Backend: vw_multi

The vw_multi backend is a wrapper around the multiclass and multilabel classification algorithms (oaa, ect, log_multi and multilabel_oaa) implemented by the [Vowpal Wabbit] machine learning system. It is probably best suited for classification tasks with a relatively small number of classes/subjects (fewer than 1000) though this depends on the specific algorithm and its parameters.

This backend also supports online learning, which means that it can be further trained during use via the learn command in the Annif CLI and REST API.

Installation

See Optional features and dependencies

Example configuration

This is a simple configuration that uses the default oaa algorithm:

[vw-multi-en]
name=vw_multi English
language=en
backend=vw_multi
analyzer=snowball(english)
bit_precision=22
probabilities=1
limit=100
chunksize=2
vocab=yso-en

This is a more advanced configuration that uses the ect algorithm with a logistic loss function, multiple passes during training and word bigrams (n-grams of two consecutive words):

[vw-multi-ect-en]
name=vw_multi ECT English
language=en
backend=vw_multi
analyzer=snowball(english)
algorithm=ect
loss_function=logistic
passes=10
bit_precision=22
limit=100
ngram=2
chunksize=2
vocab=yso-en

Backend-specific parameters

With the exception of chunksize and limit, the parameters are passed directly to the selected VW algorithm. If you omit a parameter, a default value is used.

Parameter	Description
chunksize	How many sentences per chunk
limit	Maximum number of results to return
bit_precision	Determines size of feature space. VW default is rather low, usually 22-28 are better.
learning_rate	Learning rate
loss_function	Function used when training model. Valid values: squared, logistic, hinge
l1	L1 regularization lambda value (usually a small float value)
l2	L2 regularization lambda value (usually a small float value)
passes	How many passes over training data to perform
probabilities	If set to 1, return probabilities instead of binary 1/0. Only supported by the oaa algorithm

You can check out the VW documentation about command line arguments for more details about the parameters.

The backend performs analysis for longer documents in chunks: the document is represented as a list of sentences, and that list is turned into chunks. With a chunksize of 2 (as above), each chunk is made of 2 sentences, except for the last chunk which may be shorter. Each chunk is analyzed separately and the results are averaged. Setting chunksize to a high value such as 10000 will in practice disable chunking.

Probabilities setting

VW multiclass and multilabel algorithms usually only return either a single class or, in the case of multilabel_oaa, a set of classes, but no scores or probabilities for the class(es). This differs from most Annif backends which return a distribution of scores. However, with a small chunk size, the class will be determined for each chunk separately and those results are averaged over the whole document, so the result can still be a distribution of scores.

The VW oaa algorithm, however, can optionally output class probabilities. You can enable this using probabilities=1 in the backend configuration. See Predicting probabilities in the VW wiki for some more details.

Usage

Load a vocabulary:

annif loadvoc vw-multi-en /path/to/Annif-corpora/vocab/yso-en.tsv

Train the model:

annif train vw-multi-en /path/to/Annif-corpora/training/yso-finna-en.tsv.gz

Learn from additional documents:

annif learn vw-multi-en /path/to/documents.tsv

Test the model with a single document:

cat document.txt | annif analyze vw-multi-en

Evaluate a directory full of files in fulltext document corpus format:

annif eval vw-multi-en /path/to/documents/