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a/development/_downloads/0422a2b52bca2c17c0ab975a6ae7e8db/2_parego.py +++ b/development/_downloads/0422a2b52bca2c17c0ab975a6ae7e8db/2_parego.py @@ -66,9 +66,9 @@ def configspace(self) -> ConfigurationSpace: return cs def train(self, config: Configuration, seed: int = 0, budget: int = 10) -> dict[str, float]: - lr = config["learning_rate"] if config["learning_rate"] else "constant" - lr_init = config["learning_rate_init"] if config["learning_rate_init"] else 0.001 - batch_size = config["batch_size"] if config["batch_size"] else 200 + lr = config.get("learning_rate", "constant") + lr_init = config.get("learning_rate_init", 0.001) + batch_size = config.get("batch_size", 200) start_time = time.time() diff --git a/development/_downloads/88e1c1ee9fc33236ebfa159898782a73/2_sgd_datasets.py b/development/_downloads/88e1c1ee9fc33236ebfa159898782a73/2_sgd_datasets.py index 384d1c2246..09864a963a 100644 --- a/development/_downloads/88e1c1ee9fc33236ebfa159898782a73/2_sgd_datasets.py +++ b/development/_downloads/88e1c1ee9fc33236ebfa159898782a73/2_sgd_datasets.py @@ -89,7 +89,7 @@ def train(self, config: Configuration, instance: str, seed: int = 0) -> float: # SGD classifier using given configuration clf = SGDClassifier( - loss="log", + loss="log_loss", penalty="elasticnet", alpha=config["alpha"], l1_ratio=config["l1_ratio"], diff --git a/development/_downloads/8a5c5b9a1383ea80dbf4608b637a4168/2_sgd_datasets.ipynb b/development/_downloads/8a5c5b9a1383ea80dbf4608b637a4168/2_sgd_datasets.ipynb index 2d1c763535..04e9daf060 100644 --- a/development/_downloads/8a5c5b9a1383ea80dbf4608b637a4168/2_sgd_datasets.ipynb +++ b/development/_downloads/8a5c5b9a1383ea80dbf4608b637a4168/2_sgd_datasets.ipynb @@ -15,7 +15,7 @@ }, "outputs": [], "source": [ - "from __future__ import annotations\n\nimport itertools\nimport warnings\n\nimport numpy as np\nfrom ConfigSpace import Categorical, Configuration, ConfigurationSpace, Float\nfrom sklearn import datasets\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import StratifiedKFold, cross_val_score\n\nfrom smac import MultiFidelityFacade as MFFacade\nfrom smac import Scenario\n\n__copyright__ = \"Copyright 2021, AutoML.org Freiburg-Hannover\"\n__license__ = \"3-clause BSD\"\n\n\nclass DigitsDataset:\n def __init__(self) -> None:\n self._data = datasets.load_digits()\n\n def get_instances(self) -> list[str]:\n \"\"\"Create instances from the dataset which include two classes only.\"\"\"\n return [f\"{classA}-{classB}\" for classA, classB in itertools.combinations(self._data.target_names, 2)]\n\n def get_instance_features(self) -> dict[str, list[int | float]]:\n \"\"\"Returns the mean and variance of all instances as features.\"\"\"\n features = {}\n for instance in self.get_instances():\n data, _ = self.get_instance_data(instance)\n features[instance] = [np.mean(data), np.var(data)]\n\n return features\n\n def get_instance_data(self, instance: str) -> tuple[np.ndarray, np.ndarray]:\n \"\"\"Retrieve data from the passed instance.\"\"\"\n # We split the dataset into two classes\n classA, classB = instance.split(\"-\")\n indices = np.where(np.logical_or(int(classA) == self._data.target, int(classB) == self._data.target))\n\n data = self._data.data[indices]\n target = self._data.target[indices]\n\n return data, target\n\n\nclass SGD:\n def __init__(self, dataset: DigitsDataset) -> None:\n self.dataset = dataset\n\n @property\n def configspace(self) -> ConfigurationSpace:\n \"\"\"Build the configuration space which defines all parameters and their ranges for the SGD classifier.\"\"\"\n cs = ConfigurationSpace()\n\n # We define a few possible parameters for the SGD classifier\n alpha = Float(\"alpha\", (0, 1), default=1.0)\n l1_ratio = Float(\"l1_ratio\", (0, 1), default=0.5)\n learning_rate = Categorical(\"learning_rate\", [\"constant\", \"invscaling\", \"adaptive\"], default=\"constant\")\n eta0 = Float(\"eta0\", (0.00001, 1), default=0.1, log=True)\n # Add the parameters to configuration space\n cs.add_hyperparameters([alpha, l1_ratio, learning_rate, eta0])\n\n return cs\n\n def train(self, config: Configuration, instance: str, seed: int = 0) -> float:\n \"\"\"Creates a SGD classifier based on a configuration and evaluates it on the\n digits dataset using cross-validation.\"\"\"\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\")\n\n # SGD classifier using given configuration\n clf = SGDClassifier(\n loss=\"log\",\n penalty=\"elasticnet\",\n alpha=config[\"alpha\"],\n l1_ratio=config[\"l1_ratio\"],\n learning_rate=config[\"learning_rate\"],\n eta0=config[\"eta0\"],\n max_iter=30,\n early_stopping=True,\n random_state=seed,\n )\n\n # get instance\n data, target = self.dataset.get_instance_data(instance)\n\n cv = StratifiedKFold(n_splits=4, random_state=seed, shuffle=True) # to make CV splits consistent\n scores = cross_val_score(clf, data, target, cv=cv)\n\n return 1 - np.mean(scores)\n\n\nif __name__ == \"__main__\":\n dataset = DigitsDataset()\n model = SGD(dataset)\n\n scenario = Scenario(\n model.configspace,\n walltime_limit=30, # We want to optimize for 30 seconds\n n_trials=5000, # We want to try max 5000 different trials\n min_budget=1, # Use min one instance\n max_budget=45, # Use max 45 instances (if we have a lot of instances we could constraint it here)\n instances=dataset.get_instances(),\n instance_features=dataset.get_instance_features(),\n )\n\n # Create our SMAC object and pass the scenario and the train method\n smac = MFFacade(\n scenario,\n model.train,\n overwrite=True,\n )\n\n # Now we start the optimization process\n incumbent = smac.optimize()\n\n default_cost = smac.validate(model.configspace.get_default_configuration())\n print(f\"Default cost: {default_cost}\")\n\n incumbent_cost = smac.validate(incumbent)\n print(f\"Incumbent cost: {incumbent_cost}\")" + "from __future__ import annotations\n\nimport itertools\nimport warnings\n\nimport numpy as np\nfrom ConfigSpace import Categorical, Configuration, ConfigurationSpace, Float\nfrom sklearn import datasets\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import StratifiedKFold, cross_val_score\n\nfrom smac import MultiFidelityFacade as MFFacade\nfrom smac import Scenario\n\n__copyright__ = \"Copyright 2021, AutoML.org Freiburg-Hannover\"\n__license__ = \"3-clause BSD\"\n\n\nclass DigitsDataset:\n def __init__(self) -> None:\n self._data = datasets.load_digits()\n\n def get_instances(self) -> list[str]:\n \"\"\"Create instances from the dataset which include two classes only.\"\"\"\n return [f\"{classA}-{classB}\" for classA, classB in itertools.combinations(self._data.target_names, 2)]\n\n def get_instance_features(self) -> dict[str, list[int | float]]:\n \"\"\"Returns the mean and variance of all instances as features.\"\"\"\n features = {}\n for instance in self.get_instances():\n data, _ = self.get_instance_data(instance)\n features[instance] = [np.mean(data), np.var(data)]\n\n return features\n\n def get_instance_data(self, instance: str) -> tuple[np.ndarray, np.ndarray]:\n \"\"\"Retrieve data from the passed instance.\"\"\"\n # We split the dataset into two classes\n classA, classB = instance.split(\"-\")\n indices = np.where(np.logical_or(int(classA) == self._data.target, int(classB) == self._data.target))\n\n data = self._data.data[indices]\n target = self._data.target[indices]\n\n return data, target\n\n\nclass SGD:\n def __init__(self, dataset: DigitsDataset) -> None:\n self.dataset = dataset\n\n @property\n def configspace(self) -> ConfigurationSpace:\n \"\"\"Build the configuration space which defines all parameters and their ranges for the SGD classifier.\"\"\"\n cs = ConfigurationSpace()\n\n # We define a few possible parameters for the SGD classifier\n alpha = Float(\"alpha\", (0, 1), default=1.0)\n l1_ratio = Float(\"l1_ratio\", (0, 1), default=0.5)\n learning_rate = Categorical(\"learning_rate\", [\"constant\", \"invscaling\", \"adaptive\"], default=\"constant\")\n eta0 = Float(\"eta0\", (0.00001, 1), default=0.1, log=True)\n # Add the parameters to configuration space\n cs.add_hyperparameters([alpha, l1_ratio, learning_rate, eta0])\n\n return cs\n\n def train(self, config: Configuration, instance: str, seed: int = 0) -> float:\n \"\"\"Creates a SGD classifier based on a configuration and evaluates it on the\n digits dataset using cross-validation.\"\"\"\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\")\n\n # SGD classifier using given configuration\n clf = SGDClassifier(\n loss=\"log_loss\",\n penalty=\"elasticnet\",\n alpha=config[\"alpha\"],\n l1_ratio=config[\"l1_ratio\"],\n learning_rate=config[\"learning_rate\"],\n eta0=config[\"eta0\"],\n max_iter=30,\n early_stopping=True,\n random_state=seed,\n )\n\n # get instance\n data, target = self.dataset.get_instance_data(instance)\n\n cv = StratifiedKFold(n_splits=4, random_state=seed, shuffle=True) # to make CV splits consistent\n scores = cross_val_score(clf, data, target, cv=cv)\n\n return 1 - np.mean(scores)\n\n\nif __name__ == \"__main__\":\n dataset = DigitsDataset()\n model = SGD(dataset)\n\n scenario = Scenario(\n model.configspace,\n walltime_limit=30, # We want to optimize for 30 seconds\n n_trials=5000, # We want to try max 5000 different trials\n min_budget=1, # Use min one instance\n max_budget=45, # Use max 45 instances (if we have a lot of instances we could constraint it here)\n instances=dataset.get_instances(),\n instance_features=dataset.get_instance_features(),\n )\n\n # Create our SMAC object and pass the scenario and the train method\n smac = MFFacade(\n scenario,\n model.train,\n overwrite=True,\n )\n\n # Now we start the optimization process\n incumbent = smac.optimize()\n\n default_cost = smac.validate(model.configspace.get_default_configuration())\n print(f\"Default cost: {default_cost}\")\n\n incumbent_cost = smac.validate(incumbent)\n print(f\"Incumbent cost: {incumbent_cost}\")" ] } ], diff --git a/development/_downloads/9a6a998bd05ca9b3aff17bd46b9cd502/2_parego.ipynb b/development/_downloads/9a6a998bd05ca9b3aff17bd46b9cd502/2_parego.ipynb index 8c3155c7c9..14c02b03a3 100644 --- a/development/_downloads/9a6a998bd05ca9b3aff17bd46b9cd502/2_parego.ipynb +++ b/development/_downloads/9a6a998bd05ca9b3aff17bd46b9cd502/2_parego.ipynb @@ -15,7 +15,7 @@ }, "outputs": [], "source": [ - "from __future__ import annotations\n\nimport time\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ConfigSpace import (\n Categorical,\n Configuration,\n ConfigurationSpace,\n EqualsCondition,\n Float,\n InCondition,\n Integer,\n)\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import StratifiedKFold, cross_val_score\nfrom sklearn.neural_network import MLPClassifier\n\nfrom smac import HyperparameterOptimizationFacade as HPOFacade\nfrom smac import Scenario\nfrom smac.facade.abstract_facade import AbstractFacade\nfrom smac.multi_objective.parego import ParEGO\n\n__copyright__ = \"Copyright 2021, AutoML.org Freiburg-Hannover\"\n__license__ = \"3-clause BSD\"\n\n\ndigits = load_digits()\n\n\nclass MLP:\n @property\n def configspace(self) -> ConfigurationSpace:\n cs = ConfigurationSpace()\n\n n_layer = Integer(\"n_layer\", (1, 5), default=1)\n n_neurons = Integer(\"n_neurons\", (8, 256), log=True, default=10)\n activation = Categorical(\"activation\", [\"logistic\", \"tanh\", \"relu\"], default=\"tanh\")\n solver = Categorical(\"solver\", [\"lbfgs\", \"sgd\", \"adam\"], default=\"adam\")\n batch_size = Integer(\"batch_size\", (30, 300), default=200)\n learning_rate = Categorical(\"learning_rate\", [\"constant\", \"invscaling\", \"adaptive\"], default=\"constant\")\n learning_rate_init = Float(\"learning_rate_init\", (0.0001, 1.0), default=0.001, log=True)\n\n cs.add_hyperparameters([n_layer, n_neurons, activation, solver, batch_size, learning_rate, learning_rate_init])\n\n use_lr = EqualsCondition(child=learning_rate, parent=solver, value=\"sgd\")\n use_lr_init = InCondition(child=learning_rate_init, parent=solver, values=[\"sgd\", \"adam\"])\n use_batch_size = InCondition(child=batch_size, parent=solver, values=[\"sgd\", \"adam\"])\n\n # We can also add multiple conditions on hyperparameters at once:\n cs.add_conditions([use_lr, use_batch_size, use_lr_init])\n\n return cs\n\n def train(self, config: Configuration, seed: int = 0, budget: int = 10) -> dict[str, float]:\n lr = config[\"learning_rate\"] if config[\"learning_rate\"] else \"constant\"\n lr_init = config[\"learning_rate_init\"] if config[\"learning_rate_init\"] else 0.001\n batch_size = config[\"batch_size\"] if config[\"batch_size\"] else 200\n\n start_time = time.time()\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\")\n\n classifier = MLPClassifier(\n hidden_layer_sizes=[config[\"n_neurons\"]] * config[\"n_layer\"],\n solver=config[\"solver\"],\n batch_size=batch_size,\n activation=config[\"activation\"],\n learning_rate=lr,\n learning_rate_init=lr_init,\n max_iter=int(np.ceil(budget)),\n random_state=seed,\n )\n\n # Returns the 5-fold cross validation accuracy\n cv = StratifiedKFold(n_splits=5, random_state=seed, shuffle=True) # to make CV splits consistent\n score = cross_val_score(classifier, digits.data, digits.target, cv=cv, error_score=\"raise\")\n\n return {\n \"1 - accuracy\": 1 - np.mean(score),\n \"time\": time.time() - start_time,\n }\n\n\ndef plot_pareto(smac: AbstractFacade, incumbents: list[Configuration]) -> None:\n \"\"\"Plots configurations from SMAC and highlights the best configurations in a Pareto front.\"\"\"\n average_costs = []\n average_pareto_costs = []\n for config in smac.runhistory.get_configs():\n # Since we use multiple seeds, we have to average them to get only one cost value pair for each configuration\n average_cost = smac.runhistory.average_cost(config)\n\n if config in incumbents:\n average_pareto_costs += [average_cost]\n else:\n average_costs += [average_cost]\n\n # Let's work with a numpy array\n costs = np.vstack(average_costs)\n pareto_costs = np.vstack(average_pareto_costs)\n pareto_costs = pareto_costs[pareto_costs[:, 0].argsort()] # Sort them\n\n costs_x, costs_y = costs[:, 0], costs[:, 1]\n pareto_costs_x, pareto_costs_y = pareto_costs[:, 0], pareto_costs[:, 1]\n\n plt.scatter(costs_x, costs_y, marker=\"x\", label=\"Configuration\")\n plt.scatter(pareto_costs_x, pareto_costs_y, marker=\"x\", c=\"r\", label=\"Incumbent\")\n plt.step(\n [pareto_costs_x[0]] + pareto_costs_x.tolist() + [np.max(costs_x)], # We add bounds\n [np.max(costs_y)] + pareto_costs_y.tolist() + [np.min(pareto_costs_y)], # We add bounds\n where=\"post\",\n linestyle=\":\",\n )\n\n plt.title(\"Pareto-Front\")\n plt.xlabel(smac.scenario.objectives[0])\n plt.ylabel(smac.scenario.objectives[1])\n plt.legend()\n plt.show()\n\n\nif __name__ == \"__main__\":\n mlp = MLP()\n objectives = [\"1 - accuracy\", \"time\"]\n\n # Define our environment variables\n scenario = Scenario(\n mlp.configspace,\n objectives=objectives,\n walltime_limit=30, # After 30 seconds, we stop the hyperparameter optimization\n n_trials=200, # Evaluate max 200 different trials\n n_workers=1,\n )\n\n # We want to run five random configurations before starting the optimization.\n initial_design = HPOFacade.get_initial_design(scenario, n_configs=5)\n multi_objective_algorithm = ParEGO(scenario)\n intensifier = HPOFacade.get_intensifier(scenario, max_config_calls=2)\n\n # Create our SMAC object and pass the scenario and the train method\n smac = HPOFacade(\n scenario,\n mlp.train,\n initial_design=initial_design,\n multi_objective_algorithm=multi_objective_algorithm,\n intensifier=intensifier,\n overwrite=True,\n )\n\n # Let's optimize\n incumbents = smac.optimize()\n\n # Get cost of default configuration\n default_cost = smac.validate(mlp.configspace.get_default_configuration())\n print(f\"Validated costs from default config: \\n--- {default_cost}\\n\")\n\n print(\"Validated costs from the Pareto front (incumbents):\")\n for incumbent in incumbents:\n cost = smac.validate(incumbent)\n print(\"---\", cost)\n\n # Let's plot a pareto front\n plot_pareto(smac, incumbents)" + "from __future__ import annotations\n\nimport time\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ConfigSpace import (\n Categorical,\n Configuration,\n ConfigurationSpace,\n EqualsCondition,\n Float,\n InCondition,\n Integer,\n)\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import StratifiedKFold, cross_val_score\nfrom sklearn.neural_network import MLPClassifier\n\nfrom smac import HyperparameterOptimizationFacade as HPOFacade\nfrom smac import Scenario\nfrom smac.facade.abstract_facade import AbstractFacade\nfrom smac.multi_objective.parego import ParEGO\n\n__copyright__ = \"Copyright 2021, AutoML.org Freiburg-Hannover\"\n__license__ = \"3-clause BSD\"\n\n\ndigits = load_digits()\n\n\nclass MLP:\n @property\n def configspace(self) -> ConfigurationSpace:\n cs = ConfigurationSpace()\n\n n_layer = Integer(\"n_layer\", (1, 5), default=1)\n n_neurons = Integer(\"n_neurons\", (8, 256), log=True, default=10)\n activation = Categorical(\"activation\", [\"logistic\", \"tanh\", \"relu\"], default=\"tanh\")\n solver = Categorical(\"solver\", [\"lbfgs\", \"sgd\", \"adam\"], default=\"adam\")\n batch_size = Integer(\"batch_size\", (30, 300), default=200)\n learning_rate = Categorical(\"learning_rate\", [\"constant\", \"invscaling\", \"adaptive\"], default=\"constant\")\n learning_rate_init = Float(\"learning_rate_init\", (0.0001, 1.0), default=0.001, log=True)\n\n cs.add_hyperparameters([n_layer, n_neurons, activation, solver, batch_size, learning_rate, learning_rate_init])\n\n use_lr = EqualsCondition(child=learning_rate, parent=solver, value=\"sgd\")\n use_lr_init = InCondition(child=learning_rate_init, parent=solver, values=[\"sgd\", \"adam\"])\n use_batch_size = InCondition(child=batch_size, parent=solver, values=[\"sgd\", \"adam\"])\n\n # We can also add multiple conditions on hyperparameters at once:\n cs.add_conditions([use_lr, use_batch_size, use_lr_init])\n\n return cs\n\n def train(self, config: Configuration, seed: int = 0, budget: int = 10) -> dict[str, float]:\n lr = config.get(\"learning_rate\", \"constant\")\n lr_init = config.get(\"learning_rate_init\", 0.001)\n batch_size = config.get(\"batch_size\", 200)\n\n start_time = time.time()\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\")\n\n classifier = MLPClassifier(\n hidden_layer_sizes=[config[\"n_neurons\"]] * config[\"n_layer\"],\n solver=config[\"solver\"],\n batch_size=batch_size,\n activation=config[\"activation\"],\n learning_rate=lr,\n learning_rate_init=lr_init,\n max_iter=int(np.ceil(budget)),\n random_state=seed,\n )\n\n # Returns the 5-fold cross validation accuracy\n cv = StratifiedKFold(n_splits=5, random_state=seed, shuffle=True) # to make CV splits consistent\n score = cross_val_score(classifier, digits.data, digits.target, cv=cv, error_score=\"raise\")\n\n return {\n \"1 - accuracy\": 1 - np.mean(score),\n \"time\": time.time() - start_time,\n }\n\n\ndef plot_pareto(smac: AbstractFacade, incumbents: list[Configuration]) -> None:\n \"\"\"Plots configurations from SMAC and highlights the best configurations in a Pareto front.\"\"\"\n average_costs = []\n average_pareto_costs = []\n for config in smac.runhistory.get_configs():\n # Since we use multiple seeds, we have to average them to get only one cost value pair for each configuration\n average_cost = smac.runhistory.average_cost(config)\n\n if config in incumbents:\n average_pareto_costs += [average_cost]\n else:\n average_costs += [average_cost]\n\n # Let's work with a numpy array\n costs = np.vstack(average_costs)\n pareto_costs = np.vstack(average_pareto_costs)\n pareto_costs = pareto_costs[pareto_costs[:, 0].argsort()] # Sort them\n\n costs_x, costs_y = costs[:, 0], costs[:, 1]\n pareto_costs_x, pareto_costs_y = pareto_costs[:, 0], pareto_costs[:, 1]\n\n plt.scatter(costs_x, costs_y, marker=\"x\", label=\"Configuration\")\n plt.scatter(pareto_costs_x, pareto_costs_y, marker=\"x\", c=\"r\", label=\"Incumbent\")\n plt.step(\n [pareto_costs_x[0]] + pareto_costs_x.tolist() + [np.max(costs_x)], # We add bounds\n [np.max(costs_y)] + pareto_costs_y.tolist() + [np.min(pareto_costs_y)], # We add bounds\n where=\"post\",\n linestyle=\":\",\n )\n\n plt.title(\"Pareto-Front\")\n plt.xlabel(smac.scenario.objectives[0])\n plt.ylabel(smac.scenario.objectives[1])\n plt.legend()\n plt.show()\n\n\nif __name__ == \"__main__\":\n mlp = MLP()\n objectives = [\"1 - accuracy\", \"time\"]\n\n # Define our environment variables\n scenario = Scenario(\n mlp.configspace,\n objectives=objectives,\n walltime_limit=30, # After 30 seconds, we stop the hyperparameter optimization\n n_trials=200, # Evaluate max 200 different trials\n n_workers=1,\n )\n\n # We want to run five random configurations before starting the optimization.\n initial_design = HPOFacade.get_initial_design(scenario, n_configs=5)\n multi_objective_algorithm = ParEGO(scenario)\n intensifier = HPOFacade.get_intensifier(scenario, max_config_calls=2)\n\n # Create our SMAC object and pass the scenario and the train method\n smac = HPOFacade(\n scenario,\n mlp.train,\n initial_design=initial_design,\n multi_objective_algorithm=multi_objective_algorithm,\n intensifier=intensifier,\n overwrite=True,\n )\n\n # Let's optimize\n incumbents = smac.optimize()\n\n # Get cost of default configuration\n default_cost = smac.validate(mlp.configspace.get_default_configuration())\n print(f\"Validated costs from default config: \\n--- {default_cost}\\n\")\n\n print(\"Validated costs from the Pareto front (incumbents):\")\n for incumbent in incumbents:\n cost = smac.validate(incumbent)\n print(\"---\", cost)\n\n # Let's plot a pareto front\n plot_pareto(smac, incumbents)" ] } ], diff --git a/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip b/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip index 35b1691632..7b953a503e 100644 Binary files a/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip and b/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip differ diff --git a/development/_downloads/cd745b0cfbf59a4ad87a24a2fe5463e0/1_mlp_epochs.ipynb b/development/_downloads/cd745b0cfbf59a4ad87a24a2fe5463e0/1_mlp_epochs.ipynb index 55992f769d..a340572812 100644 --- a/development/_downloads/cd745b0cfbf59a4ad87a24a2fe5463e0/1_mlp_epochs.ipynb +++ b/development/_downloads/cd745b0cfbf59a4ad87a24a2fe5463e0/1_mlp_epochs.ipynb @@ -15,7 +15,7 @@ }, "outputs": [], "source": [ - "import warnings\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ConfigSpace import (\n Categorical,\n Configuration,\n ConfigurationSpace,\n EqualsCondition,\n Float,\n InCondition,\n Integer,\n)\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import StratifiedKFold, cross_val_score\nfrom sklearn.neural_network import MLPClassifier\n\nfrom smac import MultiFidelityFacade as MFFacade\nfrom smac import Scenario\nfrom smac.facade import AbstractFacade\nfrom smac.intensifier.hyperband import Hyperband\nfrom smac.intensifier.successive_halving import SuccessiveHalving\n\n__copyright__ = \"Copyright 2021, AutoML.org Freiburg-Hannover\"\n__license__ = \"3-clause BSD\"\n\n\ndataset = load_digits()\n\n\nclass MLP:\n @property\n def configspace(self) -> ConfigurationSpace:\n # Build Configuration Space which defines all parameters and their ranges.\n # To illustrate different parameter types, we use continuous, integer and categorical parameters.\n cs = ConfigurationSpace()\n\n n_layer = Integer(\"n_layer\", (1, 5), default=1)\n n_neurons = Integer(\"n_neurons\", (8, 256), log=True, default=10)\n activation = Categorical(\"activation\", [\"logistic\", \"tanh\", \"relu\"], default=\"tanh\")\n solver = Categorical(\"solver\", [\"lbfgs\", \"sgd\", \"adam\"], default=\"adam\")\n batch_size = Integer(\"batch_size\", (30, 300), default=200)\n learning_rate = Categorical(\"learning_rate\", [\"constant\", \"invscaling\", \"adaptive\"], default=\"constant\")\n learning_rate_init = Float(\"learning_rate_init\", (0.0001, 1.0), default=0.001, log=True)\n\n # Add all hyperparameters at once:\n cs.add_hyperparameters([n_layer, n_neurons, activation, solver, batch_size, learning_rate, learning_rate_init])\n\n # Adding conditions to restrict the hyperparameter space...\n # ... since learning rate is only used when solver is 'sgd'.\n use_lr = EqualsCondition(child=learning_rate, parent=solver, value=\"sgd\")\n # ... since learning rate initialization will only be accounted for when using 'sgd' or 'adam'.\n use_lr_init = InCondition(child=learning_rate_init, parent=solver, values=[\"sgd\", \"adam\"])\n # ... since batch size will not be considered when optimizer is 'lbfgs'.\n use_batch_size = InCondition(child=batch_size, parent=solver, values=[\"sgd\", \"adam\"])\n\n # We can also add multiple conditions on hyperparameters at once:\n cs.add_conditions([use_lr, use_batch_size, use_lr_init])\n\n return cs\n\n def train(self, config: Configuration, seed: int = 0, budget: int = 25) -> float:\n # For deactivated parameters (by virtue of the conditions),\n # the configuration stores None-values.\n # This is not accepted by the MLP, so we replace them with placeholder values.\n lr = config[\"learning_rate\"] if config[\"learning_rate\"] else \"constant\"\n lr_init = config[\"learning_rate_init\"] if config[\"learning_rate_init\"] else 0.001\n batch_size = config[\"batch_size\"] if config[\"batch_size\"] else 200\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\")\n\n classifier = MLPClassifier(\n hidden_layer_sizes=[config[\"n_neurons\"]] * config[\"n_layer\"],\n solver=config[\"solver\"],\n batch_size=batch_size,\n activation=config[\"activation\"],\n learning_rate=lr,\n learning_rate_init=lr_init,\n max_iter=int(np.ceil(budget)),\n random_state=seed,\n )\n\n # Returns the 5-fold cross validation accuracy\n cv = StratifiedKFold(n_splits=5, random_state=seed, shuffle=True) # to make CV splits consistent\n score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n\n return 1 - np.mean(score)\n\n\ndef plot_trajectory(facades: list[AbstractFacade]) -> None:\n \"\"\"Plots the trajectory (incumbents) of the optimization process.\"\"\"\n plt.figure()\n plt.title(\"Trajectory\")\n plt.xlabel(\"Wallclock time [s]\")\n plt.ylabel(facades[0].scenario.objectives)\n plt.ylim(0, 0.4)\n\n for facade in facades:\n X, Y = [], []\n for item in facade.intensifier.trajectory:\n # Single-objective optimization\n assert len(item.config_ids) == 1\n assert len(item.costs) == 1\n\n y = item.costs[0]\n x = item.walltime\n\n X.append(x)\n Y.append(y)\n\n plt.plot(X, Y, label=facade.intensifier.__class__.__name__)\n plt.scatter(X, Y, marker=\"x\")\n\n plt.legend()\n plt.show()\n\n\nif __name__ == \"__main__\":\n mlp = MLP()\n\n facades: list[AbstractFacade] = []\n for intensifier_object in [SuccessiveHalving, Hyperband]:\n # Define our environment variables\n scenario = Scenario(\n mlp.configspace,\n walltime_limit=60, # After 60 seconds, we stop the hyperparameter optimization\n n_trials=500, # Evaluate max 500 different trials\n min_budget=1, # Train the MLP using a hyperparameter configuration for at least 5 epochs\n max_budget=25, # Train the MLP using a hyperparameter configuration for at most 25 epochs\n n_workers=8,\n )\n\n # We want to run five random configurations before starting the optimization.\n initial_design = MFFacade.get_initial_design(scenario, n_configs=5)\n\n # Create our intensifier\n intensifier = intensifier_object(scenario, incumbent_selection=\"highest_budget\")\n\n # Create our SMAC object and pass the scenario and the train method\n smac = MFFacade(\n scenario,\n mlp.train,\n initial_design=initial_design,\n intensifier=intensifier,\n overwrite=True,\n )\n\n # Let's optimize\n incumbent = smac.optimize()\n\n # Get cost of default configuration\n default_cost = smac.validate(mlp.configspace.get_default_configuration())\n print(f\"Default cost ({intensifier.__class__.__name__}): {default_cost}\")\n\n # Let's calculate the cost of the incumbent\n incumbent_cost = smac.validate(incumbent)\n print(f\"Incumbent cost ({intensifier.__class__.__name__}): {incumbent_cost}\")\n\n facades.append(smac)\n\n # Let's plot it\n plot_trajectory(facades)" + "import warnings\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ConfigSpace import (\n Categorical,\n Configuration,\n ConfigurationSpace,\n EqualsCondition,\n Float,\n InCondition,\n Integer,\n)\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import StratifiedKFold, cross_val_score\nfrom sklearn.neural_network import MLPClassifier\n\nfrom smac import MultiFidelityFacade as MFFacade\nfrom smac import Scenario\nfrom smac.facade import AbstractFacade\nfrom smac.intensifier.hyperband import Hyperband\nfrom smac.intensifier.successive_halving import SuccessiveHalving\n\n__copyright__ = \"Copyright 2021, AutoML.org Freiburg-Hannover\"\n__license__ = \"3-clause BSD\"\n\n\ndataset = load_digits()\n\n\nclass MLP:\n @property\n def configspace(self) -> ConfigurationSpace:\n # Build Configuration Space which defines all parameters and their ranges.\n # To illustrate different parameter types, we use continuous, integer and categorical parameters.\n cs = ConfigurationSpace()\n\n n_layer = Integer(\"n_layer\", (1, 5), default=1)\n n_neurons = Integer(\"n_neurons\", (8, 256), log=True, default=10)\n activation = Categorical(\"activation\", [\"logistic\", \"tanh\", \"relu\"], default=\"tanh\")\n solver = Categorical(\"solver\", [\"lbfgs\", \"sgd\", \"adam\"], default=\"adam\")\n batch_size = Integer(\"batch_size\", (30, 300), default=200)\n learning_rate = Categorical(\"learning_rate\", [\"constant\", \"invscaling\", \"adaptive\"], default=\"constant\")\n learning_rate_init = Float(\"learning_rate_init\", (0.0001, 1.0), default=0.001, log=True)\n\n # Add all hyperparameters at once:\n cs.add_hyperparameters([n_layer, n_neurons, activation, solver, batch_size, learning_rate, learning_rate_init])\n\n # Adding conditions to restrict the hyperparameter space...\n # ... since learning rate is only used when solver is 'sgd'.\n use_lr = EqualsCondition(child=learning_rate, parent=solver, value=\"sgd\")\n # ... since learning rate initialization will only be accounted for when using 'sgd' or 'adam'.\n use_lr_init = InCondition(child=learning_rate_init, parent=solver, values=[\"sgd\", \"adam\"])\n # ... since batch size will not be considered when optimizer is 'lbfgs'.\n use_batch_size = InCondition(child=batch_size, parent=solver, values=[\"sgd\", \"adam\"])\n\n # We can also add multiple conditions on hyperparameters at once:\n cs.add_conditions([use_lr, use_batch_size, use_lr_init])\n\n return cs\n\n def train(self, config: Configuration, seed: int = 0, budget: int = 25) -> float:\n # For deactivated parameters (by virtue of the conditions),\n # the configuration stores None-values.\n # This is not accepted by the MLP, so we replace them with placeholder values.\n lr = config.get(\"learning_rate\", \"constant\")\n lr_init = config.get(\"learning_rate_init\", 0.001)\n batch_size = config.get(\"batch_size\", 200)\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\")\n\n classifier = MLPClassifier(\n hidden_layer_sizes=[config[\"n_neurons\"]] * config[\"n_layer\"],\n solver=config[\"solver\"],\n batch_size=batch_size,\n activation=config[\"activation\"],\n learning_rate=lr,\n learning_rate_init=lr_init,\n max_iter=int(np.ceil(budget)),\n random_state=seed,\n )\n\n # Returns the 5-fold cross validation accuracy\n cv = StratifiedKFold(n_splits=5, random_state=seed, shuffle=True) # to make CV splits consistent\n score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score=\"raise\")\n\n return 1 - np.mean(score)\n\n\ndef plot_trajectory(facades: list[AbstractFacade]) -> None:\n \"\"\"Plots the trajectory (incumbents) of the optimization process.\"\"\"\n plt.figure()\n plt.title(\"Trajectory\")\n plt.xlabel(\"Wallclock time [s]\")\n plt.ylabel(facades[0].scenario.objectives)\n plt.ylim(0, 0.4)\n\n for facade in facades:\n X, Y = [], []\n for item in facade.intensifier.trajectory:\n # Single-objective optimization\n assert len(item.config_ids) == 1\n assert len(item.costs) == 1\n\n y = item.costs[0]\n x = item.walltime\n\n X.append(x)\n Y.append(y)\n\n plt.plot(X, Y, label=facade.intensifier.__class__.__name__)\n plt.scatter(X, Y, marker=\"x\")\n\n plt.legend()\n plt.show()\n\n\nif __name__ == \"__main__\":\n mlp = MLP()\n\n facades: list[AbstractFacade] = []\n for intensifier_object in [SuccessiveHalving, Hyperband]:\n # Define our environment variables\n scenario = Scenario(\n mlp.configspace,\n walltime_limit=60, # After 60 seconds, we stop the hyperparameter optimization\n n_trials=500, # Evaluate max 500 different trials\n min_budget=1, # Train the MLP using a hyperparameter configuration for at least 5 epochs\n max_budget=25, # Train the MLP using a hyperparameter configuration for at most 25 epochs\n n_workers=8,\n )\n\n # We want to run five random configurations before starting the optimization.\n initial_design = MFFacade.get_initial_design(scenario, n_configs=5)\n\n # Create our intensifier\n intensifier = intensifier_object(scenario, incumbent_selection=\"highest_budget\")\n\n # Create our SMAC object and pass the scenario and the train method\n smac = MFFacade(\n scenario,\n mlp.train,\n initial_design=initial_design,\n intensifier=intensifier,\n overwrite=True,\n )\n\n # Let's optimize\n incumbent = smac.optimize()\n\n # Get cost of default configuration\n default_cost = smac.validate(mlp.configspace.get_default_configuration())\n print(f\"Default cost ({intensifier.__class__.__name__}): {default_cost}\")\n\n # Let's calculate the cost of the incumbent\n incumbent_cost = smac.validate(incumbent)\n print(f\"Incumbent cost ({intensifier.__class__.__name__}): {incumbent_cost}\")\n\n facades.append(smac)\n\n # Let's plot it\n plot_trajectory(facades)" ] } ], diff --git a/development/_downloads/e36c26bb3a993c4641a06c811122b11e/1_mlp_epochs.py b/development/_downloads/e36c26bb3a993c4641a06c811122b11e/1_mlp_epochs.py index 9fd256c5d6..48c027d3bd 100644 --- a/development/_downloads/e36c26bb3a993c4641a06c811122b11e/1_mlp_epochs.py +++ b/development/_downloads/e36c26bb3a993c4641a06c811122b11e/1_mlp_epochs.py @@ -84,9 +84,9 @@ def train(self, config: Configuration, seed: int = 0, budget: int = 25) -> float # For deactivated parameters (by virtue of the conditions), # the configuration stores None-values. # This is not accepted by the MLP, so we replace them with placeholder values. - lr = config["learning_rate"] if config["learning_rate"] else "constant" - lr_init = config["learning_rate_init"] if config["learning_rate_init"] else 0.001 - batch_size = config["batch_size"] if config["batch_size"] else 200 + lr = config.get("learning_rate", "constant") + lr_init = config.get("learning_rate_init", 0.001) + batch_size = config.get("batch_size", 200) with warnings.catch_warnings(): warnings.filterwarnings("ignore") diff --git a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip index 38616b4386..9158a0e29d 100644 Binary files a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip and b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip differ diff --git a/development/_images/sphx_glr_1_mlp_epochs_001.png 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a/development/_sources/examples/1_basics/1_quadratic_function.rst.txt b/development/_sources/examples/1_basics/1_quadratic_function.rst.txt index bfaac9aef6..9c64cc7bf5 100644 --- a/development/_sources/examples/1_basics/1_quadratic_function.rst.txt +++ b/development/_sources/examples/1_basics/1_quadratic_function.rst.txt @@ -154,7 +154,7 @@ be applied to problems with large evaluation budgets (up to 1000 evaluations). .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 4.081 seconds) + **Total running time of the script:** ( 0 minutes 2.681 seconds) .. _sphx_glr_download_examples_1_basics_1_quadratic_function.py: diff --git a/development/_sources/examples/1_basics/2_svm_cv.rst.txt b/development/_sources/examples/1_basics/2_svm_cv.rst.txt index 9ada061f3a..36f906ed32 100644 --- a/development/_sources/examples/1_basics/2_svm_cv.rst.txt +++ b/development/_sources/examples/1_basics/2_svm_cv.rst.txt @@ -1335,7 +1335,7 @@ types as well as conditional hyperparameters. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 2.086 seconds) + **Total running time of the script:** ( 0 minutes 1.557 seconds) .. _sphx_glr_download_examples_1_basics_2_svm_cv.py: diff --git a/development/_sources/examples/1_basics/3_ask_and_tell.rst.txt b/development/_sources/examples/1_basics/3_ask_and_tell.rst.txt index f0bae3406e..9071698ea5 100644 --- a/development/_sources/examples/1_basics/3_ask_and_tell.rst.txt +++ b/development/_sources/examples/1_basics/3_ask_and_tell.rst.txt @@ -36,19 +36,21 @@ This examples show how to use the Ask-and-Tell interface. [INFO][abstract_intensifier.py:515] Added config 38310a as new incumbent because there are no incumbents yet. [INFO][abstract_intensifier.py:590] Added config a840bc and rejected config 38310a as incumbent because it is not better than the incumbents on 1 instances: [INFO][abstract_intensifier.py:590] Added config e8ddec and rejected config a840bc as incumbent because it is not better than the incumbents on 1 instances: - [INFO][abstract_intensifier.py:590] Added config e74ba7 and rejected config e8ddec as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config a9b60d and rejected config e8ddec as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config 96f88f and rejected config a9b60d as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config 0d4e03 and rejected config 96f88f as incumbent because it is not better than the incumbents on 1 instances: [INFO][smbo.py:319] Finished 50 trials. - [INFO][abstract_intensifier.py:590] Added config 1eebbc and rejected config e74ba7 as incumbent because it is not better than the incumbents on 1 instances: - [INFO][abstract_intensifier.py:590] Added config f01c34 and rejected config 1eebbc as incumbent because it is not better than the incumbents on 1 instances: - [INFO][abstract_intensifier.py:590] Added config 1df946 and rejected config f01c34 as incumbent because it is not better than the incumbents on 1 instances: - [INFO][abstract_intensifier.py:590] Added config 535c5d and rejected config 1df946 as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config 737ab9 and rejected config 0d4e03 as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config eeebb9 and rejected config 737ab9 as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config 6d9074 and rejected config eeebb9 as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config a0e706 and rejected config 6d9074 as incumbent because it is not better than the incumbents on 1 instances: [INFO][smbo.py:319] Finished 100 trials. [INFO][smbo.py:327] Configuration budget is exhausted: [INFO][smbo.py:328] --- Remaining wallclock time: inf [INFO][smbo.py:329] --- Remaining cpu time: inf [INFO][smbo.py:330] --- Remaining trials: 0 Default cost: 16916.0 - Incumbent cost: 1.1183978687059386 + Incumbent cost: 0.8499771879028607 @@ -138,7 +140,7 @@ This examples show how to use the Ask-and-Tell interface. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 4.574 seconds) + **Total running time of the script:** ( 0 minutes 3.225 seconds) .. _sphx_glr_download_examples_1_basics_3_ask_and_tell.py: diff --git a/development/_sources/examples/1_basics/4_callback.rst.txt b/development/_sources/examples/1_basics/4_callback.rst.txt index 7e9a3364f3..bd6b7b3b04 100644 --- a/development/_sources/examples/1_basics/4_callback.rst.txt +++ b/development/_sources/examples/1_basics/4_callback.rst.txt @@ -146,7 +146,7 @@ Furthermore, we print some stages of the optimization process. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.231 seconds) + **Total running time of the script:** ( 0 minutes 0.154 seconds) .. _sphx_glr_download_examples_1_basics_4_callback.py: diff --git a/development/_sources/examples/1_basics/5_continue.rst.txt b/development/_sources/examples/1_basics/5_continue.rst.txt index 7693909549..2b6e86ea08 100644 --- a/development/_sources/examples/1_basics/5_continue.rst.txt +++ b/development/_sources/examples/1_basics/5_continue.rst.txt @@ -170,7 +170,7 @@ there already is a previous run with the same meta data, this run will be contin .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 1.364 seconds) + **Total running time of the script:** ( 0 minutes 0.889 seconds) .. _sphx_glr_download_examples_1_basics_5_continue.py: diff --git a/development/_sources/examples/1_basics/6_priors.rst.txt b/development/_sources/examples/1_basics/6_priors.rst.txt index 40f8167bca..b945c97518 100644 --- a/development/_sources/examples/1_basics/6_priors.rst.txt +++ b/development/_sources/examples/1_basics/6_priors.rst.txt @@ -42,15 +42,15 @@ optimization, you have to change the acquisition function to ``PriorAcquisitionF [WARNING][prior_acqusition_function.py:107] Discretizing the prior for random forest models. [INFO][abstract_intensifier.py:515] Added config 7ec7fe as new incumbent because there are no incumbents yet. [INFO][abstract_intensifier.py:590] Added config 6acb23 and rejected config 7ec7fe as incumbent because it is not better than the incumbents on 1 instances: - [INFO][abstract_intensifier.py:590] Added config 37a8e7 and rejected config 6acb23 as incumbent because it is not better than the incumbents on 1 instances: - [INFO][abstract_intensifier.py:590] Added config 5b96ec and rejected config 37a8e7 as incumbent because it is not better than the incumbents on 1 instances: - [INFO][abstract_intensifier.py:590] Added config 35cf09 and rejected config 5b96ec as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config 95309e and rejected config 6acb23 as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config 87ba17 and rejected config 95309e as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config a3d1bd and rejected config 87ba17 as incumbent because it is not better than the incumbents on 1 instances: [INFO][smbo.py:327] Configuration budget is exhausted: [INFO][smbo.py:328] --- Remaining wallclock time: inf [INFO][smbo.py:329] --- Remaining cpu time: inf [INFO][smbo.py:330] --- Remaining trials: 0 Default cost: 0.038952336737852145 - Default cost: 0.02727174249458364 + Default cost: 0.026719281956050667 @@ -225,7 +225,7 @@ optimization, you have to change the acquisition function to ``PriorAcquisitionF .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 40.659 seconds) + **Total running time of the script:** ( 0 minutes 29.170 seconds) .. _sphx_glr_download_examples_1_basics_6_priors.py: diff --git a/development/_sources/examples/1_basics/sg_execution_times.rst.txt b/development/_sources/examples/1_basics/sg_execution_times.rst.txt index 0ec8279de0..06448f60a5 100644 --- a/development/_sources/examples/1_basics/sg_execution_times.rst.txt +++ b/development/_sources/examples/1_basics/sg_execution_times.rst.txt @@ -6,18 +6,18 @@ Computation times ================= -**00:52.995** total execution time for **examples_1_basics** files: +**00:37.675** total execution time for **examples_1_basics** files: +-----------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_1_basics_6_priors.py` (``6_priors.py``) | 00:40.659 | 0.0 MB | +| :ref:`sphx_glr_examples_1_basics_6_priors.py` (``6_priors.py``) | 00:29.170 | 0.0 MB | +-----------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_1_basics_3_ask_and_tell.py` (``3_ask_and_tell.py``) | 00:04.574 | 0.0 MB | +| :ref:`sphx_glr_examples_1_basics_3_ask_and_tell.py` (``3_ask_and_tell.py``) | 00:03.225 | 0.0 MB | +-----------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_1_basics_1_quadratic_function.py` (``1_quadratic_function.py``) | 00:04.081 | 0.0 MB | +| :ref:`sphx_glr_examples_1_basics_1_quadratic_function.py` (``1_quadratic_function.py``) | 00:02.681 | 0.0 MB | +-----------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_1_basics_2_svm_cv.py` (``2_svm_cv.py``) | 00:02.086 | 0.0 MB | +| :ref:`sphx_glr_examples_1_basics_2_svm_cv.py` (``2_svm_cv.py``) | 00:01.557 | 0.0 MB | +-----------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_1_basics_5_continue.py` (``5_continue.py``) | 00:01.364 | 0.0 MB | +| :ref:`sphx_glr_examples_1_basics_5_continue.py` (``5_continue.py``) | 00:00.889 | 0.0 MB | +-----------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_1_basics_4_callback.py` (``4_callback.py``) | 00:00.231 | 0.0 MB | +| :ref:`sphx_glr_examples_1_basics_4_callback.py` (``4_callback.py``) | 00:00.154 | 0.0 MB | +-----------------------------------------------------------------------------------------+-----------+--------+ diff --git a/development/_sources/examples/2_multi_fidelity/1_mlp_epochs.rst.txt b/development/_sources/examples/2_multi_fidelity/1_mlp_epochs.rst.txt index 555f0bea76..4027f31907 100644 --- a/development/_sources/examples/2_multi_fidelity/1_mlp_epochs.rst.txt +++ b/development/_sources/examples/2_multi_fidelity/1_mlp_epochs.rst.txt @@ -66,1278 +66,20 @@ is chosen to optimize the average accuracy on 5-fold cross validation. [INFO][smbo.py:319] Finished 0 trials. [INFO][smbo.py:319] Finished 0 trials. [INFO][smbo.py:319] Finished 0 trials. - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4655: RuntimeWarning: invalid value encountered in subtract - diff_b_a = subtract(b, a) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4655: RuntimeWarning: invalid value encountered in subtract - diff_b_a = subtract(b, a) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4658: RuntimeWarning: invalid value encountered in subtract - subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t >= 0.5) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4655: RuntimeWarning: invalid value encountered in subtract - diff_b_a = subtract(b, a) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - [INFO][abstract_intensifier.py:515] Added config c55ea1 as new incumbent because there are no incumbents yet. - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4655: RuntimeWarning: invalid value encountered in subtract - diff_b_a = subtract(b, a) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - [INFO][abstract_intensifier.py:590] Added config d0ff1f and rejected config c55ea1 as incumbent because it is not better than the incumbents on 1 instances: [INFO][smbo.py:319] Finished 50 trials. - [INFO][smbo.py:319] Finished 50 trials. - [INFO][smbo.py:319] Finished 50 trials. - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - [INFO][smbo.py:319] Finished 100 trials. - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - [INFO][abstract_intensifier.py:590] Added config bdd186 and rejected config d0ff1f as incumbent because it is not better than the incumbents on 1 instances: - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) + [INFO][abstract_intensifier.py:515] Added config 72f7f7 as new incumbent because there are no incumbents yet. + [INFO][abstract_intensifier.py:590] Added config 9d67d8 and rejected config 72f7f7 as incumbent because it is not better than the incumbents on 1 instances: [INFO][smbo.py:327] Configuration budget is exhausted: - [INFO][smbo.py:328] --- Remaining wallclock time: -6.165501117706299 + [INFO][smbo.py:328] --- Remaining wallclock time: -0.2361888885498047 [INFO][smbo.py:329] --- Remaining cpu time: inf - [INFO][smbo.py:330] --- Remaining trials: 374 - Default cost (SuccessiveHalving): inf - Incumbent cost (SuccessiveHalving): 0.024484679665738085 + [INFO][smbo.py:330] --- Remaining trials: 398 + Default cost (SuccessiveHalving): 0.36672856700711853 + Incumbent cost (SuccessiveHalving): 0.0178087279480037 [INFO][abstract_initial_design.py:82] Using `n_configs` and ignoring `n_configs_per_hyperparameter`. [INFO][abstract_facade.py:200] Workers are reduced to 8. /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use. Perhaps you already have a cluster running? - Hosting the HTTP server on port 44497 instead + Hosting the HTTP server on port 45445 instead warnings.warn( [INFO][abstract_initial_design.py:147] Using 5 initial design configurations and 0 additional configurations. [INFO][successive_halving.py:164] Successive Halving uses budget type BUDGETS with eta 3, min budget 1, and max budget 25. @@ -1357,959 +99,17 @@ is chosen to optimize the average accuracy on 5-fold cross validation. [INFO][smbo.py:319] Finished 0 trials. [INFO][smbo.py:319] Finished 0 trials. [INFO][smbo.py:319] Finished 0 trials. - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4655: RuntimeWarning: invalid value encountered in subtract - diff_b_a = subtract(b, a) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - [INFO][abstract_intensifier.py:515] Added config ceb3eb as new incumbent because there are no incumbents yet. - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4655: RuntimeWarning: invalid value encountered in subtract - diff_b_a = subtract(b, a) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4655: RuntimeWarning: invalid value encountered in subtract - diff_b_a = subtract(b, a) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4655: RuntimeWarning: invalid value encountered in subtract - diff_b_a = subtract(b, a) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/numpy/lib/function_base.py:4655: RuntimeWarning: invalid value encountered in subtract - diff_b_a = subtract(b, a) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - [INFO][abstract_intensifier.py:590] Added config d0ff1f and rejected config ceb3eb as incumbent because it is not better than the incumbents on 1 instances: - [INFO][smbo.py:319] Finished 50 trials. - [INFO][smbo.py:319] Finished 50 trials. - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - [INFO][abstract_intensifier.py:590] Added config bdd186 and rejected config d0ff1f as incumbent because it is not better than the incumbents on 1 instances: - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) - /opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/model/random_forest/random_forest.py:220: RuntimeWarning: Mean of empty slice - preds_as_array = np.log(np.nanmean(np.exp(preds_as_array), axis=2) + VERY_SMALL_NUMBER) + [INFO][abstract_intensifier.py:515] Added config 668b0c as new incumbent because there are no incumbents yet. + [INFO][abstract_intensifier.py:590] Added config 4ec18b and rejected config 668b0c as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config 72f7f7 and rejected config 4ec18b as incumbent because it is not better than the incumbents on 1 instances: + [INFO][abstract_intensifier.py:590] Added config 0a69eb and rejected config 72f7f7 as incumbent because it is not better than the incumbents on 1 instances: [INFO][smbo.py:327] Configuration budget is exhausted: - [INFO][smbo.py:328] --- Remaining wallclock time: -4.369566440582275 + [INFO][smbo.py:328] --- Remaining wallclock time: -1.0321049690246582 [INFO][smbo.py:329] --- Remaining cpu time: inf - [INFO][smbo.py:330] --- Remaining trials: 416 - Default cost (Hyperband): inf - Incumbent cost (Hyperband): 0.024484679665738085 + [INFO][smbo.py:330] --- Remaining trials: 434 + [INFO][abstract_intensifier.py:590] Added config eb39a1 and rejected config 0a69eb as incumbent because it is not better than the incumbents on 1 instances: + Default cost (Hyperband): 0.36672856700711853 + Incumbent cost (Hyperband): 0.021148251315382338 @@ -2386,9 +186,9 @@ is chosen to optimize the average accuracy on 5-fold cross validation. # For deactivated parameters (by virtue of the conditions), # the configuration stores None-values. # This is not accepted by the MLP, so we replace them with placeholder values. - lr = config["learning_rate"] if config["learning_rate"] else "constant" - lr_init = config["learning_rate_init"] if config["learning_rate_init"] else 0.001 - batch_size = config["batch_size"] if config["batch_size"] else 200 + lr = config.get("learning_rate", "constant") + lr_init = config.get("learning_rate_init", 0.001) + batch_size = config.get("batch_size", 200) with warnings.catch_warnings(): warnings.filterwarnings("ignore") @@ -2488,7 +288,7 @@ is chosen to optimize the average accuracy on 5-fold cross validation. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 3 minutes 7.903 seconds) + **Total running time of the script:** ( 2 minutes 41.402 seconds) .. _sphx_glr_download_examples_2_multi_fidelity_1_mlp_epochs.py: diff --git a/development/_sources/examples/2_multi_fidelity/2_sgd_datasets.rst.txt b/development/_sources/examples/2_multi_fidelity/2_sgd_datasets.rst.txt index b0857615db..f1892d81b1 100644 --- a/development/_sources/examples/2_multi_fidelity/2_sgd_datasets.rst.txt +++ b/development/_sources/examples/2_multi_fidelity/2_sgd_datasets.rst.txt @@ -59,10 +59,15 @@ the target function now is required to have an instance argument. [INFO][smbo.py:319] Finished 50 trials. [INFO][abstract_intensifier.py:590] Added config 884840 and rejected config 812224 as incumbent because it is not better than the incumbents on 15 instances: [INFO][smbo.py:319] Finished 100 trials. + [INFO][smbo.py:319] Finished 150 trials. + [INFO][smbo.py:319] Finished 200 trials. + [INFO][smbo.py:319] Finished 250 trials. + [INFO][smbo.py:319] Finished 300 trials. + [INFO][smbo.py:319] Finished 350 trials. [INFO][smbo.py:327] Configuration budget is exhausted: - [INFO][smbo.py:328] --- Remaining wallclock time: -0.057363271713256836 + [INFO][smbo.py:328] --- Remaining wallclock time: -0.017668962478637695 [INFO][smbo.py:329] --- Remaining cpu time: inf - [INFO][smbo.py:330] --- Remaining trials: 4852 + [INFO][smbo.py:330] --- Remaining trials: 4631 Default cost: 0.15489347419148672 Incumbent cost: 0.006249006102246613 @@ -151,7 +156,7 @@ the target function now is required to have an instance argument. # SGD classifier using given configuration clf = SGDClassifier( - loss="log", + loss="log_loss", penalty="elasticnet", alpha=config["alpha"], l1_ratio=config["l1_ratio"], @@ -204,7 +209,7 @@ the target function now is required to have an instance argument. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 47.359 seconds) + **Total running time of the script:** ( 0 minutes 37.527 seconds) .. _sphx_glr_download_examples_2_multi_fidelity_2_sgd_datasets.py: diff --git a/development/_sources/examples/2_multi_fidelity/sg_execution_times.rst.txt b/development/_sources/examples/2_multi_fidelity/sg_execution_times.rst.txt index 9c52b27f72..a07cd188b0 100644 --- a/development/_sources/examples/2_multi_fidelity/sg_execution_times.rst.txt +++ b/development/_sources/examples/2_multi_fidelity/sg_execution_times.rst.txt @@ -6,10 +6,10 @@ Computation times ================= -**03:55.263** total execution time for **examples_2_multi_fidelity** files: +**03:18.929** total execution time for **examples_2_multi_fidelity** files: +-------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_2_multi_fidelity_1_mlp_epochs.py` (``1_mlp_epochs.py``) | 03:07.903 | 0.0 MB | +| :ref:`sphx_glr_examples_2_multi_fidelity_1_mlp_epochs.py` (``1_mlp_epochs.py``) | 02:41.402 | 0.0 MB | +-------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_2_multi_fidelity_2_sgd_datasets.py` (``2_sgd_datasets.py``) | 00:47.359 | 0.0 MB | +| :ref:`sphx_glr_examples_2_multi_fidelity_2_sgd_datasets.py` (``2_sgd_datasets.py``) | 00:37.527 | 0.0 MB | +-------------------------------------------------------------------------------------+-----------+--------+ diff --git a/development/_sources/examples/3_multi_objective/1_schaffer.rst.txt b/development/_sources/examples/3_multi_objective/1_schaffer.rst.txt index fb8dbd4efd..5763705f57 100644 --- a/development/_sources/examples/3_multi_objective/1_schaffer.rst.txt +++ b/development/_sources/examples/3_multi_objective/1_schaffer.rst.txt @@ -180,158 +180,158 @@ SMAC prioritizes the second objective over the first one. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config 00947e is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 623aaa is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 7e03ee is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config e7652a is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config c17b3b is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 5f1f68 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config c9a4ab is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config c78bba is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 26907e is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config f523f5 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config d5d9b3 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 940870 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 050956 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config f21a6e is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config 04cf46 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config cd933e is a new incumbent. Total number of incumbents: 11. - [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 6a4a47 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config e3e1fc is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config 7cab7f is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config a882a8 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 4abdbb is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config cfe943 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 65680c is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 161437 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 257ec9 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config eb7876 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 0ffa3c is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 257ec9 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 3e7b8c is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 0038ea is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config b61ca9 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config b779c7 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 1e70ce is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config 9621a8 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config 26d5a4 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config fff544 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config aa878c is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config ea6007 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config c40c4c is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 850574 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config a88778 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 405f05 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 6e1528 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 016225 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 0e4ddc is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][smbo.py:319] Finished 100 trials. - [INFO][abstract_intensifier.py:598] Config c6f7e7 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 8ae00c is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config b62dbe is a new incumbent. Total number of incumbents: 11. + [INFO][smbo.py:319] Finished 100 trials. + [INFO][abstract_intensifier.py:598] Config 2fb8e3 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 5b4ebd is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 313866 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config dff21b is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 9a849e is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config c13f17 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config bf6ee6 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config ef6d00 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 1c3721 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 4051b5 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config d6acb8 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 03d947 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 2e23af is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config d2a8ac is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 4ee072 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 49eabf is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. + [INFO][abstract_intensifier.py:598] Config 17755d is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][abstract_intensifier.py:598] Config e4515e is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config a241bb is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 3c0836 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config c2b5f5 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 657da4 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 2b4703 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 75e08e is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 1d3f71 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 9ec620 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 09c690 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 1b701e is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 980177 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 38990f is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 18b4fb is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 9adac9 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 5889c8 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 9b50d4 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 38990f is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config eb89b7 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config af00a5 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 299174 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 6ea6dc is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 2ff038 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config caf420 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 4d31e6 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config be6467 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config c00b70 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config a2cbcd is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 04b3b4 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config f3c58a is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 4c851f is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 31cac4 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 1d8cc2 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 04b3b4 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 74b413 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 62390f is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config ce98d7 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config f4d7f6 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 6635ed is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config e6e911 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 5448e7 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 46a579 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 0f10d0 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 973900 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 409930 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 818cbb is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 39f1ae is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 7c3c4a is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 02e0b8 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 6582db is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 5ebade is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config d6935c is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 0b0993 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config cd4c2b is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config cd13ee is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config dd5f9b is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 04173b is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 8cae70 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 8d020c is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 759585 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 90b0e6 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 470bcf is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config b5b43a is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 36943e is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 6ef076 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 624738 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 174106 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 0d9f14 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config c5f14e is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 2f3430 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 5c0b56 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 2215da is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 2b710a is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 80f804 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 6a9164 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config 9ceac5 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config a37eca is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. - [INFO][abstract_intensifier.py:598] Config b49511 is a new incumbent. Total number of incumbents: 11. + [INFO][abstract_intensifier.py:598] Config 29ed99 is a new incumbent. Total number of incumbents: 11. [INFO][abstract_intensifier.py:623] Removed one incumbent using crowding distance because more than 10 are available. [INFO][smbo.py:319] Finished 150 trials. [INFO][smbo.py:327] Configuration budget is exhausted: @@ -344,15 +344,15 @@ SMAC prioritizes the second objective over the first one. Validated costs from the Pareto front (incumbents): --- [0.01538581 3.51922763] - --- [3.98157338e+00 2.12703017e-05] + --- [3.81477017e+00 2.19551629e-03] --- [0.66494111 1.40318427] - --- [0.27572929 2.17533202] - --- [1.23793885 0.78743083] + --- [0.28063773 2.16162765] + --- [1.21948125 0.80227625] --- [0.09228592 2.87714199] --- [3.16368713 0.04898446] - --- [2.5425014 0.16441216] - --- [1.56462636 0.56122534] - --- [2.01575389 0.33666398] + --- [2.51598724 0.17124171] + --- [1.60303937 0.53859173] + --- [2.0255363 0.33268282] @@ -464,7 +464,7 @@ SMAC prioritizes the second objective over the first one. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 18.028 seconds) + **Total running time of the script:** ( 0 minutes 11.080 seconds) .. _sphx_glr_download_examples_3_multi_objective_1_schaffer.py: diff --git a/development/_sources/examples/3_multi_objective/2_parego.rst.txt b/development/_sources/examples/3_multi_objective/2_parego.rst.txt index 52124f70ca..0cb81cc235 100644 --- a/development/_sources/examples/3_multi_objective/2_parego.rst.txt +++ b/development/_sources/examples/3_multi_objective/2_parego.rst.txt @@ -44,122 +44,26 @@ mean accuracy and run-time of each configuration. [WARNING][target_function_runner.py:72] The argument budget is not set by SMAC: Consider removing it from the target function. [INFO][abstract_initial_design.py:147] Using 5 initial design configurations and 0 additional configurations. - [WARNING][abstract_runner.py:132] Target function returned infinity or nothing at all. Result is treated as CRASHED and cost is set to [inf, inf]. - [WARNING][abstract_runner.py:138] Traceback: Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 184, in run - rval = self(config_copy, target_function, kwargs) - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 257, in __call__ - return algorithm(config, **algorithm_kwargs) - File "/home/runner/work/SMAC3/SMAC3/examples/3_multi_objective/2_parego.py", line 69, in train - lr = config["learning_rate"] if config["learning_rate"] else "constant" - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/ConfigSpace/configuration.py", line 191, in __getitem__ - raise KeyError(key) - KeyError: 'learning_rate' - - [INFO][abstract_intensifier.py:515] Added config 3623cc as new incumbent because there are no incumbents yet. - [WARNING][abstract_runner.py:132] Target function returned infinity or nothing at all. Result is treated as CRASHED and cost is set to [inf, inf]. - [WARNING][abstract_runner.py:138] Traceback: Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 184, in run - rval = self(config_copy, target_function, kwargs) - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 257, in __call__ - return algorithm(config, **algorithm_kwargs) - File "/home/runner/work/SMAC3/SMAC3/examples/3_multi_objective/2_parego.py", line 69, in train - lr = config["learning_rate"] if config["learning_rate"] else "constant" - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/ConfigSpace/configuration.py", line 191, in __getitem__ - raise KeyError(key) - KeyError: 'learning_rate' - - - [WARNING][abstract_runner.py:132] Target function returned infinity or nothing at all. Result is treated as CRASHED and cost is set to [inf, inf]. - [WARNING][abstract_runner.py:138] Traceback: Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 184, in run - rval = self(config_copy, target_function, kwargs) - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 257, in __call__ - return algorithm(config, **algorithm_kwargs) - File "/home/runner/work/SMAC3/SMAC3/examples/3_multi_objective/2_parego.py", line 69, in train - lr = config["learning_rate"] if config["learning_rate"] else "constant" - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/ConfigSpace/configuration.py", line 191, in __getitem__ - raise KeyError(key) - KeyError: 'learning_rate' - - - [WARNING][abstract_runner.py:132] Target function returned infinity or nothing at all. Result is treated as CRASHED and cost is set to [inf, inf]. - [WARNING][abstract_runner.py:138] Traceback: Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 184, in run - rval = self(config_copy, target_function, kwargs) - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 257, in __call__ - return algorithm(config, **algorithm_kwargs) - File "/home/runner/work/SMAC3/SMAC3/examples/3_multi_objective/2_parego.py", line 69, in train - lr = config["learning_rate"] if config["learning_rate"] else "constant" - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/ConfigSpace/configuration.py", line 191, in __getitem__ - raise KeyError(key) - KeyError: 'learning_rate' - - - [INFO][abstract_intensifier.py:590] Added config 0c9159 and rejected config 3623cc as incumbent because it is not better than the incumbents on 2 instances: - [WARNING][abstract_runner.py:132] Target function returned infinity or nothing at all. Result is treated as CRASHED and cost is set to [inf, inf]. - [WARNING][abstract_runner.py:138] Traceback: Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 184, in run - rval = self(config_copy, target_function, kwargs) - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 257, in __call__ - return algorithm(config, **algorithm_kwargs) - File "/home/runner/work/SMAC3/SMAC3/examples/3_multi_objective/2_parego.py", line 69, in train - lr = config["learning_rate"] if config["learning_rate"] else "constant" - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/ConfigSpace/configuration.py", line 191, in __getitem__ - raise KeyError(key) - KeyError: 'learning_rate' - - - [WARNING][abstract_runner.py:132] Target function returned infinity or nothing at all. Result is treated as CRASHED and cost is set to [inf, inf]. - [WARNING][abstract_runner.py:138] Traceback: Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 184, in run - rval = self(config_copy, target_function, kwargs) - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 257, in __call__ - return algorithm(config, **algorithm_kwargs) - File "/home/runner/work/SMAC3/SMAC3/examples/3_multi_objective/2_parego.py", line 69, in train - lr = config["learning_rate"] if config["learning_rate"] else "constant" - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/ConfigSpace/configuration.py", line 191, in __getitem__ - raise KeyError(key) - KeyError: 'learning_rate' - - - [WARNING][abstract_runner.py:132] Target function returned infinity or nothing at all. Result is treated as CRASHED and cost is set to [inf, inf]. - [WARNING][abstract_runner.py:138] Traceback: Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 184, in run - rval = self(config_copy, target_function, kwargs) - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 257, in __call__ - return algorithm(config, **algorithm_kwargs) - File "/home/runner/work/SMAC3/SMAC3/examples/3_multi_objective/2_parego.py", line 69, in train - lr = config["learning_rate"] if config["learning_rate"] else "constant" - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/ConfigSpace/configuration.py", line 191, in __getitem__ - raise KeyError(key) - KeyError: 'learning_rate' - - - [WARNING][abstract_runner.py:132] Target function returned infinity or nothing at all. Result is treated as CRASHED and cost is set to [inf, inf]. - [WARNING][abstract_runner.py:138] Traceback: Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 184, in run - rval = self(config_copy, target_function, kwargs) - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/smac/runner/target_function_runner.py", line 257, in __call__ - return algorithm(config, **algorithm_kwargs) - File "/home/runner/work/SMAC3/SMAC3/examples/3_multi_objective/2_parego.py", line 69, in train - lr = config["learning_rate"] if config["learning_rate"] else "constant" - File "/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/ConfigSpace/configuration.py", line 191, in __getitem__ - raise KeyError(key) - KeyError: 'learning_rate' - - - [INFO][abstract_intensifier.py:590] Added config 6904ad and rejected config 0c9159 as incumbent because it is not better than the incumbents on 2 instances: + [INFO][abstract_intensifier.py:598] Config 540db2 is a new incumbent. Total number of incumbents: 2. + [INFO][abstract_intensifier.py:598] Config 3b5efd is a new incumbent. Total number of incumbents: 3. + [INFO][abstract_intensifier.py:598] Config a464c2 is a new incumbent. Total number of incumbents: 4. + [INFO][abstract_intensifier.py:598] Config fa4572 is a new incumbent. Total number of incumbents: 5. + [INFO][abstract_intensifier.py:598] Config 63b370 is a new incumbent. Total number of incumbents: 4. + [INFO][abstract_intensifier.py:598] Config f262a9 is a new incumbent. Total number of incumbents: 5. [INFO][smbo.py:327] Configuration budget is exhausted: - [INFO][smbo.py:328] --- Remaining wallclock time: -3.929424524307251 + [INFO][smbo.py:328] --- Remaining wallclock time: -0.5777037143707275 [INFO][smbo.py:329] --- Remaining cpu time: inf - [INFO][smbo.py:330] --- Remaining trials: 185 + [INFO][smbo.py:330] --- Remaining trials: 175 Validated costs from default config: - --- [inf inf] + --- [0.60155834 0.24215281] Validated costs from the Pareto front (incumbents): - --- [0.193119 2.78539681] + --- [0.88924791 0.29182327] + --- [0.03561281 1.11070251] + --- [0.04396781 0.72936821] + --- [0.04229805 0.75202131] + --- [0.02838672 1.71442103] @@ -227,9 +131,9 @@ mean accuracy and run-time of each configuration. return cs def train(self, config: Configuration, seed: int = 0, budget: int = 10) -> dict[str, float]: - lr = config["learning_rate"] if config["learning_rate"] else "constant" - lr_init = config["learning_rate_init"] if config["learning_rate_init"] else 0.001 - batch_size = config["batch_size"] if config["batch_size"] else 200 + lr = config.get("learning_rate", "constant") + lr_init = config.get("learning_rate_init", 0.001) + batch_size = config.get("batch_size", 200) start_time = time.time() @@ -340,7 +244,7 @@ mean accuracy and run-time of each configuration. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 39.831 seconds) + **Total running time of the script:** ( 0 minutes 40.514 seconds) .. _sphx_glr_download_examples_3_multi_objective_2_parego.py: diff --git a/development/_sources/examples/3_multi_objective/sg_execution_times.rst.txt b/development/_sources/examples/3_multi_objective/sg_execution_times.rst.txt index 03da96f36d..3be5de6ae9 100644 --- a/development/_sources/examples/3_multi_objective/sg_execution_times.rst.txt +++ b/development/_sources/examples/3_multi_objective/sg_execution_times.rst.txt @@ -6,10 +6,10 @@ Computation times ================= -**00:57.859** total execution time for **examples_3_multi_objective** files: +**00:51.594** total execution time for **examples_3_multi_objective** files: +------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_3_multi_objective_2_parego.py` (``2_parego.py``) | 00:39.831 | 0.0 MB | +| :ref:`sphx_glr_examples_3_multi_objective_2_parego.py` (``2_parego.py``) | 00:40.514 | 0.0 MB | +------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_3_multi_objective_1_schaffer.py` (``1_schaffer.py``) | 00:18.028 | 0.0 MB | +| :ref:`sphx_glr_examples_3_multi_objective_1_schaffer.py` (``1_schaffer.py``) | 00:11.080 | 0.0 MB | +------------------------------------------------------------------------------+-----------+--------+ diff --git a/development/_sources/examples/5_commandline/1_call_target_function_script.rst.txt b/development/_sources/examples/5_commandline/1_call_target_function_script.rst.txt index e8cd118663..bfbb6d685d 100644 --- a/development/_sources/examples/5_commandline/1_call_target_function_script.rst.txt +++ b/development/_sources/examples/5_commandline/1_call_target_function_script.rst.txt @@ -112,7 +112,7 @@ This simple example shows how to call a script with the following content: .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 8.166 seconds) + **Total running time of the script:** ( 0 minutes 4.965 seconds) .. _sphx_glr_download_examples_5_commandline_1_call_target_function_script.py: diff --git a/development/_sources/examples/5_commandline/sg_execution_times.rst.txt b/development/_sources/examples/5_commandline/sg_execution_times.rst.txt index e9f456c5c1..6dfbdb2676 100644 --- a/development/_sources/examples/5_commandline/sg_execution_times.rst.txt +++ b/development/_sources/examples/5_commandline/sg_execution_times.rst.txt @@ -6,8 +6,8 @@ Computation times ================= -**00:08.166** total execution time for **examples_5_commandline** files: +**00:04.965** total execution time for **examples_5_commandline** files: +----------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_5_commandline_1_call_target_function_script.py` (``1_call_target_function_script.py``) | 00:08.166 | 0.0 MB | +| :ref:`sphx_glr_examples_5_commandline_1_call_target_function_script.py` (``1_call_target_function_script.py``) | 00:04.965 | 0.0 MB | +----------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/development/examples/1_basics/1_quadratic_function.html b/development/examples/1_basics/1_quadratic_function.html index b539b3f39c..7bbc97df3c 100644 --- a/development/examples/1_basics/1_quadratic_function.html +++ b/development/examples/1_basics/1_quadratic_function.html @@ -1134,7 +1134,7 @@ plot(smac.runhistory, incumbent) -

Total running time of the script: ( 0 minutes 4.081 seconds)

+

Total running time of the script: ( 0 minutes 2.681 seconds)

-

Total running time of the script: ( 0 minutes 2.086 seconds)

+

Total running time of the script: ( 0 minutes 1.557 seconds)

@@ -1124,7 +1126,7 @@ print(f"Incumbent cost: {incumbent_cost}")
-

Total running time of the script: ( 0 minutes 4.574 seconds)

+

Total running time of the script: ( 0 minutes 3.225 seconds)

-

Total running time of the script: ( 0 minutes 0.231 seconds)

+

Total running time of the script: ( 0 minutes 0.154 seconds)

-

Total running time of the script: ( 0 minutes 1.364 seconds)

+

Total running time of the script: ( 0 minutes 0.889 seconds)

@@ -1211,7 +1211,7 @@ print(f"Default cost: {incumbent_cost}")
-

Total running time of the script: ( 0 minutes 40.659 seconds)

+

Total running time of the script: ( 0 minutes 29.170 seconds)