8.7.2.5. sklearn.feature_extraction.text.TfidfTransformer

class sklearn.feature_extraction.text.TfidfTransformer(norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)

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.

In the SMART notation used in IR, this class implements several tf–idf variants. Tf is always “n” (natural), idf is “t” iff use_idf is given, “n” otherwise, and normalization is “c” iff norm=’l2’, “n” iff norm=None.

Parameters :

norm : ‘l1’, ‘l2’ or None, optional

Norm used to normalize term vectors. None for no normalization.

use_idf : boolean, optional

Enable inverse-document-frequency reweighting.

smooth_idf : boolean, optional

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_tf : boolean, optional

Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).

References

[Yates2011]

R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 68–74.

[MSR2008]

C.D. Manning, H. Schütze and P. Raghavan (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 121–125.

Methods

fit(X[, y])	Learn the idf vector (global term weights)
fit_transform(X[, y])	Fit to data, then transform it
get_params([deep])	Get parameters for the estimator
set_params(**params)	Set the parameters of the estimator.
transform(X[, copy])	Transform a count matrix to a tf or tf–idf representation

__init__(norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)

fit(X, y=None)

Learn the idf vector (global term weights)

Parameters :

X: sparse matrix, [n_samples, n_features] :

a matrix of term/token counts

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters :

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns :

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

Notes

This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

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

set_params(**params)

Set the parameters of the 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, copy=True)

Transform a count matrix to a tf or tf–idf representation

Parameters :

X: sparse matrix, [n_samples, n_features] :

a matrix of term/token counts

Returns :

vectors: sparse matrix, [n_samples, n_features] :