sklearn.feature_extraction.DictVectorizer¶
- class sklearn.feature_extraction.DictVectorizer(dtype=<type 'numpy.float64'>, separator='=', sparse=True)¶
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”.
Features that do not occur in a sample (mapping) will have a zero value in the resulting array/matrix.
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.
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”).
See also
- FeatureHasher
- performs vectorization using only a hash function.
- sklearn.preprocessing.OneHotEncoder
- handles nominal/categorical features encoded as columns of integers.
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.]])
Methods
fit(X[, y]) Learn a list of feature name -> indices mappings. fit_transform(X[, y]) Learn a list of feature name -> indices mappings and transform X. get_feature_names() Returns a list of feature names, ordered by their indices. get_params([deep]) Get parameters for this estimator. inverse_transform(X[, dict_type]) Transform array or sparse matrix X back to feature mappings. restrict(support[, indices]) Restrict the features to those in support. set_params(**params) Set the parameters of this estimator. transform(X[, y]) Transform feature->value dicts to array or sparse matrix. - __init__(dtype=<type 'numpy.float64'>, separator='=', sparse=True)¶
- fit(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 :
- fit_transform(X, y=None)¶
Learn a list of feature name -> indices mappings and transform X.
Like fit(X) followed by transform(X).
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.
Notes
Because this method requires two passes over X, it materializes X in memory.
- get_feature_names()¶
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.
- get_params(deep=True)¶
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- inverse_transform(X, dict_type=<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.
- restrict(support, indices=False)¶
Restrict the features to those in support.
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.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
- transform(X, y=None)¶
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).
y : (ignored)
Returns: Xa : {array, sparse matrix}
Feature vectors; always 2-d.
