def getDescriptors(self, smiles):
self.getSmilesData(smiles)
# Calculate descriptors defined in the model files
descList = self.model.varNames
savedSmilesData = dataUtilities.DataTable(self.smilesData)
#Try 3 time to get All compounds descriptors
nTry = 3
errorDesc = ""
while nTry > 0:
try:
traceLog = "Model Location:"+str(self.modelLocation)+"\n"
nBadEx = 0
# Determine Signature and non-Signature descriptor names
cinfonyDesc, clabDesc, signatureHeight, bbrcDesc, signDesc = descUtilities.getDescTypes(descList)
# Signatures
if "sign" in DescMethodsAvailable and signatureHeight:
traceLog += "Calculating signatures...\n"
print "Calculating signatures...."
preCalcData = dataUtilities.DataTable(self.preDefSignatureFile)
startHeight = 0 # Not used desc ignored in model prediction
endHeight = signatureHeight
self.smilesData = getSignatures.getSignatures(self.smilesData, startHeight, endHeight, preCalcData)
# C-Lab desc
if "clab" in DescMethodsAvailable and clabDesc:
traceLog += "Calculating C-Lab...\n"
print "Calculating C-Lab desc...."
self.smilesData = ClabUtilities.appendCLabDesc(clabDesc, self.smilesData)
# Cinfony
if cinfonyDesc:
traceLog += "Calculating Cinfony...\n"
print "Calculating Cinfony desc..."
self.smilesData = getCinfonyDesc.getCinfonyDescResults(self.smilesData, cinfonyDesc, radius = 5)
# bbrcDesc
if "bbrc" in DescMethodsAvailable and bbrcDesc:
traceLog += "Calculating BBRC...\n"
print "Calculating BBRC desc..."
self.smilesData = getBBRCDesc.getBBRCDescResult(self.smilesData, algo = "FTM", minSupPar = 1, descList = bbrcDesc)
# Detect if the descripts calaculation or something else went wrong!
for ex in self.smilesData:
if sum([ex[attr].isSpecial() for attr in self.smilesData.domain.attributes]) == len(self.smilesData.domain.attributes):
nBadEx +=1
if nBadEx:
traceLog += "WARNING: Desc. Calculation: From the "+str(len(self.smilesData))+" compounds, "+str(nBadEx)+" could not be calculated!\n"
print "WARNING: Desc. Calculation: From the "+str(len(self.smilesData))+" compounds, "+str(nBadEx)+" could not be calculated!"
print "WARNING: Tying again..."
self.smilesData = dataUtilities.DataTable(savedSmilesData)
nTry -= 1
else:
nTry = 0
except Exception, e:
errorDesc = "Error Calculating Descriptors:;"+traceLog+str(e)+";"
nTry -= 1
def getClabTasksAndSignatures(self, smiles):
if "clab" not in DescMethodsAvailable or "sign" not in DescMethodsAvailable:
return
self.getSmilesData(smiles)
# Signatures
preCalcData = dataUtilities.DataTable(self.preDefSignatureFile)
startHeight = 0
endHeight = 1
dataSign = getSignatures.getSignatures(self.smilesData, startHeight, endHeight, preCalcData)
# C-Lab descriptors
self.exToPred = ClabUtilities.appendCLabTasks(self.clabTasks, dataSign)
def getClabTasksAndSignatures(self, smiles):
if "clab" not in DescMethodsAvailable or "sign" not in DescMethodsAvailable:
return
self.getSmilesData(smiles)
# Signatures
preCalcData = dataUtilities.DataTable(self.preDefSignatureFile)
startHeight = 0
endHeight = 1
dataSign = getSignatures.getSignatures(self.smilesData, startHeight, endHeight, preCalcData)
# C-Lab descriptors
self.exToPred = ClabUtilities.appendCLabTasks(self.clabTasks, dataSign)
def getClabDescSignList(self, smiles, getMolFile=False):
# Create an Orange ExampleTable with a smiles attribute
smilesAttr = orange.EnumVariable("SMILEStoPred", values=[smiles])
myDomain = orange.Domain([smilesAttr], 0)
smilesData = dataUtilities.DataTable(myDomain, [[smiles]])
# Calculate descriptors defined in the model files
try:
descList = self.model.varNames
except: # Consensus object different
attributes = self.model.domain.variables
descList = []
for attr in attributes:
descList.append(attr.name)
# Determine Signature and non-Signature descriptor names
cinfonyDesc, clabDesc, signatureHeight, bbrcDesc, signDesc = descUtilities.getDescTypes(
descList)
# Signatures
if "sign" in DescMethodsAvailable and signatureHeight:
print "Calculating signatures..."
preCalcData = dataUtilities.DataTable(self.preDefSignatureFile)
startHeight = 0 # Not used desc ignored in model prediction
endHeight = signatureHeight
dataSign, cmpdSignDict, cmpdSignList, sdfStr = getSignatures.getSignatures(
smilesData,
startHeight,
endHeight,
preCalcData,
returnAtomID=True)
else:
cmpdSignList = [[]]
sdfStr = ""
if not getMolFile:
return (clabDesc, cmpdSignList[0])
elif not sdfStr:
return (clabDesc, cmpdSignList[0], "", "")
# create a mol file
molFile = miscUtilities.generateUniqueFile(desc="NN", ext="mol")
file = open(molFile, "w")
molStr = ""
for line in sdfStr[0]:
if "$$$$" in line:
break
molStr += line
file.write(line)
file.close()
return (clabDesc, cmpdSignList[0], molFile, molStr)
def getClabDescSignList(self, smiles, getMolFile=False):
# Create an Orange ExampleTable with a smiles attribute
smilesAttr = orange.EnumVariable("SMILEStoPred", values = [smiles])
myDomain = orange.Domain([smilesAttr], 0)
smilesData = dataUtilities.DataTable(myDomain, [[smiles]])
# Calculate descriptors defined in the model files
try:
descList = self.model.varNames
except: # Consensus object different
attributes = self.model.domain.variables
descList = []
for attr in attributes:
descList.append(attr.name)
# Determine Signature and non-Signature descriptor names
cinfonyDesc, clabDesc, signatureHeight, bbrcDesc, signDesc = descUtilities.getDescTypes(descList)
# Signatures
if "sign" in DescMethodsAvailable and signatureHeight:
print "Calculating signatures..."
preCalcData = dataUtilities.DataTable(self.preDefSignatureFile)
startHeight = 0 # Not used desc ignored in model prediction
endHeight = signatureHeight
dataSign,cmpdSignDict, cmpdSignList, sdfStr = getSignatures.getSignatures(smilesData, startHeight, endHeight, preCalcData, returnAtomID=True)
else:
cmpdSignList = [[]]
sdfStr = ""
if not getMolFile:
return (clabDesc,cmpdSignList[0])
elif not sdfStr:
return (clabDesc,cmpdSignList[0],"","")
# create a mol file
molFile = miscUtilities.generateUniqueFile(desc="NN", ext = "mol")
file= open(molFile,"w")
molStr=""
for line in sdfStr[0]:
if "$$$$" in line:
break
molStr += line
file.write(line)
file.close()
return (clabDesc,cmpdSignList[0],molFile,molStr)
def processSignificance(self,
smi,
prediction,
orderedDesc,
res,
resultsPath,
exWithDesc=None,
idx=0,
topN=1,
regMinIsDesired=True):
"""descs* = [(1.3, ["LogP"]), (0.2, ["[So2]", ...]), ...]
res = { "signature" : "",
"imgPath" : "", for placing the results
"non-signature" : "",
"molStr" : "",
"atoms" : []
"color" : [(r,g,b),(),...]}
It uses for Classificartion:
self.predictionOutcomes that must define [BADlabel, GOODlabel] in this same order
and for Regression:
self.significanceThreshold for which a GOOD prediction is BELOW the threshold
orderedDesc = { "molStr":''..., # only on specialType=1
"height":2, # only on specialType=1
"atoms":[1,2,3], # only on specialType=1
'Continuous': {
'DOWN':[ [('[F]', -0.008885456983609475), ... ('[F]',-0,0001)],
[('[O3]', -0.007324209285573964)],
[('[C3]([C3][C3])', -0.0047175657931883405)],
[('[C3]', -0.00389763719161594)]],
'UP': [[('[Car]([Car][Nar])', 0.009768981717302358)],
[('[HC]([C3])', 0.009135563633559857)]]},
'Discrete': {'DOWN': [], 'UP': []}}
"""
atomColor = None
orderedDesc_sign = {
'Continuous': {
'DOWN': [],
'UP': []
},
'Discrete': {
'DOWN': [],
'UP': []
}
}
orderedDesc_nonSign = {
'Continuous': {
'DOWN': [],
'UP': []
},
'Discrete': {
'DOWN': [],
'UP': []
}
}
if hasattr(self.model, "specialType") and self.model.specialType == 1:
print "This is a special model nr 1: It calculates itself the Significant Signatures"
endHeight = orderedDesc["height"]
try:
molStr = orderedDesc["molStr"]
atoms = eval(orderedDesc["atoms"])
except:
atoms = None
molStr = None
if not molStr or type(atoms) != list or not atoms:
atoms = None
molStr = None
orderedDesc_sign = orderedDesc
else:
atoms = None
endHeight = None
molStr = None
cinfonyDesc, clabDesc, signatureHeight, bbrcDesc, signDesc = descUtilities.getDescTypes(
[attr.name for attr in self.model.domain.attributes])
for attrType in ['Continuous', 'Discrete']:
for vector in ['UP', 'DOWN']:
for ord in range(len(orderedDesc[attrType][vector])):
signEmpty = True
nonSignEmpty = True
for attr in orderedDesc[attrType][vector][ord]:
if attr[0] in signDesc:
if signEmpty:
signEmpty = False
orderedDesc_sign[attrType][vector].append(
[])
orderedDesc_sign[attrType][vector][-1].append(
attr)
else:
if nonSignEmpty:
nonSignEmpty = False
orderedDesc_nonSign[attrType][
vector].append([])
orderedDesc_nonSign[attrType][vector][
-1].append(attr)
#Process color to use if highlight is used
outComeIsRev = None
if self.model.classVar.varType == orange.VarTypes.Discrete:
if self.predictionOutcomes is None:
print "WARNING: Cannot process Significance, Missing definition of predictionOutcomes for the EndPoint"
return
theGoodPred = str(self.predictionOutcomes[1])
theBadPred = str(self.predictionOutcomes[0])
if [str(p) for p in self.model.classVar.values
] == self.predictionOutcomes:
outComeIsRev = False
elif [str(p) for p in self.model.classVar.values
][::-1] == self.predictionOutcomes:
outComeIsRev = True
else:
print "ERROR: User outcome ordered list is not consistens toth model: ",\
self.predictionOutcomes, "<-->",self.model.classVar.values
if prediction == theGoodPred:
atomColor = 'g'
else:
atomColor = 'r'
else:
if self.significanceThreshold is None:
print "WARNING: Cannot process Significance, Missing definition of significanceThreshold for the EndPoint"
return
if prediction < self.significanceThreshold: # It is a GOOD prediction
atomColor = 'g'
else:
atomColor = 'r'
#Process Signatures
if exWithDesc: # Precalculated signatures
# cmpdSignList differ from when it is calc from smiles. However, not used.
dataSign, cmpdSignDict, cmpdSignList, sdfStr = self.getSignDataStruct(
exWithDesc)
else:
# OBS Hard coded for signatures 0 to 1.
smilesData = self.getAZOdata(smi)
if "sign" in DescMethodsAvailable:
dataSign, cmpdSignDict, cmpdSignList, sdfStr = getSignatures.getSignatures(
smilesData, 0, 1, returnAtomID=True, useClabSmiles=False)
else:
dataSign, cmpdSignDict, cmpdSignList, sdfStr = None, [
{}
] * len(smilesData), [[]] * len(smilesData), ""
# If signSVM model already returning one sign as the most significant
if not (hasattr(self.model, "specialType")
and self.model.specialType == 1):
downAbs = 0.0
rankIdxDown = 0
elemIdxDown = 0
if len(orderedDesc_sign["Continuous"]["DOWN"]):
for rankIdx in range(
len(orderedDesc_sign["Continuous"]["DOWN"])):
if downAbs != 0.0:
break
for elemIdx in range(
len(orderedDesc_sign["Continuous"]["DOWN"]
[rankIdx])):
# Test that the signature exists in the molecule
if orderedDesc_sign["Continuous"]["DOWN"][rankIdx][
elemIdx][0] in cmpdSignDict[0].keys():
downAbs = abs(orderedDesc_sign["Continuous"]
["DOWN"][rankIdx][elemIdx][1])
rankIdxDown = rankIdx
elemIdxDown = elemIdx
break
else:
if len(orderedDesc_sign["Continuous"]["DOWN"]):
downAbs = abs(orderedDesc_sign["Continuous"]["DOWN"][0][0][1])
else:
downAbs = 0.0
# If signSVM model already returning one sign as the most significant
if not (hasattr(self.model, "specialType")
and self.model.specialType == 1):
upAbs = 0.0
rankIdxUp = 0
elemIdxUp = 0
if len(orderedDesc_sign["Continuous"]["UP"]):
for rankIdx in range(len(
orderedDesc_sign["Continuous"]["UP"])):
if upAbs != 0.0:
break
for elemIdx in range(
len(orderedDesc_sign["Continuous"]["UP"]
[rankIdx])):
# Test that the signature exists in the molecule
if orderedDesc_sign["Continuous"]["UP"][rankIdx][
elemIdx][0] in cmpdSignDict[0].keys():
upAbs = abs(orderedDesc_sign["Continuous"]["UP"]
[rankIdx][elemIdx][1])
rankIdxUp = rankIdx
elemIdxUp = elemIdx
break
else:
if len(orderedDesc_sign["Continuous"]["UP"]):
upAbs = abs(orderedDesc_sign["Continuous"]["UP"][0][0][1])
else:
upAbs = 0.0
# Could happen that all derivatives are smaller than epsilon
if orderedDesc_sign["Continuous"]["UP"] or orderedDesc_sign[
"Continuous"]["DOWN"]:
if topN == 1: # Preserve syntax for Plato, only one significant signature
if upAbs > downAbs:
if not (hasattr(self.model, "specialType")
and self.model.specialType == 1):
MSDsign = orderedDesc_sign["Continuous"]["UP"][
rankIdxUp][elemIdxUp][0]
MSDdv = orderedDesc_sign["Continuous"]["UP"][
rankIdxUp][elemIdxUp][1]
else:
MSDsign = orderedDesc_sign["Continuous"]["UP"][0][0][0]
MSDdv = orderedDesc_sign["Continuous"]["UP"][0][0][1]
elif downAbs > upAbs:
if not (hasattr(self.model, "specialType")
and self.model.specialType == 1):
MSDsign = orderedDesc_sign["Continuous"]["DOWN"][
rankIdxDown][elemIdxDown][0]
MSDdv = orderedDesc_sign["Continuous"]["DOWN"][
rankIdxDown][elemIdxDown][1]
else:
MSDsign = orderedDesc_sign["Continuous"]["DOWN"][0][0][
0]
MSDdv = orderedDesc_sign["Continuous"]["DOWN"][0][0][1]
elif downAbs != 0.0:
if not (hasattr(self.model, "specialType")
and self.model.specialType == 1):
MSDsign = orderedDesc_sign["Continuous"]["DOWN"][
rankIdxDown][elemIdxDown][0]
MSDdv = orderedDesc_sign["Continuous"]["DOWN"][
rankIdxDown][elemIdxDown][1]
else:
MSDsign = orderedDesc_sign["Continuous"]["DOWN"][0][0][
0]
MSDdv = orderedDesc_sign["Continuous"]["DOWN"][0][0][1]
else:
MSDsign = None
MSDsign = 0
else:
MSDsign = []
MSDdv = []
for idx in range(topN):
try:
if abs(
orderedDesc_sign["Continuous"]["DOWN"][0][0][1]
) > abs(orderedDesc_sign["Continuous"]["UP"][0][0][1]):
MSDsign.append(orderedDesc_sign["Continuous"]
["DOWN"][0][0][0])
MSDdv.append(orderedDesc_sign["Continuous"]
["DOWN"].pop(0)[0][1])
else:
MSDsign.append(
orderedDesc_sign["Continuous"]["UP"][0][0][0])
MSDdv.append(orderedDesc_sign["Continuous"]
["UP"].pop(0)[0][1])
except:
try:
MSDsign.append(orderedDesc_sign["Continuous"]
["DOWN"][0][0][0])
MSDdv.append(orderedDesc_sign["Continuous"]
["DOWN"].pop(0)[0][1])
except:
try:
MSDsign.append(orderedDesc_sign["Continuous"]
["UP"][0][0][0])
MSDdv.append(orderedDesc_sign["Continuous"]
["UP"].pop(0)[0][1])
except:
pass
else:
MSDsign = None
MSDsign = 0
#Process non-signatures
if self.model.classVar.varType == orange.VarTypes.Discrete and outComeIsRev:
UP = "DOWN"
DOWN = "UP"
elif self.model.classVar.varType != orange.VarTypes.Discrete and not regMinIsDesired:
UP = "DOWN"
DOWN = "UP"
else:
UP = "UP"
DOWN = "DOWN"
#Process DiscreteAttrs
MSDnonSign = ""
nD_DOWN = len(orderedDesc_nonSign["Discrete"][DOWN])
if nD_DOWN:
for n in range(min(topN, nD_DOWN)):
if topN > 1:
MSDnonSign += str(n + 1) + ": "
MSDnonSign += string.join([
"Change " + x[0]
for x in orderedDesc_nonSign["Discrete"][DOWN][n]
], '\n') + '\n'
#Process Continuous attributes
for n in range(topN):
if topN > 1:
order = str(n + 1) + ": "
else:
order = ""
if len(orderedDesc_nonSign["Continuous"][DOWN]):
downAbs = abs(orderedDesc_nonSign["Continuous"][DOWN][0][0][1])
else:
downAbs = 0.0
if len(orderedDesc_nonSign["Continuous"][UP]):
upAbs = abs(orderedDesc_nonSign["Continuous"][UP][0][0][1])
else:
upAbs = 0.0
if orderedDesc_nonSign["Continuous"][UP] and upAbs >= downAbs:
TOPmsd = orderedDesc_nonSign["Continuous"][UP].pop(0)
MSDnonSign += order + string.join(
["Decrease " + x[0] for x in TOPmsd], '\n') + '\n'
if orderedDesc_nonSign["Continuous"][DOWN] and downAbs >= upAbs:
TOPmsd = orderedDesc_nonSign["Continuous"][DOWN].pop(0)
MSDnonSign += order + string.join(
["Increase " + x[0] for x in TOPmsd], '\n') + '\n'
res["non-signature"] = MSDnonSign
# Most probably Signatures will always be associated with Discrete attributes. Nevertheless, it happens that some are Continuous, and therefore
# we will be using signatures reported as Continuous if any
if not MSDsign:
res["imgPath"] = ""
res["signature"] = ""
res["signarure_deriv_val"] = 0
return
if resultsPath and os.path.isdir(resultsPath):
imgPath = os.path.join(
resultsPath, "significance_" + str(idx) + "_" +
str(time.time()).replace(".", '') + ".png")
else:
imgPath = ""
# Call the method to create the image/mol specifying the color of the hilighted atoms
if exWithDesc == None: # Don't use with precalc desc
if molStr and atoms and endHeight is not None and not imgPath:
print "Using molStr and atoms from Learner Significant Signature"
res["imgPath"] = ''
res["molStr"] = molStr
allAtoms = self.getNNAtoms(molStr, atoms, endHeight)
res["atoms"] = allAtoms
res["color"] = [atomColor] * len(allAtoms)
else:
res["imgPath"], res["molStr"], res["atoms"], res[
"color"] = self.createSignImg(smi, MSDsign, atomColor,
imgPath, endHeight)
#Fix the significant descriptors so that it is a formated string
res["signature"] = MSDsign
res["signarure_deriv_val"] = MSDdv
return res
def getDescriptors(self, smiles):
self.getSmilesData(smiles)
# Calculate descriptors defined in the model files
descList = self.model.varNames
savedSmilesData = dataUtilities.DataTable(self.smilesData)
#Try 3 time to get All compounds descriptors
nTry = 3
errorDesc = ""
while nTry > 0:
try:
#if True:
traceLog = "Model Location:" + str(self.modelLocation) + "\n"
nBadEx = 0
# Determine Signature and non-Signature descriptor names
cinfonyDesc, clabDesc, signatureHeight, bbrcDesc, signDesc = descUtilities.getDescTypes(
descList)
# Signatures
if "sign" in DescMethodsAvailable and signatureHeight:
traceLog += "Calculating signatures...\n"
print "Calculating signatures...."
preCalcData = dataUtilities.DataTable(
self.preDefSignatureFile)
startHeight = 0 # Not used desc ignored in model prediction
endHeight = signatureHeight
self.smilesData = getSignatures.getSignatures(
self.smilesData, startHeight, endHeight, preCalcData)
# C-Lab desc
if "clab" in DescMethodsAvailable and clabDesc:
traceLog += "Calculating C-Lab...\n"
print "Calculating C-Lab desc...."
self.smilesData = ClabUtilities.appendCLabDesc(
clabDesc, self.smilesData)
# Cinfony
if cinfonyDesc:
traceLog += "Calculating Cinfony...\n"
print "Calculating Cinfony desc..."
self.smilesData = getCinfonyDesc.getCinfonyDescResults(
self.smilesData, cinfonyDesc, radius=5)
# bbrcDesc
if "bbrc" in DescMethodsAvailable and bbrcDesc:
traceLog += "Calculating BBRC...\n"
print "Calculating BBRC desc..."
self.smilesData = getBBRCDesc.getBBRCDescResult(
self.smilesData,
algo="FTM",
minSupPar=1,
descList=bbrcDesc)
# Detect if the descripts calaculation or something else went wrong!
for ex in self.smilesData:
if sum([
ex[attr].isSpecial()
for attr in self.smilesData.domain.attributes
]) == len(self.smilesData.domain.attributes):
nBadEx += 1
if nBadEx:
traceLog += "WARNING: Desc. Calculation: From the " + str(
len(self.smilesData)) + " compounds, " + str(
nBadEx) + " could not be calculated!\n"
print "WARNING: Desc. Calculation: From the " + str(
len(self.smilesData)) + " compounds, " + str(
nBadEx) + " could not be calculated!"
print "WARNING: Tying again..."
self.smilesData = dataUtilities.DataTable(savedSmilesData)
nTry -= 1
else:
nTry = 0
#else:
except Exception, e:
errorDesc = "Error Calculating Descriptors:;" + traceLog + str(
e) + ";"
nTry -= 1
def createSignImg(self,
smi,
signature,
atomColor,
imgPath,
endHeight=None):
colors = []
print "Creating signature image..."
if not signature or not atomColor or not smi:
print "Missing inputs:", str([smi, signature, atomColor])
return "", "", [], []
if hasattr(self.model, "specialType") and self.model.specialType == 1:
# Create an Orange ExampleTable with a smiles attribute
smilesAttr = orange.EnumVariable("SMILEStoPred", values=[smi])
myDomain = orange.Domain([smilesAttr], 0)
smilesData = dataUtilities.DataTable(myDomain, [[smi]])
preCalcData = None
startHeight = 0
dataSign, cmpdSignDict, cmpdSignList, sdfStr = getSignatures.getSignatures(
smilesData,
startHeight,
endHeight,
preCalcData,
returnAtomID=True)
cmpdSignList = cmpdSignList[0]
CLabDesc = []
# create a mol file
tmpFile = miscUtilities.generateUniqueFile(desc="NN", ext="mol")
file = open(tmpFile, "w")
molStr = ""
for line in sdfStr[0]:
if "$$$$" in line:
break
molStr += line
file.write(line)
file.close()
else:
CLabDesc, cmpdSignList, tmpFile, molStr = self.getClabDescSignList(
smi, getMolFile=True)
if not cmpdSignList or not tmpFile:
print "Couldn't get the cmpd list or the mol file"
return "", "", [], []
# create an RDKit mol
mol = Chem.MolFromMolFile(tmpFile, True, False)
if not mol:
mol = Chem.MolFromMolFile(tmpFile, False, False)
if not mol:
print "Could not create mol for: ", smi
return "", "", [], []
adj = GetAdjacencyMatrix(mol)
# find the NN
hights = []
for i in miscUtilities.Range(0, len(cmpdSignList), mol.GetNumAtoms()):
hList = cmpdSignList[i:i + mol.GetNumAtoms()]
if len(hList):
hights.append(cmpdSignList[i:i + mol.GetNumAtoms()])
atoms = []
hight = None
for idx, h in enumerate(hights):
if signature in h:
for i, a in enumerate(h):
if a == signature:
atoms.append(i)
hight = idx
break
if len(atoms) == 0:
print "ERROR: Could not find the atom for ", signature
return "signatureNOTfound", "", [], []
#print "IniAtoms: ",atoms
visitedAtoms = []
for n in range(hight):
for atom in copy.deepcopy(atoms):
if atom not in visitedAtoms:
lNN = findNeighbors(atom, adj)
visitedAtoms.append(atom)
for lnn in lNN:
if lnn not in atoms:
atoms.append(lnn)
atoms.sort()
os.system("rm " + tmpFile)
#Specify the atom colors
colors = [atomColor] * len(atoms)
if not imgPath:
return "", molStr, atoms, colors
try:
#Draw the image
MolDrawing.elemDict = defaultdict(lambda: (0, 0, 0))
Draw.MolToImageFile(mol,
imgPath,
size=(300, 300),
kekulize=True,
wedgeBonds=True,
highlightAtoms=atoms)
#Color the Highlighted atoms with the choosen atomColor.
# Only using one color
if atomColor == 'r':
rgb = (255, 0, 0)
elif atomColor == 'g':
rgb = (0, 255, 0)
else:
rgb = (0, 0, 255) #Blue
img = Image.open(imgPath)
img = img.convert("RGBA")
pixdata = img.getdata()
newData = list()
for item in pixdata:
if item[0] == 255 and item[1] == 0 and item[2] == 0:
newData.append(rgb + (255, ))
else:
newData.append(item)
img.putdata(newData)
img.save(imgPath)
if os.path.isfile(imgPath):
return imgPath, molStr, atoms, colors
else:
return "", molStr, atoms, colors
except:
return "", molStr, atoms, colors
def processSignificance(self, smi, prediction, orderedDesc, res, resultsPath, idx = 0, topN = 1):
"""descs* = [(1.3, ["LogP"]), (0.2, ["[So2]", ...]), ...]
res = { "signature" : "",
"imgPath" : "", for placing the results
"non-signature" : "",
"molStr" : "",
"atoms" : []
"color" : [(r,g,b),(),...]}
It uses for Classificartion:
self.predictionOutcomes that must define [BADlabel, GOODlabel] in this same order
and for Regression:
self.significanceThreshold for which a GOOD prediction is BELOW the threshold
orderedDesc = { "molStr":''..., # only on specialType=1
"height":2, # only on specialType=1
"atoms":[1,2,3], # only on specialType=1
'Continuous': {
'DOWN':[ [('[F]', -0.008885456983609475), ... ('[F]',-0,0001)],
[('[O3]', -0.007324209285573964)],
[('[C3]([C3][C3])', -0.0047175657931883405)],
[('[C3]', -0.00389763719161594)]],
'UP': [[('[Car]([Car][Nar])', 0.009768981717302358)],
[('[HC]([C3])', 0.009135563633559857)]]},
'Discrete': {'DOWN': [], 'UP': []}}
"""
atomColor = None
orderedDesc_sign = {'Continuous': {'DOWN': [], 'UP': []},
'Discrete': {'DOWN': [], 'UP': []}}
orderedDesc_nonSign = {'Continuous': {'DOWN': [], 'UP': []},
'Discrete': {'DOWN': [], 'UP': []}}
if hasattr(self.model, "specialType") and self.model.specialType == 1:
print "This is a special model nr 1: It calculates itself the Significant Signatures"
endHeight = orderedDesc["height"]
try:
molStr = orderedDesc["molStr"]
atoms = eval(orderedDesc["atoms"])
except:
atoms = None
molStr = None
if not molStr or type(atoms)!=list or not atoms:
atoms = None
molStr = None
orderedDesc_sign = orderedDesc
else:
atoms = None
endHeight = None
molStr = None
cinfonyDesc, clabDesc, signatureHeight, bbrcDesc, signDesc = descUtilities.getDescTypes([attr.name for attr in self.model.domain.attributes])
for attrType in ['Continuous', 'Discrete']:
for vector in ['UP','DOWN']:
for ord in range(len(orderedDesc[attrType][vector])):
signEmpty=True
nonSignEmpty=True
for attr in orderedDesc[attrType][vector][ord]:
if attr[0] in signDesc:
if signEmpty:
signEmpty = False
orderedDesc_sign[attrType][vector].append([])
orderedDesc_sign[attrType][vector][-1].append(attr)
else:
if nonSignEmpty:
nonSignEmpty = False
orderedDesc_nonSign[attrType][vector].append([])
orderedDesc_nonSign[attrType][vector][-1].append(attr)
#Process color to use if highlight is used
outComeIsRev = None
if self.model.classVar.varType == orange.VarTypes.Discrete:
if self.predictionOutcomes is None:
print "WARNING: Cannot process Significance, Missing definition of predictionOutcomes for the EndPoint"
return
theGoodPred = str(self.predictionOutcomes[1])
theBadPred = str(self.predictionOutcomes[0])
if [str(p) for p in self.model.classVar.values] == self.predictionOutcomes:
outComeIsRev = False
elif [str(p) for p in self.model.classVar.values][::-1] == self.predictionOutcomes:
outComeIsRev = True
else:
print "ERROR: User outcome ordered list is not consistens toth model: ",\
self.predictionOutcomes, "<-->",self.model.classVar.values
if prediction == theGoodPred:
atomColor = 'g'
else:
atomColor = 'r'
else:
if self.significanceThreshold is None:
print "WARNING: Cannot process Significance, Missing definition of significanceThreshold for the EndPoint"
return
if prediction < self.significanceThreshold: # It is a GOOD prediction
atomColor = 'g'
else:
atomColor = 'r'
#Process Signatures
# OBS Hard coded for signatures 0 to 1.
smilesData = self.getAZOdata(smi)
dataSign, cmpdSignDict, cmpdSignList, sdfStr = getSignatures.getSignatures(smilesData, 0, 1, returnAtomID = True, useClabSmiles = False)
# If signSVM model already returning one sign as the most significant
if not (hasattr(self.model, "specialType") and self.model.specialType == 1):
downAbs = 0.0
rankIdxDown = 0
elemIdxDown = 0
if len(orderedDesc_sign["Continuous"]["DOWN"]):
for rankIdx in range(len(orderedDesc_sign["Continuous"]["DOWN"])):
if downAbs != 0.0:
break
for elemIdx in range(len(orderedDesc_sign["Continuous"]["DOWN"][rankIdx])):
# Test that the signature exists in the molecule
if orderedDesc_sign["Continuous"]["DOWN"][rankIdx][elemIdx][0] in cmpdSignDict[0].keys():
downAbs = abs(orderedDesc_sign["Continuous"]["DOWN"][rankIdx][elemIdx][1])
rankIdxDown = rankIdx
elemIdxDown = elemIdx
break
else:
if len(orderedDesc_sign["Continuous"]["DOWN"]):
downAbs = abs(orderedDesc_sign["Continuous"]["DOWN"][0][0][1])
else:
downAbs = 0.0
# If signSVM model already returning one sign as the most significant
if not (hasattr(self.model, "specialType") and self.model.specialType == 1):
upAbs = 0.0
rankIdxUp = 0
elemIdxUp = 0
if len(orderedDesc_sign["Continuous"]["UP"]):
for rankIdx in range(len(orderedDesc_sign["Continuous"]["UP"])):
if upAbs != 0.0:
break
for elemIdx in range(len(orderedDesc_sign["Continuous"]["UP"][rankIdx])):
# Test that the signature exists in the molecule
if orderedDesc_sign["Continuous"]["UP"][rankIdx][elemIdx][0] in cmpdSignDict[0].keys():
upAbs = abs(orderedDesc_sign["Continuous"]["UP"][rankIdx][elemIdx][1])
rankIdxUp = rankIdx
elemIdxUp = elemIdx
break
else:
if len(orderedDesc_sign["Continuous"]["UP"]):
upAbs = abs(orderedDesc_sign["Continuous"]["UP"][0][0][1])
else:
upAbs = 0.0
if upAbs > downAbs:
if not (hasattr(self.model, "specialType") and self.model.specialType == 1):
MSDsign = orderedDesc_sign["Continuous"]["UP"][rankIdxUp][elemIdxUp][0]
MSDdv = orderedDesc_sign["Continuous"]["UP"][rankIdxUp][elemIdxUp][1]
else:
MSDsign = orderedDesc_sign["Continuous"]["UP"][0][0][0]
MSDdv = orderedDesc_sign["Continuous"]["UP"][0][0][1]
elif downAbs > upAbs:
if not (hasattr(self.model, "specialType") and self.model.specialType == 1):
MSDsign = orderedDesc_sign["Continuous"]["DOWN"][rankIdxDown][elemIdxDown][0]
MSDdv = orderedDesc_sign["Continuous"]["DOWN"][rankIdxDown][elemIdxDown][1]
else:
MSDsign = orderedDesc_sign["Continuous"]["DOWN"][0][0][0]
MSDdv = orderedDesc_sign["Continuous"]["DOWN"][0][0][1]
elif downAbs != 0.0:
if not (hasattr(self.model, "specialType") and self.model.specialType == 1):
MSDsign = orderedDesc_sign["Continuous"]["DOWN"][rankIdxDown][elemIdxDown][0]
MSDdv = orderedDesc_sign["Continuous"]["DOWN"][rankIdxDown][elemIdxDown][1]
else:
MSDsign = orderedDesc_sign["Continuous"]["DOWN"][0][0][0]
MSDdv = orderedDesc_sign["Continuous"]["DOWN"][0][0][1]
else:
MSDsign = None
MSDsign = 0
#Process non-signatures
if self.model.classVar.varType == orange.VarTypes.Discrete and outComeIsRev:
UP = "DOWN"
DOWN = "UP"
else:
UP = "UP"
DOWN = "DOWN"
#Process DiscreteAttrs
MSDnonSign = ""
nD_DOWN = len(orderedDesc_nonSign["Discrete"][DOWN])
if nD_DOWN:
for n in range(min(topN,nD_DOWN)):
if topN > 1:
MSDnonSign += str(n+1)+": "
MSDnonSign += string.join(["Change "+x[0] for x in orderedDesc_nonSign["Discrete"][DOWN][n]],'\n')+'\n'
#Process Continuous attributes
for n in range(topN):
if topN > 1:
order = str(n+1)+": "
else:
order = ""
if len(orderedDesc_nonSign["Continuous"][DOWN]):
downAbs = abs(orderedDesc_nonSign["Continuous"][DOWN][0][0][1])
else:
downAbs = 0.0
if len(orderedDesc_nonSign["Continuous"][UP]):
upAbs = abs(orderedDesc_nonSign["Continuous"][UP][0][0][1])
else:
upAbs = 0.0
if orderedDesc_nonSign["Continuous"][UP] and upAbs >= downAbs:
TOPmsd = orderedDesc_nonSign["Continuous"][UP].pop(0)
MSDnonSign += order + string.join(["Decrease "+x[0] for x in TOPmsd],'\n')+'\n'
if orderedDesc_nonSign["Continuous"][DOWN] and downAbs >= upAbs:
TOPmsd = orderedDesc_nonSign["Continuous"][DOWN].pop(0)
MSDnonSign += order + string.join(["Increase "+x[0] for x in TOPmsd],'\n')+'\n'
res["non-signature"] = MSDnonSign
# Most probably Signatures will always be associated with Discrete attributes. Nevertheless, it happens that some are Continuous, and therefore
# we will be using signatures reported as Continuous if any
if not MSDsign:
res["imgPath"] = ""
res["signature"] = ""
res["signarure_deriv_val"] = 0
return
if resultsPath and os.path.isdir(resultsPath):
imgPath = os.path.join(resultsPath,"significance_"+str(idx)+"_"+str(time.time()).replace(".",'')+".png")
else:
imgPath = ""
# Call the method to create the image/mol specifying the color of the hilighted atoms
if molStr and atoms and endHeight is not None and not imgPath:
print "Using molStr and atoms from Learner Significant Signature"
res["imgPath"]=''
res["molStr"] = molStr
allAtoms = self.getNNAtoms(molStr, atoms, endHeight)
res["atoms"] = allAtoms
res["color"] = [atomColor]*len(allAtoms)
else:
res["imgPath"] , res["molStr"], res["atoms"], res["color"] = self.createSignImg(smi,MSDsign,atomColor,imgPath,endHeight)
#Fix the significant descriptors so that it is a formated string
res["signature"] = MSDsign
res["signarure_deriv_val"] = MSDdv
def createSignImg(self,smi,signature,atomColor,imgPath, endHeight = None):
colors = []
print "Creating signature image..."
if not signature or not atomColor or not smi:
print "Missing inputs:",str([smi,signature,atomColor])
return "","",[], []
if hasattr(self.model, "specialType") and self.model.specialType == 1:
# Create an Orange ExampleTable with a smiles attribute
smilesAttr = orange.EnumVariable("SMILEStoPred", values = [smi])
myDomain = orange.Domain([smilesAttr], 0)
smilesData = dataUtilities.DataTable(myDomain, [[smi]])
preCalcData = None
startHeight = 0
dataSign,cmpdSignDict, cmpdSignList, sdfStr = getSignatures.getSignatures(smilesData, startHeight, endHeight, preCalcData, returnAtomID=True)
cmpdSignList = cmpdSignList[0]
CLabDesc = []
# create a mol file
tmpFile = miscUtilities.generateUniqueFile(desc="NN", ext = "mol")
file= open(tmpFile,"w")
molStr=""
for line in sdfStr[0]:
if "$$$$" in line:
break
molStr += line
file.write(line)
file.close()
else:
CLabDesc,cmpdSignList, tmpFile, molStr = self.getClabDescSignList(smi, getMolFile=True)
if not cmpdSignList or not tmpFile:
print "Couldn't get the cmpd list or the mol file"
return "","",[], []
# create an RDKit mol
mol = Chem.MolFromMolFile(tmpFile,True,False)
if not mol:
mol = Chem.MolFromMolFile(tmpFile,False,False)
if not mol:
print "Could not create mol for: ",smi
return "","",[], []
adj = GetAdjacencyMatrix(mol)
# find the NN
hights = []
for i in miscUtilities.Range(0,len(cmpdSignList),mol.GetNumAtoms()):
hList = cmpdSignList[i:i+mol.GetNumAtoms()]
if len(hList):
hights.append(cmpdSignList[i:i+mol.GetNumAtoms()])
atoms = []
hight = None
for idx,h in enumerate(hights):
if signature in h:
for i,a in enumerate(h):
if a == signature:
atoms.append(i)
hight = idx
break
if len(atoms) == 0:
print "ERROR: Could not find the atom for ",signature
return "signatureNOTfound","",[],[]
#print "IniAtoms: ",atoms
visitedAtoms = []
for n in range(hight):
for atom in copy.deepcopy(atoms):
if atom not in visitedAtoms:
lNN = findNeighbors(atom,adj)
visitedAtoms.append(atom)
for lnn in lNN:
if lnn not in atoms:
atoms.append(lnn)
atoms.sort()
os.system("rm " + tmpFile)
#Specify the atom colors
colors=[atomColor]*len(atoms)
if not imgPath:
return "",molStr,atoms,colors
try:
#Draw the image
MolDrawing.elemDict=defaultdict(lambda : (0,0,0))
Draw.MolToImageFile(mol,imgPath,size=(300, 300), kekulize=True, wedgeBonds=True, highlightAtoms=atoms)
#Color the Highlighted atoms with the choosen atomColor.
# Only using one color
if atomColor == 'r':
rgb = (255,0,0)
elif atomColor == 'g':
rgb = (0,255,0)
else:
rgb = (0,0,255) #Blue
img = Image.open(imgPath)
img = img.convert("RGBA")
pixdata = img.getdata()
newData = list()
for item in pixdata:
if item[0] == 255 and item[1] == 0 and item[2] == 0:
newData.append(rgb + (255,) )
else:
newData.append(item)
img.putdata(newData)
img.save(imgPath)
if os.path.isfile(imgPath):
return imgPath,molStr,atoms,colors
else:
return "",molStr,atoms,colors
except:
return "",molStr,atoms,colors