ray-project--ray/python/ray/data/preprocessors/hasher.py

import collections
from typing import Any, Dict, List

import pandas as pd

from ray.data.preprocessor import SerializablePreprocessorBase
from ray.data.preprocessors.utils import (
    _PublicField,
    migrate_private_fields,
    simple_hash,
)
from ray.data.preprocessors.version_support import SerializablePreprocessor
from ray.util.annotations import PublicAPI


@PublicAPI(stability="alpha")
@SerializablePreprocessor(version=1, identifier="io.ray.preprocessors.feature_hasher")
class FeatureHasher(SerializablePreprocessorBase):
    r"""Apply the `hashing trick <https://en.wikipedia.org/wiki/Feature_hashing>`_ to a
    table that describes token frequencies.

    :class:`FeatureHasher` creates ``num_features`` columns named ``hash_{index}``,
    where ``index`` ranges from :math:`0` to ``num_features``:math:`- 1`. The column
    ``hash_{index}`` describes the frequency of tokens that hash to ``index``.

    Distinct tokens can correspond to the same index. However, if ``num_features`` is
    large enough, then columns probably correspond to a unique token.

    This preprocessor is memory efficient and quick to pickle. However, given a
    transformed column, you can't know which tokens correspond to it. This might make it
    hard to determine which tokens are important to your model.

    .. warning::
        Sparse matrices aren't supported. If you use a large ``num_features``, this
        preprocessor might behave poorly.

    Examples:

        >>> import pandas as pd
        >>> import ray
        >>> from ray.data.preprocessors import FeatureHasher

        The data below describes the frequencies of tokens in ``"I like Python"`` and
        ``"I dislike Python"``.

        >>> df = pd.DataFrame({
        ...     "I": [1, 1],
        ...     "like": [1, 0],
        ...     "dislike": [0, 1],
        ...     "Python": [1, 1]
        ... })
        >>> ds = ray.data.from_pandas(df)  # doctest: +SKIP

        :class:`FeatureHasher` hashes each token to determine its index. For example,
        the index of ``"I"`` is :math:`hash(\\texttt{"I"}) \pmod 8 = 5`.

        >>> hasher = FeatureHasher(columns=["I", "like", "dislike", "Python"], num_features=8, output_column = "hashed")
        >>> hasher.fit_transform(ds)["hashed"].to_pandas().to_numpy()  # doctest: +SKIP
        array([[0, 0, 0, 2, 0, 1, 0, 0],
               [0, 0, 0, 1, 0, 1, 1, 0]])

        Notice the hash collision: both ``"like"`` and ``"Python"`` correspond to index
        :math:`3`. You can avoid hash collisions like these by increasing
        ``num_features``.

    Args:
        columns: The columns to apply the hashing trick to. Each column should describe
            the frequency of a token.
        num_features: The number of features used to represent the vocabulary. You
            should choose a value large enough to prevent hash collisions between
            distinct tokens.
        output_column: The name of the column that contains the hashed features.

    .. seealso::
        :class:`~ray.data.preprocessors.CountVectorizer`
            Use this preprocessor to generate inputs for :class:`FeatureHasher`.

        :class:`ray.data.preprocessors.HashingVectorizer`
            If your input data describes documents rather than token frequencies,
            use :class:`~ray.data.preprocessors.HashingVectorizer`.
    """  # noqa: E501

    _is_fittable = False

    def __init__(
        self,
        columns: List[str],
        num_features: int,
        output_column: str,
    ):
        super().__init__()
        self._columns = columns
        # TODO(matt): Set default number of features.
        # This likely requires sparse matrix support to avoid explosion of columns.
        self._num_features = num_features
        self._output_column = output_column

    @property
    def columns(self) -> List[str]:
        return self._columns

    @property
    def num_features(self) -> int:
        return self._num_features

    @property
    def output_column(self) -> str:
        return self._output_column

    def _transform_pandas(self, df: pd.DataFrame):
        # TODO(matt): Use sparse matrix for efficiency.
        def row_feature_hasher(row):
            hash_counts = collections.defaultdict(int)
            for column in self._columns:
                hashed_value = simple_hash(column, self._num_features)
                hash_counts[hashed_value] += row[column]
            return {f"hash_{i}": hash_counts[i] for i in range(self._num_features)}

        feature_columns = df.loc[:, self._columns].apply(
            row_feature_hasher, axis=1, result_type="expand"
        )

        # Concatenate the hash columns
        hash_columns = [f"hash_{i}" for i in range(self._num_features)]
        concatenated = feature_columns[hash_columns].to_numpy()
        # Use a Pandas Series for column assignment to get more consistent
        # behavior across Pandas versions.
        df.loc[:, self._output_column] = pd.Series(list(concatenated))

        return df

    def get_input_columns(self) -> List[str]:
        return self._columns

    def get_output_columns(self) -> List[str]:
        return [self._output_column]

    def __repr__(self):
        return (
            f"{self.__class__.__name__}(columns={self._columns!r}, "
            f"num_features={self._num_features!r}, "
            f"output_column={self._output_column!r})"
        )

    def _get_serializable_fields(self) -> Dict[str, Any]:
        return {
            "columns": self._columns,
            "num_features": self._num_features,
            "output_column": self._output_column,
        }

    def _set_serializable_fields(self, fields: Dict[str, Any], version: int):
        # required fields
        self._columns = fields["columns"]
        self._num_features = fields["num_features"]
        self._output_column = fields["output_column"]

    def __setstate__(self, state: Dict[str, Any]) -> None:
        super().__setstate__(state)
        migrate_private_fields(
            self,
            fields={
                "_columns": _PublicField(public_field="columns"),
                "_num_features": _PublicField(public_field="num_features"),
                "_output_column": _PublicField(public_field="output_column"),
            },
        )