Search space update

autoPyTorch/api/base_task.py

@@ -46,6 +46,7 @@
 from autoPyTorch.pipeline.components.training.metrics.utils import calculate_score, get_metrics
 from autoPyTorch.utils.backend import Backend, create
 from autoPyTorch.utils.common import FitRequirement, replace_string_bool_to_bool
+from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
 from autoPyTorch.utils.logging_ import (
     PicklableClientLogger,
     get_named_client_logger,
@@ -135,6 +136,7 @@ def __init__(
             include_components: Optional[Dict] = None,
             exclude_components: Optional[Dict] = None,
             backend: Optional[Backend] = None,
+            search_space_updates: Optional[HyperparameterSearchSpaceUpdates] = None
     ) -> None:
         self.seed = seed
         self.n_jobs = n_jobs
@@ -178,6 +180,8 @@ def __init__(
 
         self.stop_logging_server = None  # type: Optional[multiprocessing.synchronize.Event]
 
+        self.search_space_updates = search_space_updates
+
     @abstractmethod
     def _get_required_dataset_properties(self, dataset: BaseDataset) -> Dict[str, Any]:
         """
@@ -252,7 +256,8 @@ def get_search_space(self, dataset: BaseDataset = None) -> ConfigurationSpace:
                 info=self._get_required_dataset_properties(dataset))
             return get_configuration_space(info=dataset.get_dataset_properties(dataset_requirements),
                                            include=self.include_components,
-                                           exclude=self.exclude_components)
+                                           exclude=self.exclude_components,
+                                           search_space_updates=self.search_space_updates)
         raise Exception("No search space initialised and no dataset passed. "
                         "Can't create default search space without the dataset")
 
@@ -816,7 +821,8 @@ def search(
                 pipeline_config={**self.pipeline_options, **budget_config},
                 ensemble_callback=proc_ensemble,
                 logger_port=self._logger_port,
-                start_num_run=num_run
+                start_num_run=num_run,
+                search_space_updates=self.search_space_updates
             )
             try:
                 self.run_history, self.trajectory, budget_type = \

autoPyTorch/api/tabular_classification.py

@@ -9,6 +9,7 @@
 from autoPyTorch.datasets.tabular_dataset import TabularDataset
 from autoPyTorch.pipeline.tabular_classification import TabularClassificationPipeline
 from autoPyTorch.utils.backend import Backend
+from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
 
 
 class TabularClassificationTask(BaseTask):
@@ -52,6 +53,7 @@ def __init__(
         include_components: Optional[Dict] = None,
         exclude_components: Optional[Dict] = None,
         backend: Optional[Backend] = None,
+        search_space_updates: Optional[HyperparameterSearchSpaceUpdates] = None
     ):
         super().__init__(
             seed=seed,
@@ -67,6 +69,7 @@ def __init__(
             include_components=include_components,
             exclude_components=exclude_components,
             backend=backend,
+            search_space_updates=search_space_updates
         )
         self.task_type = TASK_TYPES_TO_STRING[TABULAR_CLASSIFICATION]
 

autoPyTorch/evaluation/abstract_evaluator.py

@@ -42,6 +42,7 @@
     get_metrics,
 )
 from autoPyTorch.utils.backend import Backend
+from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
 from autoPyTorch.utils.logging_ import PicklableClientLogger, get_named_client_logger
 from autoPyTorch.utils.pipeline import get_dataset_requirements
 
@@ -200,7 +201,9 @@ def __init__(self, backend: Backend,
                  disable_file_output: Union[bool, List[str]] = False,
                  init_params: Optional[Dict[str, Any]] = None,
                  logger_port: Optional[int] = None,
-                 all_supported_metrics: bool = True) -> None:
+                 all_supported_metrics: bool = True,
+                 search_space_updates: Optional[HyperparameterSearchSpaceUpdates] = None
+                 ) -> None:
 
         self.starttime = time.time()
 
@@ -218,6 +221,7 @@ def __init__(self, backend: Backend,
 
         self.include = include
         self.exclude = exclude
+        self.search_space_updates = search_space_updates
 
         self.X_train, self.y_train = self.datamanager.train_tensors
 
@@ -324,6 +328,7 @@ def __init__(self, backend: Backend,
         self.pipelines: Optional[List[BaseEstimator]] = None
         self.pipeline: Optional[BaseEstimator] = None
         self.logger.debug("Fit dictionary in Abstract evaluator: {}".format(self.fit_dictionary))
+        self.logger.debug("Search space updates :{}".format(self.search_space_updates))
 
     def _get_pipeline(self) -> BaseEstimator:
         assert self.pipeline_class is not None, "Can't return pipeline, pipeline_class not initialised"
@@ -337,7 +342,8 @@ def _get_pipeline(self) -> BaseEstimator:
                                            random_state=np.random.RandomState(self.seed),
                                            include=self.include,
                                            exclude=self.exclude,
-                                           init_params=self._init_params)
+                                           init_params=self._init_params,
+                                           search_space_updates=self.search_space_updates)
         elif isinstance(self.configuration, str):
             pipeline = self.pipeline_class(config=self.configuration,
                                            dataset_properties=self.dataset_properties,

autoPyTorch/evaluation/tae.py

@@ -25,6 +25,7 @@
 from autoPyTorch.evaluation.utils import empty_queue, extract_learning_curve, read_queue
 from autoPyTorch.pipeline.components.training.metrics.base import autoPyTorchMetric
 from autoPyTorch.utils.backend import Backend
+from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
 from autoPyTorch.utils.logging_ import PicklableClientLogger, get_named_client_logger
 
 
@@ -111,6 +112,7 @@ def __init__(
             ta: typing.Optional[typing.Callable] = None,
             logger_port: int = None,
             all_supported_metrics: bool = True,
+            search_space_updates: typing.Optional[HyperparameterSearchSpaceUpdates] = None
     ):
 
         eval_function = autoPyTorch.evaluation.train_evaluator.eval_function
@@ -164,6 +166,8 @@ def __init__(
         self.resampling_strategy = dm.resampling_strategy
         self.resampling_strategy_args = dm.resampling_strategy_args
 
+        self.search_space_updates = search_space_updates
+
     def run_wrapper(
             self,
             run_info: RunInfo,
@@ -250,6 +254,7 @@ def run(
         else:
             num_run = config.config_id + self.initial_num_run
 
+        self.logger.debug("Search space updates: {}".format(self.search_space_updates))
         obj_kwargs = dict(
             queue=queue,
             config=config,
@@ -267,7 +272,8 @@ def run(
             budget_type=self.budget_type,
             pipeline_config=self.pipeline_config,
             logger_port=self.logger_port,
-            all_supported_metrics=self.all_supported_metrics
+            all_supported_metrics=self.all_supported_metrics,
+            search_space_updates=self.search_space_updates
         )
 
         info: typing.Optional[typing.List[RunValue]]

autoPyTorch/evaluation/train_evaluator.py

@@ -20,6 +20,7 @@
 from autoPyTorch.evaluation.utils import subsampler
 from autoPyTorch.pipeline.components.training.metrics.base import autoPyTorchMetric
 from autoPyTorch.utils.backend import Backend
+from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
 
 __all__ = ['TrainEvaluator', 'eval_function']
 
@@ -48,7 +49,8 @@ def __init__(self, backend: Backend, queue: Queue,
                  init_params: Optional[Dict[str, Any]] = None,
                  logger_port: Optional[int] = None,
                  keep_models: Optional[bool] = None,
-                 all_supported_metrics: bool = True) -> None:
+                 all_supported_metrics: bool = True,
+                 search_space_updates: Optional[HyperparameterSearchSpaceUpdates] = None) -> None:
         super().__init__(
             backend=backend,
             queue=queue,
@@ -65,7 +67,8 @@ def __init__(self, backend: Backend, queue: Queue,
             budget_type=budget_type,
             logger_port=logger_port,
             all_supported_metrics=all_supported_metrics,
-            pipeline_config=pipeline_config
+            pipeline_config=pipeline_config,
+            search_space_updates=search_space_updates
         )
 
         self.splits = self.datamanager.splits
@@ -77,6 +80,7 @@ def __init__(self, backend: Backend, queue: Queue,
         self.pipelines: List[Optional[BaseEstimator]] = [None] * self.num_folds
         self.indices: List[Optional[Tuple[Union[np.ndarray, List], Union[np.ndarray, List]]]] = [None] * self.num_folds
 
+        self.logger.debug("Search space updates :{}".format(self.search_space_updates))
         self.keep_models = keep_models
 
     def fit_predict_and_loss(self) -> None:
@@ -320,6 +324,7 @@ def eval_function(
         init_params: Optional[Dict[str, Any]] = None,
         logger_port: Optional[int] = None,
         all_supported_metrics: bool = True,
+        search_space_updates: Optional[HyperparameterSearchSpaceUpdates] = None,
         instance: str = None,
 ) -> None:
     evaluator = TrainEvaluator(
@@ -338,6 +343,7 @@ def eval_function(
         budget_type=budget_type,
         logger_port=logger_port,
         all_supported_metrics=all_supported_metrics,
-        pipeline_config=pipeline_config
+        pipeline_config=pipeline_config,
+        search_space_updates=search_space_updates
     )
     evaluator.fit_predict_and_loss()

autoPyTorch/optimizer/smbo.py

@@ -16,17 +16,17 @@
 from smac.tae.serial_runner import SerialRunner
 from smac.utils.io.traj_logging import TrajEntry
 
-# TODO: Enable when merged Ensemble
-# from autoPyTorch.ensemble.ensemble_builder import EnsembleBuilderManager
 from autoPyTorch.datasets.base_dataset import BaseDataset
 from autoPyTorch.datasets.resampling_strategy import (
     CrossValTypes,
     DEFAULT_RESAMPLING_PARAMETERS,
     HoldoutValTypes,
 )
+from autoPyTorch.ensemble.ensemble_builder import EnsembleBuilderManager
 from autoPyTorch.evaluation.tae import ExecuteTaFuncWithQueue, get_cost_of_crash
 from autoPyTorch.pipeline.components.training.metrics.base import autoPyTorchMetric
 from autoPyTorch.utils.backend import Backend
+from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
 from autoPyTorch.utils.logging_ import get_named_client_logger
 from autoPyTorch.utils.stopwatch import StopWatch
 
@@ -101,10 +101,9 @@ def __init__(self,
                  smac_scenario_args: typing.Optional[typing.Dict[str, typing.Any]] = None,
                  get_smac_object_callback: typing.Optional[typing.Callable] = None,
                  all_supported_metrics: bool = True,
-                 # TODO: Re-enable when ensemble merged
-                 # ensemble_callback: typing.Optional[EnsembleBuilderManager] = None,
-                 ensemble_callback: typing.Any = None,
+                 ensemble_callback: typing.Optional[EnsembleBuilderManager] = None,
                  logger_port: typing.Optional[int] = None,
+                 search_space_updates: typing.Optional[HyperparameterSearchSpaceUpdates] = None
                  ):
         """
         Interface to SMAC. This method calls the SMAC optimize method, and allows
@@ -194,6 +193,8 @@ def __init__(self,
 
         self.ensemble_callback = ensemble_callback
 
+        self.search_space_updates = search_space_updates
+
         dataset_name_ = "" if dataset_name is None else dataset_name
         if logger_port is None:
             self.logger_port = logging.handlers.DEFAULT_TCP_LOGGING_PORT
@@ -254,7 +255,8 @@ def run_smbo(self, func: typing.Optional[typing.Callable] = None
             ta=func,
             logger_port=self.logger_port,
             all_supported_metrics=self.all_supported_metrics,
-            pipeline_config=self.pipeline_config
+            pipeline_config=self.pipeline_config,
+            search_space_updates=self.search_space_updates
         )
         ta = ExecuteTaFuncWithQueue
         self.logger.info("Created TA")