def RunOnData(details, data, progressCallback=None, saveIt=1, setDescNames=0):
nExamples = data.GetNPts()
if details.lockRandom:
seed = details.randomSeed
else:
import random
seed = (random.randint(0, 1e6), random.randint(0, 1e6))
DataUtils.InitRandomNumbers(seed)
testExamples = []
if details.shuffleActivities == 1:
DataUtils.RandomizeActivities(data, shuffle=1, runDetails=details)
elif details.randomActivities == 1:
DataUtils.RandomizeActivities(data, shuffle=0, runDetails=details)
namedExamples = data.GetNamedData()
if details.splitRun == 1:
trainIdx, testIdx = SplitData.SplitIndices(len(namedExamples),
details.splitFrac,
silent=not _verbose)
trainExamples = [namedExamples[x] for x in trainIdx]
testExamples = [namedExamples[x] for x in testIdx]
else:
testExamples = []
testIdx = []
trainIdx = range(len(namedExamples))
trainExamples = namedExamples
if details.filterFrac != 0.0:
# if we're doing quantization on the fly, we need to handle that here:
if hasattr(details, 'activityBounds') and details.activityBounds:
tExamples = []
bounds = details.activityBounds
for pt in trainExamples:
pt = pt[:]
act = pt[-1]
placed = 0
bound = 0
while not placed and bound < len(bounds):
if act < bounds[bound]:
pt[-1] = bound
placed = 1
else:
bound += 1
if not placed:
pt[-1] = bound
tExamples.append(pt)
else:
bounds = None
tExamples = trainExamples
trainIdx, temp = DataUtils.FilterData(tExamples,
details.filterVal,
details.filterFrac,
-1,
indicesOnly=1)
tmp = [trainExamples[x] for x in trainIdx]
testExamples += [trainExamples[x] for x in temp]
trainExamples = tmp
counts = DataUtils.CountResults(trainExamples, bounds=bounds)
ks = counts.keys()
ks.sort()
message('Result Counts in training set:')
for k in ks:
message(str((k, counts[k])))
counts = DataUtils.CountResults(testExamples, bounds=bounds)
ks = counts.keys()
ks.sort()
message('Result Counts in test set:')
for k in ks:
message(str((k, counts[k])))
nExamples = len(trainExamples)
message('Training with %d examples' % (nExamples))
nVars = data.GetNVars()
attrs = range(1, nVars + 1)
nPossibleVals = data.GetNPossibleVals()
for i in range(1, len(nPossibleVals)):
if nPossibleVals[i - 1] == -1:
attrs.remove(i)
if details.pickleDataFileName != '':
pickleDataFile = open(details.pickleDataFileName, 'wb+')
cPickle.dump(trainExamples, pickleDataFile)
cPickle.dump(testExamples, pickleDataFile)
pickleDataFile.close()
if details.bayesModel:
composite = BayesComposite.BayesComposite()
else:
composite = Composite.Composite()
composite._randomSeed = seed
composite._splitFrac = details.splitFrac
composite._shuffleActivities = details.shuffleActivities
composite._randomizeActivities = details.randomActivities
if hasattr(details, 'filterFrac'):
composite._filterFrac = details.filterFrac
if hasattr(details, 'filterVal'):
composite._filterVal = details.filterVal
composite.SetModelFilterData(details.modelFilterFrac,
details.modelFilterVal)
composite.SetActivityQuantBounds(details.activityBounds)
nPossibleVals = data.GetNPossibleVals()
if details.activityBounds:
nPossibleVals[-1] = len(details.activityBounds) + 1
if setDescNames:
composite.SetInputOrder(data.GetVarNames())
composite.SetDescriptorNames(details._descNames)
else:
composite.SetDescriptorNames(data.GetVarNames())
composite.SetActivityQuantBounds(details.activityBounds)
if details.nModels == 1:
details.internalHoldoutFrac = 0.0
if details.useTrees:
from rdkit.ML.DecTree import CrossValidate, PruneTree
if details.qBounds != []:
from rdkit.ML.DecTree import BuildQuantTree
builder = BuildQuantTree.QuantTreeBoot
else:
from rdkit.ML.DecTree import ID3
builder = ID3.ID3Boot
driver = CrossValidate.CrossValidationDriver
pruner = PruneTree.PruneTree
composite.SetQuantBounds(details.qBounds)
nPossibleVals = data.GetNPossibleVals()
if details.activityBounds:
nPossibleVals[-1] = len(details.activityBounds) + 1
composite.Grow(trainExamples,
attrs,
nPossibleVals=[0] + nPossibleVals,
buildDriver=driver,
pruner=pruner,
nTries=details.nModels,
pruneIt=details.pruneIt,
lessGreedy=details.lessGreedy,
needsQuantization=0,
treeBuilder=builder,
nQuantBounds=details.qBounds,
startAt=details.startAt,
maxDepth=details.limitDepth,
progressCallback=progressCallback,
holdOutFrac=details.internalHoldoutFrac,
replacementSelection=details.replacementSelection,
recycleVars=details.recycleVars,
randomDescriptors=details.randomDescriptors,
silent=not _verbose)
elif details.useSigTrees:
from rdkit.ML.DecTree import CrossValidate
from rdkit.ML.DecTree import BuildSigTree
builder = BuildSigTree.SigTreeBuilder
driver = CrossValidate.CrossValidationDriver
nPossibleVals = data.GetNPossibleVals()
if details.activityBounds:
nPossibleVals[-1] = len(details.activityBounds) + 1
if hasattr(details, 'sigTreeBiasList'):
biasList = details.sigTreeBiasList
else:
biasList = None
if hasattr(details, 'useCMIM'):
useCMIM = details.useCMIM
else:
useCMIM = 0
if hasattr(details, 'allowCollections'):
allowCollections = details.allowCollections
else:
allowCollections = False
composite.Grow(trainExamples,
attrs,
nPossibleVals=[0] + nPossibleVals,
buildDriver=driver,
nTries=details.nModels,
needsQuantization=0,
treeBuilder=builder,
maxDepth=details.limitDepth,
progressCallback=progressCallback,
holdOutFrac=details.internalHoldoutFrac,
replacementSelection=details.replacementSelection,
recycleVars=details.recycleVars,
randomDescriptors=details.randomDescriptors,
biasList=biasList,
useCMIM=useCMIM,
allowCollection=allowCollections,
silent=not _verbose)
elif details.useKNN:
from rdkit.ML.KNN import CrossValidate
from rdkit.ML.KNN import DistFunctions
driver = CrossValidate.CrossValidationDriver
dfunc = ''
if (details.knnDistFunc == "Euclidean"):
dfunc = DistFunctions.EuclideanDist
elif (details.knnDistFunc == "Tanimoto"):
dfunc = DistFunctions.TanimotoDist
else:
assert 0, "Bad KNN distance metric value"
composite.Grow(trainExamples,
attrs,
nPossibleVals=[0] + nPossibleVals,
buildDriver=driver,
nTries=details.nModels,
needsQuantization=0,
numNeigh=details.knnNeighs,
holdOutFrac=details.internalHoldoutFrac,
distFunc=dfunc)
elif details.useNaiveBayes or details.useSigBayes:
from rdkit.ML.NaiveBayes import CrossValidate
driver = CrossValidate.CrossValidationDriver
if not (hasattr(details, 'useSigBayes') and details.useSigBayes):
composite.Grow(trainExamples,
attrs,
nPossibleVals=[0] + nPossibleVals,
buildDriver=driver,
nTries=details.nModels,
needsQuantization=0,
nQuantBounds=details.qBounds,
holdOutFrac=details.internalHoldoutFrac,
replacementSelection=details.replacementSelection,
mEstimateVal=details.mEstimateVal,
silent=not _verbose)
else:
if hasattr(details, 'useCMIM'):
useCMIM = details.useCMIM
else:
useCMIM = 0
composite.Grow(trainExamples,
attrs,
nPossibleVals=[0] + nPossibleVals,
buildDriver=driver,
nTries=details.nModels,
needsQuantization=0,
nQuantBounds=details.qBounds,
mEstimateVal=details.mEstimateVal,
useSigs=True,
useCMIM=useCMIM,
holdOutFrac=details.internalHoldoutFrac,
replacementSelection=details.replacementSelection,
silent=not _verbose)
## elif details.useSVM:
## from rdkit.ML.SVM import CrossValidate
## driver = CrossValidate.CrossValidationDriver
## composite.Grow(trainExamples, attrs, nPossibleVals=[0]+nPossibleVals,
## buildDriver=driver, nTries=details.nModels,
## needsQuantization=0,
## cost=details.svmCost,gamma=details.svmGamma,
## weights=details.svmWeights,degree=details.svmDegree,
## type=details.svmType,kernelType=details.svmKernel,
## coef0=details.svmCoeff,eps=details.svmEps,nu=details.svmNu,
## cache_size=details.svmCache,shrinking=details.svmShrink,
## dataType=details.svmDataType,
## holdOutFrac=details.internalHoldoutFrac,
## replacementSelection=details.replacementSelection,
## silent=not _verbose)
else:
from rdkit.ML.Neural import CrossValidate
driver = CrossValidate.CrossValidationDriver
composite.Grow(trainExamples,
attrs, [0] + nPossibleVals,
nTries=details.nModels,
buildDriver=driver,
needsQuantization=0)
composite.AverageErrors()
composite.SortModels()
modelList, counts, avgErrs = composite.GetAllData()
counts = numpy.array(counts)
avgErrs = numpy.array(avgErrs)
composite._varNames = data.GetVarNames()
for i in range(len(modelList)):
modelList[i].NameModel(composite._varNames)
# do final statistics
weightedErrs = counts * avgErrs
averageErr = sum(weightedErrs) / sum(counts)
devs = (avgErrs - averageErr)
devs = devs * counts
devs = numpy.sqrt(devs * devs)
avgDev = sum(devs) / sum(counts)
message('# Overall Average Error: %%% 5.2f, Average Deviation: %%% 6.2f' %
(100. * averageErr, 100. * avgDev))
if details.bayesModel:
composite.Train(trainExamples, verbose=0)
# blow out the saved examples and then save the composite:
composite.ClearModelExamples()
if saveIt:
composite.Pickle(details.outName)
details.model = DbModule.binaryHolder(cPickle.dumps(composite))
badExamples = []
if not details.detailedRes and (not hasattr(details, 'noScreen')
or not details.noScreen):
if details.splitRun:
message('Testing all hold-out examples')
wrong = testall(composite, testExamples, badExamples)
message('%d examples (%% %5.2f) were misclassified' %
(len(wrong),
100. * float(len(wrong)) / float(len(testExamples))))
_runDetails.holdout_error = float(len(wrong)) / len(testExamples)
else:
message('Testing all examples')
wrong = testall(composite, namedExamples, badExamples)
message('%d examples (%% %5.2f) were misclassified' %
(len(wrong),
100. * float(len(wrong)) / float(len(namedExamples))))
_runDetails.overall_error = float(len(wrong)) / len(namedExamples)
if details.detailedRes:
message('\nEntire data set:')
resTup = ScreenComposite.ShowVoteResults(range(data.GetNPts()), data,
composite, nPossibleVals[-1],
details.threshold)
nGood, nBad, nSkip, avgGood, avgBad, avgSkip, voteTab = resTup
nPts = len(namedExamples)
nClass = nGood + nBad
_runDetails.overall_error = float(nBad) / nClass
_runDetails.overall_correct_conf = avgGood
_runDetails.overall_incorrect_conf = avgBad
_runDetails.overall_result_matrix = repr(voteTab)
nRej = nClass - nPts
if nRej > 0:
_runDetails.overall_fraction_dropped = float(nRej) / nPts
if details.splitRun:
message('\nHold-out data:')
resTup = ScreenComposite.ShowVoteResults(range(len(testExamples)),
testExamples, composite,
nPossibleVals[-1],
details.threshold)
nGood, nBad, nSkip, avgGood, avgBad, avgSkip, voteTab = resTup
nPts = len(testExamples)
nClass = nGood + nBad
_runDetails.holdout_error = float(nBad) / nClass
_runDetails.holdout_correct_conf = avgGood
_runDetails.holdout_incorrect_conf = avgBad
_runDetails.holdout_result_matrix = repr(voteTab)
nRej = nClass - nPts
if nRej > 0:
_runDetails.holdout_fraction_dropped = float(nRej) / nPts
if details.persistTblName and details.dbName:
message('Updating results table %s:%s' %
(details.dbName, details.persistTblName))
details.Store(db=details.dbName, table=details.persistTblName)
if details.badName != '':
badFile = open(details.badName, 'w+')
for i in range(len(badExamples)):
ex = badExamples[i]
vote = wrong[i]
outStr = '%s\t%s\n' % (ex, vote)
badFile.write(outStr)
badFile.close()
composite.ClearModelExamples()
return composite
def GrowIt(details,composite,progressCallback=None,
saveIt=1,setDescNames=0,data=None):
""" does the actual work of building a composite model
**Arguments**
- details: a _CompositeRun.CompositeRun_ object containing details
(options, parameters, etc.) about the run
- composite: the composite model to grow
- progressCallback: (optional) a function which is called with a single
argument (the number of models built so far) after each model is built.
- saveIt: (optional) if this is nonzero, the resulting model will be pickled
and dumped to the filename specified in _details.outName_
- setDescNames: (optional) if nonzero, the composite's _SetInputOrder()_ method
will be called using the results of the data set's _GetVarNames()_ method;
it is assumed that the details object has a _descNames attribute which
is passed to the composites _SetDescriptorNames()_ method. Otherwise
(the default), _SetDescriptorNames()_ gets the results of _GetVarNames()_.
- data: (optional) the data set to be used. If this is not provided, the
data set described in details will be used.
**Returns**
the enlarged composite model
"""
details.rundate = time.asctime()
if data is None:
fName = details.tableName.strip()
if details.outName == '':
details.outName = fName + '.pkl'
if details.dbName == '':
data = DataUtils.BuildQuantDataSet(fName)
elif details.qBounds != []:
details.tableName = fName
data = details.GetDataSet()
else:
data = DataUtils.DBToQuantData(details.dbName,fName,quantName=details.qTableName,
user=details.dbUser,password=details.dbPassword)
nExamples = data.GetNPts()
seed = composite._randomSeed
DataUtils.InitRandomNumbers(seed)
testExamples = []
if details.shuffleActivities == 1:
DataUtils.RandomizeActivities(data,shuffle=1,runDetails=details)
elif details.randomActivities == 1:
DataUtils.RandomizeActivities(data,shuffle=0,runDetails=details)
namedExamples = data.GetNamedData()
trainExamples = namedExamples
nExamples = len(trainExamples)
message('Training with %d examples'%(nExamples))
message('\t%d descriptors'%(len(trainExamples[0])-2))
nVars = data.GetNVars()
nPossibleVals = composite.nPossibleVals
attrs = range(1,nVars+1)
if details.useTrees:
from rdkit.ML.DecTree import CrossValidate,PruneTree
if details.qBounds != []:
from rdkit.ML.DecTree import BuildQuantTree
builder = BuildQuantTree.QuantTreeBoot
else:
from rdkit.ML.DecTree import ID3
builder = ID3.ID3Boot
driver = CrossValidate.CrossValidationDriver
pruner = PruneTree.PruneTree
if setDescNames:
composite.SetInputOrder(data.GetVarNames())
composite.Grow(trainExamples,attrs,[0]+nPossibleVals,
buildDriver=driver,
pruner=pruner,
nTries=details.nModels,pruneIt=details.pruneIt,
lessGreedy=details.lessGreedy,needsQuantization=0,
treeBuilder=builder,nQuantBounds=details.qBounds,
startAt=details.startAt,
maxDepth=details.limitDepth,
progressCallback=progressCallback,
silent=not _verbose)
else:
from rdkit.ML.Neural import CrossValidate
driver = CrossValidate.CrossValidationDriver
composite.Grow(trainExamples,attrs,[0]+nPossibleVals,nTries=details.nModels,
buildDriver=driver,needsQuantization=0)
composite.AverageErrors()
composite.SortModels()
modelList,counts,avgErrs = composite.GetAllData()
counts = numpy.array(counts)
avgErrs = numpy.array(avgErrs)
composite._varNames = data.GetVarNames()
for i in range(len(modelList)):
modelList[i].NameModel(composite._varNames)
# do final statistics
weightedErrs = counts*avgErrs
averageErr = sum(weightedErrs)/sum(counts)
devs = (avgErrs - averageErr)
devs = devs * counts
devs = numpy.sqrt(devs*devs)
avgDev = sum(devs)/sum(counts)
if _verbose:
message('# Overall Average Error: %%% 5.2f, Average Deviation: %%% 6.2f'%(100.*averageErr,100.*avgDev))
if details.bayesModel:
composite.Train(trainExamples,verbose=0)
badExamples = []
if not details.detailedRes:
if _verbose:
message('Testing all examples')
wrong = BuildComposite.testall(composite,namedExamples,badExamples)
if _verbose:
message('%d examples (%% %5.2f) were misclassified'%(len(wrong),100.*float(len(wrong))/float(len(namedExamples))))
_runDetails.overall_error = float(len(wrong))/len(namedExamples)
if details.detailedRes:
if _verbose:
message('\nEntire data set:')
resTup = ScreenComposite.ShowVoteResults(range(data.GetNPts()),data,composite,
nPossibleVals[-1],details.threshold)
nGood,nBad,nSkip,avgGood,avgBad,avgSkip,voteTab = resTup
nPts = len(namedExamples)
nClass = nGood+nBad
_runDetails.overall_error = float(nBad) / nClass
_runDetails.overall_correct_conf = avgGood
_runDetails.overall_incorrect_conf = avgBad
_runDetails.overall_result_matrix = repr(voteTab)
nRej = nClass-nPts
if nRej > 0:
_runDetails.overall_fraction_dropped = float(nRej)/nPts
return composite