`sklearn.feature_extraction.text`.TfidfTransformer¶

class sklearn.feature_extraction.text.TfidfTransformer(norm=u'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.

The actual formula used for tf-idf is tf * (idf + 1) = tf + tf * idf, instead of tf * idf. The effect of this is that terms with zero idf, i.e. that occur in all documents of a training set, will not be entirely ignored. 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) when norm=’l2’, “n” (none) when 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.

[MRS2008]

C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 118-120.

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 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

__init__(norm=u'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.

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.

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.