364 lines
12 KiB
Python
364 lines
12 KiB
Python
# Authors: Lars Buitinck
|
|
# Dan Blanchard <dblanchard@ets.org>
|
|
# License: BSD 3 clause
|
|
|
|
from array import array
|
|
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
|
|
from ..utils.fixes import _Mapping as Mapping
|
|
|
|
|
|
def _tosequence(X):
|
|
"""Turn X into a sequence or ndarray, avoiding a copy if possible."""
|
|
if isinstance(X, Mapping): # single sample
|
|
return [X]
|
|
else:
|
|
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>`.
|
|
|
|
Parameters
|
|
----------
|
|
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
|
|
coding.
|
|
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.
|
|
|
|
Attributes
|
|
----------
|
|
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").
|
|
|
|
Examples
|
|
--------
|
|
>>> 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}]
|
|
True
|
|
>>> 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,
|
|
sort=True):
|
|
self.dtype = dtype
|
|
self.separator = separator
|
|
self.sparse = sparse
|
|
self.sort = sort
|
|
|
|
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"
|
|
" https://github.com/scikit-learn/scikit-learn/issues 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
|
|
|
|
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)
|
|
|
|
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
|
|
|
|
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
|