DictVectorizer#

classsklearn.feature_extraction.DictVectorizer(*,dtype=<class'numpy.float64'>,separator='=',sparse=True,sort=True)[source]#

Transforms lists of feature-value mappings to vectors.

This transformer turns lists of mappings (dict-like objects) of featurenames to feature values into Numpy arrays or scipy.sparse matrices for usewith 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 eachof 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 twofeatures in the output, one signifying “f=ham”, the other “f=spam”.

If a feature value is a sequence or set of strings, this transformerwill iterate over the values and will count the occurrences of each stringvalue.

However, note that this transformer will only do a binary one-hot encodingwhen feature values are of type string. If categorical features arerepresented as numeric values such as int or iterables of strings, theDictVectorizer can be followed byOneHotEncoder to completebinary one-hot encoding.

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

For an efficiency comparison of the different feature extractors, seeFeatureHasher and DictVectorizer Comparison.

Read more in theUser Guide.

Parameters:

dtypedtype, default=np.float64

The type of feature values. Passed to Numpy array/scipy.sparse matrixconstructors as the dtype argument.

separatorstr, default=”=”

Separator string used when constructing new features for one-hotcoding.

sparsebool, default=True

Whether transform should produce scipy.sparse matrices.

sortbool, default=True

Whetherfeature_names_ andvocabulary_ should besorted when fitting.

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.OrdinalEncoder

Handles nominal/categorical features encoded as columns of arbitrary data types.

Examples

>>>fromsklearn.feature_extractionimportDictVectorizer>>>v=DictVectorizer(sparse=False)>>>D=[{'foo':1,'bar':2},{'foo':3,'baz':1}]>>>X=v.fit_transform(D)>>>Xarray([[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.]])

fit(X,y=None)[source]#

Learn a list of feature name -> indices mappings.

Parameters:

XMapping or iterable over Mappings

Dict(s) or Mapping(s) from feature names (arbitrary Pythonobjects) to feature values (strings or convertible to dtype).

Changed in version 0.24:Accepts multiple string values for one categorical feature.

y(ignored)

Ignored parameter.

Returns:

selfobject

DictVectorizer class instance.

fit_transform(X,y=None)[source]#

Learn a list of feature name -> indices mappings and transform X.

Like fit(X) followed by transform(X), but does not requirematerializing X in memory.

Parameters:

XMapping or iterable over Mappings

Dict(s) or Mapping(s) from feature names (arbitrary Pythonobjects) to feature values (strings or convertible to dtype).

Changed in version 0.24:Accepts multiple string values for one categorical feature.

y(ignored)

Ignored parameter.

Returns:

Xa{array, sparse matrix}

Feature vectors; always 2-d.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Not used, present here for API consistency by convention.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

inverse_transform(X,dict_type=<class'dict'>)[source]#

Transform array or sparse matrix X back to feature mappings.

X must have been produced by this DictVectorizer’s transform orfit_transform method; it may only have passed through transformersthat preserve the number of features and their order.

In the case of one-hot/one-of-K coding, the constructed featurenames and values are returned rather than the original ones.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Sample matrix.

dict_typetype, default=dict

Constructor for feature mappings. Must conform to thecollections.Mapping API.

Returns:

X_originallist of dict_type objects of shape (n_samples,)

Feature mappings for the samples in X.

restrict(support,indices=False)[source]#

Restrict the features to those in support using feature selection.

This function modifies the estimator in-place.

Parameters:

supportarray-like

Boolean mask or list of indices (as returned by the get_supportmember of feature selectors).

indicesbool, default=False

Whether support is a list of indices.

Returns:

selfobject

DictVectorizer class instance.

Examples

>>>fromsklearn.feature_extractionimportDictVectorizer>>>fromsklearn.feature_selectionimportSelectKBest,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_out()array(['bar', 'baz', 'foo'], ...)>>>v.restrict(support.get_support())DictVectorizer()>>>v.get_feature_names_out()array(['bar', 'foo'], ...)

set_output(*,transform=None)[source]#

Set output container.

SeeIntroducing the set_output APIfor an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output oftransform andfit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• "polars": Polars output

• None: Transform configuration is unchanged

Added in version 1.4:"polars" option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects(such asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(X)[source]#

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

Named features not encountered during fit or fit_transform will besilently ignored.

Parameters:

XMapping or iterable over Mappings of shape (n_samples,)

Dict(s) or Mapping(s) from feature names (arbitrary Pythonobjects) to feature values (strings or convertible to dtype).

Returns:

Xa{array, sparse matrix}

Feature vectors; always 2-d.

Gallery examples#

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

FeatureHasher and DictVectorizer Comparison

FeatureHasher and DictVectorizer Comparison