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add a test folder

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tqchen 2014-03-05 15:20:11 -08:00
parent 74828295fe
commit 2bdcad9630
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2
demo/mushroom/mushroom.conf
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@ -10,6 +10,8 @@ test:data = "agaricus.txt.test"
booster_type = 0
loss_type = 2
bst:tree_maker=2
bst:eta=1.0
bst:gamma=1.0
bst:min_child_weight=1

8124
demo/test/agaricus-lepiota.data Normal file
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32
demo/test/agaricus-lepiota.fmap Normal file
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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

148
demo/test/agaricus-lepiota.names Normal file
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@ -0,0 +1,148 @@
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/test/dump2json.py Executable file
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#!/usr/bin/python
import sys
import json
def loadnmap( fname ):
nmap = {}
for l in open(fname):
arr = l.split()
nmap[int(arr[0])] = arr[1].strip()
return nmap
def recstats( rec, l, label ):
for it in l.split(','):
k = int( it )
if k not in rec:
rec[ k ] = (0,0)
else:
if label == 0:
rec[k] = (rec[k][0]+1,rec[k][1])
else:
rec[k] = (rec[k][0],rec[k][1]+1)
def loadstats( fname, fpath ):
res = {}
fp = open( fname )
for l in open( fpath ):
label = int( fp.readline().split()[0] )
arr = l.split()
for i in xrange( len(arr) ):
if i not in res:
res[ i ] = {}
recstats( res[ i ], arr[i], label )
return res
def mapid( idmap, fid, bid ):
if (bid, fid) not in idmap:
idmap[ (bid,fid) ] = len(idmap)
return idmap[ (bid,fid) ]
def dumpjson( fo, trees ):
fo.write('{\n')
fo.write(' \"roots\":'+json.dumps( trees['roots'], separators=(' , ',' : ') ) +',\n' )
fo.write(' \"weights\":'+json.dumps( trees['weights'], separators=(' , ',' : ') ) +',\n' )
fo.write(' \"nodes\":[\n' )
fo.write('%s\n ]' % ',\n'.join((' %s' % json.dumps( n, separators=(' , ',' : ') ) ) for n in trees['nodes']) )
fo.write('\n}\n')
fo = sys.stdout
nmap = loadnmap( 'featmap.txt' )
stat = loadstats( 'agaricus.txt.test', 'dump.path.txt' )
trees = {'roots':[], 'weights':[], 'nodes':[] }
idmap = {}
for l in open( 'dump.raw.txt'):
if l.startswith('booster['):
bid = int( l.split('[')[1].split(']')[0] )
trees['roots'].append( mapid(idmap,bid,0) )
trees['weights'].append( 1.0 )
continue
node = {}
rid = int( l.split(':')[0] )
node['id'] = mapid( idmap, bid, rid )
node['neg_cnt' ] = stat[ bid ][ rid ][ 0 ]
node['pos_cnt' ] = stat[ bid ][ rid ][ 1 ]
idx = l.find('[f')
if idx != -1:
fid = int( l[idx+2:len(l)].split('<')[0])
node['label'] = nmap[ fid ]
node['children'] = [ mapid( idmap, bid, int(it.split('=')[1]) ) for it in l.split()[1].split(',') ]
node['edge_tags'] = ['yes','no']
else:
node['label'] = l.split(':')[1].strip()
node['value'] = float(l.split(':')[1].split('=')[1])
trees['nodes'].append( node )
trees['nodes'].sort( key = lambda x:x['id'] )
dumpjson( sys.stderr, trees)

50
demo/test/mapfeat.py Executable file
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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' ):
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('\n')
fo.close()

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demo/test/mknfold.py Executable file
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#!/usr/bin/python
import sys
import random
if len(sys.argv) < 2:
print 'Usage:<filename> <k> [nfold = 5]'
exit(0)
random.seed( 10 )
k = int( sys.argv[2] )
if len(sys.argv) > 3:
nfold = int( sys.argv[3] )
else:
nfold = 5
fi = open( sys.argv[1], 'r' )
ftr = open( sys.argv[1]+'.train', 'w' )
fte = open( sys.argv[1]+'.test', 'w' )
for l in fi:
if random.randint( 1 , nfold ) == k:
fte.write( l )
else:
ftr.write( l )
fi.close()
ftr.close()
fte.close()

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demo/test/runexp.sh Executable file
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#!/bin/bash
# map feature using indicator encoding, also produce featmap.txt
python mapfeat.py
# split train and test
python mknfold.py agaricus.txt 1
# training
../../xgboost mushroom.conf
# this is what dump will looklike without feature map
../../xgboost mushroom.conf task=dump model_in=0003.model name_dump=dump.raw.txt
# this is what dump will looklike with feature map
../../xgboost mushroom.conf task=dump model_in=0003.model fmap=featmap.txt name_dump=dump.nice.txt
cat dump.nice.txt