def createStatObj(self, results=None, exp_pred=None, responseType=None, nExtFolds=None):
#Initialize res (statObj) for statistic results
res = {}
# Classification
res["CA"] = None
res["CM"] = None
res["MCC"] = None
#Regression
res["R2"] = None
res["RMSE"] = None
#Both
res["StabilityValue"] = None
res["foldStat"] = {
#Regression
"R2" : None,
"RMSE" : None,
#Classification
"CM" : None,
"CA" : None,
"MCC" : None }
if results is None or exp_pred is None or responseType is None or nExtFolds is None:
return res
#Calculate the (R2, RMSE) or (CM, CA) results depending on Classification or regression
if responseType == "Classification":
#Compute CA
res["CA"] = sum(r[0] for r in results) / self.nExtFolds
#Compute CM
res["CM"] = results[0][1] # Get the first ConfMat
for r in results[1:]:
for Lidx,line in enumerate(r[1]):
for idx,val in enumerate(line):
res["CM"][Lidx][idx] = res["CM"][Lidx][idx] + val #Add each same ConfMat position
#Compute MCC
res["MCC"] = evalUtilities.calcMCC(res["CM"])
#Compute foldStat
res["foldStat"]["CA"] = [r[0] for r in results]
res["foldStat"]["CM"] = [r[1] for r in results]
res["foldStat"]["MCC"] = [evalUtilities.calcMCC(r[1]) for r in results]
#Compute Stability
res["StabilityValue"] = evalUtilities.stability(res["foldStat"]["CA"])
else:
#compute R2
res["R2"] = evalUtilities.calcRsqrt(exp_pred)
#compute RMSE
res["RMSE"] = evalUtilities.calcRMSE(exp_pred)
#Compute foldStat
res["foldStat"]["RMSE"] = [r[0] for r in results]
res["foldStat"]["R2"] = [r[1] for r in results]
#Compute Stability
res["StabilityValue"] = evalUtilities.stability(res["foldStat"]["R2"])
return res
def getProbabilitiesAsAttribute(self, algorithm=None, minsup=None, atts=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
parameters:
algo - key for the structural feature generation algorithm (set dependent structural features that have to be calculated inside the crossvalidation)
minsup - minimum support for the algorithm
atts - attributes to be removed before learning (e.g. meta etc...)
"""
self.__log("Starting Calculating MLStatistics")
statistics = {}
if not self.__areInputsOK():
return None
if algorithm:
self.__log(" Additional features to be calculated inside of cross-validation")
self.__log(" Algorithm for structural features: " + str(algorithm))
self.__log(" Minimum support parameter: " + str(minsup))
# 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
DataIdxs = dataUtilities.SeedDataSampler(self.data, self.nExtFolds)
# 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 = {}
rocs = {}
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 range(self.nExtFolds):
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")
for ml in sortedML:
self.__log(" > " + str(ml) + "...")
try:
# Var for saving each Fols result
results[ml] = []
exp_pred[ml] = []
models[ml] = []
rocs[ml] = []
nTrainEx[ml] = []
nTestEx[ml] = []
optAcc[ml] = []
### mods TG
prediction_attribute = orange.FloatVariable("class_prob")
domain = [data.domain.attributes, prediction_attribute, data.domain.classvar]
data_new = orange.ExampleTable(domain)
logTxt = ""
for foldN in range(self.nExtFolds):
if type(self.learner) == dict:
self.paramList = None
trainData = self.data.select(DataIdxs[foldN], negate=1)
orig_len = len(trainData.domain.attributes)
# add structural descriptors to the training data (TG)
if algorithm:
trainData_structDesc = getStructuralDesc.getStructuralDescResult(trainData, algorithm, minsup)
trainData = dataUtilities.attributeDeselectionData(trainData_structDesc, atts)
testData = self.data.select(DataIdxs[foldN])
# print "IDX: ",
# print DataIdxs[foldN]
# calculate the feature values for the test data (TG)
if algorithm:
cut_off = orig_len - len(atts)
smarts = trainData.domain.attributes[cut_off:]
self.__log(" Number of structural features added: " + str(len(smarts)))
testData_structDesc = getStructuralDesc.getSMARTSrecalcDesc(testData, smarts)
testData = dataUtilities.attributeDeselectionData(testData_structDesc, atts)
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 = dataUtilities.SeedDataSampler(trainData, self.nInnerFolds)
tmpTrainData = trainData.select(tmpDataIdxs[0], negate=1)
if not self.__checkTrainData(tmpTrainData, False):
dontOptimize = True
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 = orngTest.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
strat=orange.MakeRandomIndices.StratifiedIfPossible,
randomGenerator=random.randint(0, 100),
)
CA = evalUtilities.CA(res)[0]
optAcc[ml].append(CA)
else:
res = orngTest.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
strat=orange.MakeRandomIndices.StratifiedIfPossible,
randomGenerator=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,
)
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 = orngTest.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
strat=orange.MakeRandomIndices.StratifiedIfPossible,
randomGenerator=random.randint(0, 100),
)
R2 = evalUtilities.R2(res)[0]
optAcc[ml].append(R2)
miscUtilities.removeDir(runPath)
# Train the model
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),
)
)
roc = self.aroc(testData, [model])
rocs[ml].append(roc)
# save the prediction probabilities
else:
local_exp_pred = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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
res = self.createStatObj(
results[ml],
exp_pred[ml],
nTrainEx[ml],
nTestEx[ml],
self.responseType,
self.nExtFolds,
logTxt,
rocs[ml],
)
if self.verbose > 0:
print "UnbiasedAccuracyGetter!Results " + ml + ":\n"
pprint(res)
if not res:
raise Exception("No results available!")
statistics[ml] = copy.deepcopy(res)
self.__writeResults(statistics)
self.__log(" OK")
except:
self.__log(" Learner " + str(ml) + " failed to create/optimize the model!")
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
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])
if len(consensusMLs) >= 2:
# Var for saving each Fols result
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 = "(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 = exprTest0.replace(CLASS0, CLASS1)
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])
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 = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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)
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 getAcc(self, callBack=None, algorithm=None, params=None, atts=None, holdout=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
parameters:
algorithm - list of feature generation algorithms (set dependent features that have to be calculated inside the crossvalidation)
params - dictionary of parameters
atts - attributes to be removed before learning (e.g. meta etc...)
"""
self.__log("Starting Calculating MLStatistics")
statistics = {}
if not self.__areInputsOK():
return None
if holdout:
self.nExtFolds = 1
if algorithm:
self.__log(" Additional features to be calculated inside of cross-validation")
for i in algorithm:
self.__log(" Algorithm: " + str(i))
for j, v in params.iteritems():
self.__log(" Parameter: " + str(j) + " = " + str(v))
# 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
DataIdxs = None
if holdout:
self.__log("Using hold out evaluation with " + str(holdout) + "*100 % of data for training")
DataIdxs = dataUtilities.SeedDataSampler_holdOut(self.data, holdout)
else:
DataIdxs = dataUtilities.SeedDataSampler(self.data, self.nExtFolds)
# 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 = {}
rocs = {}
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 fail at the last fold!
for foldN in range(self.nExtFolds):
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:
self.__log(" > " + str(ml) + "...")
try:
# Var for saving each Fols result
results[ml] = []
exp_pred[ml] = []
models[ml] = []
rocs[ml] = []
nTrainEx[ml] = []
nTestEx[ml] = []
optAcc[ml] = []
logTxt = ""
for foldN in range(self.nExtFolds):
if type(self.learner) == dict:
self.paramList = None
trainData = self.data.select(DataIdxs[foldN], negate=1)
orig_len = len(trainData.domain.attributes)
refs = None
methods = [
"rdk_MACCS_keys",
"rdk_topo_fps",
"rdk_morgan_fps",
"rdk_morgan_features_fps",
"rdk_atompair_fps",
]
train_domain = None
# add structural descriptors to the training data (TG)
if algorithm:
for i in range(len(algorithm)):
if algorithm[i] == "structClust":
self.__log("Algorithm " + str(i) + ": " + str(algorithm[i]))
actData = orange.ExampleTable(trainData.domain)
for d in trainData:
# only valid for simboosted qsar paper experiments!?
if d.getclass() == "2":
actData.append(d)
refs = structuralClustering.getReferenceStructures(
actData,
threshold=params["threshold"],
minClusterSize=params["minClusterSize"],
numThreads=2,
)
self.__log(
" found "
+ str(len(refs))
+ " reference structures in "
+ str(len(actData))
+ " active structures"
)
orig_len = orig_len + (len(refs) * len(methods))
trainData_sim = SimBoostedQSAR.getSimDescriptors(refs, trainData, methods)
if i == (len(algorithm) - 1):
trainData = dataUtilities.attributeDeselectionData(trainData_sim, atts)
else:
trainData = dataUtilities.attributeDeselectionData(trainData_sim, [])
elif algorithm[i] == "ECFP":
self.__log("Algorithm " + str(i) + ": " + str(algorithm[i]))
trainData_ecfp = getCinfonyDesc.getCinfonyDescResults(trainData, ["rdk.FingerPrints"])
train_domain = trainData_ecfp.domain
if i == (len(algorithm) - 1):
trainData = dataUtilities.attributeDeselectionData(trainData_ecfp, atts)
else:
trainData = dataUtilities.attributeDeselectionData(trainData_ecfp, [])
else:
self.__log("Algorithm " + str(i) + ": " + str(algorithm[i]))
trainData_structDesc = getStructuralDesc.getStructuralDescResult(
trainData, algorithm[i], params["minsup"]
)
if i == (len(algorithm) - 1):
trainData = dataUtilities.attributeDeselectionData(trainData_structDesc, atts)
else:
trainData = dataUtilities.attributeDeselectionData(trainData_structDesc, [])
# trainData.save("/home/girschic/proj/AZ/ProjDev/train.tab")
testData = self.data.select(DataIdxs[foldN])
# calculate the feature values for the test data (TG)
if algorithm:
for i in range(len(algorithm)):
if algorithm[i] == "structClust":
self.__log(str(algorithm[i]))
testData_sim = SimBoostedQSAR.getSimDescriptors(refs, testData, methods)
if i == (len(algorithm) - 1):
testData = dataUtilities.attributeDeselectionData(testData_sim, atts)
else:
testData = dataUtilities.attributeDeselectionData(testData_sim, [])
elif algorithm[i] == "ECFP":
self.__log(str(algorithm[i]))
# testData_ecfp = orange.ExampleTable(train_domain)
tmp_dat = []
for d in testData:
tmp = getCinfonyDesc.getRdkFPforTestInstance(train_domain, d)
tmp_dat.append(tmp)
testData_ecfp = orange.ExampleTable(tmp_dat[0].domain, tmp_dat)
if i == (len(algorithm) - 1):
# print "removing atts"
testData = dataUtilities.attributeDeselectionData(testData_ecfp, atts)
else:
# print "removing no atts"
testData = dataUtilities.attributeDeselectionData(testData_ecfp, [])
else:
cut_off = orig_len - len(atts)
smarts = trainData.domain.attributes[cut_off:]
self.__log(" Number of structural features added: " + str(len(smarts)))
testData_structDesc = getStructuralDesc.getSMARTSrecalcDesc(testData, smarts)
if i == (len(algorithm) - 1):
testData = dataUtilities.attributeDeselectionData(testData_structDesc, atts)
else:
testData = dataUtilities.attributeDeselectionData(testData_structDesc, [])
# testData.save("/home/girschic/proj/AZ/ProjDev/test.tab")
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 = dataUtilities.SeedDataSampler(trainData, self.nInnerFolds)
tmpTrainData = trainData.select(tmpDataIdxs[0], negate=1)
if not self.__checkTrainData(tmpTrainData, False):
dontOptimize = True
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 = orngTest.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
strat=orange.MakeRandomIndices.StratifiedIfPossible,
randomGenerator=random.randint(0, 100),
)
CA = evalUtilities.CA(res)[0]
optAcc[ml].append(CA)
else:
res = orngTest.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
strat=orange.MakeRandomIndices.StratifiedIfPossible,
randomGenerator=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)
)
# self.__log(" run path:"+str(runPath))
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,
)
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 = orngTest.crossValidation(
[MLmethods[ml]],
trainData,
folds=5,
strat=orange.MakeRandomIndices.StratifiedIfPossible,
randomGenerator=random.randint(0, 100),
)
R2 = evalUtilities.R2(res)[0]
optAcc[ml].append(R2)
miscUtilities.removeDir(runPath)
# Train the model
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),
)
)
roc = self.aroc(testData, [model])
rocs[ml].append(roc)
else:
local_exp_pred = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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
res = self.createStatObj(
results[ml],
exp_pred[ml],
nTrainEx[ml],
nTestEx[ml],
self.responseType,
self.nExtFolds,
logTxt,
rocs[ml],
)
if self.verbose > 0:
print "UnbiasedAccuracyGetter!Results " + ml + ":\n"
pprint(res)
if not res:
raise Exception("No results available!")
statistics[ml] = copy.deepcopy(res)
self.__writeResults(statistics)
self.__log(" OK")
except:
print "Unexpected error:",
print sys.exc_info()[0]
print sys.exc_info()[1]
self.__log(" Learner " + str(ml) + " failed to create/optimize the model!")
res = self.createStatObj(
results[ml],
exp_pred[ml],
nTrainEx[ml],
nTestEx[ml],
self.responseType,
self.nExtFolds,
logTxt,
rocs[ml],
)
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
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])
if len(consensusMLs) >= 2:
# Var for saving each Fols result
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 = "(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 = exprTest0.replace(CLASS0, CLASS1)
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])
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 = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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)
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 createStatObj(
self,
results=None,
exp_pred=None,
nTrainCmpds=None,
nTestCmpds=None,
responseType=None,
nExtFolds=None,
userAlert="",
rocs=None,
):
# Initialize res (statObj) for statistic results
res = {}
self.__log("Starting to create Stat Obj")
# Classification
res["CA"] = None
res["CM"] = None
res["MCC"] = None
res["ROC"] = None
# Regression
res["Q2"] = None
res["RMSE"] = None
# Both
res["StabilityValue"] = None
res["userAlert"] = userAlert
res["selected"] = False
res["stable"] = False
res["responseType"] = False
res["foldStat"] = {
"nTrainCmpds": None,
"nTestCmpds": None,
# Regression
"Q2": None,
"RMSE": None,
# Classification
"CM": None,
"CA": None,
"MCC": None,
"ROC": None,
}
if (
results is None
): # or exp_pred is None or responseType is None or nExtFolds is None or nTestCmpds is None or nTrainCmpds is None:
self.__log(" NONE...")
return res
res["responseType"] = responseType
# Calculate the (Q2, RMSE) or (CM, CA) results depending on Classification or regression
if responseType == "Classification":
# Compute CA
res["CA"] = sum(r[0] for r in results) / nExtFolds
# Compute CM
res["CM"] = copy.deepcopy(results[0][1]) # Get the first ConfMat
for r in results[1:]:
for Lidx, line in enumerate(r[1]):
for idx, val in enumerate(line):
res["CM"][Lidx][idx] = res["CM"][Lidx][idx] + val # Add each same ConfMat position
# Compute MCC
res["MCC"] = evalUtilities.calcMCC(res["CM"])
# Compute ROC
res["ROC"] = sum(ro[0] for ro in rocs) / self.nExtFolds
# Compute foldStat
res["foldStat"]["nTrainCmpds"] = [n for n in nTrainCmpds]
res["foldStat"]["nTestCmpds"] = [n for n in nTestCmpds]
res["foldStat"]["CA"] = [r[0] for r in results]
res["foldStat"]["CM"] = [r[1] for r in results]
res["foldStat"]["MCC"] = [evalUtilities.calcMCC(r[1]) for r in results]
res["foldStat"]["ROC"] = [ro for ro in rocs]
# Compute Stability
res["StabilityValue"] = evalUtilities.stability(res["foldStat"]["CA"])
else:
# compute Q2
res["Q2"] = evalUtilities.calcRsqrt(exp_pred)
# compute RMSE
res["RMSE"] = evalUtilities.calcRMSE(exp_pred)
# Compute foldStat
res["foldStat"]["nTrainCmpds"] = [n for n in nTrainCmpds]
res["foldStat"]["nTestCmpds"] = [n for n in nTestCmpds]
res["foldStat"]["RMSE"] = [r[0] for r in results]
res["foldStat"]["Q2"] = [r[1] for r in results]
# Compute Stability value
res["StabilityValue"] = evalUtilities.stability(res["foldStat"]["Q2"])
# Evaluate stability of ML
StabilityValue = res["StabilityValue"]
if StabilityValue is not None:
if responseType == "Classification":
if statc.mean(res["foldStat"]["nTestCmpds"]) > 50:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_CLASS_L
else:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_CLASS_H
else:
if statc.mean(res["foldStat"]["nTestCmpds"]) > 50:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_REG_L
else:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_REG_H
if StabilityValue < stableTH: # Select only stable models
res["stable"] = True
return res
def getStatistics(
dataset,
runningDir,
resultsFile,
mlList=[ml for ml in MLMETHODS if AZOC.MLMETHODS[ml]["useByDefault"]],
queueType="NoSGE",
verbose=0,
getAllModels=False,
callBack=None):
"""
runningDir (An existing dir for creating one job dir per fold)
|
+---- status (The overall status: "started", "finished" or the progress "1/10", "2/10", ...)
|
+---- fold_1
|
+---- fold_2
|
.
.
.
The running will be monitorized by this method.
Whenever a MLMethod fails the respective fold job is restarted
"""
if dataset.domain.classVar.varType == orange.VarTypes.Discrete:
responseType = "Classification"
else:
responseType = "Regression"
#Create the Train and test sets
DataIdxs = dataUtilities.SeedDataSampler(dataset, AZOC.QSARNEXTFOLDS)
#Check data in advance so that, by chance, it will not faill at the last fold!
#for foldN in range(AZOC.QSARNEXTFOLDS):
#trainData = dataset.select(DataIdxs,foldN,negate=1)
#checkTrainData(trainData)
jobs = {}
thisDir = os.getcwd()
os.chdir(runningDir)
#PID = os.getpid()
#print "Started getStatistics in Process with PID: "+str(PID)
#os.system('echo "'+str(PID)+'" > '+os.path.join(runningDir,"PID"))
os.system('echo "started" > ' + os.path.join(runningDir, "status"))
# Start all Fold jobs
stepsDone = 0
nTotalSteps = AZOC.QSARNEXTFOLDS
for fold in range(AZOC.QSARNEXTFOLDS):
job = str(fold)
print "Starting job for fold ", job
trainData = dataset.select(DataIdxs, fold, negate=1)
jobs[job] = {
"job": job,
"path": os.path.join(runningDir, "fold_" + job),
"running": False,
"failed": False,
"finished": False
}
# Uncomment next 3 lines for running in finished jobs dirs
#st, jID = commands.getstatusoutput("cat "+os.path.join(runningDir, "fold_"+job,"jID"))
#jobs[job]["jID"] = jID
#continue
os.system("rm -rf " + jobs[job]["path"])
os.system("mkdir -p " + jobs[job]["path"])
trainData.save(os.path.join(jobs[job]["path"], "trainData.tab"))
file_h = open(os.path.join(jobs[job]["path"], "run.sh"), "w")
file_h.write("#!/bin/tcsh\n")
file_h.write(
"source " +
os.path.join(os.environ["AZORANGEHOME"], "templateProfile") + "\n")
file_h.write("python " +
os.path.join(jobs[job]["path"], "QsubScript.py") + "\n")
file_h.close()
file_h = open(os.path.join(jobs[job]["path"], "QsubScript.py"), "w")
file_h.write("import os\n")
file_h.write("from AZutilities import dataUtilities\n")
file_h.write("from AZutilities import competitiveWorkflow\n")
file_h.write("data = dataUtilities.DataTable('" +
os.path.join(jobs[job]["path"], "trainData.tab") + "')\n")
file_h.write('os.system(\'echo "running" > ' +
os.path.join(jobs[job]["path"], "status") + ' \')\n')
file_h.write("models = competitiveWorkflow.getModel(data, mlList=" +
str(mlList) + ", savePath = '" +
os.path.join(jobs[job]["path"], "results.pkl") +
"', queueType = '" + queueType + "', getAllModels = " +
str(getAllModels) + ")\n")
file_h.write("nModelsSaved = 0\n")
file_h.write("for model in models:\n")
file_h.write(" if not models[model] is None:\n")
file_h.write(" models[model].write('" +
os.path.join(jobs[job]["path"], "model") +
"'+'_'+model)\n")
file_h.write(' nModelsSaved += 1\n')
file_h.write(
'if nModelsSaved == len([m for m in models if not models[m] is None ]):\n'
)
file_h.write(' os.system(\'echo "finished" > ' +
os.path.join(jobs[job]["path"], "status") + ' \')\n')
file_h.write('else:\n')
file_h.write(' os.system(\'echo "failed" > ' +
os.path.join(jobs[job]["path"], "status") + ' \')\n')
file_h.close()
os.chdir(os.path.join(jobs[job]["path"]))
if queueType == "NoSGE": # Serial mode
status, out = commands.getstatusoutput(
"tcsh " + os.path.join(jobs[job]["path"], "run.sh"))
if status:
print "ERROR on Job " + str(
job) + " (will be restarted latter)"
print out
else:
statusFile = os.path.join(jobs[job]["path"], "status")
if os.path.isfile(statusFile):
st, status = commands.getstatusoutput("cat " + statusFile)
else:
print "ERROR: Missing status file"
status = None
if not status:
print "ERROR! job " + job + " has no status!"
jobs[job]["failed"] = True
elif status == "failed":
print "Job " + job + " failed to build all models"
jobs[job]["failed"] = True
elif status == "finished":
jobs[job]["finished"] = True
if not isJobProgressingOK(jobs[job]):
print "Job " + job + " failed to build one or more models in getMLStatistics"
jobs[job]["failed"] = True
jobs[job]["finished"] = False
if jobs[job]["failed"]:
print "Job " + job + " FAILED"
else:
print "Finished Job " + str(job) + " with success"
if callBack:
stepsDone += 1
if not callBack((100 * stepsDone) / nTotalSteps): return None
else:
cmd = "qsub -cwd -q batch.q" + AZOC.SGE_QSUB_ARCH_OPTION_CURRENT + os.path.join(
jobs[job]["path"], "run.sh")
status, out = commands.getstatusoutput(cmd)
if status:
print "ERROR on Job " + str(job) + " (will be skipped)"
print out
#raise Exception("ERROR starting job for folder "+str(job))
# Your job 955801 ("template_run.sh") has been submitted
jID = out.strip().split(" ")[2]
print " jID: ", jID
os.system('echo "' + jID + '" > ' +
os.path.join(jobs[job]["path"], "jID"))
jobs[job]["running"] = True
jobs[job]["jID"] = jID
os.chdir(runningDir)
os.chdir(thisDir)
finished = []
if queueType == "NoSGE":
failed = []
#Report failed Jobs
for job in jobs:
if jobs[job]["finished"]:
finished.append(job)
for job in jobs:
if jobs[job]["failed"]:
failed.append(job)
print "Successful finished Jobs: ", finished
print "Failed Jobs: ", failed
else: # Monitor SGE jobs untill all are finished
#Monitor Fold jobs
updateJobsStatus(jobs)
for job in jobs:
if jobs[job]["finished"]:
finished.append(job)
print "Jobs already finished: ", finished
os.system(' echo "' + str(len(finished)) + '/' +
str(AZOC.QSARNEXTFOLDS) + '" > ' +
os.path.join(runningDir, "status"))
while len(finished) < AZOC.QSARNEXTFOLDS:
print ".",
sys.stdout.flush()
updateJobsStatus(jobs)
for job in jobs:
if jobs[job]["finished"] and job not in finished:
finished.append(job)
if callBack:
stepsDone += 1
if not callBack((100 * stepsDone) / nTotalSteps):
return None
print time.asctime() + ": Finished job " + str(job)
os.system(' echo "' + str(len(finished)) + '/' +
str(AZOC.QSARNEXTFOLDS) + '" > ' +
os.path.join(runningDir, "status"))
for job in [j for j in jobs if jobs[j]["failed"]]:
jobs[job] = restartJob(jobs[job])
time.sleep(5)
print "All fold jobs finished!"
# Gather the results
print "Gathering results..."
#Var for saving each Fols result
results = {}
exp_pred = {}
nTrainEx = {}
nTestEx = {}
# Var for saving the statistics results
statistics = {}
mlMethods = [ml for ml in AZOC.MLMETHODS] + ["Consensus"]
sortedJobs = [job for job in jobs]
sortedJobs.sort(cmp=lambda x, y: int(x) > int(y) and 1 or -1)
# Place for storing the selected models results
results["selectedML"] = []
exp_pred["selectedML"] = []
nTrainEx["selectedML"] = []
nTestEx["selectedML"] = []
foldSelectedML = []
for ml in mlMethods: # Loop over each MLMethod
try:
#Var for saving each Fols result
results[ml] = []
exp_pred[ml] = []
nTrainEx[ml] = []
nTestEx[ml] = []
logTxt = ""
for job in sortedJobs: #loop over each fold
modelPath = os.path.join(jobs[job]["path"], "model_" + ml)
if not os.path.isdir(modelPath):
if getAllModels:
print "MLMethod " + ml + " not available in fold " + job
continue
resFile = os.path.join(jobs[job]["path"], "results.pkl")
statFile_h = open(resFile)
foldStat = pickle.load(statFile_h)
statFile_h.close()
#load model
model = AZBaseClasses.modelRead(modelPath)
#Test the model
testData = dataset.select(DataIdxs, int(job))
nTrainEx[ml].append(model.NTrainEx)
nTestEx[ml].append(len(testData))
if foldStat[ml]["selected"]:
foldSelectedML.append(ml)
nTrainEx["selectedML"].append(model.NTrainEx)
nTestEx["selectedML"].append(len(testData))
if responseType == "Classification":
results[ml].append(
(evalUtilities.getClassificationAccuracy(
testData,
model), evalUtilities.getConfMat(testData, model)))
if foldStat[ml]["selected"]:
results["selectedML"].append(results[ml][-1])
else:
local_exp_pred = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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 foldStat[ml]["selected"]:
results["selectedML"].append(results[ml][-1])
exp_pred["selectedML"] += local_exp_pred
res = createStatObj(results[ml], exp_pred[ml],
nTrainEx[ml], nTestEx[ml], responseType,
len(sortedJobs), logTxt)
if not res:
raise Exception("No results available!")
if getAllModels:
statistics[ml] = copy.deepcopy(res)
writeResults(statistics, resultsFile)
print " OK", ml
except:
print "Error on MLmethod " + ml + ". It will be skipped"
ml = "selectedML"
res = createStatObj(results[ml], exp_pred[ml], nTrainEx[ml], nTestEx[ml],
responseType, len(sortedJobs), logTxt, foldSelectedML)
if not res:
raise Exception("No results available!")
statistics[ml] = copy.deepcopy(res)
writeResults(statistics, resultsFile)
os.system(' echo "finished" > ' + os.path.join(runningDir, "status"))
return statistics
def getAcc(self, algorithm = None, minsup = None, atts = None):
""" For regression problems, it returns the RMSE and the R2
For Classification problems, it returns CA and the ConfMat
The return is made in a Dict: {"RMSE":0.2,"R2":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
if (self.algorithm):
self.__log(" Additional structural features to be calculated inside of cross-validation")
self.__log(" Algorithm for structural features: "+str(self.algorithm))
self.__log(" Minimum support parameter: "+str(self.minsup))
# Set the response type
responseType = self.data.domain.classVar.varType == orange.VarTypes.Discrete and "Classification" or "Regression"
self.__log(" "+str(responseType))
#Create the Train and test sets
DataIdxs = dataUtilities.SeedDataSampler(self.data, self.nExtFolds)
#Var for saving each Fols result
results = {}
exp_pred = {}
#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]))
for ml in MLmethods:
self.__log(" > "+str(ml)+"...")
try:
#Var for saving each Fols result
results[ml] = []
exp_pred[ml] = []
models[ml] = []
for foldN in range(self.nExtFolds):
if type(self.learner) == dict:
self.paramList = None
trainData = self.data.select(DataIdxs[foldN],negate=1)
orig_len = len(trainData.domain.attributes)
if (self.algorithm):
# add structural descriptors to the training data (TG)
trainData_structDesc = getStructuralDesc.getStructuralDescResult(trainData, self.algorithm, self.minsup)
trainData = dataUtilities.attributeDeselectionData(trainData_structDesc, self.atts)
runPath = miscUtilities.createScratchDir(baseDir = AZOC.NFS_SCRATCHDIR, desc = "AccWOptParam")
trainData.save(os.path.join(runPath,"trainData.tab"))
testData = self.data.select(DataIdxs[foldN])
if (self.algorithm):
# calculate the feature values for the test data (TG)
cut_off = orig_len - len(self.atts)
smarts = trainData.domain.attributes[cut_off:]
self.__log(" Number of structural features added: "+str(len(smarts)))
testData_structDesc = getStructuralDesc.getSMARTSrecalcDesc(testData,smarts)
testData = dataUtilities.attributeDeselectionData(testData_structDesc, self.atts)
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
)
if not MLmethods[ml].optimized:
self.__log(" The learner "+str(ml)+" was not optimized.")
raise Exception("The learner "+str(ml)+" was not optimized.")
miscUtilities.removeDir(runPath)
#Train the model
model = MLmethods[ml](trainData)
models[ml].append(model)
#Test the model
if responseType == "Classification":
results[ml].append((evalUtilities.getClassificationAccuracy(testData, model), evalUtilities.getConfMat(testData, model) ) )
else:
local_exp_pred = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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
res = self.createStatObj(results[ml], exp_pred[ml], responseType, self.nExtFolds)
if self.verbose > 0:
print "AccWOptParamGetter!Results "+ml+":\n"
pprint(res)
if not res:
raise Exception("No results available!")
statistics[ml] = res.copy()
self.__writeResults(res)
self.__log(" OK")
except:
self.__log(" Learner "+str(ml)+" failed to optimize!")
res = self.createStatObj()
statistics[ml] = res.copy()
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!
stableML={}
for modelName in statistics:
if statistics[modelName]["StabilityValue"] < AZOC.QSARSTABILITYTHRESHOLD: # Select only stable models
stableML[modelName] = statistics[modelName].copy()
if len(stableML) >= 2:
self.__log("Found "+str(len(stableML))+" stable MLmethods out of "+str(len(statistics))+" MLmethods.")
if responseType == "Classification":
CLASS0 = str(self.data.domain.classVar.values[0])
CLASS1 = str(self.data.domain.classVar.values[1])
exprTest0 = "(0"
for ml in stableML:
exprTest0 += "+( "+ml+" == "+CLASS0+" )*"+str(stableML[ml]["CA"])+" "
exprTest0 += ")/IF0(sum([False"
for ml in stableML:
exprTest0 += ", "+ml+" == "+CLASS0+" "
exprTest0 += "]),1)"
exprTest1 = exprTest0.replace(CLASS0,CLASS1)
expression = [exprTest0+" >= "+exprTest1+" -> "+CLASS0," -> "+CLASS1]
else:
R2sum = sum([stableML[ml]["R2"] for ml in stableML])
expression = "(1 / "+str(R2sum)+") * (0"
for ml in stableML:
expression += " + "+str(stableML[ml]["R2"])+" * "+ml+" "
expression += ")"
#Var for saving each Fols result
Cresults = []
Cexp_pred = []
self.__log("Calculating the statistics for a Consensus model")
for foldN in range(self.nExtFolds):
testData = self.data.select(DataIdxs[foldN])
consensusClassifiers = {}
for learnerName in stableML:
consensusClassifiers[learnerName] = models[learnerName][foldN]
model = AZorngConsensus.ConsensusClassifier(classifiers = consensusClassifiers, expression = expression)
#Test the model
if responseType == "Classification":
Cresults.append((evalUtilities.getClassificationAccuracy(testData, model), evalUtilities.getConfMat(testData, model) ) )
else:
local_exp_pred = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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, responseType, self.nExtFolds)
statistics["Consensus"] = res.copy()
statistics["Consensus"]["IndividualStatistics"] = stableML.copy()
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 createStatObj(results=None,
exp_pred=None,
nTrainCmpds=None,
nTestCmpds=None,
responseType=None,
nExtFolds=None,
userAlert="",
foldSelectedML=None):
#Initialize res (statObj) for statistic results
res = {}
# Classification
res["CA"] = None
res["CM"] = None
res["MCC"] = None
#Regression
res["Q2"] = None
res["RMSE"] = None
#Both
res["StabilityValue"] = None
res["userAlert"] = userAlert
res["selected"] = False
res["stable"] = False
res["responseType"] = False
res["foldStat"] = {
"nTrainCmpds": None,
"nTestCmpds": None,
#Regression
"Q2": None,
"RMSE": None,
#Classification
"CM": None,
"CA": None,
"MCC": None
}
if not results or results is None or exp_pred is None or responseType is None or nExtFolds is None or nTestCmpds is None or nTrainCmpds is None:
return res
res["responseType"] = responseType
#Calculate the (Q2, RMSE) or (CM, CA) results depending on Classification or regression
if responseType == "Classification":
#Compute CA
res["CA"] = sum(r[0] for r in results) / nExtFolds
#Compute CM
res["CM"] = copy.deepcopy(results[0][1]) # Get the first ConfMat
for r in results[1:]:
for Lidx, line in enumerate(r[1]):
for idx, val in enumerate(line):
res["CM"][Lidx][idx] = res["CM"][Lidx][
idx] + val #Add each same ConfMat position
#Compute MCC
res["MCC"] = evalUtilities.calcMCC(res["CM"])
#Compute foldStat
res["foldStat"]["nTrainCmpds"] = [n for n in nTrainCmpds]
res["foldStat"]["nTestCmpds"] = [n for n in nTestCmpds]
res["foldStat"]["CA"] = [r[0] for r in results]
res["foldStat"]["CM"] = [r[1] for r in results]
res["foldStat"]["MCC"] = [evalUtilities.calcMCC(r[1]) for r in results]
#Compute Stability
res["StabilityValue"] = evalUtilities.stability(res["foldStat"]["CA"])
else:
#compute Q2
res["Q2"] = evalUtilities.calcRsqrt(exp_pred)
#compute RMSE
res["RMSE"] = evalUtilities.calcRMSE(exp_pred)
#Compute foldStat
res["foldStat"]["nTrainCmpds"] = [n for n in nTrainCmpds]
res["foldStat"]["nTestCmpds"] = [n for n in nTestCmpds]
res["foldStat"]["RMSE"] = [r[0] for r in results]
res["foldStat"]["Q2"] = [r[1] for r in results]
#Compute Stability value
res["StabilityValue"] = evalUtilities.stability(res["foldStat"]["Q2"])
# Save selectedMLs if passed
if foldSelectedML:
res["foldStat"]["foldSelectedML"] = [ml for ml in foldSelectedML]
#Evaluate stability of ML
StabilityValue = res["StabilityValue"]
if StabilityValue is not None:
if responseType == "Classification":
if statc.mean(res["foldStat"]["nTestCmpds"]) > 50:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_CLASS_L
else:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_CLASS_H
else:
if statc.mean(res["foldStat"]["nTestCmpds"]) > 50:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_REG_L
else:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_REG_H
if StabilityValue < stableTH: # Select only stable models
res["stable"] = True
return res
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 getAcc(self):
""" 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
DataIdxs = dataUtilities.SeedDataSampler(self.data, self.nExtFolds)
#Var for saving each Fols result
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 range(self.nExtFolds):
trainData = self.data.select(DataIdxs[foldN],negate=1)
self.__checkTrainData(trainData)
for ml in MLmethods:
self.__log(" > "+str(ml)+"...")
try:
#Var for saving each Fols result
results[ml] = []
exp_pred[ml] = []
models[ml] = []
nTrainEx[ml] = []
nTestEx[ml] = []
logTxt = ""
for foldN in range(self.nExtFolds):
if type(self.learner) == dict:
self.paramList = None
trainData = self.data.select(DataIdxs[foldN],negate=1)
testData = self.data.select(DataIdxs[foldN])
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 = dataUtilities.SeedDataSampler(trainData, self.nInnerFolds)
tmpTrainData = trainData.select(tmpDataIdxs[0],negate=1)
if not self.__checkTrainData(tmpTrainData, False):
dontOptimize = True
if dontOptimize:
logTxt += " Fold "+str(foldN)+": Too few compounds to optimize model hyper-parameters\n"
self.__log(logTxt)
else:
runPath = miscUtilities.createScratchDir(baseDir = AZOC.NFS_SCRATCHDIR, desc = "AccWOptParam")
trainData.save(os.path.join(runPath,"trainData.tab"))
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)
if not MLmethods[ml].optimized:
self.__log(" The learner "+str(ml)+" was not optimized.")
raise Exception("The learner "+str(ml)+" was not optimized.")
miscUtilities.removeDir(runPath)
#Train the model
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 = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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
res = self.createStatObj(results[ml], exp_pred[ml], nTrainEx[ml], nTestEx[ml],self.responseType, self.nExtFolds, logTxt)
if self.verbose > 0:
print "AccWOptParamGetter!Results "+ml+":\n"
pprint(res)
if not res:
raise Exception("No results available!")
statistics[ml] = res.copy()
self.__writeResults(statistics)
self.__log(" OK")
except:
self.__log(" Learner "+str(ml)+" failed to create/optimize the model!")
res = self.createStatObj()
statistics[ml] = res.copy()
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!
stableML={}
for modelName in statistics:
StabilityValue = statistics[modelName]["StabilityValue"]
if StabilityValue is not None:
if self.responseType == "Classification":
if statc.mean(statistics[modelName]["foldStat"]["nTestCmpds"]) > 50:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_CLASS_L
else:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_CLASS_H
else:
if statc.mean(statistics[modelName]["foldStat"]["nTestCmpds"]) > 50:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_REG_L
else:
stableTH = AZOC.QSARSTABILITYTHRESHOLD_REG_H
if StabilityValue < stableTH: # Select only stable models
stableML[modelName] = statistics[modelName].copy()
if len(stableML) >= 2:
self.__log("Found "+str(len(stableML))+" stable MLmethods out of "+str(len(statistics))+" MLmethods.")
if self.responseType == "Classification":
CLASS0 = str(self.data.domain.classVar.values[0])
CLASS1 = str(self.data.domain.classVar.values[1])
exprTest0 = "(0"
for ml in stableML:
exprTest0 += "+( "+ml+" == "+CLASS0+" )*"+str(stableML[ml]["CA"])+" "
exprTest0 += ")/IF0(sum([False"
for ml in stableML:
exprTest0 += ", "+ml+" == "+CLASS0+" "
exprTest0 += "]),1)"
exprTest1 = exprTest0.replace(CLASS0,CLASS1)
expression = [exprTest0+" >= "+exprTest1+" -> "+CLASS0," -> "+CLASS1]
else:
Q2sum = sum([stableML[ml]["Q2"] for ml in stableML])
expression = "(1 / "+str(Q2sum)+") * (0"
for ml in stableML:
expression += " + "+str(stableML[ml]["Q2"])+" * "+ml+" "
expression += ")"
#Var for saving each Fols result
Cresults = []
Cexp_pred = []
CnTrainEx = []
CnTestEx = []
self.__log("Calculating the statistics for a Consensus model")
for foldN in range(self.nExtFolds):
testData = self.data.select(DataIdxs[foldN])
CnTestEx.append(len(testData))
consensusClassifiers = {}
for learnerName in stableML:
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 = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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)
statistics["Consensus"] = res.copy()
statistics["Consensus"]["IndividualStatistics"] = stableML.copy()
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 getStatistics(dataset, runningDir, resultsFile, queueType = "NoSGE", verbose = 0, getAllModels = False, callBack = None):
"""
runningDir (An existing dir for creating one job dir per fold)
|
+---- status (The overall status: "started", "finished" or the progress "1/10", "2/10", ...)
|
+---- fold_1
|
+---- fold_2
|
.
.
.
The running will be monitorized by this method.
Whenever a MLMethod fails the respective fold job is restarted
"""
if dataset.domain.classVar.varType == orange.VarTypes.Discrete:
responseType = "Classification"
else:
responseType = "Regression"
#Create the Train and test sets
DataIdxs = dataUtilities.SeedDataSampler(dataset, AZOC.QSARNEXTFOLDS )
#Check data in advance so that, by chance, it will not faill at the last fold!
#for foldN in range(AZOC.QSARNEXTFOLDS):
#trainData = dataset.select(DataIdxs[foldN],negate=1)
#checkTrainData(trainData)
jobs = {}
thisDir = os.getcwd()
os.chdir(runningDir)
#PID = os.getpid()
#print "Started getStatistics in Process with PID: "+str(PID)
#os.system('echo "'+str(PID)+'" > '+os.path.join(runningDir,"PID"))
os.system('echo "started" > '+os.path.join(runningDir,"status"))
# Start all Fold jobs
stepsDone = 0
nTotalSteps = AZOC.QSARNEXTFOLDS
for fold in range(AZOC.QSARNEXTFOLDS):
job = str(fold)
print "Starting job for fold ",job
trainData = dataset.select(DataIdxs[fold],negate=1)
jobs[job] = {"job":job,"path":os.path.join(runningDir, "fold_"+job), "running":False, "failed":False, "finished":False}
# Uncomment next 3 lines for running in finished jobs dirs
#st, jID = commands.getstatusoutput("cat "+os.path.join(runningDir, "fold_"+job,"jID"))
#jobs[job]["jID"] = jID
#continue
os.system("rm -rf "+jobs[job]["path"])
os.system("mkdir -p "+jobs[job]["path"])
trainData.save(os.path.join(jobs[job]["path"],"trainData.tab"))
file_h = open(os.path.join(jobs[job]["path"],"run.sh"),"w")
file_h.write("#!/bin/tcsh\n")
file_h.write("source "+os.path.join(os.environ["AZORANGEHOME"], "templateProfile") + "\n")
file_h.write("python "+os.path.join(jobs[job]["path"],"QsubScript.py")+"\n")
file_h.close()
file_h = open(os.path.join(jobs[job]["path"],"QsubScript.py"),"w")
file_h.write("import os\n")
file_h.write("from AZutilities import dataUtilities\n")
file_h.write("from AZutilities import competitiveWorkflow\n")
file_h.write("data = dataUtilities.DataTable('"+os.path.join(jobs[job]["path"],"trainData.tab")+"')\n")
file_h.write('os.system(\'echo "running" > '+os.path.join(jobs[job]["path"],"status")+' \')\n')
file_h.write("models = competitiveWorkflow.getModel(data, savePath = '"+os.path.join(jobs[job]["path"],"results.pkl")+"', queueType = '"+queueType+"', getAllModels = "+str(getAllModels)+")\n")
file_h.write("nModelsSaved = 0\n")
file_h.write("for model in models:\n")
file_h.write(" if not models[model] is None:\n")
file_h.write(" models[model].write('"+os.path.join(jobs[job]["path"],"model")+"'+'_'+model)\n")
file_h.write(' nModelsSaved += 1\n')
file_h.write('if nModelsSaved == len([m for m in models if not models[m] is None ]):\n')
file_h.write(' os.system(\'echo "finished" > '+os.path.join(jobs[job]["path"],"status")+' \')\n')
file_h.write('else:\n')
file_h.write(' os.system(\'echo "failed" > '+os.path.join(jobs[job]["path"],"status")+' \')\n')
file_h.close()
os.chdir(os.path.join(jobs[job]["path"]))
if queueType == "NoSGE": # Serial mode
status, out = commands.getstatusoutput("tcsh " + os.path.join(jobs[job]["path"],"run.sh"))
if status:
print "ERROR on Job "+str(job)+" (will be restarted latter)"
print out
else:
statusFile = os.path.join(jobs[job]["path"],"status")
if os.path.isfile(statusFile):
st, status = commands.getstatusoutput("cat "+statusFile)
else:
print "ERROR: Missing status file"
status = None
if not status:
print "ERROR! job "+job+" has no status!"
jobs[job]["failed"] = True
elif status == "failed":
print "Job "+job+" failed to build all models"
jobs[job]["failed"] = True
elif status == "finished":
jobs[job]["finished"] = True
if not isJobProgressingOK(jobs[job]):
print "Job "+job+" failed to build one or more models in getMLStatistics"
jobs[job]["failed"] = True
jobs[job]["finished"] = False
if jobs[job]["failed"]:
print "Job "+job+" FAILED"
else:
print "Finished Job "+str(job)+" with success"
if callBack:
stepsDone += 1
if not callBack((100*stepsDone)/nTotalSteps): return None
else:
cmd = "qsub -cwd -q batch.q" + AZOC.SGE_QSUB_ARCH_OPTION_CURRENT + os.path.join(jobs[job]["path"],"run.sh")
status, out = commands.getstatusoutput(cmd)
if status:
print "ERROR on Job "+str(job)+" (will be skipped)"
print out
#raise Exception("ERROR starting job for folder "+str(job))
# Your job 955801 ("template_run.sh") has been submitted
jID = out.strip().split(" ")[2]
print " jID: ",jID
os.system('echo "'+jID+'" > '+os.path.join(jobs[job]["path"], "jID"))
jobs[job]["running"] = True
jobs[job]["jID"] = jID
os.chdir(runningDir)
os.chdir(thisDir)
finished = []
if queueType == "NoSGE":
failed = []
#Report failed Jobs
for job in jobs:
if jobs[job]["finished"]:
finished.append(job)
for job in jobs:
if jobs[job]["failed"]:
failed.append(job)
print "Successful finished Jobs: ",finished
print "Failed Jobs: ",failed
else: # Monitor SGE jobs untill all are finished
#Monitor Fold jobs
updateJobsStatus(jobs)
for job in jobs:
if jobs[job]["finished"]:
finished.append(job)
print "Jobs already finished: ",finished
os.system(' echo "'+str(len(finished))+'/'+str(AZOC.QSARNEXTFOLDS)+'" > '+os.path.join(runningDir,"status"))
while len(finished) < AZOC.QSARNEXTFOLDS:
print ".",
sys.stdout.flush()
updateJobsStatus(jobs)
for job in jobs:
if jobs[job]["finished"] and job not in finished:
finished.append(job)
if callBack:
stepsDone += 1
if not callBack((100*stepsDone)/nTotalSteps): return None
print time.asctime()+": Finished job "+str(job)
os.system(' echo "'+str(len(finished))+'/'+str(AZOC.QSARNEXTFOLDS)+'" > '+os.path.join(runningDir,"status"))
for job in [j for j in jobs if jobs[j]["failed"]]:
jobs[job] = restartJob(jobs[job])
time.sleep(5)
print "All fold jobs finished!"
# Gather the results
print "Gathering results..."
#Var for saving each Fols result
results = {}
exp_pred = {}
nTrainEx = {}
nTestEx = {}
# Var for saving the statistics results
statistics = {}
mlMethods = [ml for ml in AZOC.MLMETHODS] + ["Consensus"]
sortedJobs = [job for job in jobs]
sortedJobs.sort(cmp = lambda x,y:int(x)>int(y) and 1 or -1)
# Place for storing the selected models results
results["selectedML"] = []
exp_pred["selectedML"] = []
nTrainEx["selectedML"] = []
nTestEx["selectedML"] = []
foldSelectedML = []
for ml in mlMethods: # Loop over each MLMethod
try:
#Var for saving each Fols result
results[ml] = []
exp_pred[ml] = []
nTrainEx[ml] = []
nTestEx[ml] = []
logTxt = ""
for job in sortedJobs: #loop over each fold
modelPath = os.path.join(jobs[job]["path"], "model_"+ml)
if not os.path.isdir(modelPath):
if getAllModels: print "MLMethod "+ml+" not available in fold "+job
continue
resFile = os.path.join(jobs[job]["path"], "results.pkl")
statFile_h = open(resFile)
foldStat = pickle.load(statFile_h)
statFile_h.close()
#load model
model = AZBaseClasses.modelRead(modelPath)
#Test the model
testData = dataset.select(DataIdxs[int(job)])
nTrainEx[ml].append(model.NTrainEx)
nTestEx[ml].append(len(testData))
if foldStat[ml]["selected"]:
foldSelectedML.append(ml)
nTrainEx["selectedML"].append(model.NTrainEx)
nTestEx["selectedML"].append(len(testData))
if responseType == "Classification":
results[ml].append((evalUtilities.getClassificationAccuracy(testData, model), evalUtilities.getConfMat(testData, model) ) )
if foldStat[ml]["selected"]:
results["selectedML"].append(results[ml][-1])
else:
local_exp_pred = []
for ex in testData:
local_exp_pred.append((ex.getclass(), model(ex)))
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 foldStat[ml]["selected"]:
results["selectedML"].append(results[ml][-1])
exp_pred["selectedML"]+= local_exp_pred
res = createStatObj(results[ml], exp_pred[ml], nTrainEx[ml], nTestEx[ml],responseType, len(sortedJobs), logTxt)
if not res:
raise Exception("No results available!")
if getAllModels:
statistics[ml] = copy.deepcopy(res)
writeResults(statistics, resultsFile)
print " OK",ml
except:
print "Error on MLmethod "+ml+". It will be skipped"
ml = "selectedML"
res = createStatObj(results[ml], exp_pred[ml], nTrainEx[ml], nTestEx[ml],responseType, len(sortedJobs), logTxt, foldSelectedML)
if not res:
raise Exception("No results available!")
statistics[ml] = copy.deepcopy(res)
writeResults(statistics, resultsFile)
os.system(' echo "finished" > '+os.path.join(runningDir,"status"))
return statistics
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]]