DSF: Data Stream Filter Base Classes

In stream: Infrastructure for Data Stream Mining

Data Stream Filter Base Classes

Description

Abstract base classes for all data stream filter (DSF) classes. Data stream filters transform a data stream (DSD).

Usage

DSF(...)

## S3 method for class 'DSF'
reset_stream(dsd, pos = 1)

## S3 method for class 'DSF'
get_points(x, n = 1L, info = TRUE, ...)

## S3 method for class 'DSF'
update(object, dsd = NULL, n = 1L, return = "data", ...)

## S3 method for class 'DSF'
close_stream(dsd, ...)

Arguments

...	Further arguments passed on.
dsd	a stream object of class DSD.
pos	position in the stream.
x, object	a DSF object.
n	number of points to get/use for the update.
info	return additional columns with information about the data point (e.g., a known cluster assignment).
return	a character string indicating what update returns. The only value is currently "data" to return the transformed data. possible values depend on the DST.

Details

The DSF class cannot be instantiated, but it serve as a base class from which other DSF classes inherit.

Data stream filters transform a DSD data stream. DSF can be used in two ways.

1. DSD Adapter: When a data stream (dsd) is specified in the constructor, then the DSF acts as an a adapter for a connected data stream. The DSF implementations inherit the interface from DSD and provide:

   • get_points() get the transformed points.

   • reset_stream() reset the underlying stream

   • close_stream() close the underlying stream

2. Stream Transformer: When no data stream (dsd) is specified in the constructor, then the DSF acts like a DST data stream task and provides:

   • update() to transform the points from a specified DSD. It is convenient to use the pipe (magrittr::%>%) to apply one or more filters to data streams (see Examples section).

Methods (by generic)

• reset_stream(DSF): reset the attached stream if reset is supported.

• get_points(DSF): DSD-like interface to get points if the DSF was created with an attached stream.

• update(DSF): updates with data and returns the filtered data.

• close_stream(DSF): close the attached stream if close is supported.

Author(s)

Michael Hahsler

See Also

Other DSF: DSF_Convolve(), DSF_Downsample(), DSF_ExponentialMA(), DSF_FeatureSelection(), DSF_Func(), DSF_Scale(), DSF_dplyr()

Other DSD: DSD(), DSD_BarsAndGaussians(), DSD_Benchmark(), DSD_Cubes(), DSD_Gaussians(), DSD_MG(), DSD_Memory(), DSD_Mixture(), DSD_NULL(), DSD_ReadDB(), DSD_ReadStream(), DSD_Target(), DSD_UniformNoise(), DSD_mlbenchData(), DSD_mlbenchGenerator(), animate_data(), close_stream(), get_points(), plot.DSD(), reset_stream()

Examples

DSF()

# Example 1: Use as a DSD adapter
stream <- DSD_Gaussians(k = 3, d = 2) %>%
  DSF_Func(func = function(x) cbind(x, Xsum = x$X1 + x$X2))
stream

get_points(stream, n = 5)

# Example 2: Use as a stream transformer
trans <- DSF_Func(func = function(x) cbind(x, Xsum = x$X1 + x$X2))
trans

update(trans, stream, n = 5)

# Example 3: Use as a DST preprocessor
clusterer <- DSF_Func(func = function(x) cbind(x, X1_squared = x$X1^2)) %>%
               DST_Runner(DSC_Kmeans(k = 3))
clusterer

update(clusterer, stream, n = 100)

# Example 5: Specify a complete pipeline DSD -> DSF -> DST
pipeline <- DSD_Gaussians(k = 3, d = 2) %>%
               DSF_Func(func = function(x) cbind(x, X1_squared = x$X1^2)) %>%
               DST_Runner(DSC_Kmeans(k = 3))
pipeline

update(pipeline, n = 100)
plot(pipeline$dst)

stream documentation built on May 29, 2024, 9:43 a.m.