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change the regression demo data set

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8
demo/regression/README
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Demonstrating how to use XGBoost accomplish regression tasks on UCI mushroom dataset http://archive.ics.uci.edu/ml/datasets/Mushroom
Demonstrating how to use XGBoost accomplish regression tasks on computer hardware dataset https://archive.ics.uci.edu/ml/datasets/Computer+Hardware
Run: ./runexp.sh
Format of input: LIBSVM format
Format of featmap.txt:
<featureid> <featurename> <q or i>\n
q means continuous quantities, i means indicator features.
Feature id must be from 0 to num_features, in sorted order.

8124
demo/regression/agaricus-lepiota.data
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demo/regression/agaricus-lepiota.fmap
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1. cap-shape: bell=b,conical=c,convex=x,flat=f,knobbed=k,sunken=s
2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s
3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r,pink=p,purple=u,red=e,white=w,yellow=y
4. bruises?: bruises=t,no=f
5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f,
musty=m,none=n,pungent=p,spicy=s
6. gill-attachment: attached=a,descending=d,free=f,notched=n
7. gill-spacing: close=c,crowded=w,distant=d
8. gill-size: broad=b,narrow=n
9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g,
green=r,orange=o,pink=p,purple=u,red=e,
white=w,yellow=y
10. stalk-shape: enlarging=e,tapering=t
11. stalk-root: bulbous=b,club=c,cup=u,equal=e,
rhizomorphs=z,rooted=r,missing=?
12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,
pink=p,red=e,white=w,yellow=y
15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,
pink=p,red=e,white=w,yellow=y
16. veil-type: partial=p,universal=u
17. veil-color: brown=n,orange=o,white=w,yellow=y
18. ring-number: none=n,one=o,two=t
19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l,
none=n,pendant=p,sheathing=s,zone=z
20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r,
orange=o,purple=u,white=w,yellow=y
21. population: abundant=a,clustered=c,numerous=n,
scattered=s,several=v,solitary=y
22. habitat: grasses=g,leaves=l,meadows=m,paths=p,
urban=u,waste=w,woods=d

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demo/regression/agaricus-lepiota.names
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1. Title: Mushroom Database
2. Sources:
(a) Mushroom records drawn from The Audubon Society Field Guide to North
American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred
A. Knopf
(b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
(c) Date: 27 April 1987
3. Past Usage:
1. Schlimmer,J.S. (1987). Concept Acquisition Through Representational
Adjustment (Technical Report 87-19). Doctoral disseration, Department
of Information and Computer Science, University of California, Irvine.
--- STAGGER: asymptoted to 95% classification accuracy after reviewing
1000 instances.
2. Iba,W., Wogulis,J., & Langley,P. (1988). Trading off Simplicity
and Coverage in Incremental Concept Learning. In Proceedings of
the 5th International Conference on Machine Learning, 73-79.
Ann Arbor, Michigan: Morgan Kaufmann.
-- approximately the same results with their HILLARY algorithm
3. In the following references a set of rules (given below) were
learned for this data set which may serve as a point of
comparison for other researchers.
Duch W, Adamczak R, Grabczewski K (1996) Extraction of logical rules
from training data using backpropagation networks, in: Proc. of the
The 1st Online Workshop on Soft Computing, 19-30.Aug.1996, pp. 25-30,
available on-line at: http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/
Duch W, Adamczak R, Grabczewski K, Ishikawa M, Ueda H, Extraction of
crisp logical rules using constrained backpropagation networks -
comparison of two new approaches, in: Proc. of the European Symposium
on Artificial Neural Networks (ESANN'97), Bruge, Belgium 16-18.4.1997,
pp. xx-xx
Wlodzislaw Duch, Department of Computer Methods, Nicholas Copernicus
University, 87-100 Torun, Grudziadzka 5, Poland
e-mail: duch@phys.uni.torun.pl
WWW http://www.phys.uni.torun.pl/kmk/
Date: Mon, 17 Feb 1997 13:47:40 +0100
From: Wlodzislaw Duch <duch@phys.uni.torun.pl>
Organization: Dept. of Computer Methods, UMK
I have attached a file containing logical rules for mushrooms.
It should be helpful for other people since only in the last year I
have seen about 10 papers analyzing this dataset and obtaining quite
complex rules. We will try to contribute other results later.
With best regards, Wlodek Duch
________________________________________________________________
Logical rules for the mushroom data sets.
Logical rules given below seem to be the simplest possible for the
mushroom dataset and therefore should be treated as benchmark results.
Disjunctive rules for poisonous mushrooms, from most general
to most specific:
P_1) odor=NOT(almond.OR.anise.OR.none)
120 poisonous cases missed, 98.52% accuracy
P_2) spore-print-color=green
48 cases missed, 99.41% accuracy
P_3) odor=none.AND.stalk-surface-below-ring=scaly.AND.
(stalk-color-above-ring=NOT.brown)
8 cases missed, 99.90% accuracy
P_4) habitat=leaves.AND.cap-color=white
100% accuracy
Rule P_4) may also be
P_4') population=clustered.AND.cap_color=white
These rule involve 6 attributes (out of 22). Rules for edible
mushrooms are obtained as negation of the rules given above, for
example the rule:
odor=(almond.OR.anise.OR.none).AND.spore-print-color=NOT.green
gives 48 errors, or 99.41% accuracy on the whole dataset.
Several slightly more complex variations on these rules exist,
involving other attributes, such as gill_size, gill_spacing,
stalk_surface_above_ring, but the rules given above are the simplest
we have found.
4. Relevant Information:
This data set includes descriptions of hypothetical samples
corresponding to 23 species of gilled mushrooms in the Agaricus and
Lepiota Family (pp. 500-525). Each species is identified as
definitely edible, definitely poisonous, or of unknown edibility and
not recommended. This latter class was combined with the poisonous
one. The Guide clearly states that there is no simple rule for
determining the edibility of a mushroom; no rule like ``leaflets
three, let it be'' for Poisonous Oak and Ivy.
5. Number of Instances: 8124
6. Number of Attributes: 22 (all nominally valued)
7. Attribute Information: (classes: edible=e, poisonous=p)
1. cap-shape: bell=b,conical=c,convex=x,flat=f,
knobbed=k,sunken=s
2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s
3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r,
pink=p,purple=u,red=e,white=w,yellow=y
4. bruises?: bruises=t,no=f
5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f,
musty=m,none=n,pungent=p,spicy=s
6. gill-attachment: attached=a,descending=d,free=f,notched=n
7. gill-spacing: close=c,crowded=w,distant=d
8. gill-size: broad=b,narrow=n
9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g,
green=r,orange=o,pink=p,purple=u,red=e,
white=w,yellow=y
10. stalk-shape: enlarging=e,tapering=t
11. stalk-root: bulbous=b,club=c,cup=u,equal=e,
rhizomorphs=z,rooted=r,missing=?
12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,
pink=p,red=e,white=w,yellow=y
15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,
pink=p,red=e,white=w,yellow=y
16. veil-type: partial=p,universal=u
17. veil-color: brown=n,orange=o,white=w,yellow=y
18. ring-number: none=n,one=o,two=t
19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l,
none=n,pendant=p,sheathing=s,zone=z
20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r,
orange=o,purple=u,white=w,yellow=y
21. population: abundant=a,clustered=c,numerous=n,
scattered=s,several=v,solitary=y
22. habitat: grasses=g,leaves=l,meadows=m,paths=p,
urban=u,waste=w,woods=d
8. Missing Attribute Values: 2480 of them (denoted by "?"), all for
attribute #11.
9. Class Distribution:
-- edible: 4208 (51.8%)
-- poisonous: 3916 (48.2%)
-- total: 8124 instances

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demo/regression/machine.data Normal file
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adviser,32/60,125,256,6000,256,16,128,198,199
amdahl,470v/7,29,8000,32000,32,8,32,269,253
amdahl,470v/7a,29,8000,32000,32,8,32,220,253
amdahl,470v/7b,29,8000,32000,32,8,32,172,253
amdahl,470v/7c,29,8000,16000,32,8,16,132,132
amdahl,470v/b,26,8000,32000,64,8,32,318,290
amdahl,580-5840,23,16000,32000,64,16,32,367,381
amdahl,580-5850,23,16000,32000,64,16,32,489,381
amdahl,580-5860,23,16000,64000,64,16,32,636,749
amdahl,580-5880,23,32000,64000,128,32,64,1144,1238
apollo,dn320,400,1000,3000,0,1,2,38,23
apollo,dn420,400,512,3500,4,1,6,40,24
basf,7/65,60,2000,8000,65,1,8,92,70
basf,7/68,50,4000,16000,65,1,8,138,117
bti,5000,350,64,64,0,1,4,10,15
bti,8000,200,512,16000,0,4,32,35,64
burroughs,b1955,167,524,2000,8,4,15,19,23
burroughs,b2900,143,512,5000,0,7,32,28,29
burroughs,b2925,143,1000,2000,0,5,16,31,22
burroughs,b4955,110,5000,5000,142,8,64,120,124
burroughs,b5900,143,1500,6300,0,5,32,30,35
burroughs,b5920,143,3100,6200,0,5,20,33,39
burroughs,b6900,143,2300,6200,0,6,64,61,40
burroughs,b6925,110,3100,6200,0,6,64,76,45
c.r.d,68/10-80,320,128,6000,0,1,12,23,28
c.r.d,universe:2203t,320,512,2000,4,1,3,69,21
c.r.d,universe:68,320,256,6000,0,1,6,33,28
c.r.d,universe:68/05,320,256,3000,4,1,3,27,22
c.r.d,universe:68/137,320,512,5000,4,1,5,77,28
c.r.d,universe:68/37,320,256,5000,4,1,6,27,27
cdc,cyber:170/750,25,1310,2620,131,12,24,274,102
cdc,cyber:170/760,25,1310,2620,131,12,24,368,102
cdc,cyber:170/815,50,2620,10480,30,12,24,32,74
cdc,cyber:170/825,50,2620,10480,30,12,24,63,74
cdc,cyber:170/835,56,5240,20970,30,12,24,106,138
cdc,cyber:170/845,64,5240,20970,30,12,24,208,136
cdc,omega:480-i,50,500,2000,8,1,4,20,23
cdc,omega:480-ii,50,1000,4000,8,1,5,29,29
cdc,omega:480-iii,50,2000,8000,8,1,5,71,44
cambex,1636-1,50,1000,4000,8,3,5,26,30
cambex,1636-10,50,1000,8000,8,3,5,36,41
cambex,1641-1,50,2000,16000,8,3,5,40,74
cambex,1641-11,50,2000,16000,8,3,6,52,74
cambex,1651-1,50,2000,16000,8,3,6,60,74
dec,decsys:10:1091,133,1000,12000,9,3,12,72,54
dec,decsys:20:2060,133,1000,8000,9,3,12,72,41
dec,microvax-1,810,512,512,8,1,1,18,18
dec,vax:11/730,810,1000,5000,0,1,1,20,28
dec,vax:11/750,320,512,8000,4,1,5,40,36
dec,vax:11/780,200,512,8000,8,1,8,62,38
dg,eclipse:c/350,700,384,8000,0,1,1,24,34
dg,eclipse:m/600,700,256,2000,0,1,1,24,19
dg,eclipse:mv/10000,140,1000,16000,16,1,3,138,72
dg,eclipse:mv/4000,200,1000,8000,0,1,2,36,36
dg,eclipse:mv/6000,110,1000,4000,16,1,2,26,30
dg,eclipse:mv/8000,110,1000,12000,16,1,2,60,56
dg,eclipse:mv/8000-ii,220,1000,8000,16,1,2,71,42
formation,f4000/100,800,256,8000,0,1,4,12,34
formation,f4000/200,800,256,8000,0,1,4,14,34
formation,f4000/200ap,800,256,8000,0,1,4,20,34
formation,f4000/300,800,256,8000,0,1,4,16,34
formation,f4000/300ap,800,256,8000,0,1,4,22,34
four-phase,2000/260,125,512,1000,0,8,20,36,19
gould,concept:32/8705,75,2000,8000,64,1,38,144,75
gould,concept:32/8750,75,2000,16000,64,1,38,144,113
gould,concept:32/8780,75,2000,16000,128,1,38,259,157
hp,3000/30,90,256,1000,0,3,10,17,18
hp,3000/40,105,256,2000,0,3,10,26,20
hp,3000/44,105,1000,4000,0,3,24,32,28
hp,3000/48,105,2000,4000,8,3,19,32,33
hp,3000/64,75,2000,8000,8,3,24,62,47
hp,3000/88,75,3000,8000,8,3,48,64,54
hp,3000/iii,175,256,2000,0,3,24,22,20
harris,100,300,768,3000,0,6,24,36,23
harris,300,300,768,3000,6,6,24,44,25
harris,500,300,768,12000,6,6,24,50,52
harris,600,300,768,4500,0,1,24,45,27
harris,700,300,384,12000,6,1,24,53,50
harris,80,300,192,768,6,6,24,36,18
harris,800,180,768,12000,6,1,31,84,53
honeywell,dps:6/35,330,1000,3000,0,2,4,16,23
honeywell,dps:6/92,300,1000,4000,8,3,64,38,30
honeywell,dps:6/96,300,1000,16000,8,2,112,38,73
honeywell,dps:7/35,330,1000,2000,0,1,2,16,20
honeywell,dps:7/45,330,1000,4000,0,3,6,22,25
honeywell,dps:7/55,140,2000,4000,0,3,6,29,28
honeywell,dps:7/65,140,2000,4000,0,4,8,40,29
honeywell,dps:8/44,140,2000,4000,8,1,20,35,32
honeywell,dps:8/49,140,2000,32000,32,1,20,134,175
honeywell,dps:8/50,140,2000,8000,32,1,54,66,57
honeywell,dps:8/52,140,2000,32000,32,1,54,141,181
honeywell,dps:8/62,140,2000,32000,32,1,54,189,181
honeywell,dps:8/20,140,2000,4000,8,1,20,22,32
ibm,3033:s,57,4000,16000,1,6,12,132,82
ibm,3033:u,57,4000,24000,64,12,16,237,171
ibm,3081,26,16000,32000,64,16,24,465,361
ibm,3081:d,26,16000,32000,64,8,24,465,350
ibm,3083:b,26,8000,32000,0,8,24,277,220
ibm,3083:e,26,8000,16000,0,8,16,185,113
ibm,370/125-2,480,96,512,0,1,1,6,15
ibm,370/148,203,1000,2000,0,1,5,24,21
ibm,370/158-3,115,512,6000,16,1,6,45,35
ibm,38/3,1100,512,1500,0,1,1,7,18
ibm,38/4,1100,768,2000,0,1,1,13,20
ibm,38/5,600,768,2000,0,1,1,16,20
ibm,38/7,400,2000,4000,0,1,1,32,28
ibm,38/8,400,4000,8000,0,1,1,32,45
ibm,4321,900,1000,1000,0,1,2,11,18
ibm,4331-1,900,512,1000,0,1,2,11,17
ibm,4331-11,900,1000,4000,4,1,2,18,26
ibm,4331-2,900,1000,4000,8,1,2,22,28
ibm,4341,900,2000,4000,0,3,6,37,28
ibm,4341-1,225,2000,4000,8,3,6,40,31
ibm,4341-10,225,2000,4000,8,3,6,34,31
ibm,4341-11,180,2000,8000,8,1,6,50,42
ibm,4341-12,185,2000,16000,16,1,6,76,76
ibm,4341-2,180,2000,16000,16,1,6,66,76
ibm,4341-9,225,1000,4000,2,3,6,24,26
ibm,4361-4,25,2000,12000,8,1,4,49,59
ibm,4361-5,25,2000,12000,16,3,5,66,65
ibm,4381-1,17,4000,16000,8,6,12,100,101
ibm,4381-2,17,4000,16000,32,6,12,133,116
ibm,8130-a,1500,768,1000,0,0,0,12,18
ibm,8130-b,1500,768,2000,0,0,0,18,20
ibm,8140,800,768,2000,0,0,0,20,20
ipl,4436,50,2000,4000,0,3,6,27,30
ipl,4443,50,2000,8000,8,3,6,45,44
ipl,4445,50,2000,8000,8,1,6,56,44
ipl,4446,50,2000,16000,24,1,6,70,82
ipl,4460,50,2000,16000,24,1,6,80,82
ipl,4480,50,8000,16000,48,1,10,136,128
magnuson,m80/30,100,1000,8000,0,2,6,16,37
magnuson,m80/31,100,1000,8000,24,2,6,26,46
magnuson,m80/32,100,1000,8000,24,3,6,32,46
magnuson,m80/42,50,2000,16000,12,3,16,45,80
magnuson,m80/43,50,2000,16000,24,6,16,54,88
magnuson,m80/44,50,2000,16000,24,6,16,65,88
microdata,seq.ms/3200,150,512,4000,0,8,128,30,33
nas,as/3000,115,2000,8000,16,1,3,50,46
nas,as/3000-n,115,2000,4000,2,1,5,40,29
nas,as/5000,92,2000,8000,32,1,6,62,53
nas,as/5000-e,92,2000,8000,32,1,6,60,53
nas,as/5000-n,92,2000,8000,4,1,6,50,41
nas,as/6130,75,4000,16000,16,1,6,66,86
nas,as/6150,60,4000,16000,32,1,6,86,95
nas,as/6620,60,2000,16000,64,5,8,74,107
nas,as/6630,60,4000,16000,64,5,8,93,117
nas,as/6650,50,4000,16000,64,5,10,111,119
nas,as/7000,72,4000,16000,64,8,16,143,120
nas,as/7000-n,72,2000,8000,16,6,8,105,48
nas,as/8040,40,8000,16000,32,8,16,214,126
nas,as/8050,40,8000,32000,64,8,24,277,266
nas,as/8060,35,8000,32000,64,8,24,370,270
nas,as/9000-dpc,38,16000,32000,128,16,32,510,426
nas,as/9000-n,48,4000,24000,32,8,24,214,151
nas,as/9040,38,8000,32000,64,8,24,326,267
nas,as/9060,30,16000,32000,256,16,24,510,603
ncr,v8535:ii,112,1000,1000,0,1,4,8,19
ncr,v8545:ii,84,1000,2000,0,1,6,12,21
ncr,v8555:ii,56,1000,4000,0,1,6,17,26
ncr,v8565:ii,56,2000,6000,0,1,8,21,35
ncr,v8565:ii-e,56,2000,8000,0,1,8,24,41
ncr,v8575:ii,56,4000,8000,0,1,8,34,47
ncr,v8585:ii,56,4000,12000,0,1,8,42,62
ncr,v8595:ii,56,4000,16000,0,1,8,46,78
ncr,v8635,38,4000,8000,32,16,32,51,80
ncr,v8650,38,4000,8000,32,16,32,116,80
ncr,v8655,38,8000,16000,64,4,8,100,142
ncr,v8665,38,8000,24000,160,4,8,140,281
ncr,v8670,38,4000,16000,128,16,32,212,190
nixdorf,8890/30,200,1000,2000,0,1,2,25,21
nixdorf,8890/50,200,1000,4000,0,1,4,30,25
nixdorf,8890/70,200,2000,8000,64,1,5,41,67
perkin-elmer,3205,250,512,4000,0,1,7,25,24
perkin-elmer,3210,250,512,4000,0,4,7,50,24
perkin-elmer,3230,250,1000,16000,1,1,8,50,64
prime,50-2250,160,512,4000,2,1,5,30,25
prime,50-250-ii,160,512,2000,2,3,8,32,20
prime,50-550-ii,160,1000,4000,8,1,14,38,29
prime,50-750-ii,160,1000,8000,16,1,14,60,43
prime,50-850-ii,160,2000,8000,32,1,13,109,53
siemens,7.521,240,512,1000,8,1,3,6,19
siemens,7.531,240,512,2000,8,1,5,11,22
siemens,7.536,105,2000,4000,8,3,8,22,31
siemens,7.541,105,2000,6000,16,6,16,33,41
siemens,7.551,105,2000,8000,16,4,14,58,47
siemens,7.561,52,4000,16000,32,4,12,130,99
siemens,7.865-2,70,4000,12000,8,6,8,75,67
siemens,7.870-2,59,4000,12000,32,6,12,113,81
siemens,7.872-2,59,8000,16000,64,12,24,188,149
siemens,7.875-2,26,8000,24000,32,8,16,173,183
siemens,7.880-2,26,8000,32000,64,12,16,248,275
siemens,7.881-2,26,8000,32000,128,24,32,405,382
sperry,1100/61-h1,116,2000,8000,32,5,28,70,56
sperry,1100/81,50,2000,32000,24,6,26,114,182
sperry,1100/82,50,2000,32000,48,26,52,208,227
sperry,1100/83,50,2000,32000,112,52,104,307,341
sperry,1100/84,50,4000,32000,112,52,104,397,360
sperry,1100/93,30,8000,64000,96,12,176,915,919
sperry,1100/94,30,8000,64000,128,12,176,1150,978
sperry,80/3,180,262,4000,0,1,3,12,24
sperry,80/4,180,512,4000,0,1,3,14,24
sperry,80/5,180,262,4000,0,1,3,18,24
sperry,80/6,180,512,4000,0,1,3,21,24
sperry,80/8,124,1000,8000,0,1,8,42,37
sperry,90/80-model-3,98,1000,8000,32,2,8,46,50
sratus,32,125,2000,8000,0,2,14,52,41
wang,vs-100,480,512,8000,32,0,0,67,47
wang,vs-90,480,1000,4000,0,0,0,45,25

72
demo/regression/machine.names Normal file
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1. Title: Relative CPU Performance Data
2. Source Information
-- Creators: Phillip Ein-Dor and Jacob Feldmesser
-- Ein-Dor: Faculty of Management; Tel Aviv University; Ramat-Aviv;
Tel Aviv, 69978; Israel
-- Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779
-- Date: October, 1987
3. Past Usage:
1. Ein-Dor and Feldmesser (CACM 4/87, pp 308-317)
-- Results:
-- linear regression prediction of relative cpu performance
-- Recorded 34% average deviation from actual values
2. Kibler,D. & Aha,D. (1988). Instance-Based Prediction of
Real-Valued Attributes. In Proceedings of the CSCSI (Canadian
AI) Conference.
-- Results:
-- instance-based prediction of relative cpu performance
-- similar results; no transformations required
- Predicted attribute: cpu relative performance (numeric)
4. Relevant Information:
-- The estimated relative performance values were estimated by the authors
using a linear regression method. See their article (pp 308-313) for
more details on how the relative performance values were set.
5. Number of Instances: 209
6. Number of Attributes: 10 (6 predictive attributes, 2 non-predictive,
1 goal field, and the linear regression's guess)
7. Attribute Information:
1. vendor name: 30
(adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec,
dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson,
microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry,
sratus, wang)
2. Model Name: many unique symbols
3. MYCT: machine cycle time in nanoseconds (integer)
4. MMIN: minimum main memory in kilobytes (integer)
5. MMAX: maximum main memory in kilobytes (integer)
6. CACH: cache memory in kilobytes (integer)
7. CHMIN: minimum channels in units (integer)
8. CHMAX: maximum channels in units (integer)
9. PRP: published relative performance (integer)
10. ERP: estimated relative performance from the original article (integer)
8. Missing Attribute Values: None
9. Class Distribution: the class value (PRP) is continuously valued.
PRP Value Range: Number of Instances in Range:
0-20 31
21-100 121
101-200 27
201-300 13
301-400 7
401-500 4
501-600 2
above 600 4
Summary Statistics:
Min Max Mean SD PRP Correlation
MCYT: 17 1500 203.8 260.3 -0.3071
MMIN: 64 32000 2868.0 3878.7 0.7949
MMAX: 64 64000 11796.1 11726.6 0.8630
CACH: 0 256 25.2 40.6 0.6626
CHMIN: 0 52 4.7 6.8 0.6089
CHMAX: 0 176 18.2 26.0 0.6052
PRP: 6 1150 105.6 160.8 1.0000
ERP: 15 1238 99.3 154.8 0.9665

55
demo/regression/mapfeat.py
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#!/usr/bin/python
import sys
def loadfmap( fname ):
fmap = {}
nmap = {}
for l in open( fname ):
arr = l.split()
if arr[0].find('.') != -1:
idx = int( arr[0].strip('.') )
assert idx not in fmap
fmap[ idx ] = {}
ftype = arr[1].strip(':')
content = arr[2]
else:
content = arr[0]
for it in content.split(','):
if it.strip() == '':
continue
k , v = it.split('=')
fmap[ idx ][ v ] = len(nmap)
nmap[ len(nmap) ] = ftype+'='+k
return fmap, nmap
def write_nmap( fo, nmap ):
for i in xrange( len(nmap) ):
fo.write('%d\t%s\ti\n' % (i, nmap[i]) )
# start here
fmap, nmap = loadfmap( 'agaricus-lepiota.fmap' )
fo = open( 'featmap.txt', 'w' )
write_nmap( fo, nmap )
fo.close()
fo = open( 'agaricus.txt', 'w' )
for l in open( 'agaricus-lepiota.data' ):
fo = open( 'machine.txt', 'w' )
cnt = 6
fmap = {}
for l in open( 'machine.data' ):
arr = l.split(',')
if arr[0] == 'p':
fo.write('1')
else:
assert arr[0] == 'e'
fo.write('0')
for i in xrange( 1,len(arr) ):
fo.write( ' %d:1' % fmap[i][arr[i].strip()] )
fo.write(arr[8])
for i in xrange( 0,6 ):
fo.write( ' %d:%s' %(i,arr[i+2]) )
if arr[0] not in fmap.keys():
fmap[arr[0]] = cnt
cnt += 1
fo.write( ' %d:1' % fmap[arr[0]] )
fo.write('\n')
fo.close()

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demo/regression/runexp.sh
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@ -2,13 +2,10 @@
# map feature using indicator encoding, also produce featmap.txt
python mapfeat.py
# split train and test
python mknfold.py agaricus.txt 1
python mknfold.py machine.txt 1
# training and output the models
../../xgboost mushroom.conf
../../xgboost machine.conf
# output predictions of test data
../../xgboost mushroom.conf task=pred model_in=0003.model
../../xgboost machine.conf task=pred model_in=0003.model
# print the boosters of 00003.model in dump.raw.txt
../../xgboost mushroom.conf task=dump model_in=0003.model name_dump=dump.raw.txt
# use the feature map in printing for better visualization
../../xgboost mushroom.conf task=dump model_in=0003.model fmap=featmap.txt name_dump=dump.nice.txt
cat dump.nice.txt
../../xgboost machine.conf task=dump model_in=0003.model name_dump=dump.raw.txt