compatibility for smac3v2 new smac version

Author: KrissiHub

CHANGELOG.md

@@ -1,3 +1,8 @@
+# Version 1.3.6
+
+## New SMAC version compatibility
+- The smac3v2 converter can now also handle the output of the new smac version
+
 # Version 1.3.5
 
 ## Access Specifier

deepcave/config.py

@@ -61,7 +61,7 @@ class Config:
 
     # General config
     TITLE: str = "DeepCAVE"
-    DEBUG: bool = True
+    DEBUG: bool = False
     # How often to refresh background activities (such as update the sidebar or process button for
     # static plugins). Value in milliseconds.
     REFRESH_RATE: int = 500

logs/SMAC3v2/mlp/run_4/runhistory.json

@@ -1174,8 +1174,8 @@
       "starttime": 1748856893.299079,
       "endtime": 1748856893.353384,
       "additional_info": {
-        "traceback": "Traceback (most recent call last):\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/smac/runner/target_function_runner.py\", line 190, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/smac/runner/target_function_runner.py\", line 264, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/Users/krissi/Documents/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 106, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/utils/_param_validation.py\", line 216, in wrapper\n    return func(*args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 684, in cross_val_score\n    cv_results = cross_validate(\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/utils/_param_validation.py\", line 216, in wrapper\n    return func(*args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 411, in cross_validate\n    results = parallel(\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/utils/parallel.py\", line 77, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/joblib/parallel.py\", line 1986, in __call__\n    return output if self.return_generator else list(output)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/joblib/parallel.py\", line 1914, in _get_sequential_output\n    res = func(*args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/utils/parallel.py\", line 139, in __call__\n    return self.function(*args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/base.py\", line 1389, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 754, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 496, 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.')"
+        "traceback": "Traceback",
+        "error": "ValueError"
       }
     },
     {
@@ -4882,8 +4882,8 @@
       "starttime": 1748856917.1936471,
       "endtime": 1748856917.3327649,
       "additional_info": {
-        "traceback": "Traceback (most recent call last):\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/smac/runner/target_function_runner.py\", line 190, in run\n    rval = self(config_copy, target_function, kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/smac/runner/target_function_runner.py\", line 264, in __call__\n    return algorithm(config, **algorithm_kwargs)\n  File \"/Users/krissi/Documents/SMAC3/examples/2_multi_fidelity/1_mlp_epochs.py\", line 106, in train\n    score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/utils/_param_validation.py\", line 216, in wrapper\n    return func(*args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 684, in cross_val_score\n    cv_results = cross_validate(\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/utils/_param_validation.py\", line 216, in wrapper\n    return func(*args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 411, in cross_validate\n    results = parallel(\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/utils/parallel.py\", line 77, in __call__\n    return super().__call__(iterable_with_config)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/joblib/parallel.py\", line 1986, in __call__\n    return output if self.return_generator else list(output)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/joblib/parallel.py\", line 1914, in _get_sequential_output\n    res = func(*args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/utils/parallel.py\", line 139, in __call__\n    return self.function(*args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/base.py\", line 1389, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 754, in fit\n    return self._fit(X, y, incremental=False)\n  File \"/opt/homebrew/anaconda3/envs/smac/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py\", line 496, 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.')"
+        "traceback": "Traceback",
+        "error": "ValueError"
       }
     },
     {