import ConfigSpace.hyperparameters as CSH from ConfigSpace.configuration_space import ConfigurationSpace import numpy as np from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.setup.network_initializer.base_network_initializer import ( BaseNetworkInitializerComponent) directory = os.path.split(__file__)[0] _initializers = find_components(__package__, directory, BaseNetworkInitializerComponent) _addons = ThirdPartyComponents(BaseNetworkInitializerComponent) def add_network_initializer( initializer: BaseNetworkInitializerComponent) -> None: _addons.add_component(initializer) class NetworkInitializerChoice(autoPyTorchChoice): def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available initializer components Args: None
import ConfigSpace.hyperparameters as CSH from ConfigSpace.configuration_space import ConfigurationSpace import numpy as np from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.setup.lr_scheduler.base_scheduler import BaseLRComponent directory = os.path.split(__file__)[0] _schedulers = find_components(__package__, directory, BaseLRComponent) _addons = ThirdPartyComponents(BaseLRComponent) def add_scheduler(scheduler: BaseLRComponent) -> None: _addons.add_component(scheduler) class SchedulerChoice(autoPyTorchChoice): def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available scheduler components Args: None Returns:
from ConfigSpace.configuration_space import ConfigurationSpace import numpy as np from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.setup.network_head.base_network_head import ( NetworkHeadComponent, ) directory = os.path.split(__file__)[0] _heads = find_components(__package__, directory, NetworkHeadComponent) _addons = ThirdPartyComponents(NetworkHeadComponent) def add_head(head: NetworkHeadComponent) -> None: _addons.add_component(head) class NetworkHeadChoice(autoPyTorchChoice): def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available head components Args: None Returns:
from ConfigSpace.configuration_space import ConfigurationSpace import numpy as np from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.setup.network_embedding.base_network_embedding import ( NetworkEmbeddingComponent, ) directory = os.path.split(__file__)[0] _embeddings = find_components(__package__, directory, NetworkEmbeddingComponent) _addons = ThirdPartyComponents(NetworkEmbeddingComponent) def add_embedding(embedding: NetworkEmbeddingComponent) -> None: _addons.add_component(embedding) class NetworkEmbeddingChoice(autoPyTorchChoice): def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available embedding components Args: None Returns:
import ConfigSpace.hyperparameters as CSH from ConfigSpace.configuration_space import ConfigurationSpace from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.feature_preprocessing. \ base_feature_preprocessor import autoPyTorchFeaturePreprocessingComponent preprocessing_directory = os.path.split(__file__)[0] _preprocessors = find_components(__package__, preprocessing_directory, autoPyTorchFeaturePreprocessingComponent) _addons = ThirdPartyComponents(autoPyTorchFeaturePreprocessingComponent) def add_feature_preprocessor( feature_preprocessor: autoPyTorchFeaturePreprocessingComponent ) -> None: _addons.add_component(feature_preprocessor) class FeatureProprocessorChoice(autoPyTorchChoice): """ Allows for dynamically choosing feature_preprocessor component at runtime """ def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available feature_preprocessor components
import imgaug.augmenters as iaa import numpy as np from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, find_components, ) from autoPyTorch.pipeline.components.setup.augmentation.image.base_image_augmenter import BaseImageAugmenter augmenter_directory = os.path.split(__file__)[0] _augmenters = find_components(__package__, augmenter_directory, BaseImageAugmenter) _addons = ThirdPartyComponents(BaseImageAugmenter) def add_augmenter(augmenter: BaseImageAugmenter) -> None: _addons.add_component(augmenter) def get_components() -> Dict[str, BaseImageAugmenter]: """Returns the available augmenter components Args: None Returns:
import ConfigSpace.hyperparameters as CSH from ConfigSpace.configuration_space import ConfigurationSpace from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.preprocessing.image_preprocessing.normalise.base_normalizer import BaseNormalizer normalise_directory = os.path.split(__file__)[0] _normalizers = find_components(__package__, normalise_directory, BaseNormalizer) _addons = ThirdPartyComponents(BaseNormalizer) def add_normalizer(normalizer: BaseNormalizer) -> None: _addons.add_component(normalizer) class NormalizerChoice(autoPyTorchChoice): """ Allows for dynamically choosing normalizer component at runtime """ def get_components(self) -> Dict[str, autoPyTorchComponent]:
from typing import Dict, List, Optional import ConfigSpace.hyperparameters as CSH from ConfigSpace.configuration_space import ConfigurationSpace from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.scaling.base_scaler import BaseScaler scaling_directory = os.path.split(__file__)[0] _scalers = find_components(__package__, scaling_directory, BaseScaler) _addons = ThirdPartyComponents(BaseScaler) def add_scaler(scaler: BaseScaler) -> None: _addons.add_component(scaler) class ScalerChoice(autoPyTorchChoice): """ Allows for dynamically choosing scaling component at runtime """ def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available scaler components
import ConfigSpace.hyperparameters as CSH from ConfigSpace.configuration_space import ConfigurationSpace import numpy as np from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.setup.optimizer.base_optimizer import BaseOptimizerComponent directory = os.path.split(__file__)[0] _optimizers = find_components(__package__, directory, BaseOptimizerComponent) _addons = ThirdPartyComponents(BaseOptimizerComponent) def add_optimizer(optimizer: BaseOptimizerComponent) -> None: _addons.add_component(optimizer) class OptimizerChoice(autoPyTorchChoice): def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available optimizer components Args: None Returns:
import ConfigSpace.hyperparameters as CSH from ConfigSpace.configuration_space import ConfigurationSpace from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.encoding.base_encoder import BaseEncoder encoding_directory = os.path.split(__file__)[0] _encoders = find_components(__package__, encoding_directory, BaseEncoder) _addons = ThirdPartyComponents(BaseEncoder) def add_encoder(encoder: BaseEncoder) -> None: _addons.add_component(encoder) class EncoderChoice(autoPyTorchChoice): """ Allows for dynamically choosing encoding component at runtime """ def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available encoder components
import ConfigSpace.hyperparameters as CSH from ConfigSpace.configuration_space import ConfigurationSpace import numpy as np from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.setup.network.base_network import BaseNetworkComponent directory = os.path.split(__file__)[0] _networks = find_components(__package__, directory, BaseNetworkComponent) _addons = ThirdPartyComponents(BaseNetworkComponent) def add_network(network: BaseNetworkComponent) -> None: _addons.add_component(network) class NetworkChoice(autoPyTorchChoice): def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available network components Args: None Returns:
autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.training.losses import get_loss_instance from autoPyTorch.pipeline.components.training.metrics.utils import get_metrics from autoPyTorch.pipeline.components.training.trainer.base_trainer import ( BaseTrainerComponent, BudgetTracker, RunSummary, ) from autoPyTorch.utils.common import FitRequirement from autoPyTorch.utils.logging_ import get_named_client_logger trainer_directory = os.path.split(__file__)[0] _trainers = find_components(__package__, trainer_directory, BaseTrainerComponent) _addons = ThirdPartyComponents(BaseTrainerComponent) def add_trainer(trainer: BaseTrainerComponent) -> None: _addons.add_component(trainer) class TrainerChoice(autoPyTorchChoice): """This class is an interface to the PyTorch trainer. To map to pipeline terminology, a choice component will implement the epoch loop through fit, whereas the component who is chosen will dictate how a single epoch happens, that is, how batches of data are fed and used to train the network.
import ConfigSpace.hyperparameters as CSH from ConfigSpace.configuration_space import ConfigurationSpace import numpy as np from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.setup.traditional_ml.base_model import BaseModelComponent directory = os.path.split(__file__)[0] _models = find_components(__package__, directory, BaseModelComponent) _addons = ThirdPartyComponents(BaseModelComponent) def add_model(model: BaseModelComponent) -> None: _addons.add_component(model) class ModelChoice(autoPyTorchChoice): def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available model components Args: None Returns: Dict[str, autoPyTorchComponent]: all baseNetwork components available as choices
from ConfigSpace.configuration_space import ConfigurationSpace import numpy as np from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType from autoPyTorch.pipeline.components.base_choice import autoPyTorchChoice from autoPyTorch.pipeline.components.base_component import ( ThirdPartyComponents, autoPyTorchComponent, find_components, ) from autoPyTorch.pipeline.components.setup.network_backbone.base_network_backbone import ( NetworkBackboneComponent, ) directory = os.path.split(__file__)[0] _backbones = find_components(__package__, directory, NetworkBackboneComponent) _addons = ThirdPartyComponents(NetworkBackboneComponent) def add_backbone(backbone: NetworkBackboneComponent) -> None: _addons.add_component(backbone) class NetworkBackboneChoice(autoPyTorchChoice): def get_components(self) -> Dict[str, autoPyTorchComponent]: """Returns the available backbone components Args: None Returns: