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rdkit/Python/ML/BuildComposite.py

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# $Id$
#
# Copyright (C) 2000-2006 greg Landrum and Rational Discovery LLC
#
# @@ All Rights Reserved @@
#
""" command line utility for building composite models
#DOC
**Usage**
BuildComposite [optional args] filename
Unless indicated otherwise (via command line arguments), _filename_ is
a QDAT file.
**Command Line Arguments**
- -o *filename*: name of the output file for the pickled composite
- -n *num*: number of separate models to add to the composite
- -p *tablename*: store persistence data in the database
in table *tablename*
- -N *note*: attach some arbitrary text to the persistence data
- -b *filename*: name of the text file to hold examples from the
holdout set which are misclassified
- -s: split the data into training and hold-out sets before building
the composite
- -f *frac*: the fraction of data to use in the training set when the
data is split
- -r: randomize the activities (for testing purposes). This ignores
the initial distribution of activity values and produces each
possible activity value with equal likliehood.
- -S: shuffle the activities (for testing purposes) This produces
a permutation of the input activity values.
- -l: locks the random number generator to give consistent sets
of training and hold-out data. This is primarily intended
for testing purposes.
- -B: use a so-called Bayesian composite model.
- -d *database name*: instead of reading the data from a QDAT file,
pull it from a database. In this case, the _filename_ argument
provides the name of the database table containing the data set.
- -D: show a detailed breakdown of the composite model performance
across the training and, when appropriate, hold-out sets.
- -P *pickle file name*: write out the pickled data set to the file
- -F *filter frac*: filters the data before training to change the
distribution of activity values in the training set. *filter
frac* is the fraction of the training set that should have the
target value. **See note below on data filtering.**
- -v *filter value*: filters the data before training to change the
distribution of activity values in the training set. *filter
value* is the target value to use in filtering. **See note below
on data filtering.**
- --modelFiltFrac *model filter frac*: Similar to filter frac above,
in this case the data is filtered for each model in the composite
rather than a single overall filter for a composite. *model
filter frac* is the fraction of the training set for each model
that should have the target value (*model filter value*).
- --modelFiltVal *model filter value*: target value to use for
filtering data before training each model in the composite.
- -t *threshold value*: use high-confidence predictions for the
final analysis of the hold-out data.
- -Q *list string*: the values of quantization bounds for the
activity value. See the _-q_ argument for the format of *list
string*.
- --nRuns *count*: build *count* composite models
- --prune: prune any models built
- -h: print a usage message and exit.
- -V: print the version number and exit
*-*-*-*-*-*-*-*- Tree-Related Options -*-*-*-*-*-*-*-*
- -g: be less greedy when training the models.
- -G *number*: force trees to be rooted at descriptor *number*.
- -L *limit*: provide an (integer) limit on individual model
complexity
- -q *list string*: Add QuantTrees to the composite and use the list
specified in *list string* as the number of target quantization
bounds for each descriptor. Don't forget to include 0's at the
beginning and end of *list string* for the name and value fields.
For example, if there are 4 descriptors and you want 2 quant
bounds apiece, you would use _-q "[0,2,2,2,2,0]"_.
Two special cases:
1) If you would like to ignore a descriptor in the model
building, use '-1' for its number of quant bounds.
2) If you have integer valued data that should not be quantized
further, enter 0 for that descriptor.
- --recycle: allow descriptors to be used more than once in a tree
- --randomDescriptors=val: toggles growing random forests with val
randomly-selected descriptors available at each node.
*-*-*-*-*-*-*-*- KNN-Related Options -*-*-*-*-*-*-*-*
- --doKnn: use K-Nearest Neighbors models
- --knnK=*value*: the value of K to use in the KNN models
- --knnTanimoto: use the Tanimoto metric in KNN models
- --knnEuclid: use a Euclidean metric in KNN models
*-*-*-*-*-*-*- Naive Bayes Classifier Options -*-*-*-*-*-*-*-*
- --doNaiveBayes : use Naive Bayes classifiers
- --mEstimateVal : the value to be used in the m-estimate formula
If this is greater than 0.0, we use it to compute the conditional
probabilities by the m-estimate
*-*-*-*-*-*-*-*- SVM-Related Options -*-*-*-*-*-*-*-*
**** NOTE: THESE ARE DISABLED ****
## - --doSVM: use Support-vector machines
## - --svmKernel=*kernel*: choose the type of kernel to be used for
## the SVMs. Options are:
## The default is:
## - --svmType=*type*: choose the type of support-vector machine
## to be used. Options are:
## The default is:
## - --svmGamma=*gamma*: provide the gamma value for the SVMs. If this
## is not provided, a grid search will be carried out to determine an
## optimal *gamma* value for each SVM.
## - --svmCost=*cost*: provide the cost value for the SVMs. If this is
## not provided, a grid search will be carried out to determine an
## optimal *cost* value for each SVM.
## - --svmWeights=*weights*: provide the weight values for the
## activities. If provided this should be a sequence of (label,
## weight) 2-tuples *nActs* long. If not provided, a weight of 1
## will be used for each activity.
## - --svmEps=*epsilon*: provide the epsilon value used to determine
## when the SVM has converged. Defaults to 0.001
## - --svmDegree=*degree*: provide the degree of the kernel (when
## sensible) Defaults to 3
## - --svmCoeff=*coeff*: provide the coefficient for the kernel (when
## sensible) Defaults to 0
## - --svmNu=*nu*: provide the nu value for the kernel (when sensible)
## Defaults to 0.5
## - --svmDataType=*float*: if the data is contains only 1 and 0 s, specify by
## using binary. Defaults to float
## - --svmCache=*cache*: provide the size of the memory cache (in MB)
## to be used while building the SVM. Defaults to 40
**Notes**
- *Data filtering*: When there is a large disparity between the
numbers of points with various activity levels present in the
training set it is sometimes desirable to train on a more
homogeneous data set. This can be accomplished using filtering.
The filtering process works by selecting a particular target
fraction and target value. For example, in a case where 95% of
the original training set has activity 0 and ony 5% activity 1, we
could filter (by randomly removing points with activity 0) so that
30% of the data set used to build the composite has activity 1.
"""
import RDConfig
from utils import listutils
from ML.Composite import Composite,BayesComposite
#from ML.SVM import SVMClassificationModel as SVM
from Numeric import *
from ML.Data import DataUtils,SplitData
from ML import ScreenComposite
from Dbase import DbModule
from Dbase.DbConnection import DbConnect
from ML import CompositeRun
import sys,cPickle,time
import DataStructs
_runDetails = CompositeRun.CompositeRun()
__VERSION_STRING="3.2.3"
_verbose = 1
def message(msg):
""" emits messages to _sys.stdout_
override this in modules which import this one to redirect output
**Arguments**
- msg: the string to be displayed
"""
if _verbose: sys.stdout.write('%s\n'%(msg))
def testall(composite,examples,badExamples=[]):
""" screens a number of examples past a composite
**Arguments**
- composite: a composite model
- examples: a list of examples (with results) to be screened
- badExamples: a list to which misclassified examples are appended
**Returns**
a list of 2-tuples containing:
1) a vote
2) a confidence
these are the votes and confidence levels for **misclassified** examples
"""
wrong = []
for example in examples:
if composite.GetActivityQuantBounds():
answer = composite.QuantizeActivity(example)[-1]
else:
answer = example[-1]
res,conf = composite.ClassifyExample(example)
if res != answer:
wrong.append((res,conf))
badExamples.append(example)
return wrong
def GetCommandLine(details):
""" #DOC
"""
args = ['BuildComposite']
args.append('-n %d'%(details.nModels))
if details.filterFrac != 0.0: args.append('-F %.3f -v %d'%(details.filterFrac,details.filterVal))
if details.modelFilterFrac != 0.0: args.append('--modelFiltFrac=%.3f --modelFiltVal=%d'%(details.modelFilterFrac,
details.modelFilterVal))
if details.splitRun: args.append('-s -f %.3f'%(details.splitFrac))
if details.shuffleActivities: args.append('-S')
if details.randomActivities: args.append('-r')
if details.threshold > 0.0: args.append('-t %.3f'%(details.threshold))
if details.activityBounds: args.append('-Q "%s"'%(details.activityBoundsVals))
if details.dbName: args.append('-d %s'%(details.dbName))
if details.detailedRes: args.append('-D')
if hasattr(details,'noScreen') and details.noScreen: args.append('--noScreen')
if details.persistTblName and details.dbName:
args.append('-p %s'%(details.persistTblName))
if details.note:
args.append('-N %s'%(details.note))
if details.useTrees:
if details.limitDepth>0: args.append('-L %d'%(details.limitDepth))
if details.lessGreedy: args.append('-g')
if details.qBounds:
shortBounds = listutils.CompactListRepr(details.qBounds)
if details.qBounds: args.append('-q "%s"'%(shortBounds))
else:
if details.qBounds: args.append('-q "%s"'%(details.qBoundCount))
if details.pruneIt: args.append('--prune')
if details.startAt: args.append('-G %d'%details.startAt)
if details.recycleVars: args.append('--recycle')
if details.randomDescriptors: args.append('--randomDescriptors=%d'%details.randomDescriptors)
if details.useSigTrees:
args.append('--doSigTree')
if details.limitDepth>0: args.append('-L %d'%(details.limitDepth))
if details.randomDescriptors:
args.append('--randomDescriptors=%d'%details.randomDescriptors)
if details.useKNN:
args.append('--doKnn --knnK %d'%(details.knnNeighs))
if details.knnDistFunc=='Tanimoto':
args.append('--knnTanimoto')
else:
args.append('--knnEuclid')
if details.useNaiveBayes:
args.append('--doNaiveBayes')
if details.mEstimateVal >= 0.0 :
args.append('--mEstimateVal=%.3f'%details.mEstimateVal)
## if details.useSVM:
## args.append('--doSVM')
## if details.svmKernel:
## for k in SVM.kernels.keys():
## if SVM.kernels[k]==details.svmKernel:
## args.append('--svmKernel=%s'%k)
## break
## if details.svmType:
## for k in SVM.machineTypes.keys():
## if SVM.machineTypes[k]==details.svmType:
## args.append('--svmType=%s'%k)
## break
## if details.svmGamma:
## args.append('--svmGamma=%f'%details.svmGamma)
## if details.svmCost:
## args.append('--svmCost=%f'%details.svmCost)
## if details.svmWeights:
## args.append("--svmWeights='%s'"%str(details.svmWeights))
## if details.svmDegree:
## args.append('--svmDegree=%d'%details.svmDegree)
## if details.svmCoeff:
## args.append('--svmCoeff=%d'%details.svmCoeff)
## if details.svmEps:
## args.append('--svmEps=%f'%details.svmEps)
## if details.svmNu:
## args.append('--svmNu=%f'%details.svmNu)
## if details.svmCache:
## args.append('--svmCache=%d'%details.svmCache)
## if detail.svmDataType:
## args.append('--svmDataType=%s'%details.svmDataType)
## if not details.svmShrink:
## args.append('--svmShrink')
if details.replacementSelection: args.append('--replacementSelection')
# this should always be last:
if details.tableName: args.append(details.tableName)
return ' '.join(args)
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 ML.DecTree import CrossValidate,PruneTree
if details.qBounds != []:
from ML.DecTree import BuildQuantTree
builder = BuildQuantTree.QuantTreeBoot
else:
from 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 ML.DecTree import CrossValidate
from 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 ML.KNN import CrossValidate
from 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 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 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 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 = array(counts)
avgErrs = array(avgErrs)
composite._varNames = data.GetVarNames()
for i in xrange(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 = 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 xrange(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 RunIt(details,progressCallback=None,saveIt=1,setDescNames=0):
""" does the actual work of building a composite model
**Arguments**
- details: a _CompositeRun.CompositeRun_ object containing details
(options, parameters, etc.) about the run
- 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()_.
**Returns**
the composite model constructed
"""
details.rundate = time.asctime()
fName = details.tableName.strip()
if details.outName == '':
details.outName = fName + '.pkl'
if not details.dbName:
if details.qBounds != []:
data = DataUtils.TextFileToData(fName)
else:
data = DataUtils.BuildQuantDataSet(fName)
elif details.useSigTrees or details.useSigBayes:
details.tableName = fName
data = details.GetDataSet(pickleCol=0,pickleClass=DataStructs.ExplicitBitVect)
elif details.qBounds != [] or not details.useTrees:
details.tableName = fName
data = details.GetDataSet()
else:
data = DataUtils.DBToQuantData(details.dbName,fName,quantName=details.qTableName,
user=details.dbUser,password=details.dbPassword)
composite = RunOnData(details,data,progressCallback=progressCallback,
saveIt=saveIt,setDescNames=setDescNames)
return composite
def ShowVersion(includeArgs=0):
""" prints the version number
"""
print 'This is BuildComposite.py version %s'%(__VERSION_STRING)
if includeArgs:
import sys
print 'command line was:'
print ' '.join(sys.argv)
def Usage():
""" provides a list of arguments for when this is used from the command line
"""
import sys
print __doc__
sys.exit(-1)
def SetDefaults(runDetails=None):
""" initializes a details object with default values
**Arguments**
- details: (optional) a _CompositeRun.CompositeRun_ object.
If this is not provided, the global _runDetails will be used.
**Returns**
the initialized _CompositeRun_ object.
"""
if runDetails is None: runDetails = _runDetails
return CompositeRun.SetDefaults(runDetails)
def ParseArgs(runDetails):
""" parses command line arguments and updates _runDetails_
**Arguments**
- runDetails: a _CompositeRun.CompositeRun_ object.
"""
import getopt
args,extra = getopt.getopt(sys.argv[1:],'P:o:n:p:b:sf:F:v:hlgd:rSTt:BQ:q:DVG:N:L:',
['nRuns=','prune','profile',
'seed=','noScreen',
'modelFiltFrac=', 'modelFiltVal=',
'recycle','randomDescriptors=',
'doKnn','knnK=','knnTanimoto','knnEuclid',
'doSigTree','doCMIM=','allowCollections',
'doNaiveBayes', 'mEstimateVal=',
'doSigBayes',
## 'doSVM','svmKernel=','svmType=','svmGamma=',
## 'svmCost=','svmWeights=','svmDegree=',
## 'svmCoeff=','svmEps=','svmNu=','svmCache=',
## 'svmShrink','svmDataType=',
'replacementSelection',
])
runDetails.profileIt=0
for arg,val in args:
if arg == '-n':
runDetails.nModels = int(val)
elif arg == '-N':
runDetails.note=val
elif arg == '-o':
runDetails.outName = val
elif arg == '-Q':
qBounds = eval(val)
assert type(qBounds) in [type([]),type(())],'bad argument type for -Q, specify a list as a string'
runDetails.activityBounds=qBounds
runDetails.activityBoundsVals=val
elif arg == '-p':
runDetails.persistTblName=val
elif arg == '-P':
runDetails.pickleDataFileName= val
elif arg == '-r':
runDetails.randomActivities = 1
elif arg == '-S':
runDetails.shuffleActivities = 1
elif arg == '-b':
runDetails.badName = val
elif arg == '-B':
runDetails.bayesModels=1
elif arg == '-s':
runDetails.splitRun = 1
elif arg == '-f':
runDetails.splitFrac=float(val)
elif arg == '-F':
runDetails.filterFrac=float(val)
elif arg == '-v':
runDetails.filterVal=float(val)
elif arg == '-l':
runDetails.lockRandom = 1
elif arg == '-g':
runDetails.lessGreedy=1
elif arg == '-G':
runDetails.startAt = int(val)
elif arg == '-d':
runDetails.dbName=val
elif arg == '-T':
runDetails.useTrees = 0
elif arg == '-t':
runDetails.threshold=float(val)
elif arg == '-D':
runDetails.detailedRes = 1
elif arg == '-L':
runDetails.limitDepth = int(val)
elif arg == '-q':
qBounds = eval(val)
assert type(qBounds) in [type([]),type(())],'bad argument type for -q, specify a list as a string'
runDetails.qBoundCount=val
runDetails.qBounds = qBounds
elif arg == '-V':
ShowVersion()
sys.exit(0)
elif arg == '--nRuns':
runDetails.nRuns = int(val)
elif arg == '--modelFiltFrac':
runDetails.modelFilterFrac=float(val)
elif arg == '--modelFiltVal':
runDetails.modelFilterVal=float(val)
elif arg == '--prune':
runDetails.pruneIt=1
elif arg == '--profile':
runDetails.profileIt=1
elif arg == '--recycle':
runDetails.recycleVars=1
elif arg == '--randomDescriptors':
runDetails.randomDescriptors=int(val)
elif arg == '--doKnn':
runDetails.useKNN=1
runDetails.useTrees=0
## runDetails.useSVM=0
runDetails.useNaiveBayes=0
elif arg == '--knnK':
runDetails.knnNeighs = int(val)
elif arg == '--knnTanimoto':
runDetails.knnDistFunc="Tanimoto"
elif arg == '--knnEuclid':
runDetails.knnDistFunc="Euclidean"
elif arg == '--doSigTree':
## runDetails.useSVM=0
runDetails.useKNN=0
runDetails.useTrees=0
runDetails.useNaiveBayes=0
runDetails.useSigTrees=1
elif arg == '--doCMIM':
runDetails.useCMIM=int(val)
elif arg == '--allowCollections':
runDetails.allowCollections=True
elif arg == '--doNaiveBayes':
runDetails.useNaiveBayes=1
## runDetails.useSVM=0
runDetails.useKNN=0
runDetails.useTrees=0
runDetails.useSigBayes=0
elif arg == '--doSigBayes':
runDetails.useSigBayes=1
runDetails.useNaiveBayes=0
## runDetails.useSVM=0
runDetails.useKNN=0
runDetails.useTrees=0
elif arg == '--mEstimateVal':
runDetails.mEstimateVal=float(val)
## elif arg == '--doSVM':
## runDetails.useSVM=1
## runDetails.useKNN=0
## runDetails.useTrees=0
## runDetails.useNaiveBayes=0
## elif arg == '--svmKernel':
## if val not in SVM.kernels.keys():
## message('kernel %s not in list of available kernels:\n%s\n'%(val,SVM.kernels.keys()))
## sys.exit(-1)
## else:
## runDetails.svmKernel=SVM.kernels[val]
## elif arg == '--svmType':
## if val not in SVM.machineTypes.keys():
## message('type %s not in list of available machines:\n%s\n'%(val,SVM.machineTypes.keys()))
## sys.exit(-1)
## else:
## runDetails.svmType=SVM.machineTypes[val]
## elif arg == '--svmGamma':
## runDetails.svmGamma = float(val)
## elif arg == '--svmCost':
## runDetails.svmCost = float(val)
## elif arg == '--svmWeights':
## # FIX: this is dangerous
## runDetails.svmWeights = eval(val)
## elif arg == '--svmDegree':
## runDetails.svmDegree = int(val)
## elif arg == '--svmCoeff':
## runDetails.svmCoeff = float(val)
## elif arg == '--svmEps':
## runDetails.svmEps = float(val)
## elif arg == '--svmNu':
## runDetails.svmNu = float(val)
## elif arg == '--svmCache':
## runDetails.svmCache = int(val)
## elif arg == '--svmShrink':
## runDetails.svmShrink = 0
## elif arg == '--svmDataType':
## runDetails.svmDataType=val
elif arg== '--seed':
# FIX: dangerous
runDetails.randomSeed = eval(val)
elif arg== '--noScreen':
runDetails.noScreen=1
elif arg== '--replacementSelection':
runDetails.replacementSelection = 1
elif arg == '-h':
Usage()
else:
Usage()
runDetails.tableName=extra[0]
if __name__ == '__main__':
if len(sys.argv) < 2:
Usage()
_runDetails.cmd = ' '.join(sys.argv)
SetDefaults(_runDetails)
ParseArgs(_runDetails)
ShowVersion(includeArgs=1)
if _runDetails.nRuns > 1:
for i in range(_runDetails.nRuns):
sys.stderr.write('---------------------------------\n\tDoing %d of %d\n---------------------------------\n'%(i+1,_runDetails.nRuns))
RunIt(_runDetails)
else:
if _runDetails.profileIt:
import hotshot,hotshot.stats
prof=hotshot.Profile('prof.dat')
prof.runcall(RunIt,_runDetails)
stats = hotshot.stats.load('prof.dat')
stats.strip_dirs()
stats.sort_stats('time','calls')
stats.print_stats(30)
else:
RunIt(_runDetails)
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