Class DictVectorizerScikitsLearnNode

Transforms lists of feature-value mappings to vectors.

This node has been automatically generated by wrapping the ``sklearn.feature_extraction.dict_vectorizer.DictVectorizer`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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".

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.

Overrides: object.__init__

Transform feature->value dicts to array or sparse matrix.

This node has been automatically generated by wrapping the sklearn.feature_extraction.dict_vectorizer.DictVectorizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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).

y : (ignored)

Returns

Xa : {array, sparse matrix}

Feature vectors; always 2-d.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Learn a list of feature name -> indices mappings.

This node has been automatically generated by wrapping the sklearn.feature_extraction.dict_vectorizer.DictVectorizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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

Overrides: Node.stop_training