Source code for autogluon.features.generators.drop_unique

import logging

from pandas import DataFrame

from autogluon.common.features.feature_metadata import FeatureMetadata
from autogluon.common.features.types import R_CATEGORY, R_OBJECT, S_IMAGE_BYTEARRAY, S_IMAGE_PATH, S_TEXT

from .abstract import AbstractFeatureGenerator

logger = logging.getLogger(__name__)


# TODO: Not necessary to exist after fitting, can just update outer context feature_out/feature_in and then delete this


class DropUniqueFeatureGenerator(AbstractFeatureGenerator):
    """Drops features which only have 1 unique value or which have nearly no repeated values (based on max_unique_ratio) and are of category or object type."""

    def __init__(self, max_unique_ratio=0.99, **kwargs):
        super().__init__(**kwargs)
        self.max_unique_ratio = max_unique_ratio

    def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict):
        features_to_drop = self._drop_unique_features(
            X, self.feature_metadata_in, max_unique_ratio=self.max_unique_ratio
        )
        self._remove_features_in(features_to_drop)
        X_out = X[self.features_in]
        return X_out, self.feature_metadata_in.type_group_map_special

    def _transform(self, X: DataFrame) -> DataFrame:
        return X

    @staticmethod
    def get_default_infer_features_in_args() -> dict:
        return dict()

    # TODO: Consider NaN?
    @staticmethod
    def _drop_unique_features(X: DataFrame, feature_metadata: FeatureMetadata, max_unique_ratio) -> list:
        features_to_drop = []
        X_len = len(X)
        max_unique_value_count = X_len * max_unique_ratio
        for column in X:
            unique_value_count = len(X[column].unique())
            # Drop features that are always the same
            if unique_value_count == 1:
                features_to_drop.append(column)
            elif feature_metadata.get_feature_type_raw(column) in [R_CATEGORY, R_OBJECT] and (
                unique_value_count > max_unique_value_count
            ):
                special_types = feature_metadata.get_feature_types_special(column)
                if S_TEXT in special_types:
                    # We should not drop a text column
                    continue
                elif S_IMAGE_PATH in special_types:
                    # We should not drop an image path column
                    continue
                elif S_IMAGE_BYTEARRAY in special_types:
                    # We should not drop an image bytearray column
                    continue
                else:
                    features_to_drop.append(column)
        return features_to_drop

    def _more_tags(self):
        return {"feature_interactions": False}