sklearn.feature_extraction.text
.TfidfTransformer¶
- class sklearn.feature_extraction.text.TfidfTransformer(*, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)[source]¶
Transform a count matrix to a normalized tf or tf-idf representation.
Tf means term-frequency while tf-idf means term-frequency times inverse document-frequency. This is a common term weighting scheme in information retrieval, that has also found good use in document classification.
The goal of using tf-idf instead of the raw frequencies of occurrence of a token in a given document is to scale down the impact of tokens that occur very frequently in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus.
The formula that is used to compute the tf-idf for a term t of a document d in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is computed as idf(t) = log [ n / df(t) ] + 1 (if
smooth_idf=False
), where n is the total number of documents in the document set and df(t) is the document frequency of t; the document frequency is the number of documents in the document set that contain the term t. The effect of adding “1” to the idf in the equation above is that terms with zero idf, i.e., terms that occur in all documents in a training set, will not be entirely ignored. (Note that the idf formula above differs from the standard textbook notation that defines the idf as idf(t) = log [ n / (df(t) + 1) ]).If
smooth_idf=True
(the default), the constant “1” is added to the numerator and denominator of the idf as if an extra document was seen containing every term in the collection exactly once, which prevents zero divisions: idf(t) = log [ (1 + n) / (1 + df(t)) ] + 1.Furthermore, the formulas used to compute tf and idf depend on parameter settings that correspond to the SMART notation used in IR as follows:
Tf is “n” (natural) by default, “l” (logarithmic) when
sublinear_tf=True
. Idf is “t” when use_idf is given, “n” (none) otherwise. Normalization is “c” (cosine) whennorm='l2'
, “n” (none) whennorm=None
.Read more in the User Guide.
- Parameters:
- norm{‘l1’, ‘l2’}, default=’l2’
Each output row will have unit norm, either:
‘l2’: Sum of squares of vector elements is 1. The cosine similarity between two vectors is their dot product when l2 norm has been applied.
‘l1’: Sum of absolute values of vector elements is 1. See
preprocessing.normalize
.None: No normalization.
- use_idfbool, default=True
Enable inverse-document-frequency reweighting. If False, idf(t) = 1.
- smooth_idfbool, default=True
Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions.
- sublinear_tfbool, default=False
Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
- Attributes:
idf_
array of shape (n_features)Inverse document frequency vector, only defined if
use_idf=True
.- n_features_in_int
Number of features seen during fit.
New in version 1.0.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
See also
CountVectorizer
Transforms text into a sparse matrix of n-gram counts.
TfidfVectorizer
Convert a collection of raw documents to a matrix of TF-IDF features.
HashingVectorizer
Convert a collection of text documents to a matrix of token occurrences.
References
[Yates2011]R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 68-74.
[MRS2008]C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 118-120.
Examples
>>> from sklearn.feature_extraction.text import TfidfTransformer >>> from sklearn.feature_extraction.text import CountVectorizer >>> from sklearn.pipeline import Pipeline >>> corpus = ['this is the first document', ... 'this document is the second document', ... 'and this is the third one', ... 'is this the first document'] >>> vocabulary = ['this', 'document', 'first', 'is', 'second', 'the', ... 'and', 'one'] >>> pipe = Pipeline([('count', CountVectorizer(vocabulary=vocabulary)), ... ('tfid', TfidfTransformer())]).fit(corpus) >>> pipe['count'].transform(corpus).toarray() array([[1, 1, 1, 1, 0, 1, 0, 0], [1, 2, 0, 1, 1, 1, 0, 0], [1, 0, 0, 1, 0, 1, 1, 1], [1, 1, 1, 1, 0, 1, 0, 0]]) >>> pipe['tfid'].idf_ array([1. , 1.22314355, 1.51082562, 1. , 1.91629073, 1. , 1.91629073, 1.91629073]) >>> pipe.transform(corpus).shape (4, 8)
Methods
fit
(X[, y])Learn the idf vector (global term weights).
fit_transform
(X[, y])Fit to data, then transform it.
get_feature_names_out
([input_features])Get output feature names for transformation.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
(X[, copy])Transform a count matrix to a tf or tf-idf representation.
- fit(X, y=None)[source]¶
Learn the idf vector (global term weights).
- Parameters:
- Xsparse matrix of shape n_samples, n_features)
A matrix of term/token counts.
- yNone
This parameter is not needed to compute tf-idf.
- Returns:
- selfobject
Fitted transformer.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- Parameters:
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns:
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_feature_names_out(input_features=None)[source]¶
Get output feature names for transformation.
- Parameters:
- input_featuresarray-like of str or None, default=None
Input features.
If
input_features
isNone
, thenfeature_names_in_
is used as feature names in. Iffeature_names_in_
is not defined, then the following input feature names are generated:["x0", "x1", ..., "x(n_features_in_ - 1)"]
.If
input_features
is an array-like, theninput_features
must matchfeature_names_in_
iffeature_names_in_
is defined.
- Returns:
- feature_names_outndarray of str objects
Same as input features.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- property idf_¶
Inverse document frequency vector, only defined if
use_idf=True
.- Returns:
- ndarray of shape (n_features,)
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- transform(X, copy=True)[source]¶
Transform a count matrix to a tf or tf-idf representation.
- Parameters:
- Xsparse matrix of (n_samples, n_features)
A matrix of term/token counts.
- copybool, default=True
Whether to copy X and operate on the copy or perform in-place operations.
- Returns:
- vectorssparse matrix of shape (n_samples, n_features)
Tf-idf-weighted document-term matrix.
Examples using sklearn.feature_extraction.text.TfidfTransformer
¶
Sample pipeline for text feature extraction and evaluation
Semi-supervised Classification on a Text Dataset
Clustering text documents using k-means
FeatureHasher and DictVectorizer Comparison