427 lines
15 KiB
Python
427 lines
15 KiB
Python
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# Authors: Lars Buitinck
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# Dan Blanchard <dblanchard@ets.org>
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# License: BSD 3 clause
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from array import array
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from collections.abc import Mapping, Iterable
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from operator import itemgetter
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from numbers import Number
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import numpy as np
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import scipy.sparse as sp
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from ..base import BaseEstimator, TransformerMixin
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from ..utils import check_array, tosequence
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from ..utils.validation import _deprecate_positional_args
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def _tosequence(X):
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"""Turn X into a sequence or ndarray, avoiding a copy if possible."""
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if isinstance(X, Mapping): # single sample
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return [X]
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else:
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return tosequence(X)
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class DictVectorizer(TransformerMixin, BaseEstimator):
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"""Transforms lists of feature-value mappings to vectors.
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This transformer turns lists of mappings (dict-like objects) of feature
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names to feature values into Numpy arrays or scipy.sparse matrices for use
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with scikit-learn estimators.
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When feature values are strings, this transformer will do a binary one-hot
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(aka one-of-K) coding: one boolean-valued feature is constructed for each
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of the possible string values that the feature can take on. For instance,
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a feature "f" that can take on the values "ham" and "spam" will become two
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features in the output, one signifying "f=ham", the other "f=spam".
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If a feature value is a sequence or set of strings, this transformer
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will iterate over the values and will count the occurrences of each string
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value.
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However, note that this transformer will only do a binary one-hot encoding
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when feature values are of type string. If categorical features are
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represented as numeric values such as int or iterables of strings, the
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DictVectorizer can be followed by
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:class:`~sklearn.preprocessing.OneHotEncoder` to complete
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binary one-hot encoding.
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Features that do not occur in a sample (mapping) will have a zero value
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in the resulting array/matrix.
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Read more in the :ref:`User Guide <dict_feature_extraction>`.
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Parameters
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----------
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dtype : dtype, default=np.float64
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The type of feature values. Passed to Numpy array/scipy.sparse matrix
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constructors as the dtype argument.
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separator : str, default="="
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Separator string used when constructing new features for one-hot
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coding.
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sparse : bool, default=True
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Whether transform should produce scipy.sparse matrices.
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sort : bool, default=True
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Whether ``feature_names_`` and ``vocabulary_`` should be
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sorted when fitting.
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Attributes
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----------
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vocabulary_ : dict
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A dictionary mapping feature names to feature indices.
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feature_names_ : list
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A list of length n_features containing the feature names (e.g., "f=ham"
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and "f=spam").
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Examples
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--------
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>>> from sklearn.feature_extraction import DictVectorizer
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>>> v = DictVectorizer(sparse=False)
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>>> D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}]
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>>> X = v.fit_transform(D)
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>>> X
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array([[2., 0., 1.],
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[0., 1., 3.]])
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>>> v.inverse_transform(X) == [{'bar': 2.0, 'foo': 1.0},
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... {'baz': 1.0, 'foo': 3.0}]
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True
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>>> v.transform({'foo': 4, 'unseen_feature': 3})
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array([[0., 0., 4.]])
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See Also
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--------
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FeatureHasher : Performs vectorization using only a hash function.
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sklearn.preprocessing.OrdinalEncoder : Handles nominal/categorical
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features encoded as columns of arbitrary data types.
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"""
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@_deprecate_positional_args
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def __init__(self, *, dtype=np.float64, separator="=", sparse=True,
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sort=True):
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self.dtype = dtype
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self.separator = separator
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self.sparse = sparse
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self.sort = sort
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def _add_iterable_element(self, f, v, feature_names, vocab, *,
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fitting=True, transforming=False,
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indices=None, values=None):
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"""Add feature names for iterable of strings"""
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for vv in v:
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if isinstance(vv, str):
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feature_name = "%s%s%s" % (f, self.separator, vv)
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vv = 1
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else:
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raise TypeError(f'Unsupported type {type(vv)} in iterable '
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'value. Only iterables of string are '
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'supported.')
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if fitting and feature_name not in vocab:
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vocab[feature_name] = len(feature_names)
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feature_names.append(feature_name)
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if transforming and feature_name in vocab:
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indices.append(vocab[feature_name])
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values.append(self.dtype(vv))
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return
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def fit(self, X, y=None):
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"""Learn a list of feature name -> indices mappings.
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Parameters
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----------
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X : Mapping or iterable over Mappings
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Dict(s) or Mapping(s) from feature names (arbitrary Python
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objects) to feature values (strings or convertible to dtype).
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.. versionchanged:: 0.24
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Accepts multiple string values for one categorical feature.
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y : (ignored)
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Returns
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-------
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self
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"""
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feature_names = []
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vocab = {}
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for x in X:
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for f, v in x.items():
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if isinstance(v, str):
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feature_name = "%s%s%s" % (f, self.separator, v)
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v = 1
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elif isinstance(v, Number) or (v is None):
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feature_name = f
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elif isinstance(v, Mapping):
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raise TypeError(f'Unsupported value type {type(v)} '
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f'for {f}: {v}.\n'
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'Mapping objects are not supported.')
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elif isinstance(v, Iterable):
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feature_name = None
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self._add_iterable_element(f, v, feature_names, vocab)
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if feature_name is not None:
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if feature_name not in vocab:
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vocab[feature_name] = len(feature_names)
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feature_names.append(feature_name)
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if self.sort:
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feature_names.sort()
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vocab = {f: i for i, f in enumerate(feature_names)}
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self.feature_names_ = feature_names
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self.vocabulary_ = vocab
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return self
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def _transform(self, X, fitting):
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# Sanity check: Python's array has no way of explicitly requesting the
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# signed 32-bit integers that scipy.sparse needs, so we use the next
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# best thing: typecode "i" (int). However, if that gives larger or
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# smaller integers than 32-bit ones, np.frombuffer screws up.
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assert array("i").itemsize == 4, (
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"sizeof(int) != 4 on your platform; please report this at"
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" https://github.com/scikit-learn/scikit-learn/issues and"
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" include the output from platform.platform() in your bug report")
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dtype = self.dtype
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if fitting:
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feature_names = []
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vocab = {}
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else:
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feature_names = self.feature_names_
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vocab = self.vocabulary_
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transforming = True
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# Process everything as sparse regardless of setting
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X = [X] if isinstance(X, Mapping) else X
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indices = array("i")
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indptr = [0]
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# XXX we could change values to an array.array as well, but it
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# would require (heuristic) conversion of dtype to typecode...
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values = []
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# collect all the possible feature names and build sparse matrix at
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# same time
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for x in X:
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for f, v in x.items():
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if isinstance(v, str):
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feature_name = "%s%s%s" % (f, self.separator, v)
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v = 1
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elif isinstance(v, Number) or (v is None):
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feature_name = f
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elif isinstance(v, Mapping):
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raise TypeError(f'Unsupported value Type {type(v)} '
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f'for {f}: {v}.\n'
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'Mapping objects are not supported.')
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elif isinstance(v, Iterable):
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feature_name = None
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self._add_iterable_element(f, v, feature_names, vocab,
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fitting=fitting,
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transforming=transforming,
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indices=indices, values=values)
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if feature_name is not None:
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if fitting and feature_name not in vocab:
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vocab[feature_name] = len(feature_names)
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feature_names.append(feature_name)
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if feature_name in vocab:
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indices.append(vocab[feature_name])
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values.append(self.dtype(v))
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indptr.append(len(indices))
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if len(indptr) == 1:
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raise ValueError("Sample sequence X is empty.")
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indices = np.frombuffer(indices, dtype=np.intc)
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shape = (len(indptr) - 1, len(vocab))
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result_matrix = sp.csr_matrix((values, indices, indptr),
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shape=shape, dtype=dtype)
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# Sort everything if asked
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if fitting and self.sort:
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feature_names.sort()
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map_index = np.empty(len(feature_names), dtype=np.int32)
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for new_val, f in enumerate(feature_names):
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map_index[new_val] = vocab[f]
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vocab[f] = new_val
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result_matrix = result_matrix[:, map_index]
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if self.sparse:
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result_matrix.sort_indices()
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else:
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result_matrix = result_matrix.toarray()
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if fitting:
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self.feature_names_ = feature_names
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self.vocabulary_ = vocab
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return result_matrix
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def fit_transform(self, X, y=None):
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"""Learn a list of feature name -> indices mappings and transform X.
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Like fit(X) followed by transform(X), but does not require
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materializing X in memory.
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Parameters
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----------
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X : Mapping or iterable over Mappings
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Dict(s) or Mapping(s) from feature names (arbitrary Python
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objects) to feature values (strings or convertible to dtype).
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.. versionchanged:: 0.24
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Accepts multiple string values for one categorical feature.
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y : (ignored)
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Returns
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-------
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Xa : {array, sparse matrix}
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Feature vectors; always 2-d.
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"""
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return self._transform(X, fitting=True)
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def inverse_transform(self, X, dict_type=dict):
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"""Transform array or sparse matrix X back to feature mappings.
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X must have been produced by this DictVectorizer's transform or
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fit_transform method; it may only have passed through transformers
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that preserve the number of features and their order.
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In the case of one-hot/one-of-K coding, the constructed feature
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names and values are returned rather than the original ones.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Sample matrix.
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dict_type : type, default=dict
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Constructor for feature mappings. Must conform to the
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collections.Mapping API.
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Returns
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-------
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D : list of dict_type objects of shape (n_samples,)
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Feature mappings for the samples in X.
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"""
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# COO matrix is not subscriptable
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X = check_array(X, accept_sparse=['csr', 'csc'])
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n_samples = X.shape[0]
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names = self.feature_names_
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dicts = [dict_type() for _ in range(n_samples)]
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if sp.issparse(X):
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for i, j in zip(*X.nonzero()):
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dicts[i][names[j]] = X[i, j]
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else:
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for i, d in enumerate(dicts):
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for j, v in enumerate(X[i, :]):
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if v != 0:
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d[names[j]] = X[i, j]
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return dicts
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def transform(self, X):
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"""Transform feature->value dicts to array or sparse matrix.
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Named features not encountered during fit or fit_transform will be
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silently ignored.
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Parameters
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----------
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X : Mapping or iterable over Mappings of shape (n_samples,)
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Dict(s) or Mapping(s) from feature names (arbitrary Python
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objects) to feature values (strings or convertible to dtype).
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Returns
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-------
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Xa : {array, sparse matrix}
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Feature vectors; always 2-d.
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"""
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if self.sparse:
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return self._transform(X, fitting=False)
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else:
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dtype = self.dtype
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vocab = self.vocabulary_
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X = _tosequence(X)
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Xa = np.zeros((len(X), len(vocab)), dtype=dtype)
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for i, x in enumerate(X):
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for f, v in x.items():
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if isinstance(v, str):
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f = "%s%s%s" % (f, self.separator, v)
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v = 1
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try:
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Xa[i, vocab[f]] = dtype(v)
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except KeyError:
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pass
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return Xa
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def get_feature_names(self):
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"""Returns a list of feature names, ordered by their indices.
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If one-of-K coding is applied to categorical features, this will
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include the constructed feature names but not the original ones.
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"""
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return self.feature_names_
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def restrict(self, support, indices=False):
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"""Restrict the features to those in support using feature selection.
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This function modifies the estimator in-place.
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Parameters
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----------
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||
|
|
support : array-like
|
||
|
|
Boolean mask or list of indices (as returned by the get_support
|
||
|
|
member of feature selectors).
|
||
|
|
indices : bool, default=False
|
||
|
|
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())
|
||
|
|
DictVectorizer()
|
||
|
|
>>> 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(new_vocab.items(),
|
||
|
|
key=itemgetter(1))]
|
||
|
|
|
||
|
|
return self
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
return {'X_types': ["dict"]}
|