Source code for evalml.pipelines.components.transformers.samplers.oversamplers

from evalml.pipelines.components.transformers.samplers.base_sampler import (
    BaseOverSampler
)
from evalml.utils.woodwork_utils import infer_feature_types


[docs]class SMOTESampler(BaseOverSampler): """SMOTE Oversampler component. Works on numerical datasets only. This component is only run during training and not during predict.""" name = "SMOTE Oversampler" hyperparameter_ranges = {}
[docs] def __init__(self, sampling_ratio=0.25, k_neighbors=5, n_jobs=-1, random_seed=0, **kwargs): super().__init__("SMOTE", sampling_ratio=sampling_ratio, k_neighbors=k_neighbors, n_jobs=n_jobs, random_seed=random_seed, **kwargs)
[docs]class SMOTENCSampler(BaseOverSampler): """SMOTENC Oversampler component. Uses SMOTENC to generate synthetic samples. Works on a mix of nomerical and categorical columns. Input data must be Woodwork type, and this component is only run during training and not during predict.""" name = "SMOTENC Oversampler" hyperparameter_ranges = {}
[docs] def __init__(self, sampling_ratio=0.25, k_neighbors=5, n_jobs=-1, random_seed=0, **kwargs): self.categorical_features = None super().__init__("SMOTENC", sampling_ratio=sampling_ratio, k_neighbors=k_neighbors, n_jobs=n_jobs, random_seed=random_seed, **kwargs)
def _get_categorical(self, X): X = infer_feature_types(X) self.categorical_features = [i for i, val in enumerate(X.types['Logical Type'].items()) if str(val[1]) in {'Boolean', 'Categorical'}] self._parameters['categorical_features'] = self.categorical_features
[docs] def fit(self, X, y): # get categorical features first self._get_categorical(X) super().fit(X, y)
[docs]class SMOTENSampler(BaseOverSampler): """SMOTEN Oversampler component. Uses SMOTEN to generate synthetic samples. Works for purely categorical datasets. This component is only run during training and not during predict.""" name = "SMOTEN Oversampler" hyperparameter_ranges = {}
[docs] def __init__(self, sampling_ratio=0.25, k_neighbors=5, n_jobs=-1, random_seed=0, **kwargs): super().__init__("SMOTEN", sampling_ratio=sampling_ratio, k_neighbors=k_neighbors, n_jobs=n_jobs, random_seed=random_seed, **kwargs)