Method encode

numpy_ml/preprocessing/general.py:301–346

Encode a collection of multi-featured examples into a `n_dim`-dimensional feature matrix via feature hashing. Notes ----- Feature hashing works by applying a hash function to the features of an example and using the hash values as column indices in t

(self, examples)

        self.sparse = sparse and _SCIPY

    def encode(self, examples):
        """
        Encode a collection of multi-featured examples into a
        `n_dim`-dimensional feature matrix via feature hashing.

        Notes
        -----
        Feature hashing works by applying a hash function to the features of an
        example and using the hash values as column indices in the resulting
        feature matrix. The entries at each hashed feature column correspond to
        the values for that example and feature. For example, given the
        following two input examples:

            >>> examples = [
                {"furry": 1, "quadruped": 1, "domesticated": 1},
                {"nocturnal": 1, "quadruped": 1},
            ]

        and a hypothetical hash function `H` mapping strings to [0, 127], we have:

            >>> feature_mat = zeros(2, 128)
            >>> ex1_cols = [H("furry"), H("quadruped"), H("domesticated")]
            >>> ex2_cols = [H("nocturnal"), H("quadruped")]
            >>> feat_mat[0, ex1_cols] = 1
            >>> feat_mat[1, ex2_cols] = 1

        To better handle hash collisions, it is common to multiply the feature
        value by the sign of the digest for the corresponding feature name.

        Parameters
        ----------
        examples : dict or list of dicts
            A collection of `N` examples, each represented as a dict where keys
            correspond to the feature name and values correspond to the feature
            value.

        Returns
        -------
        table : :py:class:`ndarray <numpy.ndarray>` or :py:class:`csr_matrix <scipy.sparse.csr_matrix>` of shape `(N, n_dim)`
            The encoded feature matrix
        """
        if isinstance(examples, dict):
            examples = [examples]

        sparse = self.sparse
        return self._encode_sparse(examples) if sparse else self._encode_dense(examples)

    def _encode_dense(self, examples):
        N = len(examples)