def ClusterFromDetails(details):
""" Returns the cluster tree
"""
data = MolSimilarity.GetFingerprints(details)
if details.maxMols > 0:
data = data[:details.maxMols]
if details.outFileName:
try:
outF = open(details.outFileName, 'wb+')
except IOError:
error("Error: could not open output file %s for writing\n" %
(details.outFileName))
return None
else:
outF = None
if not data:
return None
clustTree = ClusterPoints(data,
details.metric,
details.clusterAlgo,
haveLabels=0,
haveActs=1)
if outF:
cPickle.dump(clustTree, outF)
return clustTree
def Pickle(self, fileName='foo.pkl'):
""" Writes this forest off to a file so that it can be easily loaded later
**Arguments**
fileName is the name of the file to be written
"""
pFile = open(fileName, 'wb+')
cPickle.dump(self, pFile, 1)
pFile.close()
def Pickle(self,fileName='foo.pkl'):
""" Writes this forest off to a file so that it can be easily loaded later
**Arguments**
fileName is the name of the file to be written
"""
pFile = open(fileName,'wb+')
cPickle.dump(self,pFile,1)
pFile.close()
def _writeDetailFile(self, inF, outF):
while 1:
try:
smi, refContribs = cPickle.load(inF)
except EOFError:
break
else:
mol = Chem.MolFromSmiles(smi)
if mol:
mol = Chem.AddHs(mol, 1)
smi2 = Chem.MolToSmiles(mol)
contribs = Crippen._GetAtomContribs(mol)
cPickle.dump((smi, contribs), outF)
else:
print('Problems with SMILES:', smi)
def testPkl(self):
# Test pickling
v1 = self.klass(10)
v1[1] = 1
v1[2] = 1
v1[3] = 1
pklName = 'foo.pkl'
outF = open(pklName, 'wb+')
cPickle.dump(v1, outF)
outF.close()
inF = open(pklName, 'rb')
v2 = cPickle.load(inF)
inF.close()
os.unlink(pklName)
assert tuple(v1.GetOnBits()) == tuple(v2.GetOnBits()), 'pkl failed'
def testPkl(self): # Test pickling v1 = self.klass(10) v1[1] = 1 v1[2] = 1 v1[3] = 1 pklName = 'foo.pkl' outF = open(pklName, 'wb+') cPickle.dump(v1, outF) outF.close() inF = open(pklName, 'rb') v2 = cPickle.load(inF) inF.close() os.unlink(pklName) assert tuple(v1.GetOnBits()) == tuple(v2.GetOnBits()), 'pkl failed'
def SaveState(self, fileName):
""" Writes this calculator off to a file so that it can be easily loaded later
**Arguments**
- fileName: the name of the file to be written
"""
try:
f = open(fileName, 'wb+')
except Exception:
logger.error('cannot open output file %s for writing' % (fileName))
return
cPickle.dump(self, f)
f.close()
def _writeDetailFile(self,inF,outF):
while 1:
try:
smi,refContribs = cPickle.load(inF)
except EOFError:
break
else:
mol = Chem.MolFromSmiles(smi)
if mol:
mol=Chem.AddHs(mol,1)
smi2 = Chem.MolToSmiles(mol)
contribs = Crippen._GetAtomContribs(mol)
cPickle.dump((smi,contribs),outF)
else:
print('Problems with SMILES:',smi)
def SaveState(self, fileName):
""" Writes this calculator off to a file so that it can be easily loaded later
**Arguments**
- fileName: the name of the file to be written
"""
try:
f = open(fileName, 'wb+')
except Exception:
print('cannot open output file %s for writing' % (fileName))
return
cPickle.dump(self, f)
f.close()
def testPkl(self):
""" test pickling
"""
v1 = klass(10)
v1[1] = 1
v1[2] = 1
v1[3] = 1
pklName = "foo.pkl"
outF = open(pklName, "wb+")
cPickle.dump(v1, outF)
outF.close()
inF = open(pklName, "rb")
v2 = cPickle.load(inF)
inF.close()
os.unlink(pklName)
assert tuple(v1.GetOnBits()) == tuple(v2.GetOnBits()), "pkl failed"
def SaveState(self, fileName):
""" Writes this calculator off to a file so that it can be easily loaded later
**Arguments**
- fileName: the name of the file to be written
"""
from rdkit.six.moves import cPickle
try:
f = open(fileName, 'wb+')
except:
logger.error('cannot open output file %s for writing' % (fileName))
return
cPickle.dump(self, f)
f.close()
def SaveState(self,fileName):
""" Writes this calculator off to a file so that it can be easily loaded later
**Arguments**
- fileName: the name of the file to be written
"""
from rdkit.six.moves import cPickle
try:
f = open(fileName,'wb+')
except:
print('cannot open output file %s for writing'%(fileName))
return
cPickle.dump(self,f)
f.close()
def Pickle(self,fileName='foo.pkl',saveExamples=0):
""" Writes this composite off to a file so that it can be easily loaded later
**Arguments**
- fileName: the name of the file to be written
- saveExamples: if this is zero, the individual models will have
their stored examples cleared.
"""
if not saveExamples:
self.ClearModelExamples()
pFile = open(fileName,'wb+')
cPickle.dump(self,pFile,1)
pFile.close()
def Pickle(self, fileName='foo.pkl', saveExamples=0):
""" Writes this composite off to a file so that it can be easily loaded later
**Arguments**
- fileName: the name of the file to be written
- saveExamples: if this is zero, the individual models will have
their stored examples cleared.
"""
if not saveExamples:
self.ClearModelExamples()
pFile = open(fileName, 'wb+')
cPickle.dump(self, pFile, 1)
pFile.close()
def runIt(inFileName, outFileName, smiCol=0, maxMols=-1, delim=','):
inF = gzip.open(inFileName, 'r')
outF = open(outFileName, 'wb+')
mols = []
nDone = 0
for line in inF.readlines():
if line[0] != '#':
splitL = line.strip().split(delim)
smi = splitL[smiCol].strip()
print(smi)
mol = Chem.MolFromSmiles(smi)
if mol:
contribs = Crippen._GetAtomContribs(mol)
cPickle.dump((smi, contribs), outF)
nDone += 1
if maxMols > 0 and nDone >= maxMols:
break
outF.close()
def runIt(inFileName, outFileName, smiCol=0, maxMols=-1, delim=','):
inF = gzip.open(inFileName, 'r')
outF = open(outFileName, 'wb+')
mols = []
nDone = 0
for line in inF.readlines():
if line[0] != '#':
splitL = line.strip().split(delim)
smi = splitL[smiCol].strip()
print(smi)
mol = Chem.MolFromSmiles(smi)
if mol:
contribs = Crippen._GetAtomContribs(mol)
cPickle.dump((smi, contribs), outF)
nDone += 1
if maxMols > 0 and nDone >= maxMols:
break
outF.close()
def testPkl(self):
" testing molecule pickle "
import tempfile
f,self.fName = tempfile.mkstemp('.pkl')
f=None
self.m = Chem.MolFromSmiles('CC(=O)CC')
outF = open(self.fName,'wb+')
cPickle.dump(self.m,outF)
outF.close()
inF = open(self.fName,'rb')
m2 = cPickle.load(inF)
inF.close()
try:
os.unlink(self.fName)
except:
pass
oldSmi = Chem.MolToSmiles(self.m)
newSmi = Chem.MolToSmiles(m2)
assert oldSmi==newSmi,'string compare failed'
def testPkl(self):
" testing molecule pickle "
import tempfile
f, self.fName = tempfile.mkstemp('.pkl')
f = None
self.m = Chem.MolFromSmiles('CC(=O)CC')
outF = open(self.fName, 'wb+')
cPickle.dump(self.m, outF)
outF.close()
inF = open(self.fName, 'rb')
m2 = cPickle.load(inF)
inF.close()
try:
os.unlink(self.fName)
except Exception:
pass
oldSmi = Chem.MolToSmiles(self.m)
newSmi = Chem.MolToSmiles(m2)
assert oldSmi == newSmi, 'string compare failed'
def testPkl(self):
" testing molecule pickle "
import tempfile
f, self.fName = tempfile.mkstemp(".pkl")
f = None
self.m = Chem.MolFromSmiles("CC(=O)CC")
outF = open(self.fName, "wb+")
cPickle.dump(self.m, outF)
outF.close()
inF = open(self.fName, "rb")
m2 = cPickle.load(inF)
inF.close()
try:
os.unlink(self.fName)
except:
pass
oldSmi = Chem.MolToSmiles(self.m)
newSmi = Chem.MolToSmiles(m2)
assert oldSmi == newSmi, "string compare failed"
def ClusterFromDetails(details):
""" Returns the cluster tree
"""
data = MolSimilarity.GetFingerprints(details)
if details.maxMols > 0:
data = data[:details.maxMols]
if details.outFileName:
try:
outF = open(details.outFileName, 'wb+')
except IOError:
error("Error: could not open output file %s for writing\n" % (details.outFileName))
return None
else:
outF = None
if not data:
return None
clustTree = ClusterPoints(data, details.metric, details.clusterAlgo, haveLabels=0, haveActs=1)
if outF:
cPickle.dump(clustTree, outF)
return clustTree
def WritePickledData(outName,data):
""" writes either a .qdat.pkl or a .dat.pkl file
**Arguments**
- outName: the name of the file to be used
- data: either an _MLData.MLDataSet_ or an _MLData.MLQuantDataSet_
"""
varNames = data.GetVarNames()
qBounds = data.GetQuantBounds()
ptNames = data.GetPtNames()
examples = data.GetAllData()
with open(outName,'wb+') as outFile:
cPickle.dump(varNames,outFile)
cPickle.dump(qBounds,outFile)
cPickle.dump(ptNames,outFile)
cPickle.dump(examples,outFile)
def WritePickledData(outName, data):
""" writes either a .qdat.pkl or a .dat.pkl file
**Arguments**
- outName: the name of the file to be used
- data: either an _MLData.MLDataSet_ or an _MLData.MLQuantDataSet_
"""
varNames = data.GetVarNames()
qBounds = data.GetQuantBounds()
ptNames = data.GetPtNames()
examples = data.GetAllData()
with open(outName, 'wb+') as outFile:
cPickle.dump(varNames, outFile)
cPickle.dump(qBounds, outFile)
cPickle.dump(ptNames, outFile)
cPickle.dump(examples, outFile)
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
from rdkit.Chem.PyMol import MolViewer
from rdkit.Chem.Subshape import SubshapeBuilder, SubshapeObjects, SubshapeAligner
from rdkit.six.moves import cPickle
import copy
m1 = Chem.MolFromMolFile('test_data/square1.mol')
m2 = Chem.MolFromMolFile('test_data/square2.mol')
b = SubshapeBuilder.SubshapeBuilder()
b.gridDims = (10., 10., 5)
b.gridSpacing = 0.4
b.winRad = 2.0
if 1:
print('m1:')
s1 = b.GenerateSubshapeShape(m1)
cPickle.dump(s1, open('test_data/square1.shp.pkl', 'wb+'))
print('m2:')
s2 = b.GenerateSubshapeShape(m2)
cPickle.dump(s2, open('test_data/square2.shp.pkl', 'wb+'))
ns1 = b.CombineSubshapes(s1, s2)
b.GenerateSubshapeSkeleton(ns1)
cPickle.dump(ns1, open('test_data/combined.shp.pkl', 'wb+'))
else:
s1 = cPickle.load(open('test_data/square1.shp.pkl', 'rb'))
s2 = cPickle.load(open('test_data/square2.shp.pkl', 'rb'))
#ns1 = cPickle.load(file('test_data/combined.shp.pkl','rb'))
ns1 = cPickle.load(open('test_data/combined.shp.pkl', 'rb'))
v = MolViewer()
SubshapeObjects.DisplaySubshape(v, s1, 'shape1')
SubshapeObjects.DisplaySubshape(v, ns1, 'ns1')
def Pickle(self, fileName='foo.pkl'):
""" Pickles the tree and writes it to disk
"""
with open(fileName, 'wb+') as pFile:
cPickle.dump(self, pFile)
def Pickle(self, fileName="foo.pkl"):
""" Pickles the tree and writes it to disk
"""
with open(fileName, "wb+") as pFile:
cPickle.dump(self, pFile)
def FingerprintsFromDetails(details, reportFreq=10):
data = None
if details.dbName and details.tableName:
from rdkit.Dbase.DbConnection import DbConnect
from rdkit.Dbase import DbInfo
from rdkit.ML.Data import DataUtils
try:
conn = DbConnect(details.dbName, details.tableName)
except Exception:
import traceback
error('Problems establishing connection to database: %s|%s\n' % (details.dbName,
details.tableName))
traceback.print_exc()
if not details.idName:
details.idName = DbInfo.GetColumnNames(details.dbName, details.tableName)[0]
dataSet = DataUtils.DBToData(details.dbName, details.tableName,
what='%s,%s' % (details.idName, details.smilesName))
idCol = 0
smiCol = 1
elif details.inFileName and details.useSmiles:
from rdkit.ML.Data import DataUtils
conn = None
if not details.idName:
details.idName = 'ID'
try:
dataSet = DataUtils.TextFileToData(details.inFileName,
onlyCols=[details.idName, details.smilesName])
except IOError:
import traceback
error('Problems reading from file %s\n' % (details.inFileName))
traceback.print_exc()
idCol = 0
smiCol = 1
elif details.inFileName and details.useSD:
conn = None
dataset = None
if not details.idName:
details.idName = 'ID'
dataSet = []
try:
s = Chem.SDMolSupplier(details.inFileName)
except Exception:
import traceback
error('Problems reading from file %s\n' % (details.inFileName))
traceback.print_exc()
else:
while 1:
try:
m = s.next()
except StopIteration:
break
if m:
dataSet.append(m)
if reportFreq > 0 and not len(dataSet) % reportFreq:
message('Read %d molecules\n' % (len(dataSet)))
if details.maxMols > 0 and len(dataSet) >= details.maxMols:
break
for i, mol in enumerate(dataSet):
if mol.HasProp(details.idName):
nm = mol.GetProp(details.idName)
else:
nm = mol.GetProp('_Name')
dataSet[i] = (nm, mol)
else:
dataSet = None
fps = None
if dataSet and not details.useSD:
data = dataSet.GetNamedData()
if not details.molPklName:
fps = apply(FingerprintsFromSmiles, (data, idCol, smiCol), details.__dict__)
else:
fps = apply(FingerprintsFromPickles, (data, idCol, smiCol), details.__dict__)
elif dataSet and details.useSD:
fps = apply(FingerprintsFromMols, (dataSet, ), details.__dict__)
if fps:
if details.outFileName:
outF = open(details.outFileName, 'wb+')
for i in range(len(fps)):
cPickle.dump(fps[i], outF)
outF.close()
dbName = details.outDbName or details.dbName
if details.outTableName and dbName:
from rdkit.Dbase.DbConnection import DbConnect
from rdkit.Dbase import DbUtils, DbModule
conn = DbConnect(dbName)
#
# We don't have a db open already, so we'll need to figure out
# the types of our columns...
#
colTypes = DbUtils.TypeFinder(data, len(data), len(data[0]))
typeStrs = DbUtils.GetTypeStrings([details.idName, details.smilesName], colTypes,
keyCol=details.idName)
cols = '%s, %s %s' % (typeStrs[0], details.fpColName, DbModule.binaryTypeName)
# FIX: we should really check to see if the table
# is already there and, if so, add the appropriate
# column.
#
# create the new table
#
if details.replaceTable or \
details.outTableName.upper() not in [x.upper() for x in conn.GetTableNames()]:
conn.AddTable(details.outTableName, cols)
#
# And add the data
#
for ID, fp in fps:
tpl = ID, DbModule.binaryHolder(fp.ToBinary())
conn.InsertData(details.outTableName, tpl)
conn.Commit()
return fps
for example in examples:
res = net.ClassifyExample(example[:-1])
print("%f -> %f" % (example[-1], res))
return net
def runProfile(command):
import random
random.seed(23)
import profile, pstats
datFile = "%s.prof.dat" % (command)
profile.run("%s()" % command, datFile)
stats = pstats.Stats(datFile)
stats.strip_dirs()
stats.sort_stats("time").print_stats()
if 0:
net = testXor()
print("Xor:", net)
from rdkit.six.moves import cPickle
outF = open("xornet.pkl", "wb+")
cPickle.dump(net, outF)
outF.close()
else:
# runProfile('testLinear')
net = testLinear()
# net = testOr()
cat = None
obls = None
if details.doBuild:
if not suppl:
message("We require inData to generate a catalog\n")
sys.exit(-2)
message("Building catalog\n")
t1 = time.time()
cat = BuildCatalog(suppl, maxPts=details.numMols, minPath=details.minPath,
maxPath=details.maxPath)
t2 = time.time()
message("\tThat took %.2f seconds.\n" % (t2 - t1))
if details.catalogName:
message("Dumping catalog data\n")
cPickle.dump(cat, open(details.catalogName, 'wb+'))
elif details.catalogName:
message("Loading catalog\n")
cat = cPickle.load(open(details.catalogName, 'rb'))
if details.onBitsName:
try:
obls = cPickle.load(open(details.onBitsName, 'rb'))
except Exception:
obls = None
else:
if len(obls) < (inD.count('\n') - 1):
obls = None
scores = None
if details.doScore:
if not suppl:
message("We require inData to score molecules\n")
obls = None
if details.doBuild:
if not suppl:
message("We require inData to generate a catalog\n")
sys.exit(-2)
message("Building catalog\n")
t1 = time.time()
cat = BuildCatalog(suppl,
maxPts=details.numMols,
minPath=details.minPath,
maxPath=details.maxPath)
t2 = time.time()
message("\tThat took %.2f seconds.\n" % (t2 - t1))
if details.catalogName:
message("Dumping catalog data\n")
cPickle.dump(cat, open(details.catalogName, 'wb+'))
elif details.catalogName:
message("Loading catalog\n")
cat = cPickle.load(open(details.catalogName, 'rb'))
if details.onBitsName:
try:
obls = cPickle.load(open(details.onBitsName, 'rb'))
except Exception:
obls = None
else:
if len(obls) < (inD.count('\n') - 1):
obls = None
scores = None
if details.doScore:
if not suppl:
message("We require inData to score molecules\n")
cmpd = Chem.AddHs(cmpd)
AllChem.EmbedMolecule(cmpd)
AllChem.UFFOptimizeMolecule(cmpd)
AllChem.CanonicalizeMol(cmpd)
# print(Chem.MolToMolBlock(cmpd), file=file('testmol.mol', 'w+'))
else:
cmpd = Chem.MolFromMolFile('testmol.mol')
builder = SubshapeBuilder()
if 1:
shape = builder.GenerateSubshapeShape(cmpd)
v = MolViewer()
if 1:
tmpFile = tempfile.mktemp('.grd')
v.server.deleteAll()
Geometry.WriteGridToFile(shape.grid, tmpFile)
time.sleep(1)
v.ShowMol(cmpd, name='testMol', showOnly=True)
v.server.loadSurface(tmpFile, 'testGrid', '', 2.5)
v.server.resetCGO('*')
with open('subshape.pkl', 'w+') as f:
cPickle.dump(shape, f)
for i, pt in enumerate(shape.skelPts):
v.server.sphere(tuple(pt.location), .5, (1, 0, 1), 'Pt-%d' % i)
if not hasattr(pt, 'shapeDirs'):
continue
momBeg = pt.location - pt.shapeDirs[0]
momEnd = pt.location + pt.shapeDirs[0]
v.server.cylinder(tuple(momBeg), tuple(momEnd), .1, (1, 0, 1),
'v-%d' % i)
c1.drawPolygon([(100, 100), (100, 200), (200, 200), (200, 100)],
fillColor=pid.Color(0, 0, 1))
c1.drawLines([(100, 100, 200, 200), (100, 200, 200, 100)],
color=pid.Color(0, 1, 0),
width=2)
# because the log has been instantiated with clear() as the loggerFlushCommand,
# this will blow out the log as well as the contents of the canvas.
c1.clear()
# draw some more stuff
c1.drawPolygon([(100, 100), (100, 200), (200, 200), (200, 100)],
fillColor=pid.Color(1, 0, 0))
c1.drawLines([(100, 100, 200, 200), (100, 200, 200, 100)],
color=pid.Color(0, 0, 0),
width=2)
# and write the resulting file.
c1.save()
# save the log by pickling it.
from rdkit.six.moves import cPickle
cPickle.dump(c1._LoggerGetLog(), open('foo.pkl', 'wb+'))
# create a new canvas
c2 = pidPIL.PILCanvas(sz, 'foo.png')
# read the pickled log back in
t = cPickle.load(open('foo.pkl', 'rb'))
# and play the log on the new canvas
Logger.replay(t, c2)
# there should now be a file 'foo.png' with the image
def RunOnData(details, data, progressCallback=None, saveIt=1, setDescNames=0):
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 = list(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 = list(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
t.TrainOnLine(examples, net, errTol=0.1, useAvgErr=0)
print('classifications:')
for example in examples:
res = net.ClassifyExample(example[:-1])
print('%f -> %f' % (example[-1], res))
return net
def runProfile(command):
import random
random.seed(23)
import profile
import pstats
datFile = '%s.prof.dat' % (command)
profile.run('%s()' % command, datFile)
stats = pstats.Stats(datFile)
stats.strip_dirs()
stats.sort_stats('time').print_stats()
if 0:
net = testXor()
print('Xor:', net)
from rdkit.six.moves import cPickle
outF = open('xornet.pkl', 'wb+')
cPickle.dump(net, outF)
outF.close()
else:
# runProfile('testLinear')
net = testLinear()
# net = testOr()
cmpd = Chem.MolFromSmiles('C1=CC=C1C#CC1=CC=C1')
cmpd = Chem.AddHs(cmpd)
AllChem.EmbedMolecule(cmpd)
AllChem.UFFOptimizeMolecule(cmpd)
AllChem.CanonicalizeMol(cmpd)
print >> file('testmol.mol', 'w+'), Chem.MolToMolBlock(cmpd)
else:
cmpd = Chem.MolFromMolFile('testmol.mol')
builder = SubshapeBuilder()
if 1:
shape = builder.GenerateSubshapeShape(cmpd)
v = MolViewer()
if 1:
import tempfile
tmpFile = tempfile.mktemp('.grd')
v.server.deleteAll()
Geometry.WriteGridToFile(shape.grid, tmpFile)
time.sleep(1)
v.ShowMol(cmpd, name='testMol', showOnly=True)
v.server.loadSurface(tmpFile, 'testGrid', '', 2.5)
v.server.resetCGO('*')
cPickle.dump(shape, file('subshape.pkl', 'w+'))
for i, pt in enumerate(shape.skelPts):
v.server.sphere(tuple(pt.location), .5, (1, 0, 1), 'Pt-%d' % i)
if not hasattr(pt, 'shapeDirs'): continue
momBeg = pt.location - pt.shapeDirs[0]
momEnd = pt.location + pt.shapeDirs[0]
v.server.cylinder(tuple(momBeg), tuple(momEnd), .1, (1, 0, 1),
'v-%d' % i)
def FingerprintsFromDetails(details, reportFreq=10):
data = None
if details.dbName and details.tableName:
from rdkit.Dbase.DbConnection import DbConnect
from rdkit.Dbase import DbInfo
from rdkit.ML.Data import DataUtils
try:
conn = DbConnect(details.dbName, details.tableName)
except Exception:
import traceback
error('Problems establishing connection to database: %s|%s\n' %
(details.dbName, details.tableName))
traceback.print_exc()
if not details.idName:
details.idName = DbInfo.GetColumnNames(details.dbName,
details.tableName)[0]
dataSet = DataUtils.DBToData(details.dbName,
details.tableName,
what='%s,%s' %
(details.idName, details.smilesName))
idCol = 0
smiCol = 1
elif details.inFileName and details.useSmiles:
from rdkit.ML.Data import DataUtils
conn = None
if not details.idName:
details.idName = 'ID'
try:
dataSet = DataUtils.TextFileToData(
details.inFileName,
onlyCols=[details.idName, details.smilesName])
except IOError:
import traceback
error('Problems reading from file %s\n' % (details.inFileName))
traceback.print_exc()
idCol = 0
smiCol = 1
elif details.inFileName and details.useSD:
conn = None
dataset = None
if not details.idName:
details.idName = 'ID'
dataSet = []
try:
s = Chem.SDMolSupplier(details.inFileName)
except Exception:
import traceback
error('Problems reading from file %s\n' % (details.inFileName))
traceback.print_exc()
else:
while 1:
try:
m = s.next()
except StopIteration:
break
if m:
dataSet.append(m)
if reportFreq > 0 and not len(dataSet) % reportFreq:
message('Read %d molecules\n' % (len(dataSet)))
if details.maxMols > 0 and len(
dataSet) >= details.maxMols:
break
for i, mol in enumerate(dataSet):
if mol.HasProp(details.idName):
nm = mol.GetProp(details.idName)
else:
nm = mol.GetProp('_Name')
dataSet[i] = (nm, mol)
else:
dataSet = None
fps = None
if dataSet and not details.useSD:
data = dataSet.GetNamedData()
if not details.molPklName:
fps = apply(FingerprintsFromSmiles, (data, idCol, smiCol),
details.__dict__)
else:
fps = apply(FingerprintsFromPickles, (data, idCol, smiCol),
details.__dict__)
elif dataSet and details.useSD:
fps = apply(FingerprintsFromMols, (dataSet, ), details.__dict__)
if fps:
if details.outFileName:
outF = open(details.outFileName, 'wb+')
for i in range(len(fps)):
cPickle.dump(fps[i], outF)
outF.close()
dbName = details.outDbName or details.dbName
if details.outTableName and dbName:
from rdkit.Dbase.DbConnection import DbConnect
from rdkit.Dbase import DbUtils, DbModule
conn = DbConnect(dbName)
#
# We don't have a db open already, so we'll need to figure out
# the types of our columns...
#
colTypes = DbUtils.TypeFinder(data, len(data), len(data[0]))
typeStrs = DbUtils.GetTypeStrings(
[details.idName, details.smilesName],
colTypes,
keyCol=details.idName)
cols = '%s, %s %s' % (typeStrs[0], details.fpColName,
DbModule.binaryTypeName)
# FIX: we should really check to see if the table
# is already there and, if so, add the appropriate
# column.
#
# create the new table
#
if details.replaceTable or \
details.outTableName.upper() not in [x.upper() for x in conn.GetTableNames()]:
conn.AddTable(details.outTableName, cols)
#
# And add the data
#
for ID, fp in fps:
tpl = ID, DbModule.binaryHolder(fp.ToBinary())
conn.InsertData(details.outTableName, tpl)
conn.Commit()
return fps
def GenRandomExamples(nVars=10, randScale=0.3, bitProb=0.5, nExamples=500, seed=(0, 0),
addResults=1):
random.seed(seed[0])
varWeights = numpy.array([random.random() for _ in range(nVars)]) * randScale
examples = [None] * nExamples
for i in range(nExamples):
varVals = [random.random() > bitProb for _ in range(nVars)]
temp = numpy.array(varVals) * varWeights
res = sum(temp)
if addResults:
varVals.append(res >= 1.)
examples[i] = varVals
nPossibleVals = [2] * (nExamples + 1)
attrs = list(range(nVars))
return (examples, attrs, nPossibleVals)
if __name__ == '__main__': # pragma: nocover
from rdkit.six.moves import cPickle
examples, attrs, nPossibleVals = GenRandomExamples()
outF = open('random.dat.pkl', 'wb+')
cPickle.dump(examples, outF)
cPickle.dump(attrs, outF)
cPickle.dump(nPossibleVals, outF)
tree = ID3.ID3Boot(examples, attrs, nPossibleVals)
tree.Pickle('save.pkl')
cmpd = Chem.MolFromSmiles('C1=CC=C1C#CC1=CC=C1')
cmpd = Chem.AddHs(cmpd)
AllChem.EmbedMolecule(cmpd)
AllChem.UFFOptimizeMolecule(cmpd)
AllChem.CanonicalizeMol(cmpd)
print >>file('testmol.mol','w+'),Chem.MolToMolBlock(cmpd)
else:
cmpd = Chem.MolFromMolFile('testmol.mol')
builder=SubshapeBuilder()
if 1:
shape=builder.GenerateSubshapeShape(cmpd)
v = MolViewer()
if 1:
import tempfile
tmpFile = tempfile.mktemp('.grd')
v.server.deleteAll()
Geometry.WriteGridToFile(shape.grid,tmpFile)
time.sleep(1)
v.ShowMol(cmpd,name='testMol',showOnly=True)
v.server.loadSurface(tmpFile,'testGrid','',2.5)
v.server.resetCGO('*')
cPickle.dump(shape,file('subshape.pkl','w+'))
for i,pt in enumerate(shape.skelPts):
v.server.sphere(tuple(pt.location),.5,(1,0,1),'Pt-%d'%i)
if not hasattr(pt,'shapeDirs'): continue
momBeg = pt.location-pt.shapeDirs[0]
momEnd = pt.location+pt.shapeDirs[0]
v.server.cylinder(tuple(momBeg),tuple(momEnd),.1,(1,0,1),'v-%d'%i)
# create a logged canvas and draw on it
sz = (300,300)
c1 = Logger.Logger(pidSVG.SVGCanvas,sz,'foo.svg',loggerFlushCommand='clear')
c1.drawPolygon([(100,100),(100,200),(200,200),(200,100)],fillColor=pid.Color(0,0,1))
c1.drawLines([(100,100,200,200),(100,200,200,100)],color=pid.Color(0,1,0),width=2)
# because the log has been instantiated with clear() as the loggerFlushCommand,
# this will blow out the log as well as the contents of the canvas.
c1.clear()
# draw some more stuff
c1.drawPolygon([(100,100),(100,200),(200,200),(200,100)],fillColor=pid.Color(1,0,0))
c1.drawLines([(100,100,200,200),(100,200,200,100)],color=pid.Color(0,0,0),width=2)
# and write the resulting file.
c1.save()
# save the log by pickling it.
from rdkit.six.moves import cPickle
cPickle.dump(c1._LoggerGetLog(),open('foo.pkl','wb+'))
# create a new canvas
c2 = pidPIL.PILCanvas(sz,'foo.png')
# read the pickled log back in
t = cPickle.load(open('foo.pkl','rb'))
# and play the log on the new canvas
Logger.replay(t,c2)
# there should now be a file 'foo.png' with the image
