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* [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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44 changes: 44 additions & 0 deletions
44
...ch/pipeline/components/preprocessing/tabular_preprocessing/coalescer/MinorityCoalescer.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,44 @@ | ||
| from typing import Any, Dict, Optional, Union | ||
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| from ConfigSpace.configuration_space import ConfigurationSpace | ||
| 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 | ||
| from autoPyTorch.utils.common import HyperparameterSearchSpace, add_hyperparameter | ||
| from autoPyTorch.utils.implementations import MinorityCoalesceTransformer | ||
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| class MinorityCoalescer(BaseCoalescer): | ||
| """Group together categories whose occurence is less than a specified min_frac """ | ||
| def __init__(self, min_frac: float, random_state: np.random.RandomState): | ||
| super().__init__() | ||
| self.min_frac = min_frac | ||
| self.random_state = random_state | ||
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| def fit(self, X: Dict[str, Any], y: Any = None) -> BaseCoalescer: | ||
| self.check_requirements(X, y) | ||
| self.preprocessor['categorical'] = MinorityCoalesceTransformer(min_frac=self.min_frac) | ||
| return self | ||
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| @staticmethod | ||
| def get_hyperparameter_search_space( | ||
| dataset_properties: Optional[Dict[str, Any]] = None, | ||
| min_frac: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='min_frac', | ||
| value_range=(1e-4, 0.5), | ||
| default_value=1e-2, | ||
| ), | ||
| ) -> ConfigurationSpace: | ||
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| cs = ConfigurationSpace() | ||
| add_hyperparameter(cs, min_frac, UniformFloatHyperparameter) | ||
| return cs | ||
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| @staticmethod | ||
| def get_properties(dataset_properties: Optional[Dict[str, Any]] = None) -> Dict[str, Union[str, bool]]: | ||
| return { | ||
| 'shortname': 'MinorityCoalescer', | ||
| 'name': 'MinorityCoalescer', | ||
| 'handles_sparse': False | ||
| } |
37 changes: 37 additions & 0 deletions
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autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/coalescer/NoCoalescer.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,37 @@ | ||
| 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): | ||
| def __init__(self, random_state: np.random.RandomState): | ||
| super().__init__() | ||
| self.random_state = random_state | ||
| self._processing = False | ||
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| def fit(self, X: Dict[str, Any], y: Optional[Any] = None) -> BaseCoalescer: | ||
| """ | ||
| As no coalescing happens, only check the requirements. | ||
| Args: | ||
| X (Dict[str, Any]): | ||
| fit dictionary | ||
| y (Optional[Any]): | ||
| Parameter to comply with scikit-learn API. Not used. | ||
| Returns: | ||
| instance of self | ||
| """ | ||
| self.check_requirements(X, y) | ||
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| return self | ||
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| @staticmethod | ||
| def get_properties(dataset_properties: Optional[Dict[str, Any]] = None) -> Dict[str, Union[str, bool]]: | ||
| return { | ||
| 'shortname': 'NoCoalescer', | ||
| 'name': 'NoCoalescer', | ||
| 'handles_sparse': True | ||
| } |
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