def getSMARTSrecalcDesc(data, smarts):
""" Calculates structural descriptors for test and training data.
In other words, checks for the substructure occurrence (0/1) in the
test or prediction molecules. Uses RDK.
Expects the test/prediction data and a list of SMARTS strings.
Returns the data including the new features.
"""
smilesName = dataUtilities.getSMILESAttr(data)
if not smilesName or type(smarts) != list or not len(smarts):
print "Please check the input parameters"
return None
existingAttrs = [attr for attr in smarts if attr in data.domain]
if existingAttrs:
print "The input data cannot contain the smarts to be calculated!"
return None
newdomain = orange.Domain(data.domain.attributes + \
[orange.FloatVariable(attr, numberOfDecimals=1) for attr in smarts],\
data.domain.classVar )
newdata = orange.ExampleTable(newdomain, data)
for ex in newdata:
smile = str(ex[smilesName].value)
mol = rdk.Chem.MolFromSmiles(smile)
if mol is None:
continue
for smrt in smarts:
patt = rdk.Chem.MolFromSmarts(smrt)
if mol.HasSubstructMatch(patt):
ex[smrt] = 1.0
else:
ex[smrt] = 0.0
return newdata
def getMLStatistics(trainData, mlList=[ml for ml in MLMETHODS if AZOC.MLMETHODS[ml]["useByDefault"]], savePath = None, queueType = "NoSGE", verbose = 0, logFile = None, callBack = None):
"""
Loop over all MLMETHODS to get their statistics
Write to disk the full MLStatistics including the consensus model:
Consensus model statistics will be calculated out of the a Consensus model based on MLmethods that are stable (beased on StabilityValue)
"""
log(logFile, "Running getMLStatistics...")
MLStatistics = {}
learners = {}
smilesAttr = dataUtilities.getSMILESAttr(trainData)
for ml in mlList:
learner = MLMETHODS[ml](name = ml)
if not learner.isCompatible(trainData.domain.classVar) :
log(logFile, "Ignored learner "+str(ml)+" since it's not compatible with this class.")
continue
if learner.specialType == 1 and not smilesAttr:
log(logFile, "Ignored learner "+str(ml)+" since it's special and requires a SMILES attribute.")
continue
learners[ml] = learner
# Forced queueType to NoSGE so that appspack do not fload the cluster
evaluator = getUnbiasedAccuracy.UnbiasedAccuracyGetter(data = trainData, learner = learners, paramList = None, nExtFolds = AZOC.QSARNINNERFOLDS, nInnerFolds = AZOC.QSARNCVFOLDS, queueType = "NoSGE", verbose = verbose, logFile = logFile, resultsFile = savePath)
MLStatistics = evaluator.getAcc(callBack = callBack)
saveMLStatistics(savePath, MLStatistics, logFile)
return MLStatistics
def getSMARTSrecalcDesc(data, smarts):
""" Calculates structural descriptors for test and training data.
In other words, checks for the substructure occurrence (0/1) in the
test or prediction molecules. Uses RDK.
Expects the test/prediction data and a list of SMARTS strings.
Returns the data including the new features.
"""
smilesName = dataUtilities.getSMILESAttr(data)
if not smilesName or type(smarts) != list or not len(smarts):
print "Please check the input parameters"
return None
existingAttrs = [attr for attr in smarts if attr in data.domain]
if existingAttrs:
print "The input data cannot contain the smarts to be calculated!"
return None
newdomain = orange.Domain(data.domain.attributes + \
[orange.FloatVariable(attr, numberOfDecimals=1) for attr in smarts],\
data.domain.classVar )
newdata = orange.ExampleTable(newdomain, data)
for ex in newdata:
smile = str(ex[smilesName].value)
mol = rdk.Chem.MolFromSmiles(smile)
if mol is None:
continue
for smrt in smarts:
patt = rdk.Chem.MolFromSmarts(smrt)
if mol.HasSubstructMatch(patt):
ex[smrt] = 1.0
else:
ex[smrt] = 0.0
return newdata
def getSMILESAttr(data):
# Check that the data contains a SMILES attribute
smilesName = dataUtilities.getSMILESAttr(data)
if not smilesName:
print "Warning: The data set does not contain any known smiles attribute!"
print "No similarity descriptors added!"
return None
else:
return smilesName
def getSMILESAttr(data):
# Check that the data contains a SMILES attribute
smilesName = dataUtilities.getSMILESAttr(data)
if not smilesName:
print "Warning: The data set does not contain any known smiles attribute!"
print "No similarity descriptors added!"
return None
else:
return smilesName
def getSMILESAttr(data):
# Check that the data contains a SMILES attribute
smilesName = dataUtilities.getSMILESAttr(data)
if not smilesName:
print "Warning: The data set does not contain any known smiles attribute!"
print " Expected SMILES attribute names: "+str(AZOC.SMILESNAMES)
print "No Cinfony descriptors added!"
return None
else:
return smilesName
def getSMILESAttr(data):
# Check that the data contains a SMILES attribute
smilesName = dataUtilities.getSMILESAttr(data)
if not smilesName:
print "Warning: The data set does not contain any known smiles attribute!"
print " Expected SMILES attribute names: "+str(AZOC.SMILESNAMES)
print "No Cinfony descriptors added!"
return None
else:
return smilesName
def buildConsensus(trainData, learners, MLMethods, logFile = None):
log(logFile, "Building a consensus model based on optimized MLmethods: "+str([ml for ml in MLMethods])+"...")
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
#Expression: If CAavg_{POS} ge CAavg_{NEG} -> POS else -> NEG
# where CAavg_{POS} is the average of classification accuracies of all models predicting POS.
CLASS0 = str(trainData.domain.classVar.values[0])
CLASS1 = str(trainData.domain.classVar.values[1])
#exprTest0
exprTest0 = "(0"
for ml in MLMethods:
exprTest0 += "+( "+ml+" == "+CLASS0+" )*"+str(MLMethods[ml]["optAcc"])+" "
exprTest0 += ")/IF0(sum([False"
for ml in MLMethods:
exprTest0 += ", "+ml+" == "+CLASS0+" "
exprTest0 += "]),1)"
# exprTest1
exprTest1 = "(0"
for ml in MLMethods:
exprTest1 += "+( "+ml+" == "+CLASS1+" )*"+str(MLMethods[ml]["optAcc"])+" "
exprTest1 += ")/IF0(sum([False"
for ml in MLMethods:
exprTest1 += ", "+ml+" == "+CLASS1+" "
exprTest1 += "]),1)"
# expression
expression = [exprTest0+" >= "+exprTest1+" -> "+CLASS0," -> "+CLASS1]
else:
Q2sum = sum([MLMethods[ml]["optAcc"] for ml in MLMethods])
expression = "(1 / "+str(Q2sum)+") * (0"
for ml in MLMethods:
expression += " + "+str(MLMethods[ml]["optAcc"])+" * " + ml +" "
expression += ")"
consensusLearners = {}
for learnerName in learners:
consensusLearners[learnerName] = learners[learnerName]
learner = AZorngConsensus.ConsensusLearner(learners = consensusLearners, expression = expression)
log(logFile, " Training Consensus Learner")
smilesAttr = dataUtilities.getSMILESAttr(trainData)
if smilesAttr:
log(logFile,"Found SMILES attribute:"+smilesAttr)
if learner.specialType == 1:
trainData = dataUtilities.attributeSelectionData(trainData, [smilesAttr, trainData.domain.classVar.name])
log(logFile,"Selected attrs: "+str([attr.name for attr in trainData.domain]))
else:
trainData = dataUtilities.attributeDeselectionData(trainData, [smilesAttr])
log(logFile,"Selected attrs: "+str([attr.name for attr in trainData.domain[0:3]] + ["..."] +\
[attr.name for attr in trainData.domain[len(trainData.domain)-3:]]))
return learner(trainData)
def __createBBRCInputs(self):
if not self.data:
print "ERROR: Data must be loaded first!"
return None
if self.active not in self.data.domain.classVar.values:
print "ERROR: '" + str(self.active) + "' is not part of the class values!"
return None
smilesName = dataUtilities.getSMILESAttr(self.data)
for idx, ex in enumerate(self.data):
if ex.getclass().value == self.active:
activity = 1
else:
activity = 0
ID = idx + 1 # ID is the number of coumpound in self.data which is the number os the example (1 based!)
self.MyFminer.AddCompound(str(ex[smilesName].value), ID)
self.MyFminer.AddActivity(activity, ID)
def getMLStatistics(
trainData,
mlList=[ml for ml in MLMETHODS if AZOC.MLMETHODS[ml]["useByDefault"]],
savePath=None,
queueType="NoSGE",
verbose=0,
logFile=None,
callBack=None):
"""
Loop over all MLMETHODS to get their statistics
Write to disk the full MLStatistics including the consensus model:
Consensus model statistics will be calculated out of the a Consensus model based on MLmethods that are stable (beased on StabilityValue)
"""
log(logFile, "Running getMLStatistics...")
MLStatistics = {}
learners = {}
smilesAttr = dataUtilities.getSMILESAttr(trainData)
for ml in mlList:
learner = MLMETHODS[ml](name=ml)
if not learner.isCompatible(trainData.domain.classVar):
log(
logFile, "Ignored learner " + str(ml) +
" since it's not compatible with this class.")
continue
if learner.specialType == 1 and not smilesAttr:
log(
logFile, "Ignored learner " + str(ml) +
" since it's special and requires a SMILES attribute.")
continue
learners[ml] = learner
# Forced queueType to NoSGE so that appspack do not fload the cluster
evaluator = getUnbiasedAccuracy.UnbiasedAccuracyGetter(
data=trainData,
learner=learners,
paramList=None,
nExtFolds=AZOC.QSARNINNERFOLDS,
nInnerFolds=AZOC.QSARNCVFOLDS,
queueType="NoSGE",
verbose=verbose,
logFile=logFile,
resultsFile=savePath)
MLStatistics = evaluator.getAcc(callBack=callBack)
saveMLStatistics(savePath, MLStatistics, logFile)
return MLStatistics
def __createBBRCInputs(self):
if not self.data:
print "ERROR: Data must be loaded first!"
return None
if self.active and self.active not in self.data.domain.classVar.values:
print "ERROR: '" + str(
self.active) + "' is not part of the class values!"
return None
smilesName = dataUtilities.getSMILESAttr(self.data)
print "SMILES attr detected: ", smilesName
for idx, ex in enumerate(self.data):
if not self.active:
activity = 0 #It is unknown
elif ex.getclass().value == self.active:
activity = 1
else:
activity = 0
ID = idx + 1 # ID is the number of coumpound in self.data which is the number os the example (1 based!)
self.MyFminer.AddCompound(str(ex[smilesName].value), ID)
self.MyFminer.AddActivity(activity, ID)
def buildModel(trainData,
MLMethod,
queueType="NoSGE",
verbose=0,
logFile=None):
"""
Buld the method passed in MLMethod and optimize ( "IndividualStatistics" not in MLMethod)
if MLMethod is a Consensus ("individualStatistics" in MLMethod) , build each and optimize first all models and after build the consensus!
"""
log(logFile,
"Building and optimizing learner: " + MLMethod["MLMethod"] + "...")
learners = {}
MLMethods = {}
if "IndividualStatistics" in MLMethod: #It is a consensus and will certaily not contain any
#special model as it was filtered in the getUnbiasedAcc
for ML in MLMethod["IndividualStatistics"]:
MLMethods[ML] = copy.deepcopy(MLMethod["IndividualStatistics"][ML])
else:
ML = MLMethod["MLMethod"]
if MLMETHODS[ML](
name=ML
).specialType == 1: # If is a special model and has a built-in optimizaer
log(logFile, " This is a special model")
smilesAttr = dataUtilities.getSMILESAttr(trainData)
if smilesAttr:
log(logFile, "Found SMILES attribute:" + smilesAttr)
trainData = dataUtilities.attributeSelectionData(
trainData, [smilesAttr, trainData.domain.classVar.name])
optInfo, SpecialModel = MLMETHODS[ML](name=ML).optimizePars(
trainData, folds=5)
return SpecialModel
else:
MLMethods[MLMethod["MLMethod"]] = MLMethod
smilesAttr = dataUtilities.getSMILESAttr(trainData)
if smilesAttr:
trainData = dataUtilities.attributeDeselectionData(
trainData, [smilesAttr])
# optimize all MLMethods
for ML in MLMethods:
log(logFile, " Optimizing MLmethod: " + ML)
learners[ML] = MLMETHODS[ML](name=ML)
runPath = miscUtilities.createScratchDir(
baseDir=AZOC.NFS_SCRATCHDIR, desc="competitiveWorkflow_BuildModel")
trainData.save(os.path.join(runPath, "trainData.tab"))
tunedPars = paramOptUtilities.getOptParam(learner=learners[ML],
trainDataFile=os.path.join(
runPath,
"trainData.tab"),
useGrid=False,
verbose=verbose,
queueType=queueType,
runPath=runPath,
nExtFolds=None,
logFile=logFile,
getTunedPars=True)
if not learners[ML].optimized:
print "WARNING: competitiveWorkflow: The learner " + str(
learners[ML]) + " was not optimized."
#print " Using default parameters"
print " The " + str(learners[ML]) + " will not be included"
#print " Returning None"
print " DEBUG can be made in: " + runPath
#Setting default parameters
#learners[ML] = learners[ML].__class__()
#return None
learners.pop(ML)
continue
else:
print "Optimized learner ", learners[ML]
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
MLMethods[ML]["optAcc"] = tunedPars[0]
else:
res = orngTest.crossValidation(
[learners[ML]],
trainData,
folds=5,
strat=orange.MakeRandomIndices.StratifiedIfPossible,
randomGenerator=random.randint(0, 100))
R2 = evalUtilities.R2(res)[0]
MLMethods[ML]["optAcc"] = R2
miscUtilities.removeDir(runPath)
#Train the model
if len(learners) == 1:
log(logFile, " Building the model:" + learners.keys()[0])
model = learners[learners.keys()[0]](trainData)
elif len(learners) >= 1:
model = buildConsensus(trainData, learners, MLMethods)
else:
print "ERROR: No Learners were selected!"
return None
return model
def buildConsensus(trainData, learners, MLMethods, logFile=None):
log(
logFile, "Building a consensus model based on optimized MLmethods: " +
str([ml for ml in MLMethods]) + "...")
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
#Expression: If CAavg_{POS} ge CAavg_{NEG} -> POS else -> NEG
# where CAavg_{POS} is the average of classification accuracies of all models predicting POS.
CLASS0 = str(trainData.domain.classVar.values[0])
CLASS1 = str(trainData.domain.classVar.values[1])
#exprTest0
exprTest0 = "(0"
for ml in MLMethods:
exprTest0 += "+( " + ml + " == " + CLASS0 + " )*" + str(
MLMethods[ml]["optAcc"]) + " "
exprTest0 += ")/IF0(sum([False"
for ml in MLMethods:
exprTest0 += ", " + ml + " == " + CLASS0 + " "
exprTest0 += "]),1)"
# exprTest1
exprTest1 = "(0"
for ml in MLMethods:
exprTest1 += "+( " + ml + " == " + CLASS1 + " )*" + str(
MLMethods[ml]["optAcc"]) + " "
exprTest1 += ")/IF0(sum([False"
for ml in MLMethods:
exprTest1 += ", " + ml + " == " + CLASS1 + " "
exprTest1 += "]),1)"
# expression
expression = [
exprTest0 + " >= " + exprTest1 + " -> " + CLASS0, " -> " + CLASS1
]
else:
Q2sum = sum([MLMethods[ml]["optAcc"] for ml in MLMethods])
expression = "(1 / " + str(Q2sum) + ") * (0"
for ml in MLMethods:
expression += " + " + str(
MLMethods[ml]["optAcc"]) + " * " + ml + " "
expression += ")"
consensusLearners = {}
for learnerName in learners:
consensusLearners[learnerName] = learners[learnerName]
learner = AZorngConsensus.ConsensusLearner(learners=consensusLearners,
expression=expression)
log(logFile, " Training Consensus Learner")
smilesAttr = dataUtilities.getSMILESAttr(trainData)
if smilesAttr:
log(logFile, "Found SMILES attribute:" + smilesAttr)
if learner.specialType == 1:
trainData = dataUtilities.attributeSelectionData(
trainData, [smilesAttr, trainData.domain.classVar.name])
log(
logFile, "Selected attrs: " +
str([attr.name for attr in trainData.domain]))
else:
trainData = dataUtilities.attributeDeselectionData(
trainData, [smilesAttr])
log(logFile,"Selected attrs: "+str([attr.name for attr in trainData.domain[0:3]] + ["..."] +\
[attr.name for attr in trainData.domain[len(trainData.domain)-3:]]))
return learner(trainData)
def getAcc(self, callBack = None, callBackWithFoldModel = None):
""" For regression problems, it returns the RMSE and the Q2
For Classification problems, it returns CA and the ConfMat
The return is made in a Dict: {"RMSE":0.2,"Q2":0.1,"CA":0.98,"CM":[[TP, FP],[FN,TN]]}
For the EvalResults not supported for a specific learner/datase, the respective result will be None
if the learner is a dict {"LearnerName":learner, ...} the results will be a dict with results for all Learners and for a consensus
made out of those that were stable
It some error occurred, the respective values in the Dict will be None
"""
self.__log("Starting Calculating MLStatistics")
statistics = {}
if not self.__areInputsOK():
return None
# Set the response type
self.responseType = self.data.domain.classVar.varType == orange.VarTypes.Discrete and "Classification" or "Regression"
self.__log(" "+str(self.responseType))
#Create the Train and test sets
if self.usePreDefFolds:
DataIdxs = self.preDefIndices
else:
DataIdxs = self.sampler(self.data, self.nExtFolds)
foldsN = [f for f in dict.fromkeys(DataIdxs) if f != 0] #Folds used only from 1 on ... 0 are for fixed train Bias
nFolds = len(foldsN)
#Fix the Indexes based on DataIdxs
# (0s) represents the train set ( >= 1s) represents the test set folds
if self.useVarCtrlCV:
nShifted = [0] * nFolds
for idx,isTest in enumerate(self.preDefIndices): # self.preDefIndices == 0 are to be used in TrainBias
if not isTest:
if DataIdxs[idx]:
nShifted[DataIdxs[idx]] += 1
DataIdxs[idx] = 0
for idx,shift in enumerate(nShifted):
self.__log("In fold "+str(idx)+", "+str(shift)+" examples were shifted to the train set.")
#Var for saving each Fols result
optAcc = {}
results = {}
exp_pred = {}
nTrainEx = {}
nTestEx = {}
#Set a dict of learners
MLmethods = {}
if type(self.learner) == dict:
for ml in self.learner:
MLmethods[ml] = self.learner[ml]
else:
MLmethods[self.learner.name] = self.learner
models={}
self.__log("Calculating Statistics for MLmethods:")
self.__log(" "+str([x for x in MLmethods]))
#Check data in advance so that, by chance, it will not faill at the last fold!
for foldN in foldsN:
trainData = self.data.select(DataIdxs,foldN,negate=1)
self.__checkTrainData(trainData)
#Optional!!
# Order Learners so that PLS is the first
sortedML = [ml for ml in MLmethods]
if "PLS" in sortedML:
sortedML.remove("PLS")
sortedML.insert(0,"PLS")
stepsDone = 0
nTotalSteps = len(sortedML) * self.nExtFolds
for ml in sortedML:
startTime = time.time()
self.__log(" > "+str(ml)+"...")
try:
#Var for saving each Fols result
results[ml] = []
exp_pred[ml] = []
models[ml] = []
nTrainEx[ml] = []
nTestEx[ml] = []
optAcc[ml] = []
logTxt = ""
for foldN in foldsN:
if type(self.learner) == dict:
self.paramList = None
trainData = self.data.select(DataIdxs,foldN,negate=1)
testData = self.data.select(DataIdxs,foldN)
smilesAttr = dataUtilities.getSMILESAttr(trainData)
if smilesAttr:
self.__log("Found SMILES attribute:"+smilesAttr)
if MLmethods[ml].specialType == 1:
trainData = dataUtilities.attributeSelectionData(trainData, [smilesAttr, trainData.domain.classVar.name])
testData = dataUtilities.attributeSelectionData(testData, [smilesAttr, testData.domain.classVar.name])
self.__log("Selected attrs: "+str([attr.name for attr in trainData.domain]))
else:
trainData = dataUtilities.attributeDeselectionData(trainData, [smilesAttr])
testData = dataUtilities.attributeDeselectionData(testData, [smilesAttr])
self.__log("Selected attrs: "+str([attr.name for attr in trainData.domain[0:3]] + ["..."] + [attr.name for attr in trainData.domain[len(trainData.domain)-3:]]))
nTrainEx[ml].append(len(trainData))
nTestEx[ml].append(len(testData))
#Test if trainsets inside optimizer will respect dataSize criterias.
# if not, don't optimize, but still train the model
dontOptimize = False
if self.responseType != "Classification" and (len(trainData)*(1-1.0/self.nInnerFolds) < 20):
dontOptimize = True
else:
tmpDataIdxs = self.sampler(trainData, self.nInnerFolds)
tmpTrainData = trainData.select(tmpDataIdxs,1,negate=1)
if not self.__checkTrainData(tmpTrainData, False):
dontOptimize = True
SpecialModel = None
if dontOptimize:
logTxt += " Fold "+str(foldN)+": Too few compounds to optimize model hyper-parameters\n"
self.__log(logTxt)
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
res = evalUtilities.crossValidation([MLmethods[ml]], trainData, folds=5, stratified=orange.MakeRandomIndices.StratifiedIfPossible, random_generator = random.randint(0, 100))
CA = evalUtilities.CA(res)[0]
optAcc[ml].append(CA)
else:
res = evalUtilities.crossValidation([MLmethods[ml]], trainData, folds=5, stratified=orange.MakeRandomIndices.StratifiedIfPossible, random_generator = random.randint(0, 100))
R2 = evalUtilities.R2(res)[0]
optAcc[ml].append(R2)
else:
if MLmethods[ml].specialType == 1:
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
optInfo, SpecialModel = MLmethods[ml].optimizePars(trainData, folds = 5)
optAcc[ml].append(optInfo["Acc"])
else:
res = evalUtilities.crossValidation([MLmethods[ml]], trainData, folds=5, stratified=orange.MakeRandomIndices.StratifiedIfPossible, random_generator = random.randint(0, 100))
R2 = evalUtilities.R2(res)[0]
optAcc[ml].append(R2)
else:
runPath = miscUtilities.createScratchDir(baseDir = AZOC.NFS_SCRATCHDIR, desc = "AccWOptParam", seed = id(trainData))
trainData.save(os.path.join(runPath,"trainData.tab"))
tunedPars = paramOptUtilities.getOptParam(
learner = MLmethods[ml],
trainDataFile = os.path.join(runPath,"trainData.tab"),
paramList = self.paramList,
useGrid = False,
verbose = self.verbose,
queueType = self.queueType,
runPath = runPath,
nExtFolds = None,
nFolds = self.nInnerFolds,
logFile = self.logFile,
getTunedPars = True,
fixedParams = self.fixedParams)
if not MLmethods[ml] or not MLmethods[ml].optimized:
self.__log(" WARNING: GETACCWOPTPARAM: The learner "+str(ml)+" was not optimized.")
self.__log(" It will be ignored")
#self.__log(" It will be set to default parameters")
self.__log(" DEBUG can be done in: "+runPath)
#Set learner back to default
#MLmethods[ml] = MLmethods[ml].__class__()
raise Exception("The learner "+str(ml)+" was not optimized.")
else:
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
optAcc[ml].append(tunedPars[0])
else:
res = evalUtilities.crossValidation([MLmethods[ml]], trainData, folds=5, stratified=orange.MakeRandomIndices.StratifiedIfPossible, random_generator = random.randint(0, 100))
R2 = evalUtilities.R2(res)[0]
optAcc[ml].append(R2)
miscUtilities.removeDir(runPath)
#Train the model
if SpecialModel is not None:
model = SpecialModel
else:
model = MLmethods[ml](trainData)
models[ml].append(model)
#Test the model
if self.responseType == "Classification":
results[ml].append((evalUtilities.getClassificationAccuracy(testData, model), evalUtilities.getConfMat(testData, model) ) )
else:
local_exp_pred = []
# Predict using bulk-predict
predictions = model(testData)
# Gather predictions
for n,ex in enumerate(testData):
local_exp_pred.append((ex.getclass().value, predictions[n].value))
results[ml].append((evalUtilities.calcRMSE(local_exp_pred), evalUtilities.calcRsqrt(local_exp_pred) ) )
#Save the experimental value and correspondent predicted value
exp_pred[ml] += local_exp_pred
if callBack:
stepsDone += 1
if not callBack((100*stepsDone)/nTotalSteps): return None
if callBackWithFoldModel:
callBackWithFoldModel(model)
res = self.createStatObj(results[ml], exp_pred[ml], nTrainEx[ml], nTestEx[ml],self.responseType, self.nExtFolds, logTxt, labels = hasattr(self.data.domain.classVar,"values") and list(self.data.domain.classVar.values) or None )
if self.verbose > 0:
print "UnbiasedAccuracyGetter!Results "+ml+":\n"
pprint(res)
if not res:
raise Exception("No results available!")
res["runningTime"] = time.time() - startTime
statistics[ml] = copy.deepcopy(res)
self.__writeResults(statistics)
self.__log(" OK")
except:
self.__log(" Learner "+str(ml)+" failed to create/optimize the model!")
error = str(sys.exc_info()[0]) +" "+\
str(sys.exc_info()[1]) +" "+\
str(traceback.extract_tb(sys.exc_info()[2]))
self.__log(error)
res = self.createStatObj()
statistics[ml] = copy.deepcopy(res)
self.__writeResults(statistics)
if not statistics or len(statistics) < 1:
self.__log("ERROR: No statistics to return!")
return None
elif len(statistics) > 1:
#We still need to build a consensus model out of the stable models
# ONLY if there are more that one model stable!
# When only one or no stable models, build a consensus based on all models
# ALWAYS exclude specialType models (MLmethods[ml].specialType > 0)
consensusMLs={}
for modelName in statistics:
StabilityValue = statistics[modelName]["StabilityValue"]
if StabilityValue is not None and statistics[modelName]["stable"]:
consensusMLs[modelName] = copy.deepcopy(statistics[modelName])
self.__log("Found "+str(len(consensusMLs))+" stable MLmethods out of "+str(len(statistics))+" MLmethods.")
if len(consensusMLs) <= 1: # we need more models to build a consensus!
consensusMLs={}
for modelName in statistics:
consensusMLs[modelName] = copy.deepcopy(statistics[modelName])
# Exclude specialType models
excludeThis = []
for learnerName in consensusMLs:
if models[learnerName][0].specialType > 0:
excludeThis.append(learnerName)
for learnerName in excludeThis:
consensusMLs.pop(learnerName)
self.__log(" > Excluded special model " + learnerName)
self.__log(" > Stable modules: " + str(consensusMLs.keys()))
if len(consensusMLs) >= 2:
#Var for saving each Fols result
startTime = time.time()
Cresults = []
Cexp_pred = []
CnTrainEx = []
CnTestEx = []
self.__log("Calculating the statistics for a Consensus model based on "+str([ml for ml in consensusMLs]))
for foldN in range(self.nExtFolds):
if self.responseType == "Classification":
CLASS0 = str(self.data.domain.classVar.values[0])
CLASS1 = str(self.data.domain.classVar.values[1])
# exprTest0
exprTest0 = "(0"
for ml in consensusMLs:
exprTest0 += "+( "+ml+" == "+CLASS0+" )*"+str(optAcc[ml][foldN])+" "
exprTest0 += ")/IF0(sum([False"
for ml in consensusMLs:
exprTest0 += ", "+ml+" == "+CLASS0+" "
exprTest0 += "]),1)"
# exprTest1
exprTest1 = "(0"
for ml in consensusMLs:
exprTest1 += "+( "+ml+" == "+CLASS1+" )*"+str(optAcc[ml][foldN])+" "
exprTest1 += ")/IF0(sum([False"
for ml in consensusMLs:
exprTest1 += ", "+ml+" == "+CLASS1+" "
exprTest1 += "]),1)"
# Expression
expression = [exprTest0+" >= "+exprTest1+" -> "+CLASS0," -> "+CLASS1]
else:
Q2sum = sum([optAcc[ml][foldN] for ml in consensusMLs])
expression = "(1 / "+str(Q2sum)+") * (0"
for ml in consensusMLs:
expression += " + "+str(optAcc[ml][foldN])+" * "+ml+" "
expression += ")"
testData = self.data.select(DataIdxs,foldN+1) # fold 0 if for the train Bias!!
smilesAttr = dataUtilities.getSMILESAttr(testData)
if smilesAttr:
self.__log("Found SMILES attribute:"+smilesAttr)
testData = dataUtilities.attributeDeselectionData(testData, [smilesAttr])
self.__log("Selected attrs: "+str([attr.name for attr in trainData.domain[0:3]] + ["..."] + [attr.name for attr in trainData.domain[len(trainData.domain)-3:]]))
CnTestEx.append(len(testData))
consensusClassifiers = {}
for learnerName in consensusMLs:
consensusClassifiers[learnerName] = models[learnerName][foldN]
model = AZorngConsensus.ConsensusClassifier(classifiers = consensusClassifiers, expression = expression)
CnTrainEx.append(model.NTrainEx)
#Test the model
if self.responseType == "Classification":
Cresults.append((evalUtilities.getClassificationAccuracy(testData, model), evalUtilities.getConfMat(testData, model) ) )
else:
local_exp_pred = []
# Predict using bulk-predict
predictions = model(testData)
# Gather predictions
for n,ex in enumerate(testData):
local_exp_pred.append((ex.getclass().value, predictions[n].value))
Cresults.append((evalUtilities.calcRMSE(local_exp_pred), evalUtilities.calcRsqrt(local_exp_pred) ) )
#Save the experimental value and correspondent predicted value
Cexp_pred += local_exp_pred
res = self.createStatObj(Cresults, Cexp_pred, CnTrainEx, CnTestEx, self.responseType, self.nExtFolds, labels = hasattr(self.data.domain.classVar,"values") and list(self.data.domain.classVar.values) or None )
res["runningTime"] = time.time() - startTime
statistics["Consensus"] = copy.deepcopy(res)
statistics["Consensus"]["IndividualStatistics"] = copy.deepcopy(consensusMLs)
self.__writeResults(statistics)
self.__log("Returned multiple ML methods statistics.")
return statistics
#By default return the only existing statistics!
self.__writeResults(statistics)
self.__log("Returned only one ML method statistics.")
return statistics[statistics.keys()[0]]
def buildModel(trainData, MLMethod, queueType = "NoSGE", verbose = 0, logFile = None):
"""
Buld the method passed in MLMethod and optimize ( "IndividualStatistics" not in MLMethod)
if MLMethod is a Consensus ("individualStatistics" in MLMethod) , build each and optimize first all models and after build the consensus!
"""
log(logFile, "Building and optimizing learner: "+MLMethod["MLMethod"]+"...")
learners = {}
MLMethods = {}
if "IndividualStatistics" in MLMethod: #It is a consensus and will certaily not contain any
#special model as it was filtered in the getUnbiasedAcc
for ML in MLMethod["IndividualStatistics"]:
MLMethods[ML] = copy.deepcopy(MLMethod["IndividualStatistics"][ML])
else:
ML = MLMethod["MLMethod"]
if MLMETHODS[ML](name = ML).specialType == 1: # If is a special model and has a built-in optimizaer
log(logFile, " This is a special model")
smilesAttr = dataUtilities.getSMILESAttr(trainData)
if smilesAttr:
log(logFile,"Found SMILES attribute:"+smilesAttr)
trainData = dataUtilities.attributeSelectionData(trainData, [smilesAttr, trainData.domain.classVar.name])
optInfo, SpecialModel = MLMETHODS[ML](name = ML).optimizePars(trainData, folds = 5)
return SpecialModel
else:
MLMethods[MLMethod["MLMethod"]] = MLMethod
smilesAttr = dataUtilities.getSMILESAttr(trainData)
if smilesAttr:
trainData = dataUtilities.attributeDeselectionData(trainData, [smilesAttr])
# optimize all MLMethods
for ML in MLMethods:
log(logFile, " Optimizing MLmethod: "+ML)
learners[ML] = MLMETHODS[ML](name = ML)
runPath = miscUtilities.createScratchDir(baseDir = AZOC.NFS_SCRATCHDIR, desc = "competitiveWorkflow_BuildModel")
trainData.save(os.path.join(runPath,"trainData.tab"))
tunedPars = paramOptUtilities.getOptParam(
learner = learners[ML],
trainDataFile = os.path.join(runPath,"trainData.tab"),
useGrid = False,
verbose = verbose,
queueType = queueType,
runPath = runPath,
nExtFolds = None,
logFile = logFile,
getTunedPars = True)
if not learners[ML].optimized:
print "WARNING: competitiveWorkflow: The learner "+str(learners[ML])+" was not optimized."
#print " Using default parameters"
print " The "+str(learners[ML])+" will not be included"
#print " Returning None"
print " DEBUG can be made in: "+runPath
#Setting default parameters
#learners[ML] = learners[ML].__class__()
#return None
learners.pop(ML)
continue
else:
print "Optimized learner ",learners[ML]
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
MLMethods[ML]["optAcc"] = tunedPars[0]
else:
res = orngTest.crossValidation([learners[ML]], trainData, folds=5, strat=orange.MakeRandomIndices.StratifiedIfPossible, randomGenerator = random.randint(0, 100))
R2 = evalUtilities.R2(res)[0]
MLMethods[ML]["optAcc"] = R2
miscUtilities.removeDir(runPath)
#Train the model
if len(learners) == 1:
log(logFile, " Building the model:"+learners.keys()[0])
model = learners[learners.keys()[0]](trainData)
elif len(learners) >= 1:
model = buildConsensus(trainData,learners,MLMethods)
else:
print "ERROR: No Learners were selected!"
return None
return model
def getAcc(self, callBack=None, callBackWithFoldModel=None):
""" For regression problems, it returns the RMSE and the Q2
For Classification problems, it returns CA and the ConfMat
The return is made in a Dict: {"RMSE":0.2,"Q2":0.1,"CA":0.98,"CM":[[TP, FP],[FN,TN]]}
For the EvalResults not supported for a specific learner/datase, the respective result will be None
if the learner is a dict {"LearnerName":learner, ...} the results will be a dict with results for all Learners and for a consensus
made out of those that were stable
It some error occurred, the respective values in the Dict will be None
"""
self.__log("Starting Calculating MLStatistics")
statistics = {}
if not self.__areInputsOK():
return None
# Set the response type
self.responseType = self.data.domain.classVar.varType == orange.VarTypes.Discrete and "Classification" or "Regression"
self.__log(" " + str(self.responseType))
#Create the Train and test sets
if self.usePreDefFolds:
DataIdxs = self.preDefIndices
else:
DataIdxs = self.sampler(self.data, self.nExtFolds)
foldsN = [f for f in dict.fromkeys(DataIdxs) if f != 0
] #Folds used only from 1 on ... 0 are for fixed train Bias
nFolds = len(foldsN)
#Fix the Indexes based on DataIdxs
# (0s) represents the train set ( >= 1s) represents the test set folds
if self.useVarCtrlCV:
nShifted = [0] * nFolds
for idx, isTest in enumerate(
self.preDefIndices
): # self.preDefIndices == 0 are to be used in TrainBias
if not isTest:
if DataIdxs[idx]:
nShifted[DataIdxs[idx]] += 1
DataIdxs[idx] = 0
for idx, shift in enumerate(nShifted):
self.__log("In fold " + str(idx) + ", " + str(shift) +
" examples were shifted to the train set.")
#Var for saving each Fols result
optAcc = {}
results = {}
exp_pred = {}
nTrainEx = {}
nTestEx = {}
#Set a dict of learners
MLmethods = {}
if type(self.learner) == dict:
for ml in self.learner:
MLmethods[ml] = self.learner[ml]
else:
MLmethods[self.learner.name] = self.learner
models = {}
self.__log("Calculating Statistics for MLmethods:")
self.__log(" " + str([x for x in MLmethods]))
#Check data in advance so that, by chance, it will not faill at the last fold!
for foldN in foldsN:
trainData = self.data.select(DataIdxs, foldN, negate=1)
self.__checkTrainData(trainData)
#Optional!!
# Order Learners so that PLS is the first
sortedML = [ml for ml in MLmethods]
if "PLS" in sortedML:
sortedML.remove("PLS")
sortedML.insert(0, "PLS")
stepsDone = 0
nTotalSteps = len(sortedML) * self.nExtFolds
for ml in sortedML:
startTime = time.time()
self.__log(" > " + str(ml) + "...")
try:
#Var for saving each Fols result
results[ml] = []
exp_pred[ml] = []
models[ml] = []
nTrainEx[ml] = []
nTestEx[ml] = []
optAcc[ml] = []
logTxt = ""
for foldN in foldsN:
if type(self.learner) == dict:
self.paramList = None
trainData = self.data.select(DataIdxs, foldN, negate=1)
testData = self.data.select(DataIdxs, foldN)
smilesAttr = dataUtilities.getSMILESAttr(trainData)
if smilesAttr:
self.__log("Found SMILES attribute:" + smilesAttr)
if MLmethods[ml].specialType == 1:
trainData = dataUtilities.attributeSelectionData(
trainData,
[smilesAttr, trainData.domain.classVar.name])
testData = dataUtilities.attributeSelectionData(
testData,
[smilesAttr, testData.domain.classVar.name])
self.__log(
"Selected attrs: " +
str([attr.name for attr in trainData.domain]))
else:
trainData = dataUtilities.attributeDeselectionData(
trainData, [smilesAttr])
testData = dataUtilities.attributeDeselectionData(
testData, [smilesAttr])
self.__log("Selected attrs: " + str(
[attr.name for attr in trainData.domain[0:3]] +
["..."] + [
attr.name for attr in trainData.
domain[len(trainData.domain) - 3:]
]))
nTrainEx[ml].append(len(trainData))
nTestEx[ml].append(len(testData))
#Test if trainsets inside optimizer will respect dataSize criterias.
# if not, don't optimize, but still train the model
dontOptimize = False
if self.responseType != "Classification" and (
len(trainData) *
(1 - 1.0 / self.nInnerFolds) < 20):
dontOptimize = True
else:
tmpDataIdxs = self.sampler(trainData, self.nInnerFolds)
tmpTrainData = trainData.select(tmpDataIdxs,
1,
negate=1)
if not self.__checkTrainData(tmpTrainData, False):
dontOptimize = True
SpecialModel = None
if dontOptimize:
logTxt += " Fold " + str(
foldN
) + ": Too few compounds to optimize model hyper-parameters\n"
self.__log(logTxt)
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
res = evalUtilities.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
stratified=orange.MakeRandomIndices.
StratifiedIfPossible,
random_generator=random.randint(0, 100))
CA = evalUtilities.CA(res)[0]
optAcc[ml].append(CA)
else:
res = evalUtilities.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
stratified=orange.MakeRandomIndices.
StratifiedIfPossible,
random_generator=random.randint(0, 100))
R2 = evalUtilities.R2(res)[0]
optAcc[ml].append(R2)
else:
if MLmethods[ml].specialType == 1:
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
optInfo, SpecialModel = MLmethods[
ml].optimizePars(trainData, folds=5)
optAcc[ml].append(optInfo["Acc"])
else:
res = evalUtilities.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
stratified=orange.MakeRandomIndices.
StratifiedIfPossible,
random_generator=random.randint(0, 100))
R2 = evalUtilities.R2(res)[0]
optAcc[ml].append(R2)
else:
runPath = miscUtilities.createScratchDir(
baseDir=AZOC.NFS_SCRATCHDIR,
desc="AccWOptParam",
seed=id(trainData))
trainData.save(
os.path.join(runPath, "trainData.tab"))
tunedPars = paramOptUtilities.getOptParam(
learner=MLmethods[ml],
trainDataFile=os.path.join(
runPath, "trainData.tab"),
paramList=self.paramList,
useGrid=False,
verbose=self.verbose,
queueType=self.queueType,
runPath=runPath,
nExtFolds=None,
nFolds=self.nInnerFolds,
logFile=self.logFile,
getTunedPars=True,
fixedParams=self.fixedParams)
if not MLmethods[ml] or not MLmethods[ml].optimized:
self.__log(
" WARNING: GETACCWOPTPARAM: The learner "
+ str(ml) + " was not optimized.")
self.__log(
" It will be ignored")
#self.__log(" It will be set to default parameters")
self.__log(
" DEBUG can be done in: "
+ runPath)
#Set learner back to default
#MLmethods[ml] = MLmethods[ml].__class__()
raise Exception("The learner " + str(ml) +
" was not optimized.")
else:
if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
optAcc[ml].append(tunedPars[0])
else:
res = evalUtilities.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
stratified=orange.MakeRandomIndices.
StratifiedIfPossible,
random_generator=random.randint(
0, 100))
R2 = evalUtilities.R2(res)[0]
optAcc[ml].append(R2)
miscUtilities.removeDir(runPath)
#Train the model
if SpecialModel is not None:
model = SpecialModel
else:
model = MLmethods[ml](trainData)
models[ml].append(model)
#Test the model
if self.responseType == "Classification":
results[ml].append(
(evalUtilities.getClassificationAccuracy(
testData, model),
evalUtilities.getConfMat(testData, model)))
else:
local_exp_pred = []
# Predict using bulk-predict
predictions = model(testData)
# Gather predictions
for n, ex in enumerate(testData):
local_exp_pred.append(
(ex.getclass().value, predictions[n].value))
results[ml].append(
(evalUtilities.calcRMSE(local_exp_pred),
evalUtilities.calcRsqrt(local_exp_pred)))
#Save the experimental value and correspondent predicted value
exp_pred[ml] += local_exp_pred
if callBack:
stepsDone += 1
if not callBack((100 * stepsDone) / nTotalSteps):
return None
if callBackWithFoldModel:
callBackWithFoldModel(model)
res = self.createStatObj(
results[ml],
exp_pred[ml],
nTrainEx[ml],
nTestEx[ml],
self.responseType,
self.nExtFolds,
logTxt,
labels=hasattr(self.data.domain.classVar, "values")
and list(self.data.domain.classVar.values) or None)
if self.verbose > 0:
print "UnbiasedAccuracyGetter!Results " + ml + ":\n"
pprint(res)
if not res:
raise Exception("No results available!")
res["runningTime"] = time.time() - startTime
statistics[ml] = copy.deepcopy(res)
self.__writeResults(statistics)
self.__log(" OK")
except:
self.__log(" Learner " + str(ml) +
" failed to create/optimize the model!")
error = str(sys.exc_info()[0]) +" "+\
str(sys.exc_info()[1]) +" "+\
str(traceback.extract_tb(sys.exc_info()[2]))
self.__log(error)
res = self.createStatObj()
statistics[ml] = copy.deepcopy(res)
self.__writeResults(statistics)
if not statistics or len(statistics) < 1:
self.__log("ERROR: No statistics to return!")
return None
elif len(statistics) > 1:
#We still need to build a consensus model out of the stable models
# ONLY if there are more that one model stable!
# When only one or no stable models, build a consensus based on all models
# ALWAYS exclude specialType models (MLmethods[ml].specialType > 0)
consensusMLs = {}
for modelName in statistics:
StabilityValue = statistics[modelName]["StabilityValue"]
if StabilityValue is not None and statistics[modelName][
"stable"]:
consensusMLs[modelName] = copy.deepcopy(
statistics[modelName])
self.__log("Found " + str(len(consensusMLs)) +
" stable MLmethods out of " + str(len(statistics)) +
" MLmethods.")
if len(consensusMLs
) <= 1: # we need more models to build a consensus!
consensusMLs = {}
for modelName in statistics:
consensusMLs[modelName] = copy.deepcopy(
statistics[modelName])
# Exclude specialType models
excludeThis = []
for learnerName in consensusMLs:
if models[learnerName][0].specialType > 0:
excludeThis.append(learnerName)
for learnerName in excludeThis:
consensusMLs.pop(learnerName)
self.__log(" > Excluded special model " + learnerName)
self.__log(" > Stable modules: " + str(consensusMLs.keys()))
if len(consensusMLs) >= 2:
#Var for saving each Fols result
startTime = time.time()
Cresults = []
Cexp_pred = []
CnTrainEx = []
CnTestEx = []
self.__log(
"Calculating the statistics for a Consensus model based on "
+ str([ml for ml in consensusMLs]))
for foldN in range(self.nExtFolds):
if self.responseType == "Classification":
CLASS0 = str(self.data.domain.classVar.values[0])
CLASS1 = str(self.data.domain.classVar.values[1])
# exprTest0
exprTest0 = "(0"
for ml in consensusMLs:
exprTest0 += "+( " + ml + " == " + CLASS0 + " )*" + str(
optAcc[ml][foldN]) + " "
exprTest0 += ")/IF0(sum([False"
for ml in consensusMLs:
exprTest0 += ", " + ml + " == " + CLASS0 + " "
exprTest0 += "]),1)"
# exprTest1
exprTest1 = "(0"
for ml in consensusMLs:
exprTest1 += "+( " + ml + " == " + CLASS1 + " )*" + str(
optAcc[ml][foldN]) + " "
exprTest1 += ")/IF0(sum([False"
for ml in consensusMLs:
exprTest1 += ", " + ml + " == " + CLASS1 + " "
exprTest1 += "]),1)"
# Expression
expression = [
exprTest0 + " >= " + exprTest1 + " -> " + CLASS0,
" -> " + CLASS1
]
else:
Q2sum = sum([optAcc[ml][foldN] for ml in consensusMLs])
expression = "(1 / " + str(Q2sum) + ") * (0"
for ml in consensusMLs:
expression += " + " + str(
optAcc[ml][foldN]) + " * " + ml + " "
expression += ")"
testData = self.data.select(
DataIdxs, foldN + 1) # fold 0 if for the train Bias!!
smilesAttr = dataUtilities.getSMILESAttr(testData)
if smilesAttr:
self.__log("Found SMILES attribute:" + smilesAttr)
testData = dataUtilities.attributeDeselectionData(
testData, [smilesAttr])
self.__log("Selected attrs: " + str(
[attr.name
for attr in trainData.domain[0:3]] + ["..."] + [
attr.name for attr in
trainData.domain[len(trainData.domain) - 3:]
]))
CnTestEx.append(len(testData))
consensusClassifiers = {}
for learnerName in consensusMLs:
consensusClassifiers[learnerName] = models[
learnerName][foldN]
model = AZorngConsensus.ConsensusClassifier(
classifiers=consensusClassifiers,
expression=expression)
CnTrainEx.append(model.NTrainEx)
#Test the model
if self.responseType == "Classification":
Cresults.append(
(evalUtilities.getClassificationAccuracy(
testData, model),
evalUtilities.getConfMat(testData, model)))
else:
local_exp_pred = []
# Predict using bulk-predict
predictions = model(testData)
# Gather predictions
for n, ex in enumerate(testData):
local_exp_pred.append(
(ex.getclass().value, predictions[n].value))
Cresults.append(
(evalUtilities.calcRMSE(local_exp_pred),
evalUtilities.calcRsqrt(local_exp_pred)))
#Save the experimental value and correspondent predicted value
Cexp_pred += local_exp_pred
res = self.createStatObj(
Cresults,
Cexp_pred,
CnTrainEx,
CnTestEx,
self.responseType,
self.nExtFolds,
labels=hasattr(self.data.domain.classVar, "values")
and list(self.data.domain.classVar.values) or None)
res["runningTime"] = time.time() - startTime
statistics["Consensus"] = copy.deepcopy(res)
statistics["Consensus"][
"IndividualStatistics"] = copy.deepcopy(consensusMLs)
self.__writeResults(statistics)
self.__log("Returned multiple ML methods statistics.")
return statistics
#By default return the only existing statistics!
self.__writeResults(statistics)
self.__log("Returned only one ML method statistics.")
return statistics[statistics.keys()[0]]
