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[feat] Add coalescer (#376)
* [fix] Add check dataset in transform as well for test dataset, which does not require fit * [test] Migrate tests from the francisco's PR without modifications * [fix] Modify so that tests pass * [test] Increase the coverage
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‎autoPyTorch/configs/greedy_portfolio.json

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[{"data_loader:batch_size": 60,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_backbone:ShapedMLPBackbone:max_dropout": 0.023271935735825866},
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{"data_loader:batch_size": 255,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_backbone:ShapedResNetBackbone:max_dropout": 0.7662454727603789},
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{"data_loader:batch_size": 165,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_head:fully_connected:units_layer_1": 128},
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{"data_loader:batch_size": 299,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_head:fully_connected:units_layer_1": 128},
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{"data_loader:batch_size": 183,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_backbone:ShapedResNetBackbone:max_dropout": 0.27204101593048097},
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{"data_loader:batch_size": 21,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_head:fully_connected:units_layer_1": 128},
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{"data_loader:batch_size": 159,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "TruncatedSVD",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_head:fully_connected:units_layer_1": 128},
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{"data_loader:batch_size": 442,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "TruncatedSVD",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_head:fully_connected:units_layer_1": 128},
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{"data_loader:batch_size": 140,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "TruncatedSVD",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_head:fully_connected:units_layer_1": 128},
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{"data_loader:batch_size": 48,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_head:fully_connected:units_layer_1": 128},
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{"data_loader:batch_size": 168,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_backbone:ShapedResNetBackbone:max_dropout": 0.8992826006547855},
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{"data_loader:batch_size": 21,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_head:fully_connected:units_layer_1": 128},
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{"data_loader:batch_size": 163,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_backbone:ShapedResNetBackbone:max_dropout": 0.6341848343636569},
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{"data_loader:batch_size": 150,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "NoFeaturePreprocessor",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_backbone:ShapedResNetBackbone:max_dropout": 0.7133813761319248},
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{"data_loader:batch_size": 151,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "TruncatedSVD",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",
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"network_head:fully_connected:units_layer_1": 128},
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{"data_loader:batch_size": 42,
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"encoder:__choice__": "OneHotEncoder",
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"coalescer:__choice__": "NoCoalescer",
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"feature_preprocessor:__choice__": "TruncatedSVD",
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"imputer:numerical_strategy": "mean",
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"lr_scheduler:__choice__": "CosineAnnealingLR",

‎autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/coalescer/MinorityCoalescer.py

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from typing import Any, Dict, Optional, Union
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from ConfigSpace.configuration_space import ConfigurationSpace
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from ConfigSpace.hyperparameters import UniformFloatHyperparameter
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import numpy as np
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from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.coalescer.base_coalescer import BaseCoalescer
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from autoPyTorch.utils.common import HyperparameterSearchSpace, add_hyperparameter
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from autoPyTorch.utils.implementations import MinorityCoalesceTransformer
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class MinorityCoalescer(BaseCoalescer):
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"""Group together categories whose occurence is less than a specified min_frac """
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def __init__(self, min_frac: float, random_state: np.random.RandomState):
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super().__init__()
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self.min_frac = min_frac
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self.random_state = random_state
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def fit(self, X: Dict[str, Any], y: Any = None) -> BaseCoalescer:
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self.check_requirements(X, y)
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self.preprocessor['categorical'] = MinorityCoalesceTransformer(min_frac=self.min_frac)
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return self
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@staticmethod
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def get_hyperparameter_search_space(
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dataset_properties: Optional[Dict[str, Any]] = None,
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min_frac: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='min_frac',
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value_range=(1e-4, 0.5),
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default_value=1e-2,
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),
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) -> ConfigurationSpace:
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cs = ConfigurationSpace()
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add_hyperparameter(cs, min_frac, UniformFloatHyperparameter)
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return cs
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@staticmethod
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def get_properties(dataset_properties: Optional[Dict[str, Any]] = None) -> Dict[str, Union[str, bool]]:
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return {
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'shortname': 'MinorityCoalescer',
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'name': 'MinorityCoalescer',
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'handles_sparse': False
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}

‎autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/coalescer/NoCoalescer.py

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from typing import Any, Dict, Optional, Union
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import numpy as np
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from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.coalescer.base_coalescer import BaseCoalescer
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class NoCoalescer(BaseCoalescer):
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def __init__(self, random_state: np.random.RandomState):
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super().__init__()
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self.random_state = random_state
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self._processing = False
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def fit(self, X: Dict[str, Any], y: Optional[Any] = None) -> BaseCoalescer:
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"""
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As no coalescing happens, only check the requirements.
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Args:
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X (Dict[str, Any]):
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fit dictionary
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y (Optional[Any]):
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Parameter to comply with scikit-learn API. Not used.
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Returns:
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instance of self
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"""
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self.check_requirements(X, y)
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return self
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@staticmethod
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def get_properties(dataset_properties: Optional[Dict[str, Any]] = None) -> Dict[str, Union[str, bool]]:
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return {
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'shortname': 'NoCoalescer',
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'name': 'NoCoalescer',
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'handles_sparse': True
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}

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