave.layouts.sidebar)
            • SMAC @@ -1919,10 +2087,14 @@

              S

            • SMAC3v1Run (class in deepcave.runs.converters.smac3v1)
            • SMAC3v2Run (class in deepcave.runs.converters.smac3v2) +
              • +
            • start() (deepcave.runs.recorder.Recorder method)
            • StaticPlugin (class in deepcave.plugins.static)
            • Status (class in deepcave.runs.status) +
              • +
            • string_to_hash() (in module deepcave.utils.hash)
            • StyledPlot (class in deepcave.utils.styled_plot)
              • @@ -1934,10 +2106,18 @@

              S

              T

              @@ -1949,13 +2129,15 @@

              U

            • update() (deepcave.runs.handler.RunHandler method)
              • -
            • update_dict() (in module deepcave.utils.data_structures) -
              • +

              X

              + + +
              + +

              Y

              + + +
              + diff --git a/development/objects.inv b/development/objects.inv index b75e31f816c33f71f427dc2338eeb19e330fae71..50698a2ee34886bf70d83f808e57c3ebaeb22fac 100644 GIT binary patch delta 5837 zcmV;;7BcCJDWfisihpz5HWI$`SFp;y&UmA}<>n}H;;JOpTB)2=#p00A3?shGhpgFO zzW~7pKmzCn3CiA!wv<;i0s{Qi%V^+j%O8kHMVVS&( zmompolcjIC)Zwjv@KiFS%urZV4vTku4D;eS3?7S$)D0S4%73+v>-eGj^Ld7}{PyUxS0d@7-T*?MXNWfMXEQN=t)4Unm$VFB&@J zw0R7dYZQe`IEnsQG#}W1kY%26Sb84L_;y0s^z6CtD+=T zb6pJb`E_eIjN|550boWO(Us2B5hdCAe-P6)+A`F z)DhfclCmz6j-A~BW>g`C=ZKxQ?tgLlYj+2nl_RFps@(&Vb^aY%K}}K; zH6$Kw)mF@^XiGOnFD=42h)GHEnB>vbof+wX-J(q`$r+?$xUv1XK&0=My4Eg`Y&;5k z(5}EP0r^XDfwN>tGzlJ$YvH@CWmwZz zVSld6l{SDftU?MzDugpE$-{49CO`i)w&4=;Zs9$Clyxxs?0^azxL0{=JF8-60yV&@j^t<86#3Ep*bphOv$)<&M@u{Q23&s|tGNtpgT^oPXW zxR2BuI2{;uXKHLR=FYU(W-*FUTdy|Tjp zA6l+8a<8Z6T1#lFVR8D0L{0KcU;{Xc!d7k)jf%V*-#bNus6AN`Epu<|B)%rq`)1tL>S4EmoN@ zr)sRSTGkpcUo%ZfO1o)M#R+vWF@HfLbAB7^rObcVKfIN7LYgwYN{)2dW=dX1y|vcm 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"deepcave.utils.dash.flash"]], "deepcave.utils.data_structures": [[51, "module-deepcave.utils.data_structures"]], "update_dict() (in module deepcave.utils.data_structures)": [[51, "deepcave.utils.data_structures.update_dict"]], "deepcave.utils.docs": [[52, "module-deepcave.utils.docs"]], "rst_to_md() (in module deepcave.utils.docs)": [[52, "deepcave.utils.docs.rst_to_md"]], "deepcave.utils.files": [[53, "module-deepcave.utils.files"]], "make_dirs() (in module deepcave.utils.files)": [[53, "deepcave.utils.files.make_dirs"]], "deepcave.utils.hash": [[54, "module-deepcave.utils.hash"]], "file_to_hash() (in module deepcave.utils.hash)": [[54, "deepcave.utils.hash.file_to_hash"]], "string_to_hash() (in module deepcave.utils.hash)": [[54, "deepcave.utils.hash.string_to_hash"]], "create_table() (in module deepcave.utils.layout)": [[55, "deepcave.utils.layout.create_table"]], "deepcave.utils.layout": [[55, "module-deepcave.utils.layout"]], "get_checklist_options() (in module deepcave.utils.layout)": [[55, "deepcave.utils.layout.get_checklist_options"]], "get_radio_options() (in module deepcave.utils.layout)": [[55, "deepcave.utils.layout.get_radio_options"]], "get_select_options() (in module deepcave.utils.layout)": [[55, "deepcave.utils.layout.get_select_options"]], "get_slider_marks() (in module deepcave.utils.layout)": [[55, "deepcave.utils.layout.get_slider_marks"]], "help_button() (in module deepcave.utils.layout)": [[55, "deepcave.utils.layout.help_button"]], "deepcave.utils.logs": [[56, "module-deepcave.utils.logs"]], "get_logger() (in module deepcave.utils.logs)": [[56, "deepcave.utils.logs.get_logger"]], "notification (class in deepcave.utils.notification)": [[57, "deepcave.utils.notification.Notification"]], "deepcave.utils.notification": [[57, "module-deepcave.utils.notification"]], "get_latest() (deepcave.utils.notification.notification method)": [[57, "deepcave.utils.notification.Notification.get_latest"]], "reset() 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deepcave.utils.styled_plotty)": [[60, "deepcave.utils.styled_plotty.get_hyperparameter_ticks_from_values"]], "hex_to_rgb() (in module deepcave.utils.styled_plotty)": [[60, "deepcave.utils.styled_plotty.hex_to_rgb"]], "prettify_label() (in module deepcave.utils.styled_plotty)": [[60, "deepcave.utils.styled_plotty.prettify_label"]], "save_image() (in module deepcave.utils.styled_plotty)": [[60, "deepcave.utils.styled_plotty.save_image"]], "convert_symb() (in module deepcave.utils.symbolic_regression)": [[61, "deepcave.utils.symbolic_regression.convert_symb"]], "deepcave.utils.symbolic_regression": [[61, "module-deepcave.utils.symbolic_regression"]], "exp() (in module deepcave.utils.symbolic_regression)": [[61, "deepcave.utils.symbolic_regression.exp"]], "get_function_set() (in module deepcave.utils.symbolic_regression)": [[61, "deepcave.utils.symbolic_regression.get_function_set"]], "create_url() (in module deepcave.utils.url)": [[62, "deepcave.utils.url.create_url"]], "deepcave.utils.url": [[62, "module-deepcave.utils.url"]], "parse_url() (in module deepcave.utils.url)": [[62, "deepcave.utils.url.parse_url"]], "deepcave.utils.util": [[63, "module-deepcave.utils.util"]], "get_latest_change() (in module deepcave.utils.util)": [[63, "deepcave.utils.util.get_latest_change"]], "get_random_string() (in module deepcave.utils.util)": [[63, "deepcave.utils.util.get_random_string"]], "short_string() (in module deepcave.utils.util)": [[63, "deepcave.utils.util.short_string"]], "bo": [[79, "term-BO"]], "budget": [[79, "term-Budget"]], "objective": [[79, "term-Objective"]], "smac": [[79, "term-SMAC"]]}}) \ No newline at end of file