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rdkit/Python/ML/AnalyzeComposite.py

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# $Id$
#
# Copyright (C) 2002-2006 greg Landrum and Rational Discovery LLC
#
# @@ All Rights Reserved @@
#
""" command line utility to report on the contributions of descriptors to
tree-based composite models
Usage: AnalyzeComposite [optional args] <models>
<models>: file name(s) of pickled composite model(s)
(this is the name of the db table if using a database)
Optional Arguments:
-n number: the number of levels of each model to consider
-d dbname: the database from which to read the models
-N Note: the note string to search for to pull models from the database
-X: Send the results to Excel. Note: will alter the current
worksheet (by adding data to the end) and only works on
systems with Excel installed. It *is* safe to call this
multiple times with a single worksheet.
-v: be verbose whilst screening
"""
from Numeric import *
import sys,cPickle
from ML.DecTree import TreeUtils,Tree
from ML.Data import Stats
from Dbase.DbConnection import DbConnect
from ML import ScreenComposite
try:
from Excel.ExcelWrapper import ExcelWrapper as Excel
except ImportError:
Excel = None
__VERSION_STRING="2.2.0"
def ProcessIt(composites,nToConsider=3,verbose=0,reportToExcel=0):
composite=composites[0]
nComposites =len(composites)
ns = composite.GetDescriptorNames()
#nDesc = len(ns)-2
if len(ns)>2:
#globalRes = zeros((nDesc,nToConsider),Float)
globalRes = {}
nDone = 1
descNames = {}
for composite in composites:
if verbose > 0:
print '#------------------------------------'
print 'Doing: ',nDone
nModels = len(composite)
nDone += 1
res = {}
for i in range(len(composite)):
model = composite.GetModel(i)
if isinstance(model,Tree.TreeNode):
levels = TreeUtils.CollectLabelLevels(model,{},0,nToConsider)
TreeUtils.CollectDescriptorNames(model,descNames,0,nToConsider)
for descId in levels.keys():
v = res.get(descId,zeros(nToConsider,Float))
v[levels[descId]] += 1./nModels
res[descId] = v
for k in res:
v = globalRes.get(k,zeros(nToConsider,Float))
v += res[k]/nComposites
globalRes[k] = v
if verbose > 0:
for k in res.keys():
name = descNames[k]
strRes = ', '.join(['%4.2f'%x for x in res[k]])
print '%s,%s,%5.4f'%(name,strRes,sum(res[k]))
print
if verbose >= 0:
print '# Average Descriptor Positions'
retVal = []
if reportToExcel and Excel is not None:
xl = Excel()
xlCol = 1
xlRow = xl.FindLastRow(1,xlCol)
xlRow+=1
xl[xlRow,xlCol]=' '.join(sys.argv)
xlRow+=1
else:
xl = None
for k in globalRes.keys():
name = descNames[k]
if verbose >= 0:
strRes = ', '.join(['%4.2f'%x for x in globalRes[k]])
print '%s,%s,%5.4f'%(name,strRes,sum(globalRes[k]))
if xl:
xlCol=1
xl[xlRow,xlCol]=name
xlCol += 1
for v in globalRes[k]:
xl[xlRow,xlCol]=v
xlCol+=1
xl[xlRow,xlCol]=sum(globalRes[k])
xlRow += 1
tmp = [name]
tmp.extend(globalRes[k])
tmp.append(sum(globalRes[k]))
retVal.append(tmp)
if verbose >= 0:
print
else:
retVal = []
return retVal
def ErrorStats(conn,where,enrich=1):
fields = 'overall_error,holdout_error,overall_result_matrix,holdout_result_matrix,overall_correct_conf,overall_incorrect_conf,holdout_correct_conf,holdout_incorrect_conf'
try:
data = conn.GetData(fields=fields,where=where)
except:
import traceback
traceback.print_exc()
return None
nPts = len(data)
if not nPts:
sys.stderr.write('no runs found\n')
return None
overall = zeros(nPts,Float)
overallEnrich = zeros(nPts,Float)
oCorConf = 0.0
oInCorConf = 0.0
holdout = zeros(nPts,Float)
holdoutEnrich = zeros(nPts,Float)
hCorConf = 0.0
hInCorConf = 0.0
overallMatrix = None
holdoutMatrix = None
for i in range(nPts):
if data[i][0] is not None:
overall[i] = data[i][0]
oCorConf += data[i][4]
oInCorConf += data[i][5]
if data[i][1] is not None:
holdout[i] = data[i][1]
haveHoldout=1
else:
haveHoldout=0
tmpOverall = 1.*eval(data[i][2])
if enrich >=0:
overallEnrich[i] = ScreenComposite.CalcEnrichment(tmpOverall,tgt=enrich)
if haveHoldout:
tmpHoldout = 1.*eval(data[i][3])
if enrich >=0:
holdoutEnrich[i] = ScreenComposite.CalcEnrichment(tmpHoldout,tgt=enrich)
if overallMatrix is None:
if data[i][2] is not None:
overallMatrix = tmpOverall
if haveHoldout and data[i][3] is not None:
holdoutMatrix = tmpHoldout
else:
overallMatrix += tmpOverall
if haveHoldout:
holdoutMatrix += tmpHoldout
if haveHoldout:
hCorConf += data[i][6]
hInCorConf += data[i][7]
avgOverall = sum(overall)/nPts
oCorConf /= nPts
oInCorConf /= nPts
overallMatrix /= nPts
oSort = argsort(overall)
oMin = overall[oSort[0]]
overall -= avgOverall
devOverall = sqrt(sum(overall**2)/(nPts-1))
res = {}
res['oAvg'] = 100*avgOverall
res['oDev'] = 100*devOverall
res['oCorrectConf'] = 100*oCorConf
res['oIncorrectConf'] = 100*oInCorConf
res['oResultMat']=overallMatrix
res['oBestIdx']=oSort[0]
res['oBestErr']=100*oMin
if enrich>=0:
mean,dev = Stats.MeanAndDev(overallEnrich)
res['oAvgEnrich'] = mean
res['oDevEnrich'] = dev
if haveHoldout:
avgHoldout = sum(holdout)/nPts
hCorConf /= nPts
hInCorConf /= nPts
holdoutMatrix /= nPts
hSort = argsort(holdout)
hMin = holdout[hSort[0]]
holdout -= avgHoldout
devHoldout = sqrt(sum(holdout**2)/(nPts-1))
res['hAvg'] = 100*avgHoldout
res['hDev'] = 100*devHoldout
res['hCorrectConf'] = 100*hCorConf
res['hIncorrectConf'] = 100*hInCorConf
res['hResultMat']=holdoutMatrix
res['hBestIdx']=hSort[0]
res['hBestErr']=100*hMin
if enrich>=0:
mean,dev = Stats.MeanAndDev(holdoutEnrich)
res['hAvgEnrich'] = mean
res['hDevEnrich'] = dev
return res
def ShowStats(statD,enrich=1):
statD = statD.copy()
statD['oBestIdx'] = statD['oBestIdx']+1
txt="""
# Error Statistics:
\tOverall: %(oAvg)6.3f%% (%(oDev)6.3f) %(oCorrectConf)4.1f/%(oIncorrectConf)4.1f
\t\tBest: %(oBestIdx)d %(oBestErr)6.3f%%"""%(statD)
if statD.has_key('hAvg'):
statD['hBestIdx'] = statD['hBestIdx']+1
txt += """
\tHoldout: %(hAvg)6.3f%% (%(hDev)6.3f) %(hCorrectConf)4.1f/%(hIncorrectConf)4.1f
\t\tBest: %(hBestIdx)d %(hBestErr)6.3f%%
"""%(statD)
print txt
print
print '# Results matrices:'
print '\tOverall:'
tmp = transpose(statD['oResultMat'])
colCounts = sum(tmp)
rowCounts = sum(tmp,1)
for i in range(len(tmp)):
if rowCounts[i]==0: rowCounts[i]=1
row = tmp[i]
print '\t\t',
for j in range(len(row)):
print '% 6.2f'%row[j],
print '\t| % 4.2f'%(100.*tmp[i,i]/rowCounts[i])
print '\t\t',
for i in range(len(tmp)):
print '------',
print
print '\t\t',
for i in range(len(tmp)):
if colCounts[i]==0: colCounts[i]=1
print '% 6.2f'%(100.*tmp[i,i]/colCounts[i]),
print
if enrich>-1 and statD.has_key('oAvgEnrich'):
print '\t\tEnrich(%d): %.3f (%.3f)'%(enrich,statD['oAvgEnrich'],statD['oDevEnrich'])
if statD.has_key('hResultMat'):
print '\tHoldout:'
tmp = transpose(statD['hResultMat'])
colCounts = sum(tmp)
rowCounts = sum(tmp,1)
for i in range(len(tmp)):
if rowCounts[i]==0: rowCounts[i]=1
row = tmp[i]
print '\t\t',
for j in range(len(row)):
print '% 6.2f'%row[j],
print '\t| % 4.2f'%(100.*tmp[i,i]/rowCounts[i])
print '\t\t',
for i in range(len(tmp)):
print '------',
print
print '\t\t',
for i in range(len(tmp)):
if colCounts[i]==0: colCounts[i]=1
print '% 6.2f'%(100.*tmp[i,i]/colCounts[i]),
print
if enrich>-1 and statD.has_key('hAvgEnrich'):
print '\t\tEnrich(%d): %.3f (%.3f)'%(enrich,statD['hAvgEnrich'],statD['hDevEnrich'])
return
def Usage():
print __doc__
sys.exit(-1)
if __name__ == "__main__":
import getopt
try:
args,extras = getopt.getopt(sys.argv[1:],'n:d:N:vX',('skip',
'enrich=',
))
except:
Usage()
count = 3
db = None
note = ''
verbose = 0
skip = 0
enrich = 1
reportToExcel=0
for arg,val in args:
if arg == '-n':
count = int(val)+1
elif arg == '-d':
db = val
elif arg == '-N':
note = val
elif arg == '-v':
verbose = 1
elif arg == '-X':
if Excel is not None:
reportToExcel = 1
else:
ScreenComposite.message('NOTE: Excel support not enabled, -X option ignored.')
elif arg == '--skip':
skip = 1
elif arg == '--enrich':
enrich = int(val)
composites = []
if db is None:
for arg in extras:
composite = cPickle.load(open(arg,'rb'))
composites.append(composite)
else:
tbl = extras[0]
conn = DbConnect(db,tbl)
if note:
where="where note='%s'"%(note)
else:
where = ''
if not skip:
pkls = conn.GetData(fields='model',where=where)
composites = []
for pkl in pkls:
pkl = str(pkl[0])
comp = cPickle.loads(pkl)
composites.append(comp)
if len(composites):
ProcessIt(composites,count,verbose=verbose,reportToExcel=reportToExcel)
elif not skip:
print 'ERROR: no composite models found'
sys.exit(-1)
if db:
res = ErrorStats(conn,where,enrich=enrich)
if res:
ShowStats(res)
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