DST_SlidingWindow: DST_SlidingWindow - Call R Functions on a Sliding Window

In stream: Infrastructure for Data Stream Mining

DST_SlidingWindow – Call R Functions on a Sliding Window

Description

Keeps a sliding window of the data stream an calls a function at regular intervals with the contents of the window.

Usage

DST_SlidingWindow(f, window, rebuild, ...)

## S3 method for class 'DST_SlidingWindow'
update(
  object,
  dsd,
  n = 1L,
  return = c("nothing", "model"),
  rebuild = FALSE,
  ...
)

## S3 method for class 'DST_SlidingWindow'
predict(object, newdata, ...)

Arguments

f	the function to be called.
window	size of the sliding window.
rebuild	logical; perform a rebuild after the update.
...	additional parameters passed on to the predict() function of the underlying model.
object	the updated DST_SlidingWindow object.
dsd	A DSD object with the data stream.
n	number of points from dsd to use for the update.
return	a character string indicating what update returns. The default is "nothing" and "model" returns the aggregated data.
newdata	dataframe with the new data.

Details

Keeps a sliding window of the data stream an calls a function at regular intervals with the contents of the window. The function needs to have the form

f <- function(data, ...) {...}

where data is the data.frame with the points in the sliding window (See get_points() in DSAggregate_Window). The function will be executed at regular intervals after update() was called with fixed number of points. update(..., rebuild = TRUE) can be used to force recomputing the function. This can be used with n = 0 to recompute it even without adding more points. The last valid function result can be retrieved/

Many modelling functions provide a formula interface which lets them be directly used inside a DST_SlidingWindow (see Examples section).

If the function returns a model that supports predict(), then predict can directly be called on the DST_SlidingWindow object.

Value

An object of class DST_SlidingWindow.

Author(s)

Michael Hahsler

See Also

Other DST: DSAggregate(), DSC(), DSClassifier(), DSOutlier(), DSRegressor(), DST(), DST_WriteStream(), evaluate, predict(), stream_pipeline, update()

Examples

library(stream)

# create a data stream for the iris dataset
data <- iris[sample(nrow(iris)), ]
stream <- DSD_Memory(data)
stream

## Example 1: Use a function on the sliding window
summarizer <- function(data) summary(data)

s <- DST_SlidingWindow(summarizer,
  window = 100, rebuild = 50)
s

# update window with 49 points. The function is not yet called
update(s, stream, 49)
get_model(s)

# updating with the 50th point will trigger a function call (see rebuild parameter)
# note that the window is only 1/2 full and we have 50 NAs
update(s, stream, 1)
get_model(s)

# 50 more points and the function will be recomputed
update(s, stream, 50)
get_model(s)


## Example 2: Use classifier on the sliding window
reset_stream(stream)

# rpart, like most models in R, already have a formula interface that uses a
# data parameter. We can use these types of models directly
library(rpart)
cl <- DST_SlidingWindow(
  rpart, formula = Species ~ Petal.Length + Petal.Width,
  window = 100, rebuild = 50)
cl

# update window with 50 points so the model is built
update(cl, stream, 50)
get_model(cl)

# update with 40 more points and force the function to be recomputed even though it would take
#  50 points to automatically rebuild.
update(cl, stream, 40, rebuild = TRUE)
get_model(cl)

# rpart supports predict, so we can use it directly with the DST_SlidingWindow
new_points <- get_points(stream, n = 5)
predict(cl, new_points, type = "class")

## Example 3: Regression using a sliding window
reset_stream(stream)

## lm can be directly used
reg <- DST_SlidingWindow(
  lm, formula = Sepal.Length ~ Petal.Width + Petal.Length,
  window = 100, rebuild = 50)
reg

update(reg, stream, 100)
get_model(reg)

# lm supports predict, so we can use it directly with the DST_SlidingWindow
new_points <- get_points(stream, n = 5)
predict(reg, new_points)

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