| Title: | Ordinal Relations |
|---|---|
| Description: | Functions to handle ordinal relations reflected within the feature space. Those function allow to search for ordinal relations in multi-class datasets. One can check whether proposed relations are reflected in a specific feature representation. Furthermore, it provides functions to filter, organize and further analyze those ordinal relations. |
| Authors: | L Lausser, LM Schaefer, R Szekely, HA Kestler |
| Maintainer: | HA Kestler <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.3 |
| Built: | 2025-01-24 03:58:25 UTC |
| Source: | https://github.com/cran/ORION |
Converts from a Subcascades object to a weighted edge list.
as.edgedataframe(subcascades)as.edgedataframe(subcascades)
subcascades |
A Subcascades object as it is returned by |
Converts a Subcascades object to a data.frame that can be used to generate a graph.
A data.frame that can be used to generate a graph. The first and second column correspond to all pairwise relations (from - to) of the cascades within the Subcascades object. The 'CASC_ID' column contains the same ID for all edges belonging to the same cascade. The 'SIZE' column gives the size of the cascade to which the repective column bleongs. The method returns NULL if the object subcascades is empty.
library(TunePareto) library(igraph) data(esl) data = esl$data labels = esl$labels predMap = predictionMap(data, labels, classifier = tunePareto.svm(), kernel='linear') # generate a dataframe subcascades = subcascades(predMap,thresh=0.65,size=4) edges = as.edgedataframe(subcascades) g = graph_from_data_frame(edges[,c(1,2)], directed = TRUE) E(g)$weight = edges[,3] plot(g,edge.color=edges[,3],edge.arrow.size=0.5, edge.curved =seq(-0.5, 1, length = ecount(g)))library(TunePareto) library(igraph) data(esl) data = esl$data labels = esl$labels predMap = predictionMap(data, labels, classifier = tunePareto.svm(), kernel='linear') # generate a dataframe subcascades = subcascades(predMap,thresh=0.65,size=4) edges = as.edgedataframe(subcascades) g = graph_from_data_frame(edges[,c(1,2)], directed = TRUE) E(g)$weight = edges[,3] plot(g,edge.color=edges[,3],edge.arrow.size=0.5, edge.curved =seq(-0.5, 1, length = ecount(g)))
Converts from a Groupwise object to a Subcascades object.
as.subcascades(groupwise = NULL)as.subcascades(groupwise = NULL)
groupwise |
A Groupwise object, which comprises a two-leveled list. The first level collects cascades of the same size. The second level contains a list of unique class combinations, labelled as a character string with '-' separating the different classes. For each unique set of class combinations the corresponding orders and their performance are given as a matrix, where each row contains a cascade, given as a character string of type '1>2>3', and the columns the sensitivity for the class at the corresponding position. Each matrix is sorted row-wise according to the achieved minimal classwise sensitivites of the cascades (decreasing). |
Converts a Groupwise object to a Subcascades object.
A Subcascades object comprising the evaluated cascades and their performances. The Subcascades object is made up of a list of matrices. Each matrix comprises the evaluation results of cascades of a specific length and is sorted row-wise according to the achieved minimal classwise sensitivities of the cascades (decreasing). The rownames show the class order by a character string of type '1>2>3' and the entries the sensitivity for each position of the cascade.
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate a Groupwise object subc = subcascades(predMap,thresh=0.7) groupwise = groupwise(subc) #convert it to a Subcascades object converted.subcascades = as.subcascades(groupwise)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate a Groupwise object subc = subcascades(predMap,thresh=0.7) groupwise = groupwise(subc) #convert it to a Subcascades object converted.subcascades = as.subcascades(groupwise)
Sensitivities of all pairwise binary classifiers for all classes.
conf(predictionMap = NULL)conf(predictionMap = NULL)
predictionMap |
A PredictionMap object as it is returned by |
The Conf object contains all class sensitivities for each binary trained classifiers. The $fC-part is a matrix with one column and contains the sensitivities for the first class of each pairwise classifier. The rows stand for the pairwise classifiers, whereby 0vs1 means that this classifier was trained for class 0 against class 1, with class 0 being the first class. The number '-1' is used as placeholder. The $sC-part is a matrix and contains the preformance measures for all second classes, which are meant here as all classes except the first class. The rows correspond to the binary classifiers and the columns to the classes.
Object of class Conf. Consists of a list of two numeric matrices fC and sC.
summary.Conf, print.Conf, plot.Conf
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') conf = conf(predMap)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') conf = conf(predMap)
Confusion table and class assignments of one cascade.
confusionTable( predictionMap = NULL, cascade = NULL, other.classes = NULL, sort = TRUE )confusionTable( predictionMap = NULL, cascade = NULL, other.classes = NULL, sort = TRUE )
predictionMap |
A PredictionMap object as it is returned by |
cascade |
A numeric vector of classes or a character string of type '1>2>3' of at least two class labels reflected in 'predictionMap'. |
other.classes |
This parameter can be either NULL, 'all' or a numeric vector of classes that are not part of the cascade parameter. If other.classes is: - NULL, only the cascade classes are evaluated. - 'all', all remaining classes are evaluated. - a vector of classes, those classes are evaluated. |
sort |
If TRUE (default) the classes that are not part of cascade are sorted based on their confusion. |
An object of type ConfusionTable including the sensitivities, with the label of the predicted classes in the rows and the labels of the original class in the columns.
summary.ConfusionTable, print.ConfusionTable, plot.ConfusionTable
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # Calculation of the confusion matrix for '0>2>3>4'. confTable = confusionTable(predMap, cascade = '0>2>3>4') # Calculation of the confusion matrix for '0>2>3>4' # and the assignment of all samples of the other classes. confTable = confusionTable(predMap, cascade = '0>2>3>4', other.classes='all', sort = TRUE)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # Calculation of the confusion matrix for '0>2>3>4'. confTable = confusionTable(predMap, cascade = '0>2>3>4') # Calculation of the confusion matrix for '0>2>3>4' # and the assignment of all samples of the other classes. confTable = confusionTable(predMap, cascade = '0>2>3>4', other.classes='all', sort = TRUE)
dropSets filters out specific cascade sets.
dropSets( subcascades = NULL, sets = NULL, direction = "sub", ordered = FALSE, neighborhood = "direct", type = "any" )dropSets( subcascades = NULL, sets = NULL, direction = "sub", ordered = FALSE, neighborhood = "direct", type = "any" )
subcascades |
A Subcascades object as it is returned by |
sets |
Contains the set used for filtering. It is either a list of numeric vectors, a numeric vector, or a vector of characters representing a cascade of the following format '1>2>4'. Empty vectors are not allowed. |
direction |
Either 'sub','super' or 'exact', indicating whether the subset, the superset or the exact set is filtered.
The 'exact' case filters exactly the cascades defined in |
ordered |
Either TRUE or FALSE indicating whether the order of the classes in sets is considered or not. |
neighborhood |
Either 'direct' or 'indirect' defines whether the given classes have to be direct neighbors ('direct') or there are other classes allowed inbetween ('indirect'). |
type |
If more than one cascade is defined in sets, this parameter defines whether the filtered cascade has to fit to at least one of the sets ('any') or to all of the given sets ('all'). |
This function allows for filtering the Subcascades object.
If direction is set to 'exact' the parameter neighborhood is ignored and the parameter type is used as its default and cannot be changed.
There has to be an exact match between the classes of the sets parameter cascades and the cascade of the Subcascades object,
so not more but also not less classes are allowed. If the ordered parameter is set to TRUE also the order of the classes within the
sets parameter and within the Subcascades object has be be exactly the same.
If direction is set to 'sub' the Subcascades object is filtered for subsets of the given cascades.
If the type parameter is set to 'any' each cascade is considered individually otherwise all cascades are used as reference for filtering.
This means that either for each cascade of the sets parameter individually or for taking all together the Subcascades object is filtered for cascades that are
made up of the same classes, a subset of classes (ordered = FALSE) or a cascade, part of a cascade (ordered = TRUE),
resulting in a set of cascades that might contain less classes.
The parameter neighbourhood is not considered.
If direction is set to 'super' the Subcascades object is filtered for a superset of the given cascades.
If the type parameter is set to 'any' each cascade is considered individually otherwise all cascades are used as reference for filtering.
A superset are cascades that are made up of (parts) of the given cascades (ordered = TRUE) or classes (ordered=FALSE) but can contain also more classes.
Depending on the parameter neighborhood only classes defined in the sets parameter are allowed as neighbors ('direct').
If neighborhood is set to 'indirect' the filtering is less strict and the direct neighborhood defined in sets is not considered.
A Subcascades object comprising the evaluated cascades and their performances. The Subcascades object is made up of a list of matrices. Each matrix comprises the evaluation results of cascades of a specific length and is sorted row-wise according to the achieved minimal classwise sensitivities of the cascades (decreasing). The rownames show the class order by a character string of type '1>2>3' and the entries the sensitivity for each position of the cascade.
dropSize, keepSize, keepSets, dropThreshold, keepThreshold
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) #define sets set1 = list(c(1,2,3),c(2,3,4)) set2 = c('1>2>3','2>3>4') # filter for the subset cascades that contain either the classes # {1,2,3} or {2,3,4} independent of the order, but neighbored dropSets(subc, sets = set1, direction = 'sub', ordered = FALSE, neighborhood = 'direct') dropSets(subc, sets = set2, direction = 'sub', ordered = FALSE, neighborhood = 'direct') # filter for the superset cascades that contain either the classes # {1,2,3} or {2,3,4} independent of the order, but neighbored dropSets(subc, sets = set1, direction = 'super', ordered = FALSE, neighborhood = 'direct') dropSets(subc, sets = set2, direction = 'super', ordered = FALSE, neighborhood = 'direct') # filter for the superset cascades that contain both the classes # {1,2,3} and {2,3,4} in exactly the given order, but allowing # for other classes inbetween dropSets(subc, sets = set1, direction = 'super', ordered = TRUE, neighborhood = 'indirect', type = 'all') dropSets(subc, sets = set2, direction = 'super', ordered = TRUE, neighborhood = 'indirect', type = 'all') # filter for the exact cascades # sets can be a numeric list result <- dropSets(subc, list(c(0,1,2),c(2,3,4,1)), direction = 'exact', ordered=TRUE) unlist(t(lapply(result,rownames))) # or sets can be a character vector result <- dropSets(subc, c('0>1>2','2>3>4>1'), direction = 'exact',ordered=TRUE) unlist(t(lapply(result,rownames)))library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) #define sets set1 = list(c(1,2,3),c(2,3,4)) set2 = c('1>2>3','2>3>4') # filter for the subset cascades that contain either the classes # {1,2,3} or {2,3,4} independent of the order, but neighbored dropSets(subc, sets = set1, direction = 'sub', ordered = FALSE, neighborhood = 'direct') dropSets(subc, sets = set2, direction = 'sub', ordered = FALSE, neighborhood = 'direct') # filter for the superset cascades that contain either the classes # {1,2,3} or {2,3,4} independent of the order, but neighbored dropSets(subc, sets = set1, direction = 'super', ordered = FALSE, neighborhood = 'direct') dropSets(subc, sets = set2, direction = 'super', ordered = FALSE, neighborhood = 'direct') # filter for the superset cascades that contain both the classes # {1,2,3} and {2,3,4} in exactly the given order, but allowing # for other classes inbetween dropSets(subc, sets = set1, direction = 'super', ordered = TRUE, neighborhood = 'indirect', type = 'all') dropSets(subc, sets = set2, direction = 'super', ordered = TRUE, neighborhood = 'indirect', type = 'all') # filter for the exact cascades # sets can be a numeric list result <- dropSets(subc, list(c(0,1,2),c(2,3,4,1)), direction = 'exact', ordered=TRUE) unlist(t(lapply(result,rownames))) # or sets can be a character vector result <- dropSets(subc, c('0>1>2','2>3>4>1'), direction = 'exact',ordered=TRUE) unlist(t(lapply(result,rownames)))
Filters out the Subcascades object for the given sizes.
dropSize(subcascades = NULL, size = NA)dropSize(subcascades = NULL, size = NA)
subcascades |
A Subcascades object as it is returned by |
size |
A numeric value that defines the size of the cascades that should be returned. The smallest size is 2 and the largest the maximal number of classes of the current dataset. |
A Subcascades object as in subcascades, that does not include cascades of the specific lengths that hve been filtered.
keepSize, dropSets, keepSets, dropThreshold, keepThreshold
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) # filters out cascades that have a length of 3 dropSize(subc,size=3) # filters out cascades that have a length of 3 or 4 dropSize(subc, size=c(3,4))library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) # filters out cascades that have a length of 3 dropSize(subc,size=3) # filters out cascades that have a length of 3 or 4 dropSize(subc, size=c(3,4))
Filters out all cascades that match the comparison with a minimal classwise sensitivity threshold.
dropThreshold(subcascades = NULL, comparison = ">=", thresh = 0)dropThreshold(subcascades = NULL, comparison = ">=", thresh = 0)
subcascades |
A Subcascades object as it is returned by |
comparison |
Defines the comparison type (<,>,<=,>=) for the threshold. |
thresh |
A numeric value between 0 and 1. The minimal sensitivity threshold used to filter the returned cascades. Only cascades that pass this threshold are returned. If 0 is used the returned cascades are filtered for >0 and otherwise >= thresh. For low thresholds the calculation lasts longer. |
A Subcascades object comprising the evaluated cascades and their performances. The Subcascades object is made up of a list of matrices. Each matrix comprises the evaluation results of cascades of a specific length and is sorted row-wise according to the achieved minimal classwise sensitivities of the cascades (decreasing). The rownames show the class order by a character string of type '1>2>3' and the entries the sensitivity for each position of the cascade.
dropSize, keepSize, dropSets, keepSets, keepThreshold
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.5) # filters for cascades that # 1. have a minimal classwise sensitivity >= 0.6 dropThreshold(subc,thresh=0.6) # 2. have a minimal classwise sensitivity <= 0.6 dropThreshold(subc, comparison = '<=', thresh=0.6)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.5) # filters for cascades that # 1. have a minimal classwise sensitivity >= 0.6 dropThreshold(subc,thresh=0.6) # 2. have a minimal classwise sensitivity <= 0.6 dropThreshold(subc, comparison = '<=', thresh=0.6)
The original ESL data set contains 488 profiles of applicants for certain industrial jobs. Expert psychologists of a recruiting company, based upon psychometric test results and interviews with the candidates, determined the values of the input attributes. The output is the an overall score corresponding to the degree of fitness of the candidate to this type of job. Here classes 1-3 are merged to one group and all classes >6 are merged to one group. Furtheremore, the data is relabeled to start at 0 and only 20 samples per class are included.
data(esl)data(esl)
An object of class list of length 2.
Weka dataset (datasets-arie_ben_david.tar.gz, 11,348 Bytes)
ordinal, real-world datasets donated by Dr. Arie Ben David (Holon Inst. of Technology/Israel) Donor: Arie Ben David MIS, Dept. of Technology Management Holon Academic Inst. of Technology 52 Golomb St. Holon 58102 Israel [email protected] Owner: Yoav Ganzah Business Administration School Tel Aviv Univerity Ramat Aviv 69978 Israel
data(esl)data(esl)
The ESL data set contains 488 profiles of applicants for certain industrial jobs. Expert psychologists of a recruiting company, based upon psychometric test results and interviews with the candidates, determined the values of the input attributes. The output is the an overall score corresponding to the degree of fitness of the candidate to this type of job.
data(esl_org)data(esl_org)
An object of class data.frame with 488 rows and 5 columns.
Weka dataset (datasets-arie_ben_david.tar.gz, 11,348 Bytes)
ordinal, real-world datasets donated by Dr. Arie Ben David (Holon Inst. of Technology/Israel) Donor: Arie Ben David MIS, Dept. of Technology Management Holon Academic Inst. of Technology 52 Golomb St. Holon 58102 Israel [email protected] Owner: Yoav Ganzah Business Administration School Tel Aviv Univerity Ramat Aviv 69978 Israel
data(esl_org)data(esl_org)
Converts from a Subcascades object to a Groupwise object.
groupwise(subcascades = NULL, maxCl = 50)groupwise(subcascades = NULL, maxCl = 50)
subcascades |
A Subcascades object as it is returned by |
maxCl |
An integer defining the lower bound for the maximal number of classes. Has only to be set if the analyzed dataset has more than 50 classes. |
This function re-sorts the Subcascades object in a way that the cascades made up of the same classes are grouped.
A Groupwise object, which comprises a two-leveled list. The first level collects cascades of the same size. The second level contains a list of unique class combinations, labelled as a character string with '-' separating the different classes. For each unique set of class combinations the corresponding orders and their performance are given as a matrix, where each row contains a cascade, given as a character string of type '1>2>3', and the columns the sensitivity for the class at the corresponding position. Each matrix is sorted row-wise according to the achieved minimal classwise sensitivites of the cascades (decreasing).
as.subcascades, summary.Groupwise, print.Groupwise, plot.Groupwise, as.edgedataframe
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subcascades = subcascades(predMap,thresh=0.7) #create a Groupwise object groupwise = groupwise(subcascades)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subcascades = subcascades(predMap,thresh=0.7) #create a Groupwise object groupwise = groupwise(subcascades)
keepSets filters specific cascade sets.
keepSets( subcascades = NULL, sets = NULL, direction = "sub", ordered = FALSE, neighborhood = "direct", type = "any" )keepSets( subcascades = NULL, sets = NULL, direction = "sub", ordered = FALSE, neighborhood = "direct", type = "any" )
subcascades |
A Subcascades object as it is returned by |
sets |
Contains the set used for filtering. It is either a list of numeric vectors, a numeric vector, or a vector of characters representing a cascade of the following format '1>2>4'. Empty vectors are not allowed. |
direction |
Either 'sub','super' or 'exact', indicating whether the subset, the superset or the exact set is filtered.
The 'exact' case filters exactly the cascades defined in |
ordered |
Either TRUE or FALSE indicating whether the order of the classes in sets is considered or not. |
neighborhood |
Either 'direct' or 'indirect' defines whether the given classes have to be direct neighbors ('direct') or there are other classes allowed inbetween ('indirect'). |
type |
If more than one cascade is defined in sets, this parameter defines whether the filtered cascade has to fit to at least one of the sets ('any') or to all of the given sets ('all'). |
This function allows for filtering the Subcascades object.
If direction is set to 'exact' the parameter neighborhood is ignored and the parameter type is used as its default and cannot be changed.
There has to be an exact match between the classes of the sets parameter cascades and the cascade of the Subcascades object,
so not more but also not less classes are allowed. If the ordered parameter is set to TRUE also the order of the classes within the
sets parameter and within the Subcascades object has be be exactly the same.
If direction is set to 'sub' the Subcascades object is filtered for subsets of the given cascades.
If the type parameter is set to 'any' each cascade is considered individually otherwise all cascades are used as reference for filtering.
This means that either for each cascade of the sets parameter individually or for taking all together the Subcascades object is filtered for cascades that are
made up of the same classes, a subset of classes (ordered = FALSE) or a cascade, part of a cascade (ordered = TRUE),
resulting in a set of cascades that might contain less classes.
The parameter neighbourhood is not considered.
If direction is set to 'super' the Subcascades object is filtered for a superset of the given cascades.
If the type parameter is set to 'any' each cascade is considered individually otherwise all cascades are used as reference for filtering.
A superset are cascades that are made up of (parts) of the given cascades (ordered = TRUE) or classes (ordered=FALSE) but can contain also more classes.
Depending on the parameter neighborhood only classes defined in the sets parameter are allowed as neighbors ('direct').
If neighborhood is set to 'indirect' the filtering is less strict and the direct neighborhood defined in sets is not considered.
A Subcascades object comprising the evaluated cascades and their performances. The Subcascades object is made up of a list of matrices. Each matrix comprises the evaluation results of cascades of a specific length and is sorted row-wise according to the achieved minimal classwise sensitivities of the cascades (decreasing). The rownames show the class order by a character string of type '1>2>3' and the entries the sensitivity for each position of the cascade.
dropSize, keepSize, dropSets, dropThreshold, keepThreshold
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) #define sets set1 = list(c(1,2,3),c(2,3,4)) set2 = c('1>2>3','2>3>4') # filter for the subset cascades that contain either the classes # {1,2,3} or {2,3,4} independent of the order, but neighbored keepSets(subc, sets = set1, direction = 'sub', ordered = FALSE, neighborhood = 'direct') keepSets(subc, sets = set2, direction = 'sub', ordered = FALSE, neighborhood = 'direct') # filter for the superset cascades that contain either the classes # {1,2,3} or {2,3,4} independent of the order, but neighbored keepSets(subc, sets = set1, direction = 'super', ordered = FALSE, neighborhood = 'direct') keepSets(subc, sets = set2, direction = 'super', ordered = FALSE, neighborhood = 'direct') # filter for the superset cascades that contain both the classes # {1,2,3} and {2,3,4} in exactly the given order, but allowing # for other classes inbetween keepSets(subc, sets = set1, direction = 'super', ordered = TRUE, neighborhood = 'indirect', type = 'all') keepSets(subc, sets = set2, direction = 'super', ordered = TRUE, neighborhood = 'indirect', type = 'all') # filter for the exact cascades # sets can be a numeric list result <- keepSets(subc, list(c(0,1,2),c(2,3,4,1)), direction = 'exact', ordered=TRUE) unlist(t(lapply(result,rownames))) # or sets can be a character vector result <- keepSets(subc, c('0>1>2','2>3>4>1'), direction = 'exact',ordered=TRUE) unlist(t(lapply(result,rownames)))library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) #define sets set1 = list(c(1,2,3),c(2,3,4)) set2 = c('1>2>3','2>3>4') # filter for the subset cascades that contain either the classes # {1,2,3} or {2,3,4} independent of the order, but neighbored keepSets(subc, sets = set1, direction = 'sub', ordered = FALSE, neighborhood = 'direct') keepSets(subc, sets = set2, direction = 'sub', ordered = FALSE, neighborhood = 'direct') # filter for the superset cascades that contain either the classes # {1,2,3} or {2,3,4} independent of the order, but neighbored keepSets(subc, sets = set1, direction = 'super', ordered = FALSE, neighborhood = 'direct') keepSets(subc, sets = set2, direction = 'super', ordered = FALSE, neighborhood = 'direct') # filter for the superset cascades that contain both the classes # {1,2,3} and {2,3,4} in exactly the given order, but allowing # for other classes inbetween keepSets(subc, sets = set1, direction = 'super', ordered = TRUE, neighborhood = 'indirect', type = 'all') keepSets(subc, sets = set2, direction = 'super', ordered = TRUE, neighborhood = 'indirect', type = 'all') # filter for the exact cascades # sets can be a numeric list result <- keepSets(subc, list(c(0,1,2),c(2,3,4,1)), direction = 'exact', ordered=TRUE) unlist(t(lapply(result,rownames))) # or sets can be a character vector result <- keepSets(subc, c('0>1>2','2>3>4>1'), direction = 'exact',ordered=TRUE) unlist(t(lapply(result,rownames)))
Filters the Subcascades object for cascades of the given sizes.
keepSize(subcascades = NULL, size = NA)keepSize(subcascades = NULL, size = NA)
subcascades |
A Subcascades object as it is returned by |
size |
A numeric value that defines the size of the cascades that should be returned. The smallest size is 2 and the largest the maximal number of classes of the current dataset. |
A Subcascades object as in subcascades, that only includes cascades of specific lengths.
dropSize, dropSets, keepSets, dropThreshold, keepThreshold
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) # filters for cascades that have a length of 3 keepSize(subc,size=3) # filters for cascades that have a length of 3 or 4 keepSize(subc, size=c(3,4))library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) # filters for cascades that have a length of 3 keepSize(subc,size=3) # filters for cascades that have a length of 3 or 4 keepSize(subc, size=c(3,4))
Filters for all cascades that match the comparison with a minimal classwise sensitivity threshold.
keepThreshold(subcascades = NULL, thresh = 0, comparison = ">=")keepThreshold(subcascades = NULL, thresh = 0, comparison = ">=")
subcascades |
A Subcascades object as it is returned by |
thresh |
A numeric value between 0 and 1. The minimal sensitivity threshold used to filter the returned cascades. Only cascades that pass this threshold are returned. If 0 is used the returned cascades are filtered for >0 and otherwise >= thresh. For low thresholds the calculation lasts longer. |
comparison |
Defines the comparison type (<,>,<=,>=) for the threshold. |
A Subcascades object comprising the evaluated cascades and their performances. The Subcascades object is made up of a list of matrices. Each matrix comprises the evaluation results of cascades of a specific length and is sorted row-wise according to the achieved minimal classwise sensitivities of the cascades (decreasing). The rownames show the class order by a character string of type '1>2>3' and the entries the sensitivity for each position of the cascade.
dropSize, keepSize, dropSets, keepSets, dropThreshold
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.5) # filters for cascades that # 1. have a minimal classwise sensitivity >= 0.6 keepThreshold(subc,thresh=0.6) # 2. have a minimal classwise sensitivity <= 0.6 keepThreshold(subc, comparison = '<=', thresh=0.6)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.5) # filters for cascades that # 1. have a minimal classwise sensitivity >= 0.6 keepThreshold(subc,thresh=0.6) # 2. have a minimal classwise sensitivity <= 0.6 keepThreshold(subc, comparison = '<=', thresh=0.6)
mergeSubcascades adds the cascades from subcascades2 to the subcascades1, that have not been part of subcascades1.
mergeSubcascades(subcascades1 = NULL, subcascades2 = NULL)mergeSubcascades(subcascades1 = NULL, subcascades2 = NULL)
subcascades1 |
A Subcascades object as it is returned by |
subcascades2 |
A Subcascades object like subcascades1 |
A Subcascades object comprising the evaluated cascades and their performances. The Subcascades object is made up of a list of matrices. Each matrix comprises the evaluation results of cascades of a specific length and is sorted row-wise according to the achieved minimal classwise sensitivities of the cascades (decreasing). The rownames show the class order by a character string of type '1>2>3' and the entries the sensitivity for each position of the cascade.
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # make two Subcascades objects subc1 = subcascades(predMap, size = c(3,4), thresh = 0.6) subc2 = subcascades(predMap, size = c(4), thresh = 0.5) # add the cascades of subcascades2 to subcascades1 mergeSubcascades(subc1, subc2)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # make two Subcascades objects subc1 = subcascades(predMap, size = c(3,4), thresh = 0.6) subc2 = subcascades(predMap, size = c(4), thresh = 0.5) # add the cascades of subcascades2 to subcascades1 mergeSubcascades(subc1, subc2)
It comprises a list of two elements: A data matrix ("data") and a label vector ("labels"). The samples are given in the rows and the features in the columns of the matrix. The sampled dataset comprises 5 classes with 40 samples, each. It is embedded in a two dimensional feature space and contains 5 classes with no ordinal sequence.
data(nonOrdinalData)data(nonOrdinalData)
An object of class list of length 2.
data(nonOrdinalData)data(nonOrdinalData)
It comprises a list of two elements: A data matrix ("data") and a label vector ("labels"). The samples are given in the rows and the features in the columns of the matrix. The sampled dataset comprises 5 classes with 40 samples, each. It is embedded in a two dimensional feature space and contains 1 ordinal sequence with 5 classes.
data(ordinalData)data(ordinalData)
An object of class list of length 2.
data(ordinalData)data(ordinalData)
Plots a heatmap that shows base classifier performance.
## S3 method for class 'Conf' plot( x = NULL, classNames = NULL, onlySens = FALSE, symmetric = FALSE, main = "summary of base classifiers", xlab = "classes", ylab = "base classifiers", digits = 3, ignore = 0, first.colors = c("#f5f5f5", "#b2182b"), second.colors = c("#f5f5f5", "#2166ac"), other.colors = c("#f5f5f5", "#1b7837"), na.color = c("yellow"), las = 1, srt = 30, color.key = FALSE, cex = 1, cex.lab = 1, ... )## S3 method for class 'Conf' plot( x = NULL, classNames = NULL, onlySens = FALSE, symmetric = FALSE, main = "summary of base classifiers", xlab = "classes", ylab = "base classifiers", digits = 3, ignore = 0, first.colors = c("#f5f5f5", "#b2182b"), second.colors = c("#f5f5f5", "#2166ac"), other.colors = c("#f5f5f5", "#1b7837"), na.color = c("yellow"), las = 1, srt = 30, color.key = FALSE, cex = 1, cex.lab = 1, ... )
x |
A Conf object as it is returned by |
classNames |
Vector of the original class names. If not given the class number is used instead. |
onlySens |
A logical indicating if the plot should should be restricted to the class-wise sensitivities of the base classifiers. |
symmetric |
Logical indicating whether a symmetric base classifier (TRUE) is used. Only used when onlySens is TRUE. |
main |
See |
xlab |
A title for the x axis (see |
ylab |
A title for the y axis (see |
digits |
Integer indicating the number of decimal places to be used (see |
ignore |
A numeric value between 0 and 1. All confusion and purity values below this number are not written as string into the corresponding element. |
first.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the first class. The color palette is calcuated by an interpolation between the 2 given colors. |
second.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the second class. The color palette is calcuated by an interpolation between the 2 given colors. |
other.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the other class. The color palette is calcuated by an interpolation between the 2 given colors. |
na.color |
Color, which is used for indicating the empty elements (if the given class is not part of the cascade). |
las |
See |
srt |
Angle used to rotate the strings of the x-axis and y-axis labels (see |
color.key |
Specifies whether a color key is drawn (TRUE) or not (FALSE). |
cex |
See |
cex.lab |
See |
... |
Further arguments passed from other methods. |
This function plots a heatmap of the base classifier performance.
No return value, called to generate the heatmap plot.
conf, plot.Subcascades, plot.ConfusionTable
library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object conf <- conf(predMap) plot(conf, onlySens=TRUE, symmetric=TRUE)library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object conf <- conf(predMap) plot(conf, onlySens=TRUE, symmetric=TRUE)
Plots an extended confusion table.
## S3 method for class 'ConfusionTable' plot( x = NULL, classNames = NULL, main = "extended confusion table", xlab = "original class labels", ylab = "predicted class labels", cascLab = "inner classes", otherLab = "outer classes", digits = 3, ignore = 0, casc.colors = c("#f5f5f5", "#8c510a"), other.colors = c("#f5f5f5", "#01665e"), colSep = "#b2182b", las = 1, color.key = TRUE, cex = 1, cex.lab = 1, ... )## S3 method for class 'ConfusionTable' plot( x = NULL, classNames = NULL, main = "extended confusion table", xlab = "original class labels", ylab = "predicted class labels", cascLab = "inner classes", otherLab = "outer classes", digits = 3, ignore = 0, casc.colors = c("#f5f5f5", "#8c510a"), other.colors = c("#f5f5f5", "#01665e"), colSep = "#b2182b", las = 1, color.key = TRUE, cex = 1, cex.lab = 1, ... )
x |
A ConfusionTable object as it is returned by |
classNames |
Vector of the original class names. If not given the class number is used instead. |
main |
See |
xlab |
A title for the x axis (see |
ylab |
A title for the y axis (see |
cascLab |
Character string used as header for the cascade part of the extended confusion table. |
otherLab |
Character string used as header for the other class part of the extended confusion table. |
digits |
Integer indicating the number of decimal places to be used (see |
ignore |
A numeric value between 0 and 1. All confusion and purity values below this number are not written as string into the corresponding element. |
casc.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the first class. The color palette is calcuated by an interpolation between the 2 given colors. |
other.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the second class. The color palette is calcuated by an interpolation between the 2 given colors. |
colSep |
Color, which is used for the vertical line separating the cascade classes and the other classes. |
las |
See |
color.key |
Specifies whether a color key is drawn (TRUE) or not (FALSE). |
cex |
See |
cex.lab |
See |
... |
Further arguments passed from other methods. |
No return value, called to generate the confusion table plot.
confusionTable, plot.Subcascades, plot.Conf
library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object conf.table <- confusionTable(predMap,cascade='0>1>3>4',other.classes = 'all') plot(conf.table)library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object conf.table <- confusionTable(predMap,cascade='0>1>3>4',other.classes = 'all') plot(conf.table)
Plots a heatmap, that shows the performance of cascades of a Groupwise object.
## S3 method for class 'Groupwise' plot( x = NULL, class.sort = "", row.sort = "sens", main = "groupwise", xlab = "classes", ylab = "cascades", digits = 3, ignore = 0, casc.colors = c("#f5f5f5", "#01665e"), na.color = c("#f5f5f5"), color.key = TRUE, las = 1, cex = 1, cex.lab = 1, srt = 30, ... )## S3 method for class 'Groupwise' plot( x = NULL, class.sort = "", row.sort = "sens", main = "groupwise", xlab = "classes", ylab = "cascades", digits = 3, ignore = 0, casc.colors = c("#f5f5f5", "#01665e"), na.color = c("#f5f5f5"), color.key = TRUE, las = 1, cex = 1, cex.lab = 1, srt = 30, ... )
x |
A Groupwise object as it is returned by |
class.sort |
The classes can be sorted either according to the first cascade in the Groupwise object (”), or based on the class frequency ('max'). |
row.sort |
The cascade can be sorted either based on the maximal-minimal class-wise sensitivity ('sens') or according the class frequency. |
main |
See |
xlab |
A title for the x axis (see |
ylab |
A title for the y axis (see |
digits |
Integer indicating the number of decimal places to be used (see |
ignore |
A numeric value between 0 and 1. All confusion and purity values below this number are not written as string into the corresponding element. |
casc.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity. The color palette is calcuated by an interpolation between the 2 given colors. |
na.color |
Color, which is used for indicating the empty elements (if the given class is not part of the cascade). |
color.key |
Specifies whether a color key is drawn (TRUE) or not (FALSE). |
las |
See |
cex |
See |
cex.lab |
See |
srt |
Angle used to rotate the strings of the x-axis and y-axis labels (see |
... |
Further arguments passed from other methods. |
This function plots a heatmap with the cascades of the Groupwise object in the rows and all classes present in any of the cascades in the columns. The colors indicate whether a given class is present in the corresponding cascade and with which sensitivity. Internally converts the Groupwise object to a Subcascades object and plots the corresponding heatmap.
No return value, called to generate the heatmap plot of the Groupwise Object.
groupwise, subcascades, plot.Subcascades
library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc <- subcascades(predMap,thresh=0.7,size=c(3,4)) groupwise <- groupwise(subc) plot(groupwise,row.sort='max')library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc <- subcascades(predMap,thresh=0.7,size=c(3,4)) groupwise <- groupwise(subc) plot(groupwise,row.sort='max')
Plots a heatmap, that shows the predictions of a PredictionMap object and the real labels in a cross-validation or reclassification experiment.
## S3 method for class 'PredictionMap' plot( x = NULL, xlab = "samples", ylab = "base classifiers", main = "Prediction map", las = 1, srt = 30, cex = 1, cex.lab = 1, label.colors = NULL, plot.sampleIDs = FALSE, plot.cv.runs = TRUE, plot.class.labels = TRUE, ... )## S3 method for class 'PredictionMap' plot( x = NULL, xlab = "samples", ylab = "base classifiers", main = "Prediction map", las = 1, srt = 30, cex = 1, cex.lab = 1, label.colors = NULL, plot.sampleIDs = FALSE, plot.cv.runs = TRUE, plot.class.labels = TRUE, ... )
x |
A PredictonMap object as it is returned by |
xlab |
A title for the x axis (see |
ylab |
A title for the y axis (see |
main |
See |
las |
See |
srt |
Angle used to rotate the strings of the x-axis and y-axis labels (see |
cex |
See |
cex.lab |
See |
label.colors |
A vector of the color for the class labels. If NULL, automatically use rainbow color scheme. |
plot.sampleIDs |
Specifices if the sample IDs should be plotted along the x axis (TRUE or FALSE). |
plot.cv.runs |
Specifices if the cross-validation runs should be plotted (TRUE or FALSE). Cross-validation runs are visually separated by straight lines. |
plot.class.labels |
Specificies if the numerical class labels should additionally plotted (TRUE or FALSE). |
... |
Further arguments passed from other methods. |
This function plots a heatmap with color-decoded predictions made by the specified classifier. Here, the rows indicate the different binary base classifiers and the columns the samples in the specified resampling experiment (reclassification or cross-validation). Labels are visualized in the top row and decoded color-wise.
No return value, called to a heatmap plot of the predictionMap Object.
library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') plot(predMap)library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') plot(predMap)
Plots a heatmap, that shows the performance of cascades of a Subcascades object.
## S3 method for class 'Subcascades' plot( x = NULL, classNames = NULL, class.sort = "", row.sort = "", main = "subcascades", xlab = "classes", ylab = "cascades", digits = 3, ignore = 0, casc.colors = c("#f5f5f5", "#01665e"), na.color = c("#f5f5f5"), color.key = TRUE, las = 1, cex = 1, cex.lab = 1, srt = 30, ... )## S3 method for class 'Subcascades' plot( x = NULL, classNames = NULL, class.sort = "", row.sort = "", main = "subcascades", xlab = "classes", ylab = "cascades", digits = 3, ignore = 0, casc.colors = c("#f5f5f5", "#01665e"), na.color = c("#f5f5f5"), color.key = TRUE, las = 1, cex = 1, cex.lab = 1, srt = 30, ... )
x |
A Subcascades object as it is returned by |
classNames |
Character vector including the names of the classes. |
class.sort |
The classes can be sorted either by: "" = unsorted, "max": sorted according to maximal occurrence of a class or "sens": sorted according to highest class-wise sensitivity |
row.sort |
The cascade can be sorted by: "" = unsorted, "max": sorted according to maximal occurrence of maximal length or "sens": sorted according to highest class-wise sensitivity |
main |
See |
xlab |
A title for the x axis (see |
ylab |
A title for the y axis (see |
digits |
Integer indicating the number of decimal places to be used (see |
ignore |
A numeric value between 0 and 1. All confusion and purity values below this number are not written as string into the corresponding element. |
casc.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity. The color palette is calcuated by an interpolation between the 2 given colors. |
na.color |
Color, which is used for indicating the empty elements (if the given class is not part of the cascade). |
color.key |
Specifies whether a color key is drawn (TRUE) or not (FALSE). |
las |
See |
cex |
See |
cex.lab |
See |
srt |
Angle used to rotate the strings of the x-axis and y-axis labels (see |
... |
Further arguments passed from other methods. |
This function plots a heatmap with the cascades of the Subcascades object in the rows and all classes present in any of the cascades in the columns. The colors indicate whether a given class is present in the corresponding cascade and with which sensitivity.
No return value, called to generate the heatmap plot of the subcascades Object.
subcascades, plot.Conf, plot.ConfusionTable
library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc <- subcascades(predMap,thresh=0.7,size=c(3,4)) plot(subc,row.sort='max')library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc <- subcascades(predMap,thresh=0.7,size=c(3,4)) plot(subc,row.sort='max')
Plots a heatmap that shows base classifier performance.
plotBaseClassifier( x = NULL, classNames = NULL, symmetric = FALSE, main = "summary of base classifiers", xlab = "second class", ylab = "first class", digits = 3, ignore = 0, first.colors = c("#f5f5f5", "#8c510a"), second.colors = c("#f5f5f5", "#01665e"), na.color = c("yellow"), las = 1, color.key = TRUE, cex = 1, cex.lab = 1, ... )plotBaseClassifier( x = NULL, classNames = NULL, symmetric = FALSE, main = "summary of base classifiers", xlab = "second class", ylab = "first class", digits = 3, ignore = 0, first.colors = c("#f5f5f5", "#8c510a"), second.colors = c("#f5f5f5", "#01665e"), na.color = c("yellow"), las = 1, color.key = TRUE, cex = 1, cex.lab = 1, ... )
x |
A Conf object as it is returned by |
classNames |
Vector of the original class names. If not given the class number is used instead. |
symmetric |
Logical indicating whether a symmetric base classifier (TRUE) is used. |
main |
See |
xlab |
A title for the x axis (see |
ylab |
A title for the y axis (see |
digits |
Integer indicating the number of decimal places to be used (see |
ignore |
A numeric value between 0 and 1. All confusion and purity values below this number are not written as string into the corresponding element. |
first.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the first class. The color palette is calcuated by an interpolation between the 2 given colors. |
second.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the second class. The color palette is calcuated by an interpolation between the 2 given colors. |
na.color |
Color, which is used for indicating the empty elements (if the given class is not part of the cascade). |
las |
See |
color.key |
Specifies whether a color key is drawn (TRUE) or not (FALSE). |
cex |
See |
cex.lab |
See |
... |
Further arguments passed from other methods. |
This function plots a heatmap of the base classifier performance.
No return value, called to generate the heatmap plot.
Plots a heatmap that shows base classifier performance.
plotConf( x = NULL, classNames = NULL, main = "summary of base classifiers", xlab = "classes", ylab = "base classifiers", digits = 3, ignore = 0, first.colors = c("#f5f5f5", "#b2182b"), second.colors = c("#f5f5f5", "#2166ac"), other.colors = c("#f5f5f5", "#1b7837"), las = 1, srt = 30, color.key = FALSE, cex = 1, cex.lab = 1, ... )plotConf( x = NULL, classNames = NULL, main = "summary of base classifiers", xlab = "classes", ylab = "base classifiers", digits = 3, ignore = 0, first.colors = c("#f5f5f5", "#b2182b"), second.colors = c("#f5f5f5", "#2166ac"), other.colors = c("#f5f5f5", "#1b7837"), las = 1, srt = 30, color.key = FALSE, cex = 1, cex.lab = 1, ... )
x |
A Conf object as it is returned by |
classNames |
Vector of the original class names. If not given the class number is used instead. |
main |
See |
xlab |
A title for the x axis (see |
ylab |
A title for the y axis (see |
digits |
Integer indicating the number of decimal places to be used (see |
ignore |
A numeric value between 0 and 1. All confusion and purity values below this number are not written as string into the corresponding element. |
first.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the first class. The color palette is calcuated by an interpolation between the 2 given colors. |
second.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the second class. The color palette is calcuated by an interpolation between the 2 given colors. |
other.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the other class. The color palette is calcuated by an interpolation between the 2 given colors. |
las |
See |
srt |
Angle used to rotate the strings of the x-axis and y-axis labels (see |
color.key |
Specifies whether a color key is drawn (TRUE) or not (FALSE). |
cex |
See |
cex.lab |
See |
... |
Further arguments passed from other methods. |
This function plots a heatmap of the base classifier performance.
No return value, called to generate the heatmap plot.
conf, plot.Subcascades, plot.ConfusionTable
Makes a PredictionMap object for the given data.
predictionMap( data = NULL, labels = NULL, foldList = NULL, parallel = FALSE, classifier = NULL, ... )predictionMap( data = NULL, labels = NULL, foldList = NULL, parallel = FALSE, classifier = NULL, ... )
data |
The data either as matrix or data.frame with samples in rows and features in columns. |
labels |
A vector of labels of data, consecutively labelled starting with 0. |
foldList |
A set of partitions for a cross-validation experiment.
This list comprises as many elements as cross-validation runs.
Each run is a list of as many vectors as folds. The entries are the indices of the samples that are left out in the folds.
This fold list can be generated by using |
parallel |
Either TRUE or FALSE (default). If TRUE the pairwise training is performed parallelized. |
classifier |
A TunePareto classifier object. |
... |
Further parameters of the classifier object. |
Using a reclassification or a cross-validation set up this function performs a pairwise training for all class combinations and evaluates all samples using the trained classifiers. This means that for each run, fold and binary classifier the predicted class for each sample is calculated.
A PredictionMap object. It is made up of a list of two matrices, which are called meta and pred. Both matrices provide information for individual samples column-wise. The meta information in meta connects the values in the pred-matrix to a specific fold, run, sample and contains the original label. The rownames of the pred-matrix (e.g. [0vs1]) show the classes of the binary base classifier. The elements are the prediction result of a specific training. The rows that correspond to base classifiers that would separate the same class consists of -1. Those rows are not used within the analysis.
summary.PredictionMap, print.PredictionMap, plot.PredictionMap
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) # svm with linear kernel predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # knn with k = 3 predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.knn(), k = 3) # randomForest predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.randomForest())library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) # svm with linear kernel predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # knn with k = 3 predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.knn(), k = 3) # randomForest predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.randomForest())
Prints all details about a Conf object.
## S3 method for class 'Conf' print(x, printfC = TRUE, printsC = TRUE, ...)## S3 method for class 'Conf' print(x, printfC = TRUE, printsC = TRUE, ...)
x |
A Conf object as it is returned by |
printfC |
If TRUE, the sensitivities of the first classes are printed (TP = true positives). |
printsC |
If TRUE, the values of sC are printed (TN = true negatives). The values of sC correspond to the rate of samples of a given class (column), which are assigned to the second class of the base classifier (row). |
... |
Further parameters |
Prints all details about a given Conf object. A Conf object provides the conditional prediction rates of all base classifiers applied to all classes.
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') conf = conf(predMap) print(conf)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') conf = conf(predMap) print(conf)
Prints all details about a ConfusionTable object.
## S3 method for class 'ConfusionTable' print(x, ...)## S3 method for class 'ConfusionTable' print(x, ...)
x |
A ConfusionTable object as it is returned by |
... |
Further parameters |
Prints all conditional prediction rates for the specified cascade.
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # Calculation of the confusion matrix for '0>2>3>4'. confTable = confusionTable(predMap, cascade = '0>2>3>4') print(confTable)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # Calculation of the confusion matrix for '0>2>3>4'. confTable = confusionTable(predMap, cascade = '0>2>3>4') print(confTable)
Prints all details about a Groupwise object.
## S3 method for class 'Groupwise' print(x, printSizes = length(x), ...)## S3 method for class 'Groupwise' print(x, printSizes = length(x), ...)
x |
A Groupwise object as it is returned by |
printSizes |
Integer that specifies how many of the cascade sizes (starting with the largest cascades) should be printed. |
... |
Further parameters |
Prints all details about a given Groupwise object. A Groupwise object re-sorts a Subcascades object in a way that the cascades made up of the same classes are grouped. The printSizes parameter can be used to control the cascade sizes to be printed.
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) #create a Groupwise object groupwise = groupwise(subc) print(groupwise, printSizes = 2)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) #create a Groupwise object groupwise = groupwise(subc) print(groupwise, printSizes = 2)
Prints all details about a PredictionMap object.
## S3 method for class 'PredictionMap' print(x, showMeta = TRUE, showPred = TRUE, ...)## S3 method for class 'PredictionMap' print(x, showMeta = TRUE, showPred = TRUE, ...)
x |
A PredictionMap object generated by |
showMeta |
If TRUE the meta data of the PredictionMap object will be printed. |
showPred |
If TRUE the predictions of the PredictionMap object will be printed. |
... |
Further parameters |
Prints all details about a given PredictionMap object and information about the meta information as well as the predictions.
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) # svm with linear kernel predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') print(predMap, showMeta=TRUE, showPred=TRUE)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) # svm with linear kernel predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') print(predMap, showMeta=TRUE, showPred=TRUE)
Prints all details about a Subcascades object.
## S3 method for class 'Subcascades' print(x, printSizes = length(x), ...)## S3 method for class 'Subcascades' print(x, printSizes = length(x), ...)
x |
A Subcascades object generated by |
printSizes |
Integer specifying how many elements of a Subcascades object should be printed, sorted decreasingly after cascade length. |
... |
Further parameters |
Prints all details about a given Subcascades object.
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # use default parameter settings # -> returns cascades of all lengths that show a minimal classwise sensitivity >0. subc = subcascades(predMap) # print subcascades for the largest 2 sizes print(subc, printSize=2)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # use default parameter settings # -> returns cascades of all lengths that show a minimal classwise sensitivity >0. subc = subcascades(predMap) # print subcascades for the largest 2 sizes print(subc, printSize=2)
It comprises a list of two elements: A data matrix ("data") and a label vector ("labels"). The samples are given in the rows and the features in the columns of the matrix. The sampled dataset comprises 6 classes with 40 samples, each. It is embedded in a two dimensional feature space and contains 2 ordinal sequences with 5 classes.
data(screeningData)data(screeningData)
An object of class list of length 2.
data(screeningData)data(screeningData)
Subcascades returns all cascades found within the data or evaluates a set of specific cascades.
subcascades( predictionMap = NULL, sets = NULL, thresh = 0, size = NA, numSol = 1000 )subcascades( predictionMap = NULL, sets = NULL, thresh = 0, size = NA, numSol = 1000 )
predictionMap |
A PredictionMap object as it is returned by |
sets |
Contains the set used for filtering. It is either a list of numeric vectors, a numeric vector, or a vector of characters representing a cascade of the following format '1>2>4'. Empty vectors are not allowed. |
thresh |
A numeric value between 0 and 1. The minimal sensitivity threshold used to filter the returned cascades. Only cascades that pass this threshold are returned. If 0 is used the returned cascades are filtered for >0 and otherwise >= thresh. For low thresholds the calculation lasts longer. |
size |
A numeric value that defines the size of the cascades that should be returned. The smallest size is 2 and the largest the maximal number of classes of the current dataset. If size is NA (the default setting), all cascades from 2 to the maximal number of classes are evaluated. |
numSol |
The maximum number of cascades that should pass the first sensitivity bound and are further evaluated. |
This function can either be used to evaluate the performance of a specific cascade, a set of cascades or to filter out the set of cascades of a specific size and passing a given threshold. If the sets-variable is given no size can be set.
A Subcascades object comprising the evaluated cascades and their performances. The Subcascades object is made up of a list of matrices. Each matrix comprises the evaluation results of cascades of a specific length and is sorted row-wise according to the achieved minimal classwise sensitivities of the cascades (decreasing). The rownames show the class order by a character string of type '1>2>3' and the entries the sensitivity for each position of the cascade.
print.Subcascades, plot.Subcascades, summary.Subcascades
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # use default parameter settings # -> returns cascades of all lengths that show a minimal classwise sensitivity >0. subc = subcascades(predMap) # change the threshold # -> returns cascades of all lengths that show a minimal classwise sensitivity >=0.6. subc = subcascades(predMap, thresh=0.6) # search only for cascades of length 2 and 4 # -> returns cascades of length 2 and 4 that show a minimal classwise sensitivity >=0.6. subc = subcascades(predMap, thresh=0.6, size=c(2,4)) # evaluates the performance of the cascade '0>1>2>3>4'. subc = subcascades(predMap, sets = c('0>1>2>3>4'))library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # use default parameter settings # -> returns cascades of all lengths that show a minimal classwise sensitivity >0. subc = subcascades(predMap) # change the threshold # -> returns cascades of all lengths that show a minimal classwise sensitivity >=0.6. subc = subcascades(predMap, thresh=0.6) # search only for cascades of length 2 and 4 # -> returns cascades of length 2 and 4 that show a minimal classwise sensitivity >=0.6. subc = subcascades(predMap, thresh=0.6, size=c(2,4)) # evaluates the performance of the cascade '0>1>2>3>4'. subc = subcascades(predMap, sets = c('0>1>2>3>4'))
summary.Conf returns a summarizing overview of a Conf object.
## S3 method for class 'Conf' summary(object = NULL, ...)## S3 method for class 'Conf' summary(object = NULL, ...)
object |
A Conf object as it is returned by |
... |
Further arguments passed from other methods. |
This function gives an overview of the Conf object. The overall number of classes, the highest and lowest sensitivities of the first class as well as the highest and lowest performance of the second class are recorded.
conf, summary.PredictionMap, summary.Subcascades, summary.Groupwise, summary.ConfusionTable
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') conf = conf(predMap) summary(conf)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') conf = conf(predMap) summary(conf)
summary.ConfusionTable returns a summarizing overview.
## S3 method for class 'ConfusionTable' summary(object = NULL, ...)## S3 method for class 'ConfusionTable' summary(object = NULL, ...)
object |
A ConfusionTable object as it is returned by |
... |
Further arguments passed from other methods. |
This function gives an overview of the characteristics of the ConfusionTable object. The cascade with the corresponding mean accuracy are given.
confusionTable, summary.PredictionMap, summary.Subcascades, summary.Groupwise, summary.Conf
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # Calculation of the confusion matrix for '0>2>3>4'. confusionTable = confusionTable(predMap, cascade = '0>2>3>4') summary(confusionTable)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # Calculation of the confusion matrix for '0>2>3>4'. confusionTable = confusionTable(predMap, cascade = '0>2>3>4') summary(confusionTable)
summary.Groupwise returns a summarizing overview. For each length the number of permutations consisting of the same set of classes is given.
## S3 method for class 'Groupwise' summary(object = NULL, ...)## S3 method for class 'Groupwise' summary(object = NULL, ...)
object |
A Groupwise object as it is returned by |
... |
Further arguments passed from other methods. |
This function gives an overview of the characteristics of the Groupwise object. The number of permutations per size is given. A permutation means that the corresponding cascades contain the same classes but with different order.
groupwise, summary.PredictionMap, summary.Subcascades, summary.Conf, summary.ConfusionTable
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) groupwise = groupwise(subc,maxCl=50) summary(groupwise)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc = subcascades(predMap,thresh=0.7) groupwise = groupwise(subc,maxCl=50) summary(groupwise)
summary.PredictionMap returns a summarizing overview of a PredictionMap object.
## S3 method for class 'PredictionMap' summary(object = NULL, ...)## S3 method for class 'PredictionMap' summary(object = NULL, ...)
object |
A PredictionMap object as it is returned by |
... |
Further arguments passed from other methods. |
This function gives an overview of the PredictionMap object. A short summary about the utilized data and labels is given as well as the number of runs and folds of the cross-validation. The summary also includes if the prediction map was generated in parallel and the name of the utilized TunePareto classifier as well as its specified parameters.
predictionMap, summary.Subcascades, summary.Groupwise, summary.Conf, summary.ConfusionTable
library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') summary(predMap)library(TunePareto) data(esl) data = esl$data labels = esl$labels foldList = generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap = predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') summary(predMap)
Generates a general overview of the characteristics of the Subcascades object.
## S3 method for class 'Subcascades' summary(object = NULL, includeClassSummary = TRUE, digits = 3, ...)## S3 method for class 'Subcascades' summary(object = NULL, includeClassSummary = TRUE, digits = 3, ...)
object |
A Subcascades object as it is returned by |
includeClassSummary |
Boolean indicating if the occurrence of classes by size should be included in the summary. |
digits |
Integer defining the number of decimal places as it is used in the |
... |
Further arguments passed from other methods. |
This function gives a general overview of characteristics of the Subcascades object, like number of cascades or maximal cascade length.
subcascades, summary.PredictionMap, summary.Groupwise, summary.Conf, summary.ConfusionTable
library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc <- subcascades(predMap,thresh=0.7,numSol=10000) summary(subc)library(TunePareto) data(esl) data <- esl$data labels <- esl$labels foldList <- generateCVRuns(labels = labels, ntimes = 2, nfold = 2, leaveOneOut = FALSE, stratified = TRUE) predMap <- predictionMap(data, labels, foldList = foldList, classifier = tunePareto.svm(), kernel='linear') # generate Subcascades object subc <- subcascades(predMap,thresh=0.7,numSol=10000) summary(subc)
summaryClasses returns the occurrence of classes by size
summaryClasses(subcascades = NULL)summaryClasses(subcascades = NULL)
subcascades |
A Subcascades object as it is returned by |
This function gives an overview of the classes of the Subcascades object. For each length in the Subcascades object the occurence of classes is given.
A matrix summarizing the overview characteristics of the Subcascades object.
summarySubcascades returns a summarizing overview. For each length the number of cascades and the minimal class-wise sensitivity is given.
summarySubcascades(subcascades = NULL)summarySubcascades(subcascades = NULL)
subcascades |
A Subcascades object as it is returned by |
This function gives an overview of the Subcascades object. For each length in the Subcascades object the number of cascades of that length, as well as their minimal classwise sensitivity is given.
A matrix summarizing the overview characteristics of the Subcascades object. For each size (rows) the number of cascades within the Subcascades object (number) and the minimal classwise sensitivity (min.class.sens) are given.
TunePareto wrapper for the ordinal classifier cascade.
tunePareto.occ(base.classifier)tunePareto.occ(base.classifier)
base.classifier |
A predefined TuneParetoClassifier object used as binary classifier
for the ordinal classifier cascade. There exist five classifier types that
can be used: tunePareto.knn(), tunePareto.svm(), tunePareto.tree(), tunePareto.randomForest(),
tunePareto.NaiveBayes(). For more information about these classifier functions please refer to
the corresponding help page of like |
The "tunePareto.occ" encapsulates the classifier of an ordinal classifier cascade. Additionally, to the parameters of the corresponding base classifier the "class.order" parameter can be provided. It is either a character vector, a numeric vector, or a vector representing a cascade of the following format '1>2>4'.
Returns an object of class TuneParetoClassifier (see: TunePareto::tuneParetoClassifier).
This can be passed to the function trainTuneParetoClassifier (see: TunePareto::trainTuneParetoClassifier).
library(TunePareto) data(esl) data = esl$data labels = esl$labels # train classifier model <- trainTuneParetoClassifier( classifier = tunePareto.occ( base.classifier = tunePareto.svm()), trainData = data, trainLabels = labels, class.order = as.character(c(4,3,1,0)), kernel = "linear", cost = 1) # predict labels prediction <- predict(object = model, newdata = data)library(TunePareto) data(esl) data = esl$data labels = esl$labels # train classifier model <- trainTuneParetoClassifier( classifier = tunePareto.occ( base.classifier = tunePareto.svm()), trainData = data, trainLabels = labels, class.order = as.character(c(4,3,1,0)), kernel = "linear", cost = 1) # predict labels prediction <- predict(object = model, newdata = data)