Repository: automl/DeepCAVE (language: python)

Issue:
Feature/add converter for smac 2.0



Discussion / hints:

Fix patch:
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a412e8ee..b3921a30 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,7 +3,7 @@
 # If things break on update, raise an issue
 repos:
   - repo: https://github.com/pycqa/isort
-    rev: 5.10.1
+    rev: 5.12.0
     hooks:
       - id: isort
         name: isort imports deepcave
@@ -14,7 +14,7 @@ repos:
         files: tests
 
   - repo: https://github.com/ambv/black
-    rev: 22.1.0
+    rev: 23.1.0
     hooks:
       - id: black
         name: black formatter deepcave
@@ -29,7 +29,7 @@ repos:
         files: examples
 
   - repo: https://github.com/pycqa/pydocstyle
-    rev: 6.1.1
+    rev: 6.3.0
     hooks:
       - id: pydocstyle
         files: deepcave
@@ -42,8 +42,8 @@ repos:
         name: mypy deepcave
         files: deepcave
 
-  - repo: https://gitlab.com/pycqa/flake8
-    rev: 4.0.1
+  - repo: https://github.com/PyCQA/flake8
+    rev: 6.0.0
     hooks:
       - id: flake8
         name: flake8 deepcave
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0a651574..59fe86d0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,11 @@
+# Version 1.1
+
+## Converters
+- SMAC 2.0
+
+## Dependencies
+- Remove SMAC dependency by adding required function directly
+
 # Version 1.0.1
 
 ## Bug-Fixes
diff --git a/deepcave/__init__.py b/deepcave/__init__.py
index 71754a9d..5f5b6608 100644
--- a/deepcave/__init__.py
+++ b/deepcave/__init__.py
@@ -17,7 +17,7 @@
     "Source Code": "https://github.com/automl/deepcave",
 }
 copyright = f"Copyright {datetime.date.today().strftime('%Y')}, {author}"
-version = "1.0.1"
+version = "1.1"
 
 _exec_file = sys.argv[0]
 _exec_files = ["server.py", "worker.py", "sphinx-build"]
diff --git a/deepcave/config.py b/deepcave/config.py
index c41379d2..c568bbf5 100644
--- a/deepcave/config.py
+++ b/deepcave/config.py
@@ -83,6 +83,7 @@ def PLUGINS(self) -> Dict[str, List["Plugin"]]:
     def CONVERTERS(self) -> List[Type["Run"]]:
         from deepcave.runs.converters.bohb import BOHBRun
         from deepcave.runs.converters.deepcave import DeepCAVERun
-        from deepcave.runs.converters.smac import SMACRun
+        from deepcave.runs.converters.smac3v1 import SMAC3v1Run
+        from deepcave.runs.converters.smac3v2 import SMAC3v2Run
 
-        return [DeepCAVERun, BOHBRun, SMACRun]
+        return [DeepCAVERun, BOHBRun, SMAC3v1Run, SMAC3v2Run]
diff --git a/deepcave/evaluators/epm/random_forest.py b/deepcave/evaluators/epm/random_forest.py
index 8fcb3017..fc39e001 100644
--- a/deepcave/evaluators/epm/random_forest.py
+++ b/deepcave/evaluators/epm/random_forest.py
@@ -16,7 +16,8 @@
 from sklearn.decomposition import PCA
 from sklearn.exceptions import NotFittedError
 from sklearn.preprocessing import MinMaxScaler
-from smac.epm.util_funcs import get_types
+
+from deepcave.evaluators.epm.utils import get_types
 
 VERY_SMALL_NUMBER = 1e-10
 PYRFR_MAPPING = {
diff --git a/deepcave/evaluators/epm/utils.py b/deepcave/evaluators/epm/utils.py
new file mode 100644
index 00000000..6505fe96
--- /dev/null
+++ b/deepcave/evaluators/epm/utils.py
@@ -0,0 +1,117 @@
+import typing
+
+import numpy as np
+from ConfigSpace import ConfigurationSpace
+from ConfigSpace.hyperparameters import (
+    BetaFloatHyperparameter,
+    BetaIntegerHyperparameter,
+    CategoricalHyperparameter,
+    Constant,
+    NormalFloatHyperparameter,
+    NormalIntegerHyperparameter,
+    OrdinalHyperparameter,
+    UniformFloatHyperparameter,
+    UniformIntegerHyperparameter,
+)
+
+
+def get_types(
+    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.
+    """
+    # Extract types vector for rf from config space and the bounds
+    types = [0] * len(config_space.get_hyperparameters())
+    bounds = [(np.nan, np.nan)] * len(types)
+
+    for i, param in enumerate(config_space.get_hyperparameters()):
+        parents = config_space.get_parents_of(param.name)
+        if len(parents) == 0:
+            can_be_inactive = False
+        else:
+            can_be_inactive = True
+
+        if isinstance(param, (CategoricalHyperparameter)):
+            n_cats = len(param.choices)
+            if can_be_inactive:
+                n_cats = len(param.choices) + 1
+            types[i] = n_cats
+            bounds[i] = (int(n_cats), np.nan)
+        elif isinstance(param, (OrdinalHyperparameter)):
+            n_cats = len(param.sequence)
+            types[i] = 0
+            if can_be_inactive:
+                bounds[i] = (0, int(n_cats))
+            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
+            # parameter
+            if can_be_inactive:
+                bounds[i] = (2, np.nan)
+                types[i] = 2
+            else:
+                bounds[i] = (0, np.nan)
+                types[i] = 0
+            # and we leave the bounds to be 0 for now
+        elif isinstance(param, UniformFloatHyperparameter):
+            # Are sampled on the unit hypercube thus the bounds
+            # are always 0.0, 1.0
+            if can_be_inactive:
+                bounds[i] = (-1.0, 1.0)
+            else:
+                bounds[i] = (0, 1.0)
+        elif isinstance(param, UniformIntegerHyperparameter):
+            if can_be_inactive:
+                bounds[i] = (-1.0, 1.0)
+            else:
+                bounds[i] = (0, 1.0)
+        elif isinstance(param, NormalFloatHyperparameter):
+            if can_be_inactive:
+                raise ValueError(
+                    "Inactive parameters not supported for Beta and Normal Hyperparameters"
+                )
+
+            bounds[i] = (param._lower, param._upper)
+        elif isinstance(param, NormalIntegerHyperparameter):
+            if can_be_inactive:
+                raise ValueError(
+                    "Inactive parameters not supported for Beta and Normal Hyperparameters"
+                )
+
+            bounds[i] = (param.nfhp._lower, param.nfhp._upper)
+        elif isinstance(param, BetaFloatHyperparameter):
+            if can_be_inactive:
+                raise ValueError(
+                    "Inactive parameters not supported for Beta and Normal Hyperparameters"
+                )
+
+            bounds[i] = (param._lower, param._upper)
+        elif isinstance(param, BetaIntegerHyperparameter):
+            if can_be_inactive:
+                raise ValueError(
+                    "Inactive parameters not supported for Beta and Normal Hyperparameters"
+                )
+
+            bounds[i] = (param.bfhp._lower, param.bfhp._upper)
+        elif not isinstance(
+            param,
+            (
+                UniformFloatHyperparameter,
+                UniformIntegerHyperparameter,
+                OrdinalHyperparameter,
+                CategoricalHyperparameter,
+                NormalFloatHyperparameter,
+                NormalIntegerHyperparameter,
+                BetaFloatHyperparameter,
+                BetaIntegerHyperparameter,
+            ),
+        ):
+            raise TypeError("Unknown hyperparameter type %s" % type(param))
+
+    if instance_features is not None:
+        types = types + [0] * instance_features.shape[1]
+
+    return types, bounds
diff --git a/deepcave/runs/__init__.py b/deepcave/runs/__init__.py
index 5e5a54c9..7443f84f 100644
--- a/deepcave/runs/__init__.py
+++ b/deepcave/runs/__init__.py
@@ -482,7 +482,6 @@ def get_status(self, config_id: int, budget: Optional[Union[int, float]] = None)
         # Unfortunately, we have to iterate through the history to find the status
         # TODO: Cache the stati
         for trial in self.history:
-
             if trial_key == trial.get_key():
                 return trial.status
 
@@ -893,7 +892,7 @@ def get_encoded_data(
 
 def check_equality(
     runs: List[AbstractRun],
-    meta: bool = True,
+    meta: bool = False,
     configspace: bool = True,
     objectives: bool = True,
     budgets: bool = True,
diff --git a/deepcave/runs/converters/smac.py b/deepcave/runs/converters/smac3v1.py
similarity index 98%
rename from deepcave/runs/converters/smac.py
rename to deepcave/runs/converters/smac3v1.py
index e5820bd1..ea66cdc3 100644
--- a/deepcave/runs/converters/smac.py
+++ b/deepcave/runs/converters/smac3v1.py
@@ -9,8 +9,8 @@
 from deepcave.utils.hash import file_to_hash
 
 
-class SMACRun(Run):
-    prefix = "SMAC"
+class SMAC3v1Run(Run):
+    prefix = "SMAC3v1"
     _initial_order = 2
 
     @property
@@ -65,7 +65,7 @@ def from_path(cls, path):
                     meta[arg] = value
 
         # Let's create a new run object
-        run = SMACRun(
+        run = SMAC3v1Run(
             name=path.stem, configspace=configspace, objectives=[objective1, objective2], meta=meta
         )
 
diff --git a/deepcave/runs/converters/smac3v2.py b/deepcave/runs/converters/smac3v2.py
new file mode 100644
index 00000000..800a57d8
--- /dev/null
+++ b/deepcave/runs/converters/smac3v2.py
@@ -0,0 +1,147 @@
+import json
+from pathlib import Path
+
+import numpy as np
+
+from deepcave.runs import Status
+from deepcave.runs.objective import Objective
+from deepcave.runs.run import Run
+from deepcave.utils.hash import file_to_hash
+
+
+class SMAC3v2Run(Run):
+    prefix = "SMAC3v2"
+    _initial_order = 2
+
+    @property
+    def hash(self):
+        if self.path is None:
+            return ""
+
+        # Use hash of history.json as id
+        return file_to_hash(self.path / "runhistory.json")
+
+    @property
+    def latest_change(self):
+        if self.path is None:
+            return 0
+
+        return Path(self.path / "runhistory.json").stat().st_mtime
+
+    @classmethod
+    def from_path(cls, path):
+        """
+        Based on working_dir/run_name/*, return a new trials object.
+        """
+        path = Path(path)
+
+        # Read configspace
+        from ConfigSpace.read_and_write import json as cs_json
+
+        with (path / "configspace.json").open("r") as f:
+            configspace = cs_json.read(f.read())
+
+        # Read objectives
+        # We have to define it ourselves, because we don't know the type of the objective
+        # Only lock lower
+        objective1 = Objective("Cost", lower=0)
+        objective2 = Objective("Time", lower=0)
+
+        # Read meta
+        with (path / "scenario.json").open() as json_file:
+            meta = json.load(json_file)
+            meta["run_objectives"] = meta.pop("objectives")
+
+        # Let's create a new run object
+        run = SMAC3v2Run(
+            name=path.stem, configspace=configspace, objectives=[objective1, objective2], meta=meta
+        )
+
+        # We have to set the path manually
+        run._path = path
+
+        # Iterate over the runhistory
+        with (path / "runhistory.json").open() as json_file:
+            all_data = json.load(json_file)
+            data = all_data["data"]
+            config_origins = all_data["config_origins"]
+            configs = all_data["configs"]
+
+        instance_ids = []
+
+        first_starttime = None
+        seeds = []
+        for (
+            config_id,
+            instance_id,
+            seed,
+            budget,
+            cost,
+            time,
+            status,
+            starttime,
+            endtime,
+            additional_info,
+        ) in data:
+            if instance_id not in instance_ids:
+                instance_ids += [instance_id]
+
+            if len(instance_ids) > 1:
+                raise RuntimeError("Instances are not supported.")
+
+            config_id = str(config_id)
+            config = configs[config_id]
+
+            if seed not in seeds:
+                seeds.append(seed)
+
+            if len(seeds) > 1:
+                raise RuntimeError("Multiple seeds are not supported.")
+
+            if first_starttime is None:
+                first_starttime = starttime
+
+            starttime = starttime - first_starttime
+            endtime = endtime - first_starttime
+
+            if status == 0:
+                # still running
+                continue
+            elif status == 1:
+                status = Status.SUCCESS
+            elif status == 3:
+                status = Status.TIMEOUT
+            elif status == 4:
+                status = Status.MEMORYOUT
+            else:
+                status = Status.CRASHED
+
+            if status != Status.SUCCESS:
+                # We don't want cost included which are failed
+                cost = None
+                time = None
+            else:
+                time = endtime - starttime
+
+            # Round budget
+            if budget:
+                budget = np.round(budget, 2)
+            else:
+                budget = 0.0
+
+            origin = None
+            if config_id in config_origins:
+                origin = config_origins[config_id]
+
+            run.add(
+                costs=[cost, time],
+                config=config,
+                budget=budget,
+                start_time=starttime,
+                end_time=endtime,
+                status=status,
+                origin=origin,
+                additional=additional_info,
+            )
+
+        return run
diff --git a/deepcave/runs/group.py b/deepcave/runs/group.py
index 3517c63a..8ca639b8 100644
--- a/deepcave/runs/group.py
+++ b/deepcave/runs/group.py
@@ -21,7 +21,8 @@ def __init__(self, name: str, runs: List[AbstractRun]):
 
         try:
             attributes = check_equality(self.runs)
-            self.meta = attributes["meta"]
+            # abstract run requires meta to contain budgets / objectives
+            self.meta = {"budgets": attributes["budgets"], "objectives": attributes["objectives"]}
             self.configspace = attributes["configspace"]
             self.objectives = attributes["objectives"]
             self.budgets = attributes["budgets"]
@@ -82,8 +83,8 @@ def __init__(self, name: str, runs: List[AbstractRun]):
 
                     # And update highest budget
                     self._update_highest_budget(new_config_id, trial.budget, trial.status)
-        except Exception:
-            raise NotMergeableError("Runs can not be merged.")
+        except Exception as e:
+            raise NotMergeableError(f"Runs can not be merged: {e}")
 
     def __iter__(self):
         for run in self.runs:
diff --git a/docs/converters.rst b/docs/converters.rst
index 7adbcea2..10d8e336 100644
--- a/docs/converters.rst
+++ b/docs/converters.rst
@@ -7,7 +7,8 @@ sure that folders are put into the right format so that the files can be interpr
 Currently, DeepCAVE supports the following converters:
 
 - DeepCAVE (native)
-- SMAC (v1.1.1)
+- SMAC (v1.4)
+- SMAC (v2.0.0)
 - BOHB
 - Auto-Sklearn
 - Auto-PyTorch
diff --git a/logs/SMAC/mlp/run_1/configspace.json b/logs/SMAC3v1/mlp/run_1/configspace.json
similarity index 100%
rename from logs/SMAC/mlp/run_1/configspace.json
rename to logs/SMAC3v1/mlp/run_1/configspace.json
diff --git a/logs/SMAC/mlp/run_1/runhistory.json b/logs/SMAC3v1/mlp/run_1/runhistory.json
similarity index 100%
rename from logs/SMAC/mlp/run_1/runhistory.json
rename to logs/SMAC3v1/mlp/run_1/runhistory.json
diff --git a/logs/SMAC/mlp/run_1/scenario.txt b/logs/SMAC3v1/mlp/run_1/scenario.txt
similarity index 100%
rename from logs/SMAC/mlp/run_1/scenario.txt
rename to logs/SMAC3v1/mlp/run_1/scenario.txt
diff --git a/logs/SMAC/mlp/run_1/stats.json b/logs/SMAC3v1/mlp/run_1/stats.json
similarity index 100%
rename from logs/SMAC/mlp/run_1/stats.json
rename to logs/SMAC3v1/mlp/run_1/stats.json
diff --git a/logs/SMAC/mlp/run_1/traj.json b/logs/SMAC3v1/mlp/run_1/traj.json
similarity index 100%
rename from logs/SMAC/mlp/run_1/traj.json
rename to logs/SMAC3v1/mlp/run_1/traj.json
diff --git a/logs/SMAC/mlp/run_1/traj_aclib2.json b/logs/SMAC3v1/mlp/run_1/traj_aclib2.json
similarity index 100%
rename from logs/SMAC/mlp/run_1/traj_aclib2.json
rename to logs/SMAC3v1/mlp/run_1/traj_aclib2.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/configspace.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/configspace.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/configspace.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/configspace.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/configspace.pcs b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/configspace.pcs
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/configspace.pcs
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/configspace.pcs
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/history_05-16-22_20-31-59_CEST.pkl b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/history_05-16-22_20-31-59_CEST.pkl
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/history_05-16-22_20-31-59_CEST.pkl
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/history_05-16-22_20-31-59_CEST.pkl
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/incumbent_05-16-22_20-31-59_CEST.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/incumbent_05-16-22_20-31-59_CEST.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/incumbent_05-16-22_20-31-59_CEST.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/incumbent_05-16-22_20-31-59_CEST.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/runhistory.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/runhistory.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/runhistory.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/runhistory.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/scenario.txt b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/scenario.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/scenario.txt
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/scenario.txt
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/stats.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/stats.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/stats.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/stats.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/train_insts.txt b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/train_insts.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/train_insts.txt
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/train_insts.txt
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/traj.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/traj.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/traj.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/traj.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/traj_aclib2.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/traj_aclib2.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/traj_aclib2.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/traj_aclib2.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/traj_old.csv b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/traj_old.csv
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-0/traj_old.csv
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-0/traj_old.csv
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/configspace.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/configspace.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/configspace.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/configspace.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/configspace.pcs b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/configspace.pcs
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/configspace.pcs
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/configspace.pcs
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/history_05-16-22_23-31-45_CEST.pkl b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/history_05-16-22_23-31-45_CEST.pkl
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/history_05-16-22_23-31-45_CEST.pkl
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/history_05-16-22_23-31-45_CEST.pkl
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/incumbent_05-16-22_23-31-45_CEST.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/incumbent_05-16-22_23-31-45_CEST.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/incumbent_05-16-22_23-31-45_CEST.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/incumbent_05-16-22_23-31-45_CEST.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/runhistory.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/runhistory.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/runhistory.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/runhistory.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/scenario.txt b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/scenario.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/scenario.txt
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/scenario.txt
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/stats.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/stats.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/stats.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/stats.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/train_insts.txt b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/train_insts.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/train_insts.txt
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/train_insts.txt
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/traj.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/traj.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/traj.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/traj.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/traj_aclib2.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/traj_aclib2.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/traj_aclib2.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/traj_aclib2.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/traj_old.csv b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/traj_old.csv
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25/traj_old.csv
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25/traj_old.csv
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/configspace.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/configspace.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/configspace.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/configspace.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/configspace.pcs b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/configspace.pcs
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/configspace.pcs
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/configspace.pcs
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/history_05-17-22_02-14-09_CEST.pkl b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/history_05-17-22_02-14-09_CEST.pkl
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/history_05-17-22_02-14-09_CEST.pkl
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/history_05-17-22_02-14-09_CEST.pkl
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/incumbent_05-17-22_02-14-09_CEST.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/incumbent_05-17-22_02-14-09_CEST.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/incumbent_05-17-22_02-14-09_CEST.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/incumbent_05-17-22_02-14-09_CEST.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/runhistory.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/runhistory.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/runhistory.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/runhistory.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/scenario.txt b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/scenario.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/scenario.txt
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/scenario.txt
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/stats.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/stats.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/stats.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/stats.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/train_insts.txt b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/train_insts.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/train_insts.txt
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/train_insts.txt
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/traj.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/traj.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/traj.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/traj.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/traj_aclib2.json b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/traj_aclib2.json
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/traj_aclib2.json
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/traj_aclib2.json
diff --git a/logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/traj_old.csv b/logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/traj_old.csv
similarity index 100%
rename from logs/SMAC/outlier-detection/DEHB-pendigits-015-0-50/traj_old.csv
rename to logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-50/traj_old.csv
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/configspace.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/configspace.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/configspace.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/configspace.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/configspace.pcs b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/configspace.pcs
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/configspace.pcs
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/configspace.pcs
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/runhistory.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/runhistory.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/runhistory.json
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diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/scenario.txt b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/scenario.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/scenario.txt
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/scenario.txt
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/stats.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/stats.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/stats.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/stats.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/train_insts.txt b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/train_insts.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/train_insts.txt
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/train_insts.txt
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/traj.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/traj.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/traj.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/traj.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/traj_aclib2.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/traj_aclib2.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/traj_aclib2.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/traj_aclib2.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/traj_old.csv b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/traj_old.csv
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0/traj_old.csv
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0/traj_old.csv
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/configspace.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/configspace.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/configspace.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/configspace.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/configspace.pcs b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/configspace.pcs
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/configspace.pcs
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/configspace.pcs
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/runhistory.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/runhistory.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/runhistory.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/runhistory.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/scenario.txt b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/scenario.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/scenario.txt
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/scenario.txt
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/stats.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/stats.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/stats.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/stats.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/train_insts.txt b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/train_insts.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/train_insts.txt
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/train_insts.txt
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/traj.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/traj.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/traj.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/traj.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/traj_aclib2.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/traj_aclib2.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/traj_aclib2.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/traj_aclib2.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/traj_old.csv b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/traj_old.csv
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-25/traj_old.csv
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-25/traj_old.csv
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/configspace.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/configspace.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/configspace.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/configspace.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/configspace.pcs b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/configspace.pcs
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/configspace.pcs
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/configspace.pcs
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/runhistory.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/runhistory.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/runhistory.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/runhistory.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/scenario.txt b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/scenario.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/scenario.txt
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/scenario.txt
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/stats.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/stats.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/stats.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/stats.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/train_insts.txt b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/train_insts.txt
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/train_insts.txt
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/train_insts.txt
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/traj.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/traj.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/traj.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/traj.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/traj_aclib2.json b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/traj_aclib2.json
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/traj_aclib2.json
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/traj_aclib2.json
diff --git a/logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/traj_old.csv b/logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/traj_old.csv
similarity index 100%
rename from logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50/traj_old.csv
rename to logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50/traj_old.csv
diff --git a/logs/SMAC3v2/mlp/run_1/configspace.json b/logs/SMAC3v2/mlp/run_1/configspace.json
new file mode 100644
index 00000000..1841073d
--- /dev/null
+++ b/logs/SMAC3v2/mlp/run_1/configspace.json
@@ -0,0 +1,98 @@
+{
+  "hyperparameters": [
+    {
+      "name": "activation",
+      "type": "categorical",
+      "choices": [
+        "logistic",
+        "tanh",
+        "relu"
+      ],
+      "default": "tanh",
+      "weights": null
+    },
+    {
+      "name": "n_layer",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 5,
+      "default": 1
+    },
+    {
+      "name": "n_neurons",
+      "type": "uniform_int",
+      "log": true,
+      "lower": 8,
+      "upper": 256,
+      "default": 10
+    },
+    {
+      "name": "solver",
+      "type": "categorical",
+      "choices": [
+        "lbfgs",
+        "sgd",
+        "adam"
+      ],
+      "default": "adam",
+      "weights": null
+    },
+    {
+      "name": "batch_size",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 30,
+      "upper": 300,
+      "default": 200
+    },
+    {
+      "name": "learning_rate",
+      "type": "categorical",
+      "choices": [
+        "constant",
+        "invscaling",
+        "adaptive"
+      ],
+      "default": "constant",
+      "weights": null
+    },
+    {
+      "name": "learning_rate_init",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.0001,
+      "upper": 1.0,
+      "default": 0.001
+    }
+  ],
+  "conditions": [
+    {
+      "child": "batch_size",
+      "parent": "solver",
+      "type": "IN",
+      "values": [
+        "sgd",
+        "adam"
+      ]
+    },
+    {
+      "child": "learning_rate",
+      "parent": "solver",
+      "type": "EQ",
+      "value": "sgd"
+    },
+    {
+      "child": "learning_rate_init",
+      "parent": "solver",
+      "type": "IN",
+      "values": [
+        "sgd",
+        "adam"
+      ]
+    }
+  ],
+  "forbiddens": [],
+  "python_module_version": "0.6.1",
+  "json_format_version": 0.4
+}
\ No newline at end of file
diff --git a/logs/SMAC3v2/mlp/run_1/intensifier.json b/logs/SMAC3v2/mlp/run_1/intensifier.json
new file mode 100644
index 00000000..41a3f368
--- /dev/null
+++ b/logs/SMAC3v2/mlp/run_1/intensifier.json
@@ -0,0 +1,324 @@
+{
+  "incumbent_ids": [
+    225
+  ],
+  "rejected_config_ids": [
+    13,
+    6,
+    26,
+    111
+  ],
+  "incumbents_changed": 5,
+  "trajectory": [
+    {
+      "config_ids": [
+        13
+      ],
+      "costs": [
+        0.05008975549365524
+      ],
+      "trial": 66,
+      "walltime": 5.8023521900177
+    },
+    {
+      "config_ids": [
+        6
+      ],
+      "costs": [
+        0.030055710306406747
+      ],
+      "trial": 72,
+      "walltime": 6.461397409439087
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+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1085, in __call__\n    if self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
+      82,
+      null,
+      209652396,
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+      0.13521046115753632,
+      1.6590993404388428,
+      1,
+      1677764121.5921943,
+      1677764123.2525449,
+      {}
+    ],
+    [
+      83,
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+      2.7777777777777777,
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+      1677764121.7402987,
+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1085, in __call__\n    if self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
+      84,
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+      {}
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+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1088, in __call__\n    while self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
+      67,
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+      {}
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+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1085, in __call__\n    if self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
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+      1677764126.0575383,
+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1088, in __call__\n    while self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
+      82,
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+      1677764131.4935918,
+      {}
+    ],
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+      {}
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+      {}
+    ],
+    [
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+      Infinity,
+      0.0,
+      2,
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+      1677764128.0119393,
+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1085, in __call__\n    if self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
+      102,
+      null,
+      209652396,
+      2.7777777777777777,
+      Infinity,
+      0.0,
+      2,
+      1677764128.036637,
+      1677764128.2285743,
+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1088, in __call__\n    while self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
+      103,
+      null,
+      209652396,
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+      1677764128.284444,
+      {}
+    ],
+    [
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+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1088, in __call__\n    while self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
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+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1088, in __call__\n    while self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
+      119,
+      null,
+      209652396,
+      2.7777777777777777,
+      Infinity,
+      0.0,
+      2,
+      1677764133.498564,
+      1677764133.8883476,
+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1088, in __call__\n    while self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
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+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1085, in __call__\n    if self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
+      127,
+      null,
+      209652396,
+      2.7777777777777777,
+      Infinity,
+      0.0,
+      2,
+      1677764136.833416,
+      1677764137.4211633,
+      {
+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1085, in __call__\n    if self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
+    ],
+    [
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+    "277": "Acquisition Function Maximizer: Local Search"
+  }
+}
\ No newline at end of file
diff --git a/logs/SMAC3v2/mlp/run_1/scenario.json b/logs/SMAC3v2/mlp/run_1/scenario.json
new file mode 100644
index 00000000..885637ab
--- /dev/null
+++ b/logs/SMAC3v2/mlp/run_1/scenario.json
@@ -0,0 +1,157 @@
+{
+    "name": "a51c7eb78bfca7abe982cbd5dcd9b3ff",
+    "deterministic": false,
+    "objectives": "cost",
+    "crash_cost": Infinity,
+    "termination_cost_threshold": Infinity,
+    "walltime_limit": 60,
+    "cputime_limit": Infinity,
+    "trial_walltime_limit": null,
+    "trial_memory_limit": null,
+    "n_trials": 500,
+    "instances": null,
+    "instance_features": null,
+    "min_budget": 1,
+    "max_budget": 25,
+    "seed": 0,
+    "n_workers": 8,
+    "_meta": {
+        "facade": {
+            "name": "MultiFidelityFacade"
+        },
+        "runner": {
+            "name": "DaskParallelRunner"
+        },
+        "model": {
+            "name": "RandomForest",
+            "types": [
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+                0,
+                0,
+                3,
+                0,
+                4,
+                0
+            ],
+            "bounds": [
+                [
+                    3,
+                    NaN
+                ],
+                [
+                    0,
+                    1.0
+                ],
+                [
+                    0,
+                    1.0
+                ],
+                [
+                    3,
+                    NaN
+                ],
+                [
+                    -1.0,
+                    1.0
+                ],
+                [
+                    4,
+                    NaN
+                ],
+                [
+                    -1.0,
+                    1.0
+                ]
+            ],
+            "pca_components": 7,
+            "n_trees": 10,
+            "n_points_per_tree": -1,
+            "ratio_features": 1.0,
+            "min_samples_split": 2,
+            "min_samples_leaf": 1,
+            "max_depth": 1048576,
+            "eps_purity": 1e-08,
+            "max_nodes": 1048576,
+            "bootstrapping": true
+        },
+        "acquisition_maximizer": {
+            "name": "LocalAndSortedRandomSearch",
+            "acquisition_function": {
+                "name": "EI",
+                "xi": 0.0,
+                "log": true
+            },
+            "challengers": 10000,
+            "seed": 0,
+            "random_search": {
+                "name": "RandomSearch",
+                "acquisition_function": {
+                    "name": "EI",
+                    "xi": 0.0,
+                    "log": true
+                },
+                "challengers": 5000,
+                "seed": 0
+            },
+            "local_search": {
+                "name": "LocalSearch",
+                "acquisition_function": {
+                    "name": "EI",
+                    "xi": 0.0,
+                    "log": true
+                },
+                "challengers": 5000,
+                "seed": 0,
+                "max_steps": null,
+                "n_steps_plateau_walk": 10,
+                "vectorization_min_obtain": 2,
+                "vectorization_max_obtain": 64
+            }
+        },
+        "acquisition_function": {
+            "name": "EI",
+            "xi": 0.0,
+            "log": true
+        },
+        "intensifier": {
+            "name": "SuccessiveHalving",
+            "max_incumbents": 10,
+            "seed": 0,
+            "eta": 3,
+            "instance_seed_order": "shuffle_once",
+            "incumbent_selection": "highest_budget"
+        },
+        "initial_design": {
+            "name": "RandomInitialDesign",
+            "n_configs": 5,
+            "n_configs_per_hyperparameter": 10,
+            "additional_configs": [],
+            "seed": 0
+        },
+        "random_design": {
+            "name": "ProbabilityRandomDesign",
+            "seed": 0,
+            "probability": 0.2
+        },
+        "runhistory_encoder": {
+            "name": "RunHistoryLogScaledEncoder",
+            "considered_states": [
+                1,
+                2,
+                4
+            ],
+            "lower_budget_states": [],
+            "scale_percentage": 5,
+            "seed": 0
+        },
+        "multi_objective_algorithm": null,
+        "config_selector": {
+            "name": "ConfigSelector",
+            "retrain_after": 8,
+            "retries": 16,
+            "min_trials": 1
+        },
+        "version": "2.0.0b2"
+    },
+    "output_directory": "smac3_output/a51c7eb78bfca7abe982cbd5dcd9b3ff/0"
+}
\ No newline at end of file
diff --git a/logs/SMAC3v2/mlp/run_2/configspace.json b/logs/SMAC3v2/mlp/run_2/configspace.json
new file mode 100644
index 00000000..1841073d
--- /dev/null
+++ b/logs/SMAC3v2/mlp/run_2/configspace.json
@@ -0,0 +1,98 @@
+{
+  "hyperparameters": [
+    {
+      "name": "activation",
+      "type": "categorical",
+      "choices": [
+        "logistic",
+        "tanh",
+        "relu"
+      ],
+      "default": "tanh",
+      "weights": null
+    },
+    {
+      "name": "n_layer",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 5,
+      "default": 1
+    },
+    {
+      "name": "n_neurons",
+      "type": "uniform_int",
+      "log": true,
+      "lower": 8,
+      "upper": 256,
+      "default": 10
+    },
+    {
+      "name": "solver",
+      "type": "categorical",
+      "choices": [
+        "lbfgs",
+        "sgd",
+        "adam"
+      ],
+      "default": "adam",
+      "weights": null
+    },
+    {
+      "name": "batch_size",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 30,
+      "upper": 300,
+      "default": 200
+    },
+    {
+      "name": "learning_rate",
+      "type": "categorical",
+      "choices": [
+        "constant",
+        "invscaling",
+        "adaptive"
+      ],
+      "default": "constant",
+      "weights": null
+    },
+    {
+      "name": "learning_rate_init",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.0001,
+      "upper": 1.0,
+      "default": 0.001
+    }
+  ],
+  "conditions": [
+    {
+      "child": "batch_size",
+      "parent": "solver",
+      "type": "IN",
+      "values": [
+        "sgd",
+        "adam"
+      ]
+    },
+    {
+      "child": "learning_rate",
+      "parent": "solver",
+      "type": "EQ",
+      "value": "sgd"
+    },
+    {
+      "child": "learning_rate_init",
+      "parent": "solver",
+      "type": "IN",
+      "values": [
+        "sgd",
+        "adam"
+      ]
+    }
+  ],
+  "forbiddens": [],
+  "python_module_version": "0.6.1",
+  "json_format_version": 0.4
+}
\ No newline at end of file
diff --git a/logs/SMAC3v2/mlp/run_2/intensifier.json b/logs/SMAC3v2/mlp/run_2/intensifier.json
new file mode 100644
index 00000000..beb90e4a
--- /dev/null
+++ b/logs/SMAC3v2/mlp/run_2/intensifier.json
@@ -0,0 +1,281 @@
+{
+  "incumbent_ids": [
+    181
+  ],
+  "rejected_config_ids": [
+    13,
+    6,
+    26,
+    36,
+    133
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+  "incumbents_changed": 6,
+  "trajectory": [
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+      "walltime": 6.415576934814453
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+      "walltime": 6.729609251022339
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+      "walltime": 9.927663564682007
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+    {
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+        36
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+      "costs": [
+        0.0233720210461158
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+      "trial": 159,
+      "walltime": 19.656972646713257
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+    {
+      "config_ids": [
+        133
+      ],
+      "costs": [
+        0.021149798823893562
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+      "trial": 242,
+      "walltime": 50.021228075027466
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+    {
+      "config_ids": [
+        181
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+        0.020592695759826696
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+      "trial": 280,
+      "walltime": 63.389174938201904
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+  ],
+  "state": {
+    "tracker": {
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+              "n_neurons": 145,
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+              "learning_rate": "constant",
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+              "n_neurons": 223,
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+              "learning_rate": "constant",
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+            {
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+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
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+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
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+}
\ No newline at end of file
diff --git a/logs/SMAC3v2/mlp/run_2/scenario.json b/logs/SMAC3v2/mlp/run_2/scenario.json
new file mode 100644
index 00000000..885637ab
--- /dev/null
+++ b/logs/SMAC3v2/mlp/run_2/scenario.json
@@ -0,0 +1,157 @@
+{
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\ No newline at end of file
diff --git a/logs/SMAC3v2/mlp/run_3/configspace.json b/logs/SMAC3v2/mlp/run_3/configspace.json
new file mode 100644
index 00000000..1841073d
--- /dev/null
+++ b/logs/SMAC3v2/mlp/run_3/configspace.json
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\ No newline at end of file
diff --git a/logs/SMAC3v2/mlp/run_3/intensifier.json b/logs/SMAC3v2/mlp/run_3/intensifier.json
new file mode 100644
index 00000000..3b41d398
--- /dev/null
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\ No newline at end of file
diff --git a/logs/SMAC3v2/mlp/run_3/optimization.json b/logs/SMAC3v2/mlp/run_3/optimization.json
new file mode 100644
index 00000000..5c62dd3b
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+        "traceback": "Traceback (most recent call last):\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 158, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/smac/runner/target_function_runner.py\", line 231, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/home/skrebs/Projects/AutoML/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 107, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 515, in cross_val_score\n    cv_results = cross_validate(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 266, in cross_validate\n    results = parallel(\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 1085, in __call__\n    if self.dispatch_one_batch(iterator):\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n    self.results = batch()\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n    return self.function(*args, **kwargs)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 747, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/home/skrebs/miniconda3/envs/test_smac/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 491, in _fit\n    raise ValueError(\nValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.\n",
+        "error": "ValueError('Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.')"
+      }
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+}
\ No newline at end of file
diff --git a/logs/SMAC3v2/mlp/run_3/scenario.json b/logs/SMAC3v2/mlp/run_3/scenario.json
new file mode 100644
index 00000000..885637ab
--- /dev/null
+++ b/logs/SMAC3v2/mlp/run_3/scenario.json
@@ -0,0 +1,157 @@
+{
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+                [
+                    0,
+                    1.0
+                ],
+                [
+                    3,
+                    NaN
+                ],
+                [
+                    -1.0,
+                    1.0
+                ],
+                [
+                    4,
+                    NaN
+                ],
+                [
+                    -1.0,
+                    1.0
+                ]
+            ],
+            "pca_components": 7,
+            "n_trees": 10,
+            "n_points_per_tree": -1,
+            "ratio_features": 1.0,
+            "min_samples_split": 2,
+            "min_samples_leaf": 1,
+            "max_depth": 1048576,
+            "eps_purity": 1e-08,
+            "max_nodes": 1048576,
+            "bootstrapping": true
+        },
+        "acquisition_maximizer": {
+            "name": "LocalAndSortedRandomSearch",
+            "acquisition_function": {
+                "name": "EI",
+                "xi": 0.0,
+                "log": true
+            },
+            "challengers": 10000,
+            "seed": 0,
+            "random_search": {
+                "name": "RandomSearch",
+                "acquisition_function": {
+                    "name": "EI",
+                    "xi": 0.0,
+                    "log": true
+                },
+                "challengers": 5000,
+                "seed": 0
+            },
+            "local_search": {
+                "name": "LocalSearch",
+                "acquisition_function": {
+                    "name": "EI",
+                    "xi": 0.0,
+                    "log": true
+                },
+                "challengers": 5000,
+                "seed": 0,
+                "max_steps": null,
+                "n_steps_plateau_walk": 10,
+                "vectorization_min_obtain": 2,
+                "vectorization_max_obtain": 64
+            }
+        },
+        "acquisition_function": {
+            "name": "EI",
+            "xi": 0.0,
+            "log": true
+        },
+        "intensifier": {
+            "name": "SuccessiveHalving",
+            "max_incumbents": 10,
+            "seed": 0,
+            "eta": 3,
+            "instance_seed_order": "shuffle_once",
+            "incumbent_selection": "highest_budget"
+        },
+        "initial_design": {
+            "name": "RandomInitialDesign",
+            "n_configs": 5,
+            "n_configs_per_hyperparameter": 10,
+            "additional_configs": [],
+            "seed": 0
+        },
+        "random_design": {
+            "name": "ProbabilityRandomDesign",
+            "seed": 0,
+            "probability": 0.2
+        },
+        "runhistory_encoder": {
+            "name": "RunHistoryLogScaledEncoder",
+            "considered_states": [
+                1,
+                2,
+                4
+            ],
+            "lower_budget_states": [],
+            "scale_percentage": 5,
+            "seed": 0
+        },
+        "multi_objective_algorithm": null,
+        "config_selector": {
+            "name": "ConfigSelector",
+            "retrain_after": 8,
+            "retries": 16,
+            "min_trials": 1
+        },
+        "version": "2.0.0b2"
+    },
+    "output_directory": "smac3_output/a51c7eb78bfca7abe982cbd5dcd9b3ff/0"
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index e07bf630..35deeab5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,7 +8,7 @@ matplotlib>=3.5.1
 
 # AutoML packages
 ConfigSpace
-smac<1.4.0
+pyrfr>=0.9.0
 hpbandster==0.7.4
 
 # Upgrading to 2.1.0 or higher breaks the slider because string keys in marks


Test patch:
diff --git a/tests/test_evaluators/test_epm.py b/tests/test_evaluators/test_epm.py
index 88ae4ce8..d152a7f2 100644
--- a/tests/test_evaluators/test_epm.py
+++ b/tests/test_evaluators/test_epm.py
@@ -7,14 +7,14 @@
 from deepcave.evaluators.epm.fanova_forest import FanovaForest
 from deepcave.evaluators.epm.random_forest import RandomForest
 from deepcave.runs import AbstractRun
-from deepcave.runs.converters.smac import SMACRun
+from deepcave.runs.converters.smac3v1 import SMAC3v1Run
 from deepcave.runs.status import Status
 
 
 class TestEPM(unittest.TestCase):
     def setUp(self):
         # Initiate run here
-        self.run: AbstractRun = SMACRun.from_path("logs/SMAC/mlp/run_1")
+        self.run: AbstractRun = SMAC3v1Run.from_path("logs/SMAC3v1/mlp/run_1")
         self.cs = self.run.configspace
         self.hp_names = self.cs.get_hyperparameter_names()
 
diff --git a/tests/test_evaluators/test_fanova.py b/tests/test_evaluators/test_fanova.py
index d2838f31..af57f535 100644
--- a/tests/test_evaluators/test_fanova.py
+++ b/tests/test_evaluators/test_fanova.py
@@ -6,13 +6,13 @@
 from deepcave.constants import COMBINED_COST_NAME
 from deepcave.evaluators.fanova import fANOVA as Evaluator
 from deepcave.runs import AbstractRun
-from deepcave.runs.converters.smac import SMACRun
+from deepcave.runs.converters.smac3v1 import SMAC3v1Run
 
 
 class TestFanova(unittest.TestCase):
     def setUp(self):
         # Initiate run here
-        self.run: AbstractRun = SMACRun.from_path("logs/SMAC/mlp/run_1")
+        self.run: AbstractRun = SMAC3v1Run.from_path("logs/SMAC3v1/mlp/run_1")
         self.hp_names = self.run.configspace.get_hyperparameter_names()
         self.evaluator = Evaluator(self.run)
 
diff --git a/tests/test_evaluators/test_lpi.py b/tests/test_evaluators/test_lpi.py
index 097cf891..bc467f1d 100644
--- a/tests/test_evaluators/test_lpi.py
+++ b/tests/test_evaluators/test_lpi.py
@@ -6,13 +6,13 @@
 from deepcave.constants import COMBINED_COST_NAME
 from deepcave.evaluators.lpi import LPI as Evaluator
 from deepcave.runs import AbstractRun
-from deepcave.runs.converters.smac import SMACRun
+from deepcave.runs.converters.smac3v1 import SMAC3v1Run
 
 
 class TestLPI(unittest.TestCase):
     def setUp(self):
         # Initiate run here
-        self.run: AbstractRun = SMACRun.from_path("logs/SMAC/mlp/run_1")
+        self.run: AbstractRun = SMAC3v1Run.from_path("logs/SMAC3v1/mlp/run_1")
         self.hp_names = self.run.configspace.get_hyperparameter_names()
         self.evaluator = Evaluator(self.run)
 
diff --git a/tests/test_runs/test_groups.py b/tests/test_runs/test_groups.py
index c94a2bc9..a2e402f9 100644
--- a/tests/test_runs/test_groups.py
+++ b/tests/test_runs/test_groups.py
@@ -7,30 +7,41 @@
 from requests import check_compatibility
 
 from deepcave.runs import AbstractRun, check_equality
-from deepcave.runs.converters.smac import SMACRun
+from deepcave.runs.converters.smac3v1 import SMAC3v1Run
+from deepcave.runs.converters.smac3v2 import SMAC3v2Run
 from deepcave.runs.group import Group
 
 
 class TestRun(unittest.TestCase):
     def setUp(self) -> None:
         # Initiate run here
-        self.run1: AbstractRun = SMACRun.from_path(
-            "logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0"
+        self.run1: AbstractRun = SMAC3v1Run.from_path(
+            "logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0"
         )
-        self.run2: AbstractRun = SMACRun.from_path(
-            "logs/SMAC/outlier-detection/DEHB-pendigits-015-0-25"
+        self.run2: AbstractRun = SMAC3v1Run.from_path(
+            "logs/SMAC3v1/outlier-detection/DEHB-pendigits-015-0-25"
         )
-        self.run3: AbstractRun = SMACRun.from_path(
-            "logs/SMAC/outlier-detection/SMAC-pendigits-015-0-50"
+        self.run3: AbstractRun = SMAC3v1Run.from_path(
+            "logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-50"
         )
+        self.run1_v2: AbstractRun = SMAC3v2Run.from_path("logs/SMAC3v2/mlp/run_1")
+        self.run2_v2: AbstractRun = SMAC3v2Run.from_path("logs/SMAC3v2/mlp/run_2")
+        self.run3_v2: AbstractRun = SMAC3v2Run.from_path("logs/SMAC3v2/mlp/run_3")
 
-    def test(self) -> None:
+    def test_group_v1(self) -> None:
         group1 = Group("blub", runs=[self.run1, self.run2])
         check_equality([self.run1, group1])
 
         group2 = Group("blub", runs=[self.run1, self.run2, self.run3])
         check_equality([self.run1, self.run2, self.run3, group2])
 
+    def test_group_v2(self) -> None:
+        group1 = Group("blub", runs=[self.run1_v2, self.run2_v2])
+        check_equality([self.run1_v2, group1])
+
+        group2 = Group("blub", runs=[self.run1_v2, self.run2_v2, self.run3_v2])
+        check_equality([self.run1_v2, self.run2_v2, self.run3_v2, group2])
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_runs/test_run.py b/tests/test_runs/test_run.py
index 0f60e66c..d3ecf486 100644
--- a/tests/test_runs/test_run.py
+++ b/tests/test_runs/test_run.py
@@ -3,23 +3,32 @@
 import unittest
 
 from deepcave.runs import AbstractRun
-from deepcave.runs.converters.smac import SMACRun
+from deepcave.runs.converters.smac3v1 import SMAC3v1Run
+from deepcave.runs.converters.smac3v2 import SMAC3v2Run
 
 
 class TestRun(unittest.TestCase):
     def setUp(self):
         # Initiate run here
-        self.run: AbstractRun = SMACRun.from_path(
-            "logs/SMAC/outlier-detection/SMAC-pendigits-015-0-0"
+        self.run: AbstractRun = SMAC3v1Run.from_path(
+            "logs/SMAC3v1/outlier-detection/SMAC-pendigits-015-0-0"
         )
+        self.run_v2: AbstractRun = SMAC3v2Run.from_path("logs/SMAC3v2/mlp/run_1")
 
-    def test_configs(self):
+    def test_configs_v1(self):
         # Get a random config_id
         config_id = 5
         config = self.run.get_config(config_id)
         normalized_config = self.run.encode_config(config)
         assert isinstance(normalized_config, List)
 
+    def test_configs_v2(self):
+        # Get a random config_id
+        config_id = 5
+        config = self.run_v2.get_config(config_id)
+        normalized_config = self.run.encode_config(config)
+        assert isinstance(normalized_config, List)
+
 
 if __name__ == "__main__":
     unittest.main()