OnlineLDAsvi (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.text.topicmodel.OnlineLDAsvi

All Implemented Interfaces:

Parameterized
```
public class OnlineLDAsvi
extends Object
implements Parameterized
```
This class provides an implementation of Latent Dirichlet Allocation for learning a topic model from a set of documents. This implementation is based on Stochastic Variational Inference and is meant for large collections (more than 100,000 data points) and can learn in an online fashion.

For LDA it is common to set α = η = 1/K, where K is the number of topics to be learned. Note that η is not a learning rate parameter, as the symbol is usually used.
For this algorithm, some potential parameter combinations (by column) for batch size, κ, and τ₀ are:

batch size 256 1024 4096

κ 0.6 0.5 0.5

τ₀ 1024 256 64

For smaller corpuses, reducing τ₀ can improve the performance (even down to τ₀ = 1)
See:
- Blei, D. M., Ng, A. Y.,&Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(4-5), 993–1022. doi:10.1162/jmlr.2003.3.4-5.993
- Hoffman, M., Blei, D.,&Bach, F. (2010). Online Learning for Latent Dirichlet Allocation. In Advances in Neural Information Processing Systems (pp. 856–864). Retrieved from here
- Hoffman, M. D., Blei, D. M., Wang, C.,&Paisley, J. (2013). Stochastic Variational Inference. The Journal of Machine Learning Research, 14(1), 1303–1347.
- Hoffman, M. D. (2013). Lazy updates for online LDA. Retrieved from here
Author:

Edward Raff

batch size	256	1024	4096
κ	0.6	0.5	0.5
τ₀	1024	256	64

Constructor Summary

Constructors
Constructor and Description
`OnlineLDAsvi()` Creates a new Online LDA learner.
`OnlineLDAsvi(int K, int D, int W)` Creates a new Online LDA learner that is ready for online updates

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`getAlpha()`
`int`	`getD()` Returns the approximate number of documents that will be observed, or `-1` if this object is not ready to learn
`int`	`getEpochs()` Returns the number of training iterations over the data set that will be used
`double`	`getEta()`
`int`	`getK()` Returns the number of topics to learn, or `-1` if this object is not ready to learn
`double`	`getKappa()`
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`Vec`	`getTopics(Vec doc)` Computes the topic distribution for the given document. Note that the returned vector will be dense, but many of the values may be very nearly zero.
`Vec`	`getTopicVec(int k)` Returns the topic vector for a given topic.
`int`	`getVocabSize()` Returns the size of the vocabulary for LDA, or `-1` if this object is not ready to learn
`void`	`model(DataSet dataSet, int topics)` Fits the LDA model against the given data set
`void`	`model(DataSet dataSet, int topics, ExecutorService ex)` Fits the LDA model against the given data set
`void`	`setAlpha(double alpha)` Sets the prior for the on weight vector theta.
`void`	`setD(int D)` Sets the approximate number of documents that will be observed
`void`	`setEpochs(int epochs)` Sets the number of training epochs when learning in a "batch" setting
`void`	`setEta(double eta)` Prior on topics.
`void`	`setK(int K)` Sets the number of topics that LDA will try to learn
`void`	`setKappa(double kappa)` The "forgetfulness" factor in the learning rate.
`void`	`setMiniBatchSize(int miniBatchSize)` Sets the number of data points used at a time to perform one update of the model parameters
`void`	`setTau0(double tau0)` A learning rate constant to control the influence of early iterations on the solution.
`void`	`setVocabSize(int W)` Sets the vocabulary size for LDA, which is the number of dimensions in the input feature vectors.
`void`	`update(List<Vec> docs)` Performs an update of the LDA topic distributions based on the given mini-batch of documents.
`void`	`update(List<Vec> docs, ExecutorService ex)` Performs an update of the LDA topic distribution based on the given mini-batch of documents.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - OnlineLDAsvi
```
public OnlineLDAsvi()
```
    Creates a new Online LDA learner. The number of topics, expected number of documents, and the vocabulary size must be set before it can be used.
  - OnlineLDAsvi
```
public OnlineLDAsvi(int K,
                    int D,
                    int W)
```
    Creates a new Online LDA learner that is ready for online updates
    
    Parameters:
    
    K - the number of topics to learn
    
    D - the expected number of documents to see
    
    W - the vocabulary size
- Method Detail
  - setK
```
public void setK(int K)
```
    Sets the number of topics that LDA will try to learn
    
    Parameters:
    
    K - the number of topics to learn
  - getK
```
public int getK()
```
    Returns the number of topics to learn, or -1 if this object is not ready to learn
    
    Returns:
    
    the number of topics that will be learned
  - setD
```
public void setD(int D)
```
    Sets the approximate number of documents that will be observed
    
    Parameters:
    
    D - the number of documents that will be observed
  - getD
```
public int getD()
```
    Returns the approximate number of documents that will be observed, or -1 if this object is not ready to learn
    
    Returns:
    
    the number of documents that will be observed
  - setVocabSize
```
public void setVocabSize(int W)
```
    Sets the vocabulary size for LDA, which is the number of dimensions in the input feature vectors.
    
    Parameters:
    
    W - the vocabulary size for LDA
  - getVocabSize
```
public int getVocabSize()
```
    Returns the size of the vocabulary for LDA, or -1 if this object is not ready to learn
    
    Returns:
    
    the vocabulary size for LDA
  - setAlpha
```
public void setAlpha(double alpha)
```
    Sets the prior for the on weight vector theta. 1/K is a common choice.
    
    Parameters:
    
    alpha - the positive prior value
  - getAlpha
```
public double getAlpha()
```
    Returns:
    
    the weight vector prior over theta
  - setEta
```
public void setEta(double eta)
```
    Prior on topics. 1/K is a common choice.
    
    Parameters:
    
    eta - the positive prior for topics
  - getEta
```
public double getEta()
```
    Returns:
    
    the topic prior
  - setTau0
```
public void setTau0(double tau0)
```
    A learning rate constant to control the influence of early iterations on the solution. Larger values reduce the influence of earlier iterations, smaller values increase the weight of earlier iterations.
    
    Parameters:
    
    tau0 - a learning rate parameter that must be greater than 0 (usually at least 1)
  - setEpochs
```
public void setEpochs(int epochs)
```
    Sets the number of training epochs when learning in a "batch" setting
    
    Parameters:
    
    epochs - the number of iterations to go over the data set
  - getEpochs
```
public int getEpochs()
```
    Returns the number of training iterations over the data set that will be used
    
    Returns:
    
    the number of training iterations over the data set that will be used
  - setKappa
```
public void setKappa(double kappa)
```
    The "forgetfulness" factor in the learning rate. Larger values increase the rate at which old information is "forgotten"
    
    Parameters:
    
    kappa - the forgetfulness factor in [0.5, 1]
  - getKappa
```
public double getKappa()
```
    Returns:
    
    the forgetfulness factor
  - setMiniBatchSize
```
public void setMiniBatchSize(int miniBatchSize)
```
    Sets the number of data points used at a time to perform one update of the model parameters
    
    Parameters:
    
    miniBatchSize - the batch size to use
  - getTopicVec
```
public Vec getTopicVec(int k)
```
    Returns the topic vector for a given topic. The vector should not be altered, and is scaled so that the sum of all term weights sums to one.
    
    Parameters:
    
    k - the topic to get the vector for
    
    Returns:
    
    the raw topic vector for the requested topic.
  - update
```
public void update(List<Vec> docs)
```
    Performs an update of the LDA topic distributions based on the given mini-batch of documents.
    
    Parameters:
    
    docs - the list of document vectors to update from
  - update
```
public void update(List<Vec> docs,
                   ExecutorService ex)
```
    Performs an update of the LDA topic distribution based on the given mini-batch of documents.
    
    Parameters:
    
    docs - the list of document vectors to update from
    
    ex - the source of threads for parallel execution
  - model
```
public void model(DataSet dataSet,
                  int topics)
```
    Fits the LDA model against the given data set
    
    Parameters:
    
    dataSet - the data set to learn a topic model for
    
    topics - the number of topics to learn
  - model
```
public void model(DataSet dataSet,
                  int topics,
                  ExecutorService ex)
```
    Fits the LDA model against the given data set
    
    Parameters:
    
    dataSet - the data set to learn a topic model for
    
    topics - the number of topics to learn
    
    ex - the source of threads for parallel execution
  - getTopics
```
public Vec getTopics(Vec doc)
```
    Computes the topic distribution for the given document.
    Note that the returned vector will be dense, but many of the values may be very nearly zero.
    
    Parameters:
    
    doc - the document to find the topics for
    
    Returns:
    
    a vector of the topic distribution for the given document
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class OnlineLDAsvi

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

OnlineLDAsvi

OnlineLDAsvi

Method Detail

setK

getK

setD

getD

setVocabSize

getVocabSize

setAlpha

getAlpha

setEta

getEta

setTau0

setEpochs

getEpochs

setKappa

getKappa

setMiniBatchSize

getTopicVec

update

update

model

model

getTopics

getParameters

getParameter