def _apply_default_pipeline_settings(pipeline):
from autoPyTorch.pipeline.nodes.network_selector import NetworkSelector
from autoPyTorch.pipeline.nodes.loss_module_selector import LossModuleSelector
from autoPyTorch.pipeline.nodes.metric_selector import MetricSelector
from autoPyTorch.pipeline.nodes.train_node import TrainNode
from autoPyTorch.pipeline.nodes.cross_validation import CrossValidation
import torch.nn as nn
from autoPyTorch.components.metrics.standard_metrics import multilabel_accuracy
from autoPyTorch.components.preprocessing.loss_weight_strategies import LossWeightStrategyWeightedBinary
AutoNetFeatureData._apply_default_pipeline_settings(pipeline)
net_selector = pipeline[NetworkSelector.get_name()]
net_selector.add_final_activation('sigmoid', nn.Sigmoid())
loss_selector = pipeline[LossModuleSelector.get_name()]
loss_selector.add_loss_module('bce_with_logits', nn.BCEWithLogitsLoss,
None, False)
loss_selector.add_loss_module('bce_with_logits_weighted',
nn.BCEWithLogitsLoss,
LossWeightStrategyWeightedBinary(),
False)
metric_selector = pipeline[MetricSelector.get_name()]
metric_selector.add_metric('multilabel_accuracy', multilabel_accuracy)
train_node = pipeline[TrainNode.get_name()]
train_node.default_minimize_value = False
cv = pipeline[CrossValidation.get_name()]
cv.use_stratified_cv_split_default = False
def _apply_default_pipeline_settings(pipeline):
from autoPyTorch.pipeline.nodes.network_selector import NetworkSelector
from autoPyTorch.pipeline.nodes.loss_module_selector import LossModuleSelector
from autoPyTorch.pipeline.nodes.metric_selector import MetricSelector
from autoPyTorch.pipeline.nodes.train_node import TrainNode
from autoPyTorch.pipeline.nodes.cross_validation import CrossValidation
import torch.nn as nn
from autoPyTorch.components.metrics.standard_metrics import mean_distance
AutoNetFeatureData._apply_default_pipeline_settings(pipeline)
net_selector = pipeline[NetworkSelector.get_name()]
net_selector.add_final_activation('none', nn.Sequential())
loss_selector = pipeline[LossModuleSelector.get_name()]
loss_selector.add_loss_module('l1_loss', nn.L1Loss)
metric_selector = pipeline[MetricSelector.get_name()]
metric_selector.add_metric('mean_distance', mean_distance)
train_node = pipeline[TrainNode.get_name()]
train_node.default_minimize_value = True
cv = pipeline[CrossValidation.get_name()]
cv.use_stratified_cv_split_default = False
def _apply_default_pipeline_settings(pipeline):
from autoPyTorch.pipeline.nodes.network_selector import NetworkSelector
from autoPyTorch.pipeline.nodes.loss_module_selector import LossModuleSelector
from autoPyTorch.pipeline.nodes.metric_selector import MetricSelector
from autoPyTorch.pipeline.nodes.train_node import TrainNode
from autoPyTorch.pipeline.nodes.resampling_strategy_selector import ResamplingStrategySelector
from autoPyTorch.pipeline.nodes.cross_validation import CrossValidation
from autoPyTorch.pipeline.nodes.one_hot_encoding import OneHotEncoding
from autoPyTorch.pipeline.nodes.resampling_strategy_selector import ResamplingStrategySelector
from autoPyTorch.components.preprocessing.resampling import RandomOverSamplingWithReplacement, RandomUnderSamplingWithReplacement, SMOTE, \
TargetSizeStrategyAverageSample, TargetSizeStrategyDownsample, TargetSizeStrategyMedianSample, TargetSizeStrategyUpsample
import torch.nn as nn
from autoPyTorch.components.metrics.standard_metrics import accuracy
from autoPyTorch.components.preprocessing.loss_weight_strategies import LossWeightStrategyWeighted
AutoNetFeatureData._apply_default_pipeline_settings(pipeline)
net_selector = pipeline[NetworkSelector.get_name()]
net_selector.add_final_activation('softmax', nn.Softmax(1))
loss_selector = pipeline[LossModuleSelector.get_name()]
loss_selector.add_loss_module('cross_entropy', nn.CrossEntropyLoss,
None, True)
loss_selector.add_loss_module('cross_entropy_weighted',
nn.CrossEntropyLoss,
LossWeightStrategyWeighted(), True)
metric_selector = pipeline[MetricSelector.get_name()]
metric_selector.add_metric('accuracy', accuracy)
resample_selector = pipeline[ResamplingStrategySelector.get_name()]
resample_selector.add_over_sampling_method(
'random', RandomOverSamplingWithReplacement)
resample_selector.add_over_sampling_method('smote', SMOTE)
resample_selector.add_under_sampling_method(
'random', RandomUnderSamplingWithReplacement)
resample_selector.add_target_size_strategy('upsample',
TargetSizeStrategyUpsample)
resample_selector.add_target_size_strategy(
'downsample', TargetSizeStrategyDownsample)
resample_selector.add_target_size_strategy(
'average', TargetSizeStrategyAverageSample)
resample_selector.add_target_size_strategy(
'median', TargetSizeStrategyMedianSample)
train_node = pipeline[TrainNode.get_name()]
train_node.default_minimize_value = False
cv = pipeline[CrossValidation.get_name()]
cv.use_stratified_cv_split_default = True
one_hot_encoding_node = pipeline[OneHotEncoding.get_name()]
one_hot_encoding_node.encode_Y = True
return pipeline
def test_loss_selector(self):
pipeline = Pipeline([LossModuleSelector()])
selector = pipeline[LossModuleSelector.get_name()]
selector.add_loss_module("L1", nn.L1Loss)
selector.add_loss_module("cross_entropy", nn.CrossEntropyLoss,
LossWeightStrategyWeighted(), True)
pipeline_config = pipeline.get_pipeline_config(
loss_modules=["L1", "cross_entropy"])
pipeline_hyperparameter_config = pipeline.get_hyperparameter_search_space(
**pipeline_config).sample_configuration()
pipeline_hyperparameter_config["LossModuleSelector:loss_module"] = "L1"
pipeline.fit_pipeline(
hyperparameter_config=pipeline_hyperparameter_config,
train_indices=np.array([0, 1, 2]),
X=np.random.rand(3, 3),
Y=np.random.rand(3, 2),
pipeline_config=pipeline_config,
tmp=None)
selected_loss = pipeline[
selector.get_name()].fit_output['loss_function']
self.assertEqual(type(selected_loss.function), nn.L1Loss)
pipeline_hyperparameter_config[
"LossModuleSelector:loss_module"] = "cross_entropy"
pipeline.fit_pipeline(
hyperparameter_config=pipeline_hyperparameter_config,
train_indices=np.array([0, 1, 2]),
X=np.random.rand(3, 3),
Y=np.array([[1, 0], [0, 1], [1, 0]]),
pipeline_config=pipeline_config,
tmp=None)
selected_loss = pipeline[
selector.get_name()].fit_output['loss_function']
self.assertEqual(type(selected_loss.function), nn.CrossEntropyLoss)
self.assertEqual(
selected_loss(torch.tensor([[0.0, 10000.0]]),
torch.tensor([[0, 1]])), 0)
def get_default_pipeline(cls):
from autoPyTorch.pipeline.base.pipeline import Pipeline
from autoPyTorch.pipeline.nodes.autonet_settings import AutoNetSettings
from autoPyTorch.pipeline.nodes.optimization_algorithm import OptimizationAlgorithm
from autoPyTorch.pipeline.nodes.cross_validation import CrossValidation
from autoPyTorch.pipeline.nodes.imputation import Imputation
from autoPyTorch.pipeline.nodes.normalization_strategy_selector import NormalizationStrategySelector
from autoPyTorch.pipeline.nodes.one_hot_encoding import OneHotEncoding
from autoPyTorch.pipeline.nodes.preprocessor_selector import PreprocessorSelector
from autoPyTorch.pipeline.nodes.resampling_strategy_selector import ResamplingStrategySelector
from autoPyTorch.pipeline.nodes.embedding_selector import EmbeddingSelector
from autoPyTorch.pipeline.nodes.network_selector import NetworkSelector
from autoPyTorch.pipeline.nodes.optimizer_selector import OptimizerSelector
from autoPyTorch.pipeline.nodes.lr_scheduler_selector import LearningrateSchedulerSelector
from autoPyTorch.pipeline.nodes.log_functions_selector import LogFunctionsSelector
from autoPyTorch.pipeline.nodes.metric_selector import MetricSelector
from autoPyTorch.pipeline.nodes.loss_module_selector import LossModuleSelector
from autoPyTorch.pipeline.nodes.train_node import TrainNode
# build the pipeline
pipeline = Pipeline([
AutoNetSettings(),
OptimizationAlgorithm([
CrossValidation([
Imputation(),
NormalizationStrategySelector(),
OneHotEncoding(),
PreprocessorSelector(),
ResamplingStrategySelector(),
EmbeddingSelector(),
NetworkSelector(),
OptimizerSelector(),
LearningrateSchedulerSelector(),
LogFunctionsSelector(),
MetricSelector(),
LossModuleSelector(),
TrainNode()
])
])
])
cls._apply_default_pipeline_settings(pipeline)
return pipeline