Class TfidfTransformerScikitsLearnNode

Transform a count matrix to a normalized tf or tf-idf representation

This node has been automatically generated by wrapping the ``sklearn.feature_extraction.text.TfidfTransformer`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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.

Read more in the :ref:`User Guide <text_feature_extraction>`.

**Parameters**

norm : 'l1', 'l2' or None, optional
    Norm used to normalize term vectors. None for no normalization.

use_idf : boolean, default=True
    Enable inverse-document-frequency reweighting.

smooth_idf : boolean, 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_tf : boolean, default=False
    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.`

Overrides: object.__init__

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

This node has been automatically generated by wrapping the sklearn.feature_extraction.text.TfidfTransformer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : sparse matrix, [n_samples, n_features]

a matrix of term/token counts

copy : boolean, default True

Whether to copy X and operate on the copy or perform in-place operations.

Returns

vectors : sparse matrix, [n_samples, n_features]

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Learn the idf vector (global term weights)

This node has been automatically generated by wrapping the sklearn.feature_extraction.text.TfidfTransformer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : sparse matrix, [n_samples, n_features]

a matrix of term/token counts

Overrides: Node.stop_training