-i>SSKJr SSKJrJr SSKJr SSKJr SSKr SSK J r SSK Jr SSKJrJrJr SS KJr SS KJr "S S \\5rg) )array)IterableMapping)Number) itemgetterN)metadata_routing) BaseEstimatorTransformerMixin _fit_context) check_array)check_is_fittedc^\rSrSr%SrS\R 0rS\/S/S/S.r \ \ S'\ RSS S S.S jrS S S S S .Sjr\"S S9SSj5rSr\"S S9SSj5r\ 4SjrSrSSjrSSjrU4SjrSrU=r$)DictVectorizera 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". If a feature value is a sequence or set of strings, this transformer will iterate over the values and will count the occurrences of each string value. 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 or iterables of strings, the DictVectorizer can be followed by :class:`~sklearn.preprocessing.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. For an efficiency comparison of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. Read more in the :ref:`User Guide `. Parameters ---------- dtype : dtype, default=np.float64 The type of feature values. Passed to Numpy array/scipy.sparse matrix constructors as the dtype argument. separator : str, default="=" Separator string used when constructing new features for one-hot coding. sparse : bool, default=True Whether transform should produce scipy.sparse matrices. sort : bool, default=True Whether ``feature_names_`` and ``vocabulary_`` should be sorted 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 -------- >>> 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.]]) dict_type no_validationbooleandtype separatorsparsesort_parameter_constraints=Tc4XlX lX0lX@lgNr)selfrrrrs ^/var/www/html/venv/lib/python3.13/site-packages/sklearn/feature_extraction/_dict_vectorizer.py__init__DictVectorizer.__init__js "  FNfitting transformingindicesvaluesczUHn [U [5(aU<UR<U <3n Sn O[S[ U 5S35eU(a#X;a[ U5XJ'UR U 5 U(dM{X;dMUR XJ5 UR URU 55 M g)z)Add feature names for iterable of stringszUnsupported type z; in iterable value. Only iterables of string are supported.N) isinstancestrr TypeErrortypelenappendr) rfv feature_namesvocabr$r%r&r'vv feature_names r_add_iterable_element$DictVectorizer._add_iterable_elementpsB"c""+,dnnbA 'Rz2!! <4&)-&8#$$\2| 5u23 djjn-!r")prefer_skip_nested_validationc /n0nUHnUR5Hupg[U[5(aU<UR<U<3nOw[U[5(dUcUnO\[U[ 5(a[ S[U5SUSUS35e[U[5(aSnURXgX45 WcMX;dM[U5XH'URU5 M M UR(a/UR5 [U5V Vs0sHupXi_M nn nX0lX@lU$s snn f)aLearn a list of feature name -> indices mappings. Parameters ---------- X : Mapping or iterable over Mappings Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype). .. versionchanged:: 0.24 Accepts multiple string values for one categorical feature. y : (ignored) Ignored parameter. Returns ------- self : object DictVectorizer class instance. NzUnsupported value type  for : z$. Mapping objects are not supported.)itemsr*r+rrrr,r-rr6r.r/r enumeratefeature_names_ vocabulary_) rXyr2r3xr0r1r5is rfitDictVectorizer.fits2* A a%%/0$..!#DL6**qy#$L7++#1$q'; cA3'==  8,,#'L..q]J+#0.1-.@+%,,\:%"* 99    &/ &>?&>daQT&>E?+  @s"Ec[S5RS:XdS5eURnU(a/n0nOURnURnSn[ U[ 5(aU/OUn[S5nS/n/n UGHWn U R5GH$up[ U [5(aU <UR<U <3n Sn O[ U [5(dU cU n Om[ U [ 5(d.[ U [5(aSn URU U UUUUUU S9 O*[S[U 5S U S U S [U 5S 3 5eU cMU(a#X;a[U5X]'UR!U 5 X;dMUR!X]5 U R!URU 55 GM' UR![U55 GMZ [U5S:Xa [#S 5e[$R&"U[$R(S9n[U5S- [U54n[*R,"XU4XS9nU(avUR.(aeUR/5 [$R0"[U5[$R2S9n[5U5Hunn X[UU'UX['M USS2U4nUR6(aUR95 OUR;5nU(a X@lXPlU$)NrCzsizeof(int) != 4 on your platform; please report this at https://github.com/scikit-learn/scikit-learn/issues and include the output from platform.platform() in your bug reportTrr)r#zUnsupported value Type r:r;z. z objects are not supported.zSample sequence X is empty.r)shaper)ritemsizerr>r?r*rr<r+rrrr6r,r-r.r/ ValueErrornp frombufferintcsp csr_matrixremptyint32r=r sort_indicestoarray)rr@r$rr2r3r%r&indptrr'rBr0r1r5rI result_matrix map_indexnew_vals r _transformDictVectorizer._transforms Sz""a'  N '   ME //M$$E a))QCq*A a%%/0$..!#DLA6**qy#$L#Aw//Jq(4K4K#'L..% '%1 '%/ $1$q'; cA3c7)#>@  +<#<.1-.@+%,,\:#,u':; djjm4A"D MM#g, 'GJ v;! :; ;--rww7Vq#e*- f %U tyy    ]!3288DI' 6 %*X '""7*!Y,7M ;;  & & ()113M "/ $ r"c"URUSS9$)a,Learn a list of feature name -> indices mappings and transform X. Like fit(X) followed by transform(X), but does not require materializing X in memory. Parameters ---------- X : Mapping or iterable over Mappings Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype). .. versionchanged:: 0.24 Accepts multiple string values for one categorical feature. y : (ignored) Ignored parameter. Returns ------- Xa : {array, sparse matrix} Feature vectors; always 2-d. Tr$)rY)rr@rAs r fit_transformDictVectorizer.fit_transform(s0q$//r"c[US5 [USS/S9nURSnURn[ U5Vs/sH oR"5PM nn[ R "U5(a.[UR56HupxXU4XgXH'M U$[U5H2upy[XSS245HupU S:wdM XU4XU'M M4 U$s snf)aTransform array or sparse matrix X back to feature mappings. X must have been produced by this DictVectorizer's transform or fit_transform method; it may only have passed through transformers that preserve the number of features and their order. In the case of one-hot/one-of-K coding, the constructed feature names and values are returned rather than the original ones. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Sample matrix. dict_type : type, default=dict Constructor for feature mappings. Must conform to the collections.Mapping API. Returns ------- X_original : list of dict_type objects of shape (n_samples,) Feature mappings for the samples in X. r>csrcsc) accept_sparserN) rr rIr>rangerOissparsezipnonzeror=) rr@r n_samplesnames_dictsrCjdr1s rinverse_transform DictVectorizer.inverse_transformBs. ./ % 8GGAJ ##&+I&67&6&67 ;;q>>QYY[)%&!tW"* "%(%a1g.DAAv&'1g( /)  8sCc>[USS/5 URUSS9$)aTransform 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 of shape (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. r>r?Fr\)rrY)rr@s r transformDictVectorizer.transformms'" /?@q%00r"c[US5 [SUR55(a&URVs/sHn[U5PM nnO URn[R "U[ S9$s snf)aGet output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Not used, present here for API consistency by convention. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. r>c3L# UHn[U[5(+v M g7fr)r*r+).0names r 7DictVectorizer.get_feature_names_out..sI5HT:dC(((5Hs"$rH)ranyr>r+rLasarrayobject)rinput_featuresrur2s rget_feature_names_out$DictVectorizer.get_feature_names_outsj ./ IT5H5HI I I373F3FG3F4SY3FMGM //Mzz-v66HsA9c:[US5 U(d[R"U5SnURn0nUHn[ U5XCU'M X@l[ UR5[S5S9VVs/sHupeUPM snnUlU$s snnf)aeRestrict 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 : bool, default=False Whether support is a list of indices. Returns ------- self : object DictVectorizer class instance. 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_out() array(['bar', 'baz', 'foo'], ...) >>> v.restrict(support.get_support()) DictVectorizer() >>> v.get_feature_names_out() array(['bar', 'foo'], ...) r>rr))key) rrLwherer>r.r?sortedr<r)rsupportr&rh new_vocabrCr0s rrestrictDictVectorizer.restrictsB ./hhw'*G## A"%i.IAh % !2 1 F F$!AF   s>Bch>[TU]5nSURlSURlU$)NTF)super__sklearn_tags__ input_tagsdict two_d_array)rtags __class__s rrDictVectorizer.__sklearn_tags__s-w')#&+# r")rr>rrrr?r)F)__name__ __module__ __qualname____firstlineno____doc__rUNUSED4_DictVectorizer__metadata_request__inverse_transformr+rr__annotations__rLfloat64r r6r rDrYr]rmrpr|rr__static_attributes__ __classcell__)rs@rrrsJZ.9:J:Q:Q,R)!U+ $D!# c$T.>5363jaF50602.2)V1(7(0dr"r)rcollections.abcrrnumbersroperatorrnumpyrL scipy.sparserrO sklearn.utilsrbaser r r utilsr utils.validationrrr"rrs9-*@@.x%}xr"