Package 'MDFS'

Add contrast variables to data

Description

This function is deprecated. Please use GenContrastVariables instead.

Usage

AddContrastVariables(data, n.contrast = max(ncol(data)/10, 30))

Arguments

data	data organized in matrix with separate variables in columns
n.contrast	number of constrast variables (defaults to max of 1/10 of variables number and 30)

Value

A list with the following key names:

• indices – vector of indices of input variables used to construct contrast variables

• x – data with constrast variables appended to it

• mask – vector of booleans making it easy to select just contrast variables

---

as.data.frame S3 method implementation for MDFS

Description

as.data.frame S3 method implementation for MDFS

Usage

## S3 method for class 'MDFS'
as.data.frame(x, ...)

Arguments

x	an MDFS object
...	ignored

Value

data.frame

---

Interesting tuples

Description

Interesting tuples

Usage

ComputeInterestingTuples(
  data,
  decision = NULL,
  dimensions = 2,
  divisions = 1,
  discretizations = 1,
  seed = NULL,
  range = NULL,
  pc.xi = 0.25,
  ig.thr = 0,
  I.lower = NULL,
  interesting.vars = vector(mode = "integer"),
  require.all.vars = FALSE,
  return.matrix = FALSE,
  stat_mode = "MI",
  average = FALSE
)

Arguments

data	input data where columns are variables and rows are observations (all numeric)
decision	decision variable as a binary sequence of length equal to number of observations
dimensions	number of dimensions (a positive integer; 5 max)
divisions	number of divisions (from 1 to 15)
discretizations	number of discretizations
seed	seed for PRNG used during discretizations (NULL for random)
range	discretization range (from 0.0 to 1.0; NULL selects probable optimal number)
pc.xi	parameter xi used to compute pseudocounts (the default is recommended not to be changed)
ig.thr	IG threshold above which the tuple is interesting (0 and negative mean no filtering)
I.lower	IG values computed for lower dimension (1D for 2D, etc.)
interesting.vars	variables for which to check the IGs (none = all)
require.all.vars	boolean whether to require tuple to consist of only interesting.vars
return.matrix	boolean whether to return a matrix instead of a list (ignored if not using the optimised method variant)
stat_mode	character, one of: "MI" (mutual information, the default; becomes information gain when decision is given), "H" (entropy; becomes conditional entropy when decision is given), "VI" (variation of information; becomes target information difference when decision is given); decides on the value computed
average	boolean whether to average over discretisations instead of maximising (the default)

Details

If running in 2D and no filtering is applied, this function is able to run in an optimised fashion. It is recommended to avoid filtering in 2D if only it is feasible.

This function calculates what stat_mode dictates. When decision is omitted, the stat_mode is calculated on the descriptive variables. When decision is given, the stat_mode is calculated on the decision variable, conditional on the other variables. Translate "IG" to that value in the rest of this function's description.

Value

A data.frame or NULL (following a warning) if no tuples are found.

The following columns are present in the data.frame:

• Var – interesting variable index

• Tuple.1, Tuple.2, ... – corresponding tuple (up to dimensions columns)

• IG – information gain achieved by var in Tuple.*

Additionally attribute named run.params with run parameters is set on the result.

Examples

ig.1d <- ComputeMaxInfoGains(madelon$data, madelon$decision, dimensions = 1, divisions = 1,
                             range = 0, seed = 0)
ComputeInterestingTuples(madelon$data, madelon$decision, dimensions = 2, divisions = 1,
                         range = 0, seed = 0, ig.thr = 100, I.lower = ig.1d$IG)

---

Interesting tuples (discrete)

Description

Interesting tuples (discrete)

Usage

ComputeInterestingTuplesDiscrete(
  data,
  decision = NULL,
  dimensions = 2,
  pc.xi = 0.25,
  ig.thr = 0,
  I.lower = NULL,
  interesting.vars = vector(mode = "integer"),
  require.all.vars = FALSE,
  return.matrix = FALSE,
  stat_mode = "MI"
)

Arguments

data	input data where columns are variables and rows are observations (all discrete with the same number of categories)
decision	decision variable as a binary sequence of length equal to number of observations
dimensions	number of dimensions (a positive integer; 5 max)
pc.xi	parameter xi used to compute pseudocounts (the default is recommended not to be changed)
ig.thr	IG threshold above which the tuple is interesting (0 and negative mean no filtering)
I.lower	IG values computed for lower dimension (1D for 2D, etc.)
interesting.vars	variables for which to check the IGs (none = all)
require.all.vars	boolean whether to require tuple to consist of only interesting.vars
return.matrix	boolean whether to return a matrix instead of a list (ignored if not using the optimised method variant)
stat_mode	character, one of: "MI" (mutual information, the default; becomes information gain when decision is given), "H" (entropy; becomes conditional entropy when decision is given), "VI" (variation of information; becomes target information difference when decision is given); decides on the value computed

Details

If running in 2D and no filtering is applied, this function is able to run in an optimised fashion. It is recommended to avoid filtering in 2D if only it is feasible.

This function calculates what stat_mode dictates. When decision is omitted, the stat_mode is calculated on the descriptive variables. When decision is given, the stat_mode is calculated on the decision variable, conditional on the other variables. Translate "IG" to that value in the rest of this function's description.

Value

A data.frame or NULL (following a warning) if no tuples are found.

The following columns are present in the data.frame:

• Var – interesting variable index

• Tuple.1, Tuple.2, ... – corresponding tuple (up to dimensions columns)

• IG – information gain achieved by var in Tuple.*

Additionally attribute named run.params with run parameters is set on the result.

Examples

ig.1d <- ComputeMaxInfoGainsDiscrete(madelon$data > 500, madelon$decision, dimensions = 1)
ComputeInterestingTuplesDiscrete(madelon$data > 500, madelon$decision, dimensions = 2,
                                 ig.thr = 100, I.lower = ig.1d$IG)

---

Max information gains

Description

Max information gains

Usage

ComputeMaxInfoGains(
  data,
  decision,
  contrast_data = NULL,
  dimensions = 1,
  divisions = 1,
  discretizations = 1,
  seed = NULL,
  range = NULL,
  pc.xi = 0.25,
  return.tuples = FALSE,
  interesting.vars = vector(mode = "integer"),
  require.all.vars = FALSE,
  use.CUDA = FALSE
)

Arguments

data	input data where columns are variables and rows are observations (all numeric)
decision	decision variable as a binary sequence of length equal to number of observations
contrast_data	the contrast counterpart of data, has to have the same number of observations - not supported with CUDA
dimensions	number of dimensions (a positive integer; 5 max)
divisions	number of divisions (from 1 to 15; additionally limited by dimensions if using CUDA)
discretizations	number of discretizations
seed	seed for PRNG used during discretizations (NULL for random)
range	discretization range (from 0.0 to 1.0; NULL selects probable optimal number)
pc.xi	parameter xi used to compute pseudocounts (the default is recommended not to be changed)
return.tuples	whether to return tuples (and relevant discretization number) where max IG was observed (one tuple and relevant discretization number per variable) - not supported with CUDA nor in 1D
interesting.vars	variables for which to check the IGs (none = all) - not supported with CUDA
require.all.vars	boolean whether to require tuple to consist of only interesting.vars
use.CUDA	whether to use CUDA acceleration (must be compiled with CUDA)

Value

A data.frame with the following columns:

• IG – max information gain (of each variable)

• Tuple.1, Tuple.2, ... – corresponding tuple (up to dimensions columns, available only when return.tuples == T)

• Discretization.nr – corresponding discretization number (available only when return.tuples == T)

Additionally attribute named run.params with run parameters is set on the result.

Examples

ComputeMaxInfoGains(madelon$data, madelon$decision, dimensions = 2, divisions = 1,
                    range = 0, seed = 0)

---

Max information gains (discrete)

Description

Max information gains (discrete)

Usage

ComputeMaxInfoGainsDiscrete(
  data,
  decision,
  contrast_data = NULL,
  dimensions = 1,
  pc.xi = 0.25,
  return.tuples = FALSE,
  interesting.vars = vector(mode = "integer"),
  require.all.vars = FALSE
)

Arguments

data	input data where columns are variables and rows are observations (all discrete with the same number of categories)
decision	decision variable as a binary sequence of length equal to number of observations
contrast_data	the contrast counterpart of data, has to have the same number of observations
dimensions	number of dimensions (a positive integer; 5 max)
pc.xi	parameter xi used to compute pseudocounts (the default is recommended not to be changed)
return.tuples	whether to return tuples where max IG was observed (one tuple per variable) - not supported with CUDA nor in 1D
interesting.vars	variables for which to check the IGs (none = all) - not supported with CUDA
require.all.vars	boolean whether to require tuple to consist of only interesting.vars

Value

A data.frame with the following columns:

• IG – max information gain (of each variable)

• Tuple.1, Tuple.2, ... – corresponding tuple (up to dimensions columns, available only when return.tuples == T)

• Discretization.nr – always 1 (for compatibility with the non-discrete function; available only when return.tuples == T)

Additionally attribute named run.params with run parameters is set on the result.

Examples

ComputeMaxInfoGainsDiscrete(madelon$data > 500, madelon$decision, dimensions = 2)

---

Compute p-values from information gains and return MDFS

Description

Compute p-values from information gains and return MDFS

Usage

ComputePValue(
  IG,
  dimensions,
  divisions,
  response.divisions = 1,
  df = NULL,
  contrast.mask = NULL,
  ig.in.bits = TRUE,
  ig.doubled = FALSE,
  one.dim.mode = "exp",
  irr.vars.num = NULL,
  ign.low.ig.vars.num = NULL,
  min.irr.vars.num = NULL,
  max.ign.low.ig.vars.num = NULL,
  search.points = 8,
  level = 0.05
)

Arguments

IG	max conditional information gains
dimensions	number of dimensions
divisions	number of divisions
response.divisions	number of response divisions (i.e. categories-1)
df	vector of degrees of freedom for each variable (optional)
contrast.mask	boolean mask on IG specifying which variables are contrast variables (or NULL if none, otherwise at least 3 variables must be marked)
ig.in.bits	TRUE if input is in binary log (as opposed to natural log)
ig.doubled	TRUE if input is doubled (to follow the chi-squared distribution)
one.dim.mode	'exp' for exponential distribution, 'lin' for linear function of chi-squared or 'raw' for raw chi-squared
irr.vars.num	if not NULL, number of irrelevant variables, specified by the user
ign.low.ig.vars.num	if not NULL, number of ignored low IG variables, specified by the user
min.irr.vars.num	minimum number of irrelevant variables (NULL selects probable optimal number)
max.ign.low.ig.vars.num	maximum number of ignored low IG variables (NULL selects probable optimal number)
search.points	number of points in search procedure for the optimal number of ignored variables
level	acceptable error level of goodness-of-fit one-sample Kolmogorov-Smirnov test (used only for warning)

Value

A data.frame with class set to MDFS. Can be coerced back to data.frame using as.data.frame.

The following columns are present:

• IG – information gains (input copy)

• chi.squared.p.value – chi-squared p-values

• p.value – theoretical p-values

Additionally the following attributes are set:

• run.params – run parameters

• sq.dev – vector of square deviations used to estimate the number of irrelevant variables

• dist.param – distribution parameter

• err.param – squared error of the distribution parameter

• fit.p.value – p-value of fit

Examples

ComputePValue(madelon$IG.2D, dimensions = 2, divisions = 1)

---

Discretize variable on demand

Description

Discretize variable on demand

Usage

Discretize(data, variable.idx, divisions, discretization.nr, seed, range)

Arguments

data	input data where columns are variables and rows are observations (all numeric)
variable.idx	variable index (as it appears in data)
divisions	number of divisions
discretization.nr	discretization number (positive integer)
seed	seed for PRNG
range	discretization range

Value

Discretized variable.

Examples

Discretize(madelon$data, 3, 1, 1, 0, 0.5)

---

Generate contrast variables from data

Description

Generate contrast variables from data

Usage

GenContrastVariables(data, n.contrast = max(ncol(data), 30))

Arguments

data	data organized in matrix with separate variables in columns
n.contrast	number of constrast variables (defaults to max of 1/10 of variables number and 30)

Value

A list with the following key names:

• indices – vector of indices of input variables used to construct contrast variables

• x – data with constrast variables appended to it

• mask – vector of booleans making it easy to select just contrast variables

Examples

GenContrastVariables(madelon$data)

---

Get the recommended range for multiple discretisations

Description

Get the recommended range for multiple discretisations

Usage

GetRange(k = 3, n, dimensions, divisions = 1)

Arguments

k	the assumed minimum number of objects in a bucket (the default is the recommended value)
n	the total number of objects considered
dimensions	the number of dimensions of analysis
divisions	the number of divisions of discretisations

Value

The recommended range value (a floating point number).

Examples

GetRange(n = 250, dimensions = 2)

---

An artificial dataset called MADELON

Description

An artificial dataset containing data points grouped in 32 clusters placed on the vertices of a five dimensional hypercube and randomly labeled 0/1.

Usage

madelon

Format

A list of two elements:

data

2000 by 500 matrix of 2000 objects with 500 features

decision

vector of 2000 decisions (labels 0/1)

IG.2D

example 2D IG computed using ComputeMaxInfoGains

Details

The five dimensions constitute 5 informative features. 15 linear combinations of those features are added to form a set of 20 (redundant) informative features. There are 480 distractor features called 'probes' having no predictive power.

Included is the original training set with label -1 changed to 0.

Source

https://archive.ics.uci.edu/ml/datasets/Madelon

---

Run end-to-end MDFS

Description

Run end-to-end MDFS

Usage

MDFS(
  data,
  decision,
  n.contrast = max(ncol(data), 30),
  dimensions = 1,
  divisions = 1,
  discretizations = 1,
  range = NULL,
  pc.xi = 0.25,
  p.adjust.method = "holm",
  level = 0.05,
  seed = NULL,
  use.CUDA = FALSE
)

Arguments

data	input data where columns are variables and rows are observations (all numeric)
decision	decision variable as a boolean vector of length equal to number of observations
n.contrast	number of constrast variables (defaults to max of 1/10 of variables number and 30)
dimensions	number of dimensions (a positive integer; on CUDA limited to 2–5 range)
divisions	number of divisions (from 1 to 15)
discretizations	number of discretizations
range	discretization range (from 0.0 to 1.0; NULL selects probable optimal number)
pc.xi	parameter xi used to compute pseudocounts (the default is recommended not to be changed)
p.adjust.method	method as accepted by p.adjust ("BY" is recommended for FDR, see Details)
level	statistical significance level
seed	seed for PRNG used during discretizations (NULL for random)
use.CUDA	whether to use CUDA acceleration (must be compiled with CUDA; NOTE: the CUDA version might provide a slightly lower sensitivity due to a lack of native support for contrast_data)

Details

In case of FDR control it is recommended to use Benjamini-Hochberg-Yekutieli p-value adjustment method ("BY" in p.adjust) due to unknown dependencies between tests.

Value

A list with the following fields:

• contrast.indices – indices of variables chosen to build contrast variables

• contrast.variables – built contrast variables

• MIG.Result – result of ComputeMaxInfoGains

• MDFS – result of ComputePValue (the MDFS object)

• statistic – vector of statistic's values (IGs) for corresponding variables

• p.value – vector of p-values for corresponding variables

• adjusted.p.value – vector of adjusted p-values for corresponding variables

• relevant.variables – vector of relevant variables indices

Examples

MDFS(madelon$data, madelon$decision, dimensions = 2, divisions = 1,
     range = 0, seed = 0)

---

Call omp_set_num_threads

Description

Call omp_set_num_threads

Usage

mdfs_omp_set_num_threads(num_threads)

Arguments

num_threads

input data where columns are variables and rows are observations (all numeric)

Value

No return value, called for side effects.

---

Plot MDFS details

Description

Plot MDFS details

Usage

## S3 method for class 'MDFS'
plot(x, plots = c("ig", "c", "p"), ...)

Arguments

x	an MDFS object
plots	plots to plot (ig for max IG, c for chi-squared p-values, p for p-values)
...	passed on to plot

Value

No return value, called for side effects.

---

Find indices of relevant variables

Description

Find indices of relevant variables

Usage

RelevantVariables(fs, ...)

Arguments

fs	feature selector
...	arguments passed to methods

Value

indices of important variables

---

Find indices of relevant variables from MDFS

Description

Find indices of relevant variables from MDFS

Usage

## S3 method for class 'MDFS'
RelevantVariables(fs, level = 0.05, p.adjust.method = "holm", ...)

Arguments

fs	an MDFS object
level	statistical significance level
p.adjust.method	method as accepted by p.adjust ("BY" is recommended for FDR, see Details)
...	ignored

Details

In case of FDR control it is recommended to use Benjamini-Hochberg-Yekutieli p-value adjustment method ("BY" in p.adjust) due to unknown dependencies between tests.

Value

indices of relevant variables

---