LargeViz (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.datatransform.visualization.LargeViz

All Implemented Interfaces:

Serializable, Cloneable, VisualizationTransform
```
public class LargeViz
extends Object
implements VisualizationTransform
```
LargeViz is an algorithm for creating low dimensional embeddings for visualization. It is meant to be faster and better quality than t-SNE without requiring any parameter tuning to get good results. LargeViz is related to t-SNE in how the neighbor graph is constructed, and the perplexity parameter for LargeViz has the same meaning and impact as the perplexity parameter in t-SNE.

NOTE: The origina LargeViz paper includes a faster scheme for approximately constructing the nearest neighbor graph. This is not yet implemented, but has no negative impact on the quality of the result.
See: Tang, J., Liu, J., Zhang, M., & Mei, Q. (2016). Visualizing Large-scale and High-dimensional Data. In Proceedings of the 25th International Conference on World Wide Web (pp. 287–297). Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. doi:10.1145/2872427.2883041

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

LargeViz()

Constructors
Constructor and Description
`LargeViz()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`getGamma()`
`int`	`getNegativeSamples()`
`double`	`getPerplexity()`
`int`	`getTargetDimension()`
`void`	`setDistanceMetricEmbedding(DistanceMetric dm)` Sets the distance metric to use for the embedded space.
`void`	`setDistanceMetricSource(DistanceMetric dm)` Sets the distance metric to use for the original space.
`void`	`setGamma(double gamma)` Gamma controls the negative weight assigned to negative edges in the optimization problem.
`void`	`setNegativeSamples(int M)` Sets the number of negative neighbor samples to obtain for each data point.
`void`	`setPerplexity(double perplexity)` Sets the target perplexity of the gaussian used over each data point.
`boolean`	`setTargetDimension(int target)` Sets the target dimension to embed new dataset to.
`<Type extends DataSet> Type`	`transform(DataSet<Type> d)` Transforms the given data set, returning a dataset of the same type.
`<Type extends DataSet> Type`	`transform(DataSet<Type> d, ExecutorService ex)` Transforms the given data set, returning a dataset of the same type.

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

- Constructor Detail
  - LargeViz
```
public LargeViz()
```
- Method Detail
  - setPerplexity
```
public void setPerplexity(double perplexity)
```
    Sets the target perplexity of the gaussian used over each data point. The perplexity can be thought of as a quasi desired number of nearest neighbors to be considered, but is adapted based on the distribution of the data. Increasing the perplexity can increase the amount of time it takes to get an embedding. Using a value in the range of [5, 100] is recommended.
    
    Parameters:
    
    perplexity - the quasi number of neighbors to consider for each data point
  - getPerplexity
```
public double getPerplexity()
```
    Returns:
    
    the target perplexity to use for each data point
  - setDistanceMetricSource
```
public void setDistanceMetricSource(DistanceMetric dm)
```
    Sets the distance metric to use for the original space. This will determine the target nearest neighbors to keep close to each other in the embedding space
    
    Parameters:
    
    dm - the distance metric to use
  - setDistanceMetricEmbedding
```
public void setDistanceMetricEmbedding(DistanceMetric dm)
```
    Sets the distance metric to use for the embedded space. This will determine the actual nearest neighbors as the occur in the embedded space.
    
    Parameters:
    
    dm - the distance metric to use
  - setNegativeSamples
```
public void setNegativeSamples(int M)
```
    Sets the number of negative neighbor samples to obtain for each data point. The default recommended value is 5.
    
    Parameters:
    
    M - the number of negative samples to use for each update
  - getNegativeSamples
```
public int getNegativeSamples()
```
    Returns:
    
    the number of negative samples to use for each update
  - setGamma
```
public void setGamma(double gamma)
```
    Gamma controls the negative weight assigned to negative edges in the optimization problem. Large values will place a higher emphasis on separating non-neighbors in the embedded space. The default recommend value is 7.
    
    Parameters:
    
    gamma - the weight for negative edge samples
  - getGamma
```
public double getGamma()
```
    Returns:
    
    the weight for negative edge samples
  - getTargetDimension
```
public int getTargetDimension()
```
    Specified by:
    
    getTargetDimension in interface VisualizationTransform
    
    Returns:
    
    the number of dimensions that a dataset will be embedded down to
  - setTargetDimension
```
public boolean setTargetDimension(int target)
```
    Description copied from interface: VisualizationTransform
    
    Sets the target dimension to embed new dataset to. Many visualization methods may only support a target of 2 or 3 dimensions, or only one of those options. For that reason a boolean value will be returned indicating if the target size was acceptable. If not, no change to the object will occur.
    
    Specified by:
    
    setTargetDimension in interface VisualizationTransform
    
    Parameters:
    
    target - the new target dimension size when VisualizationTransform.transform(jsat.DataSet) is called.
    
    Returns:
    
    true if this transform supports that dimension and it was set, false if the target dimension is unsupported and the previous value will be used instead.
  - transform
```
public <Type extends DataSet> Type transform(DataSet<Type> d)
```
    Description copied from interface: VisualizationTransform
    
    Transforms the given data set, returning a dataset of the same type.
    
    Specified by:
    
    transform in interface VisualizationTransform
    
    Type Parameters:
    
    Type - the dataset type
    
    Parameters:
    
    d - the data set to transform
    
    Returns:
    
    the lower dimension dataset for visualization.
  - transform
```
public <Type extends DataSet> Type transform(DataSet<Type> d,
                                             ExecutorService ex)
```
    Description copied from interface: VisualizationTransform
    
    Transforms the given data set, returning a dataset of the same type.
    
    Specified by:
    
    transform in interface VisualizationTransform
    
    Type Parameters:
    
    Type - the dataset type
    
    Parameters:
    
    d - the data set to transform
    
    ex - the source of threads for parallel computation
    
    Returns:
    
    the lower dimension dataset for visualization.

Class LargeViz

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

LargeViz

Method Detail

setPerplexity

getPerplexity

setDistanceMetricSource

setDistanceMetricEmbedding

setNegativeSamples

getNegativeSamples

setGamma

getGamma

getTargetDimension

setTargetDimension

transform

transform