plot.idgraph: Plots interdependencies graph
In rmcfs: The MCFS-ID Algorithm for Feature Selection and Interdependency Discovery
Description
Usage
Arguments
Examples
Description
Invokes plot.igraph with predefined parameters to visualize interdependencies
graph (ID-Graph). Standard plot function with custom parameters may be used instead of this one.
Usage
1
2
3
4
5
Arguments
x
idgraph/igraph S3 object representing feature interdependencies. This object is produced by build.idgraph function.
label_dist
space between the node's label and the corresponding node in the plot.
color
it defines color of the graph nodes.
cex
size of fonts.
...
additional plotting parameters.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
## Not run: ###dontrunbegin
# create input data
adata <- artificial.data(rnd_features = 10)
showme(adata)
# Parametrize and run MCFS-ID procedure
result <- mcfs(class~., adata, cutoffPermutations = 0, featureFreq = 50,
finalCV = FALSE, finalRuleset = FALSE, threadsNumber = 2)
# build interdependencies graph for top 6 features
# and top 12 interdependencies and plot all nodes
gid <- build.idgraph(result, size = 6, size_ID = 12, orphan_nodes = TRUE)
plot(gid, label_dist = 1)
## End(Not run)###dontrunend
Example output
Loading required package: rJava
########################
## rmcfs 1.2.15 ##
########################
If used please cite the following paper:
M. Draminski, J. Koronacki (2018),
rmcfs: An R Package for Monte Carlo Feature Selection and Interdependency Discovery,
Journal of Statistical Software, vol 85(12), 1-28, doi:10.18637/jss.v085.i12.
X1 X2 X3 X4 X5 X6 X7
1 0.3930107 0.3663493 0.38472302 0.5231210 0.41198267 0.76316701 0.7403338
2 0.8477112 0.8862161 0.43740693 0.9837048 0.25369210 0.94306892 0.2809859
3 0.3980598 0.8071308 0.15242067 0.1138398 0.91578190 0.59179029 0.7064991
4 0.8443938 0.7690093 0.03827253 0.3638340 0.80776492 0.23910017 0.9315151
5 0.5370111 0.8292374 0.75562139 0.3587937 0.29421959 0.00236669 0.7572942
6 0.4070033 0.6478332 0.44078957 0.5373605 0.59909905 0.71363226 0.4231788
7 0.3533752 0.9916727 0.87594382 0.9287339 0.28275152 0.34097983 0.9032967
8 0.7734430 0.1969377 0.46840625 0.4158277 0.02713330 0.76381899 0.1317639
9 0.3626167 0.4714013 0.99050051 0.7332733 0.03945014 0.89841888 0.3601390
10 0.1374891 0.4289984 0.52236197 0.7735007 0.91963210 0.58737566 0.9399527
X8 X9 X10
1 0.3771495 0.95244865 0.637382733
2 0.3240378 0.46141117 0.834912962
3 0.7286271 0.08268236 0.931144820
4 0.1606327 0.86374901 0.712248333
5 0.9766857 0.59797595 0.554702510
6 0.5707007 0.95430054 0.574245517
7 0.5775714 0.25045984 0.914132439
8 0.2733582 0.99839912 0.007631675
9 0.8197607 0.07040937 0.037980088
10 0.2716991 0.97478223 0.105096398
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class
60 0.664640874 0.03549135 0.2912184 0.71342019 0 0 0 0 0 0 B
61 0.257072543 0.95822999 0.4837183 0.46706456 0 0 0 0 0 0 C
62 0.424140241 0.27672091 0.5769849 0.05890492 0 0 0 0 C C C
63 0.740464086 0.06420468 0.3993345 0.77116243 0 0 0 0 C C C
64 0.915609306 0.95802624 0.1607511 0.07035358 0 0 0 0 C C C
65 0.347891173 0.56451597 0.4230502 0.12820831 0 0 0 0 0 0 C
66 0.431517157 0.29369966 0.3349596 0.63414046 0 0 0 0 0 0 C
67 0.009327838 0.56759436 0.9663640 0.17126916 0 0 0 0 C C C
68 0.763069531 0.64192371 0.5744504 0.62816292 0 0 0 0 0 0 C
69 0.560870141 0.66840742 0.5962220 0.78485115 0 0 0 0 C C C
70 0.996192478 0.47594241 0.6291487 0.37286428 0 0 0 0 C C C
class: 'data.frame' size: 70 x 17Checking input data...
Exporting params...
Exporting input data...
Running MCFS-ID...
*****************************************
******** dmLab *******
*** ver. 2.2.1 2016.10.27 ***
*****************************************
Created by Michal Draminski [mdramins@ipipan.waw.pl]
http://www.ipipan.eu/staff/m.draminski/
Polish Academy of Sciences - Institute of Computer Science
**************************************************************************
'MCFS-ID' and 'ADX' are developed by Michal Draminski
'rmcfs' developed by Michal Draminski & Julian Zubek
'SLIQ' developed by Mariusz Gromada
**************************************************************************
If you want to use dmLab or 'MCFS-ID' in your work, please cite the paper:
M.Draminski, A.Rada-Iglesias, S.Enroth, C.Wadelius, J. Koronacki, J.Komorowski
'Monte Carlo feature selection for supervised classification', BIOINFORMATICS 24(1): 110-117 (2008)
**************************************************************************
Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'.
**************************
*** MCFS-ID Experiment ***
**************************
Loading data: 'input.adx'...
attributes: 17 events: 70
Data loaded.
Nominal target detected - using J48 model.
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = [B, C, A], Sizes = [20, 10, 40], classSizeRatio = 0.25, balanceValue = 1.0
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Sat Oct 05 20:32:21 UTC 2019
Running: 2 threads.
10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
All 2 threads are finished.
1000 trees built in 1.8 s.
Confusion Matrix
predicted
class B C A other
B 4932.0 315.0 1823.0 0.0
C 877.0 1689.0 1474.0 0.0
A 546.0 223.0 13371.0 0.0
other 0.0 0.0 0.0 0.0
Accuracy = 0.7917
WeightedAccuracy = 0.6870
True Positive Rate
B: 0.6975
C: 0.4180
A: 0.9456
False Positive Rate
B: 0.0782
C: 0.0253
A: 0.2967
Minimal (based on linear regression angle) RI = 0.0293235
Minimal (based on k-means clustering) RI = 0.4564096
Minimal important (mean based on cutoff methods) RI = 0.0293235
Size of important (mean based on cutoff methods) attributes set = 6
*** Saving filtered data ***
*** Calculations for input data: 'input' are finished! Processing time: 1.9 s. ***
Reading results...
Done.
Selected 6 nodes and 12 edges.
rmcfs documentation built on Sept. 18, 2021, 5:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Examples
Description
Invokes plot.igraph with predefined parameters to visualize interdependencies
graph (ID-Graph). Standard plot function with custom parameters may be used instead of this one.
Usage
1 2 3 4 5 |
Arguments
x |
idgraph/igraph S3 object representing feature interdependencies. This object is produced by |
label_dist |
space between the node's label and the corresponding node in the plot. |
color |
it defines color of the graph nodes. |
cex |
size of fonts. |
... |
additional plotting parameters. |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## Not run: ###dontrunbegin
# create input data
adata <- artificial.data(rnd_features = 10)
showme(adata)
# Parametrize and run MCFS-ID procedure
result <- mcfs(class~., adata, cutoffPermutations = 0, featureFreq = 50,
finalCV = FALSE, finalRuleset = FALSE, threadsNumber = 2)
# build interdependencies graph for top 6 features
# and top 12 interdependencies and plot all nodes
gid <- build.idgraph(result, size = 6, size_ID = 12, orphan_nodes = TRUE)
plot(gid, label_dist = 1)
## End(Not run)###dontrunend
|
Example output
Loading required package: rJava
########################
## rmcfs 1.2.15 ##
########################
If used please cite the following paper:
M. Draminski, J. Koronacki (2018),
rmcfs: An R Package for Monte Carlo Feature Selection and Interdependency Discovery,
Journal of Statistical Software, vol 85(12), 1-28, doi:10.18637/jss.v085.i12.
X1 X2 X3 X4 X5 X6 X7
1 0.3930107 0.3663493 0.38472302 0.5231210 0.41198267 0.76316701 0.7403338
2 0.8477112 0.8862161 0.43740693 0.9837048 0.25369210 0.94306892 0.2809859
3 0.3980598 0.8071308 0.15242067 0.1138398 0.91578190 0.59179029 0.7064991
4 0.8443938 0.7690093 0.03827253 0.3638340 0.80776492 0.23910017 0.9315151
5 0.5370111 0.8292374 0.75562139 0.3587937 0.29421959 0.00236669 0.7572942
6 0.4070033 0.6478332 0.44078957 0.5373605 0.59909905 0.71363226 0.4231788
7 0.3533752 0.9916727 0.87594382 0.9287339 0.28275152 0.34097983 0.9032967
8 0.7734430 0.1969377 0.46840625 0.4158277 0.02713330 0.76381899 0.1317639
9 0.3626167 0.4714013 0.99050051 0.7332733 0.03945014 0.89841888 0.3601390
10 0.1374891 0.4289984 0.52236197 0.7735007 0.91963210 0.58737566 0.9399527
X8 X9 X10
1 0.3771495 0.95244865 0.637382733
2 0.3240378 0.46141117 0.834912962
3 0.7286271 0.08268236 0.931144820
4 0.1606327 0.86374901 0.712248333
5 0.9766857 0.59797595 0.554702510
6 0.5707007 0.95430054 0.574245517
7 0.5775714 0.25045984 0.914132439
8 0.2733582 0.99839912 0.007631675
9 0.8197607 0.07040937 0.037980088
10 0.2716991 0.97478223 0.105096398
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class
60 0.664640874 0.03549135 0.2912184 0.71342019 0 0 0 0 0 0 B
61 0.257072543 0.95822999 0.4837183 0.46706456 0 0 0 0 0 0 C
62 0.424140241 0.27672091 0.5769849 0.05890492 0 0 0 0 C C C
63 0.740464086 0.06420468 0.3993345 0.77116243 0 0 0 0 C C C
64 0.915609306 0.95802624 0.1607511 0.07035358 0 0 0 0 C C C
65 0.347891173 0.56451597 0.4230502 0.12820831 0 0 0 0 0 0 C
66 0.431517157 0.29369966 0.3349596 0.63414046 0 0 0 0 0 0 C
67 0.009327838 0.56759436 0.9663640 0.17126916 0 0 0 0 C C C
68 0.763069531 0.64192371 0.5744504 0.62816292 0 0 0 0 0 0 C
69 0.560870141 0.66840742 0.5962220 0.78485115 0 0 0 0 C C C
70 0.996192478 0.47594241 0.6291487 0.37286428 0 0 0 0 C C C
class: 'data.frame' size: 70 x 17Checking input data...
Exporting params...
Exporting input data...
Running MCFS-ID...
*****************************************
******** dmLab *******
*** ver. 2.2.1 2016.10.27 ***
*****************************************
Created by Michal Draminski [mdramins@ipipan.waw.pl]
http://www.ipipan.eu/staff/m.draminski/
Polish Academy of Sciences - Institute of Computer Science
**************************************************************************
'MCFS-ID' and 'ADX' are developed by Michal Draminski
'rmcfs' developed by Michal Draminski & Julian Zubek
'SLIQ' developed by Mariusz Gromada
**************************************************************************
If you want to use dmLab or 'MCFS-ID' in your work, please cite the paper:
M.Draminski, A.Rada-Iglesias, S.Enroth, C.Wadelius, J. Koronacki, J.Komorowski
'Monte Carlo feature selection for supervised classification', BIOINFORMATICS 24(1): 110-117 (2008)
**************************************************************************
Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'.
**************************
*** MCFS-ID Experiment ***
**************************
Loading data: 'input.adx'...
attributes: 17 events: 70
Data loaded.
Nominal target detected - using J48 model.
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = [B, C, A], Sizes = [20, 10, 40], classSizeRatio = 0.25, balanceValue = 1.0
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Sat Oct 05 20:32:21 UTC 2019
Running: 2 threads.
10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
All 2 threads are finished.
1000 trees built in 1.8 s.
Confusion Matrix
predicted
class B C A other
B 4932.0 315.0 1823.0 0.0
C 877.0 1689.0 1474.0 0.0
A 546.0 223.0 13371.0 0.0
other 0.0 0.0 0.0 0.0
Accuracy = 0.7917
WeightedAccuracy = 0.6870
True Positive Rate
B: 0.6975
C: 0.4180
A: 0.9456
False Positive Rate
B: 0.0782
C: 0.0253
A: 0.2967
Minimal (based on linear regression angle) RI = 0.0293235
Minimal (based on k-means clustering) RI = 0.4564096
Minimal important (mean based on cutoff methods) RI = 0.0293235
Size of important (mean based on cutoff methods) attributes set = 6
*** Saving filtered data ***
*** Calculations for input data: 'input' are finished! Processing time: 1.9 s. ***
Reading results...
Done.
Selected 6 nodes and 12 edges.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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