Source code for sklearn.feature_extraction.dict_vectorizer

# Authors: Lars Buitinck
#          Dan Blanchard <>
# License: BSD 3 clause

from array import array
from collections import Mapping
from operator import itemgetter

import numpy as np
import scipy.sparse as sp

from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..externals.six.moves import xrange
from ..utils import check_array, tosequence

def _tosequence(X):
    """Turn X into a sequence or ndarray, avoiding a copy if possible."""
    if isinstance(X, Mapping):  # single sample
        return [X]
        return tosequence(X)

class DictVectorizer(BaseEstimator, TransformerMixin):
    """Transforms lists of feature-value mappings to vectors.

    This transformer turns lists of mappings (dict-like objects) of feature
    names to feature values into Numpy arrays or scipy.sparse matrices for use
    with scikit-learn estimators.

    When feature values are strings, this transformer will do a binary one-hot
    (aka one-of-K) coding: one boolean-valued feature is constructed for each
    of the possible string values that the feature can take on. For instance,
    a feature "f" that can take on the values "ham" and "spam" will become two
    features in the output, one signifying "f=ham", the other "f=spam".

    However, note that this transformer will only do a binary one-hot encoding
    when feature values are of type string. If categorical features are
    represented as numeric values such as int, the DictVectorizer can be
    followed by OneHotEncoder to complete binary one-hot encoding.

    Features that do not occur in a sample (mapping) will have a zero value
    in the resulting array/matrix.

    Read more in the :ref:`User Guide <dict_feature_extraction>`.

    dtype : callable, optional
        The type of feature values. Passed to Numpy array/scipy.sparse matrix
        constructors as the dtype argument.
    separator : string, optional
        Separator string used when constructing new features for one-hot
    sparse : boolean, optional.
        Whether transform should produce scipy.sparse matrices.
        True by default.
    sort : boolean, optional.
        Whether ``feature_names_`` and ``vocabulary_`` should be sorted when fitting.
        True by default.

    vocabulary_ : dict
        A dictionary mapping feature names to feature indices.

    feature_names_ : list
        A list of length n_features containing the feature names (e.g., "f=ham"
        and "f=spam").

    >>> from sklearn.feature_extraction import DictVectorizer
    >>> v = DictVectorizer(sparse=False)
    >>> D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}]
    >>> X = v.fit_transform(D)
    >>> X
    array([[ 2.,  0.,  1.],
           [ 0.,  1.,  3.]])
    >>> v.inverse_transform(X) == \
        [{'bar': 2.0, 'foo': 1.0}, {'baz': 1.0, 'foo': 3.0}]
    >>> v.transform({'foo': 4, 'unseen_feature': 3})
    array([[ 0.,  0.,  4.]])

    See also
    FeatureHasher : performs vectorization using only a hash function.
    sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features
      encoded as columns of integers.

    def __init__(self, dtype=np.float64, separator="=", sparse=True,
        self.dtype = dtype
        self.separator = separator
        self.sparse = sparse
        self.sort = sort

[docs] def fit(self, X, y=None): """Learn a list of feature name -> indices mappings. Parameters ---------- X : Mapping or iterable over Mappings Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype). y : (ignored) Returns ------- self """ feature_names = [] vocab = {} for x in X: for f, v in six.iteritems(x): if isinstance(v, six.string_types): f = "%s%s%s" % (f, self.separator, v) if f not in vocab: feature_names.append(f) vocab[f] = len(vocab) if self.sort: feature_names.sort() vocab = dict((f, i) for i, f in enumerate(feature_names)) self.feature_names_ = feature_names self.vocabulary_ = vocab return self
def _transform(self, X, fitting): # Sanity check: Python's array has no way of explicitly requesting the # signed 32-bit integers that scipy.sparse needs, so we use the next # best thing: typecode "i" (int). However, if that gives larger or # smaller integers than 32-bit ones, np.frombuffer screws up. assert array("i").itemsize == 4, ( "sizeof(int) != 4 on your platform; please report this at" " and" " include the output from platform.platform() in your bug report") dtype = self.dtype if fitting: feature_names = [] vocab = {} else: feature_names = self.feature_names_ vocab = self.vocabulary_ # Process everything as sparse regardless of setting X = [X] if isinstance(X, Mapping) else X indices = array("i") indptr = array("i", [0]) # XXX we could change values to an array.array as well, but it # would require (heuristic) conversion of dtype to typecode... values = [] # collect all the possible feature names and build sparse matrix at # same time for x in X: for f, v in six.iteritems(x): if isinstance(v, six.string_types): f = "%s%s%s" % (f, self.separator, v) v = 1 if f in vocab: indices.append(vocab[f]) values.append(dtype(v)) else: if fitting: feature_names.append(f) vocab[f] = len(vocab) indices.append(vocab[f]) values.append(dtype(v)) indptr.append(len(indices)) if len(indptr) == 1: raise ValueError("Sample sequence X is empty.") indices = np.frombuffer(indices, dtype=np.intc) indptr = np.frombuffer(indptr, dtype=np.intc) shape = (len(indptr) - 1, len(vocab)) result_matrix = sp.csr_matrix((values, indices, indptr), shape=shape, dtype=dtype) # Sort everything if asked if fitting and self.sort: feature_names.sort() map_index = np.empty(len(feature_names), dtype=np.int32) for new_val, f in enumerate(feature_names): map_index[new_val] = vocab[f] vocab[f] = new_val result_matrix = result_matrix[:, map_index] if self.sparse: result_matrix.sort_indices() else: result_matrix = result_matrix.toarray() if fitting: self.feature_names_ = feature_names self.vocabulary_ = vocab return result_matrix
[docs] def fit_transform(self, X, y=None): """Learn a list of feature name -> indices mappings and transform X. Like fit(X) followed by transform(X), but does not require materializing X in memory. Parameters ---------- X : Mapping or iterable over Mappings Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype). y : (ignored) Returns ------- Xa : {array, sparse matrix} Feature vectors; always 2-d. """ return self._transform(X, fitting=True)
[docs] def inverse_transform(self, X, dict_type=dict): """Transform array or sparse matrix X back to feature mappings. X must have been produced by this DictVectorizer's transform or fit_transform method; it may only have passed through transformers that preserve the number of features and their order. In the case of one-hot/one-of-K coding, the constructed feature names and values are returned rather than the original ones. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Sample matrix. dict_type : callable, optional Constructor for feature mappings. Must conform to the collections.Mapping API. Returns ------- D : list of dict_type objects, length = n_samples Feature mappings for the samples in X. """ # COO matrix is not subscriptable X = check_array(X, accept_sparse=['csr', 'csc']) n_samples = X.shape[0] names = self.feature_names_ dicts = [dict_type() for _ in xrange(n_samples)] if sp.issparse(X): for i, j in zip(*X.nonzero()): dicts[i][names[j]] = X[i, j] else: for i, d in enumerate(dicts): for j, v in enumerate(X[i, :]): if v != 0: d[names[j]] = X[i, j] return dicts
[docs] def transform(self, X): """Transform feature->value dicts to array or sparse matrix. Named features not encountered during fit or fit_transform will be silently ignored. Parameters ---------- X : Mapping or iterable over Mappings, length = n_samples Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype). Returns ------- Xa : {array, sparse matrix} Feature vectors; always 2-d. """ if self.sparse: return self._transform(X, fitting=False) else: dtype = self.dtype vocab = self.vocabulary_ X = _tosequence(X) Xa = np.zeros((len(X), len(vocab)), dtype=dtype) for i, x in enumerate(X): for f, v in six.iteritems(x): if isinstance(v, six.string_types): f = "%s%s%s" % (f, self.separator, v) v = 1 try: Xa[i, vocab[f]] = dtype(v) except KeyError: pass return Xa
def get_feature_names(self): """Returns a list of feature names, ordered by their indices. If one-of-K coding is applied to categorical features, this will include the constructed feature names but not the original ones. """ return self.feature_names_ def restrict(self, support, indices=False): """Restrict the features to those in support using feature selection. This function modifies the estimator in-place. Parameters ---------- support : array-like Boolean mask or list of indices (as returned by the get_support member of feature selectors). indices : boolean, optional Whether support is a list of indices. Returns ------- self Examples -------- >>> from sklearn.feature_extraction import DictVectorizer >>> from sklearn.feature_selection import SelectKBest, chi2 >>> v = DictVectorizer() >>> D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}] >>> X = v.fit_transform(D) >>> support = SelectKBest(chi2, k=2).fit(X, [0, 1]) >>> v.get_feature_names() ['bar', 'baz', 'foo'] >>> v.restrict(support.get_support()) # doctest: +ELLIPSIS DictVectorizer(dtype=..., separator='=', sort=True, sparse=True) >>> v.get_feature_names() ['bar', 'foo'] """ if not indices: support = np.where(support)[0] names = self.feature_names_ new_vocab = {} for i in support: new_vocab[names[i]] = len(new_vocab) self.vocabulary_ = new_vocab self.feature_names_ = [f for f, i in sorted(six.iteritems(new_vocab), key=itemgetter(1))] return self