SGT {dataset} R Documentation

Sequence Graph Transform (SGT) — Sequence Embedding for Clustering, Classification, and Search

Description


Sequence Graph Transform (SGT) is a sequence embedding function. SGT extracts
the short- and long-term sequence features and embeds them in a finite-dimensional
feature space. The long and short term patterns embedded in SGT can be tuned
without any increase in the computation.

>https://github.com/cran2367/sgt/blob/25bf28097788fbbf9727abad91ec6e59873947cc/python/sgt-package/sgt/sgt.py

Usage

SGT(
    alphabets = NULL,
    kappa = 1,
    length.sensitive = FALSE,
    mode = Full);

Arguments

alphabets

Optional, except if mode is Spark. The set of alphabets that make up all the sequences in the dataset. If not passed, the alphabet set is automatically computed as the unique set of elements that make all the sequences. A list or 1d-array of the set of elements that make up the sequences. For example, np.array(["A", "B", "C"]. If mode is 'spark', the alphabets are necessary. [as string]

kappa

Tuning parameter, kappa > 0, to change the extraction of long-term dependency. Higher the value the lesser the long-term dependency captured in the embedding. Typical values for kappa are 1, 5, 10. [as double]

length.sensitive

Default False. This is set to true if the embedding of should have the information of the length of the sequence. If set to false then the embedding of two sequences with similar pattern but different lengths will be the same. lengthsensitive = false is similar to length-normalization. [as boolean]

Details

Compute embedding of a single or a collection of discrete item sequences. A discrete item sequence is a sequence made from a set discrete elements, also known as alphabet set. For example, suppose the alphabet set is the set of roman letters, {A, B, ..., Z}. This set is made of discrete elements. Examples of sequences from such a set are AABADDSA, UADSFJPFFFOIHOUGD, etc. Such sequence datasets are commonly found in online industry, for example, item purchase history, where the alphabet set is the set of all product items. Sequence datasets are abundant in bioinformatics as protein sequences. Using the embeddings created here, classification and clustering models can be built for sequence datasets. Read more inhttps://arxiv.org/pdf/1608.03533.pdf

Authors

MLkit

Value

this function returns data object of type SequenceGraphTransform.

clr value class

Examples

[Package dataset version 1.0.0.0 Index]