def get_default_pipeline(cls):
from autoPyTorch.pipeline.base.pipeline import Pipeline
from autoPyTorch.pipeline.nodes import AutoNetSettings, OptimizationAlgorithm, \
CrossValidation, Imputation, NormalizationStrategySelector, OneHotEncoding, PreprocessorSelector, ResamplingStrategySelector, \
EmbeddingSelector, NetworkSelector, OptimizerSelector, LearningrateSchedulerSelector, LogFunctionsSelector, MetricSelector, \
LossModuleSelector, TrainNode, CreateDataLoader, CreateDatasetInfo, InitializationSelector
# build the pipeline
pipeline = Pipeline([
AutoNetSettings(),
CreateDatasetInfo(),
OptimizationAlgorithm([
CrossValidation([
Imputation(),
NormalizationStrategySelector(),
OneHotEncoding(),
PreprocessorSelector(),
ResamplingStrategySelector(),
EmbeddingSelector(),
NetworkSelector(),
InitializationSelector(),
OptimizerSelector(),
LearningrateSchedulerSelector(),
LogFunctionsSelector(),
MetricSelector(),
LossModuleSelector(),
CreateDataLoader(),
TrainNode()
])
]),
])
cls._apply_default_pipeline_settings(pipeline)
return pipeline
def get_default_ensemble_pipeline(cls):
"""Construct a default pipeline, include nodes for Ensemble.
Returns:
Pipeline -- The constructed default pipeline
"""
from autoPyTorch.pipeline.base.pipeline import Pipeline
from autoPyTorch.pipeline.nodes import AutoNetSettings, OptimizationAlgorithm, \
CrossValidation, Imputation, NormalizationStrategySelector, OneHotEncoding, PreprocessorSelector, ResamplingStrategySelector, \
EmbeddingSelector, NetworkSelector, OptimizerSelector, LearningrateSchedulerSelector, LogFunctionsSelector, MetricSelector, \
LossModuleSelector, TrainNode, CreateDataLoader, CreateDatasetInfo, EnableComputePredictionsForEnsemble, SavePredictionsForEnsemble, \
BuildEnsemble, EnsembleServer, InitializationSelector, BaselineTrainer
# build the pipeline
pipeline = Pipeline([
AutoNetSettings(),
CreateDatasetInfo(),
EnsembleServer(),
OptimizationAlgorithm([
CrossValidation([
Imputation(),
BaselineTrainer(),
NormalizationStrategySelector(),
OneHotEncoding(),
PreprocessorSelector(),
ResamplingStrategySelector(),
EmbeddingSelector(),
NetworkSelector(),
InitializationSelector(),
OptimizerSelector(),
LearningrateSchedulerSelector(),
LogFunctionsSelector(),
MetricSelector(),
EnableComputePredictionsForEnsemble(),
LossModuleSelector(),
CreateDataLoader(),
TrainNode(),
SavePredictionsForEnsemble()
])
]),
BuildEnsemble()
])
cls._apply_default_pipeline_settings(pipeline)
return pipeline
def _apply_default_pipeline_settings(pipeline):
"""Add the components to the pipeline
Arguments:
pipeline {pipeline} -- The pipelines to add the components to
"""
from autoPyTorch.pipeline.nodes import NormalizationStrategySelector, PreprocessorSelector, EmbeddingSelector, NetworkSelector, \
OptimizerSelector, LearningrateSchedulerSelector, TrainNode, CrossValidation, InitializationSelector
from autoPyTorch.components.networks.feature import MlpNet, ResNet, ShapedMlpNet, ShapedResNet
from autoPyTorch.components.networks.initialization import SimpleInitializer, SparseInitialization
from autoPyTorch.components.optimizer.optimizer import AdamOptimizer, SgdOptimizer
from autoPyTorch.components.lr_scheduler.lr_schedulers import SchedulerCosineAnnealingWithRestartsLR, SchedulerNone, \
SchedulerCyclicLR, SchedulerExponentialLR, SchedulerReduceLROnPlateau, SchedulerReduceLROnPlateau, SchedulerStepLR
from autoPyTorch.components.networks.feature import LearnedEntityEmbedding
from sklearn.preprocessing import MinMaxScaler, StandardScaler, MaxAbsScaler
from sklearn.model_selection import KFold
from autoPyTorch.components.preprocessing.feature_preprocessing import \
TruncatedSVD, FastICA, RandomKitchenSinks, KernelPCA, Nystroem, PowerTransformer
from autoPyTorch.components.training.early_stopping import EarlyStopping
from autoPyTorch.components.regularization.mixup import Mixup
pre_selector = pipeline[PreprocessorSelector.get_name()]
pre_selector.add_preprocessor('truncated_svd', TruncatedSVD)
pre_selector.add_preprocessor('power_transformer', PowerTransformer)
pre_selector.add_preprocessor('fast_ica', FastICA)
pre_selector.add_preprocessor('kitchen_sinks', RandomKitchenSinks)
pre_selector.add_preprocessor('kernel_pca', KernelPCA)
pre_selector.add_preprocessor('nystroem', Nystroem)
norm_selector = pipeline[NormalizationStrategySelector.get_name()]
norm_selector.add_normalization_strategy('minmax', MinMaxScaler)
norm_selector.add_normalization_strategy('standardize', StandardScaler)
norm_selector.add_normalization_strategy('maxabs', MaxAbsScaler)
emb_selector = pipeline[EmbeddingSelector.get_name()]
emb_selector.add_embedding_module('learned', LearnedEntityEmbedding)
net_selector = pipeline[NetworkSelector.get_name()]
net_selector.add_network('mlpnet', MlpNet)
net_selector.add_network('shapedmlpnet', ShapedMlpNet)
net_selector.add_network('resnet', ResNet)
net_selector.add_network('shapedresnet', ShapedResNet)
init_selector = pipeline[InitializationSelector.get_name()]
init_selector.add_initialization_method("sparse", SparseInitialization)
init_selector.add_initializer("simple_initializer", SimpleInitializer)
opt_selector = pipeline[OptimizerSelector.get_name()]
opt_selector.add_optimizer('adam', AdamOptimizer)
opt_selector.add_optimizer('sgd', SgdOptimizer)
lr_selector = pipeline[LearningrateSchedulerSelector.get_name()]
lr_selector.add_lr_scheduler('cosine_annealing', SchedulerCosineAnnealingWithRestartsLR)
lr_selector.add_lr_scheduler('cyclic', SchedulerCyclicLR)
lr_selector.add_lr_scheduler('exponential', SchedulerExponentialLR)
lr_selector.add_lr_scheduler('step', SchedulerStepLR)
lr_selector.add_lr_scheduler('plateau', SchedulerReduceLROnPlateau)
lr_selector.add_lr_scheduler('none', SchedulerNone)
train_node = pipeline[TrainNode.get_name()]
train_node.add_training_technique("early_stopping", EarlyStopping)
train_node.add_batch_loss_computation_technique("mixup", Mixup)
cv = pipeline[CrossValidation.get_name()]
cv.add_cross_validator("k_fold", KFold)