def Consensusread(dirPath,verbose = 0):
"""Read a Consensus model from disk and return as a ConsensusClassifier instance. """
# Read data from disk
#This removes any trailing '/'
dirPath = os.path.realpath(str(dirPath))
# This assures that all related files will be inside a folder
try:
domainFile = dataUtilities.DataTable(os.path.join(dirPath,"trainDomain.tab"))
#Load the models
modelFiles = glob.glob(os.path.join(dirPath,'C*.model'))
if len(modelFiles) < 2:
if self.verbose > 0: print "ERROR: Missing model files in ",dirPath
return None
else:
classifiers = []
for mFile in modelFiles:
classifiers.append(AZBaseClasses.modelRead(mFile))
if not classifiers[-1]:
if self.verbose > 0: print "ERROR: Could not load the model ",mFile
return None
except:
if verbose > 0: print "ERROR: It was not possible to load the Consensus model"
return None
return ConsensusClassifier(classifiers=classifiers ,classVar = domainFile.domain.classVar, verbose = verbose, domain = domainFile.domain)
def Consensusread(dirPath,verbose = 0):
"""Read a Consensus model from disk and return as a ConsensusClassifier instance. """
# Read data from disk
#This removes any trailing '/'
dirPath = os.path.realpath(str(dirPath))
basicStat = None
NTrainEx = None
imputeData = None
# This assures that all related files will be inside a folder
try:
domainFile = dataUtilities.DataTable(os.path.join(dirPath,"trainDomain.tab"))
#Load the models
modelFiles = glob.glob(os.path.join(dirPath,'C*.model'))
if len(modelFiles) < 2:
if verbose > 0: print "ERROR: Missing model files in ",dirPath
return None
else:
classifiers = []
for mFile in modelFiles:
classifiers.append(AZBaseClasses.modelRead(mFile))
if not classifiers[-1]:
if verbose > 0: print "ERROR: Could not load the model ",mFile
return None
else:
#Try to load the imputeData, basicStat and NTrainEx from a model that saved it!
if hasattr(classifiers[-1], "basicStat") and classifiers[-1].basicStat and not basicStat:
basicStat = classifiers[-1].basicStat
if hasattr(classifiers[-1], "NTrainEx") and classifiers[-1].NTrainEx and not NTrainEx:
NTrainEx = classifiers[-1].NTrainEx
if hasattr(classifiers[-1], "imputeData") and classifiers[-1].imputeData and not imputeData:
imputeData = classifiers[-1].imputeData
domainFile = imputeData #This is needed for domain compatibilitu betwene imputer and domain var
except:
if verbose > 0: print "ERROR: It was not possible to load the Consensus model"
return None
return ConsensusClassifier(classifiers=classifiers ,varNames = [attr.name for attr in domainFile.domain.attributes],classVar = domainFile.domain.classVar, verbose = verbose, domain = domainFile.domain, basicStat = basicStat, NTrainEx = NTrainEx, imputeData = imputeData)
def Consensusread(dirPath, verbose=0):
"""Read a Consensus model from disk and return as a ConsensusClassifier instance. """
# Read data from disk
#This removes any trailing '/'
dirPath = os.path.realpath(str(dirPath))
basicStat = None
NTrainEx = None
imputeData = None
expression = None
weights = None
# This assures that all related files will be inside a folder
try:
domainFile = dataUtilities.DataTable(
os.path.join(dirPath, "trainDomain.tab"))
learnerFilename = os.path.join(dirPath, 'learnerDict.pkl')
expressionListFilename = os.path.join(dirPath, 'expressionList.pkl')
expressionFilename = os.path.join(dirPath, 'expression.pkl')
weightsFilename = os.path.join(dirPath, 'weights.pkl')
#Load the models
modelFiles = glob.glob(os.path.join(dirPath, 'C*.model'))
modelFiles.sort()
if len(modelFiles) < 2:
if verbose > 0: print "ERROR: Missing model files in ", dirPath
return None
else:
if os.path.exists(learnerFilename):
#
# We have a custom expression to read
#
dictionaryFile = open(learnerFilename, 'rb')
classifiers = pickle.load(dictionaryFile)
dictionaryFile.close()
models = []
for mFile in modelFiles:
models.append(AZBaseClasses.modelRead(mFile))
for k, v in classifiers.iteritems():
classifiers[k] = models[v]
#Try to load the imputeData, basicStat and NTrainEx from a model that saved it!
if hasattr(classifiers.itervalues().next(),
"basicStat") and classifiers.itervalues().next(
).basicStat and not basicStat:
basicStat = classifiers.itervalues().next().basicStat
if hasattr(classifiers.itervalues().next(),
"NTrainEx") and classifiers.itervalues().next(
).NTrainEx and not NTrainEx:
NTrainEx = classifiers.itervalues().next().NTrainEx
if hasattr(classifiers.itervalues().next(),
"imputeData") and classifiers.itervalues().next(
).imputeData and not imputeData:
imputeData = classifiers.itervalues().next().imputeData
domainFile = imputeData #This is needed for domain compatibility between imputer and domain var
if os.path.exists(expressionListFilename):
file = open(expressionListFilename)
expression = pickle.load(file)
file.close()
else:
file = open(expressionFilename)
expression = pickle.load(file)
file.close()
if os.path.exists(weightsFilename):
file = open(weightsFilename)
weights = pickle.load(file)
file.close()
else:
#
# Default expression to read
#
classifiers = []
for mFile in modelFiles:
classifiers.append(AZBaseClasses.modelRead(mFile))
if not classifiers[-1]:
if verbose > 0:
print "ERROR: Could not load the model ", mFile
return None
else:
#Try to load the imputeData, basicStat and NTrainEx from a model that saved it!
if hasattr(
classifiers[-1], "basicStat"
) and classifiers[-1].basicStat and not basicStat:
basicStat = classifiers[-1].basicStat
if hasattr(classifiers[-1], "NTrainEx"
) and classifiers[-1].NTrainEx and not NTrainEx:
NTrainEx = classifiers[-1].NTrainEx
if hasattr(
classifiers[-1], "imputeData"
) and classifiers[-1].imputeData and not imputeData:
imputeData = classifiers[-1].imputeData
domainFile = imputeData #This is needed for domain compatibilitu betwene imputer and domain var
except:
if verbose > 0:
print "ERROR: It was not possible to load the Consensus model"
return None
return ConsensusClassifier(
classifiers=classifiers,
expression=expression,
weights=weights,
varNames=[attr.name for attr in domainFile.domain.attributes],
classVar=domainFile.domain.classVar,
verbose=verbose,
domain=domainFile.domain,
basicStat=basicStat,
NTrainEx=NTrainEx,
imputeData=imputeData)
def Consensusread(dirPath,verbose = 0):
"""Read a Consensus model from disk and return as a ConsensusClassifier instance. """
# Read data from disk
#This removes any trailing '/'
dirPath = os.path.realpath(str(dirPath))
basicStat = None
NTrainEx = None
imputeData = None
expression = None
weights = None
# This assures that all related files will be inside a folder
try:
domainFile = dataUtilities.DataTable(os.path.join(dirPath, "trainDomain.tab"))
learnerFilename = os.path.join(dirPath, 'learnerDict.pkl')
expressionListFilename = os.path.join(dirPath, 'expressionList.pkl')
expressionFilename = os.path.join(dirPath, 'expression.pkl')
weightsFilename = os.path.join(dirPath, 'weights.pkl')
#Load the models
modelFiles = glob.glob(os.path.join(dirPath,'C*.model'))
modelFiles.sort()
if len(modelFiles) < 2:
if verbose > 0: print "ERROR: Missing model files in ",dirPath
return None
else:
if os.path.exists(learnerFilename):
#
# We have a custom expression to read
#
dictionaryFile = open(learnerFilename, 'rb')
classifiers = pickle.load(dictionaryFile)
dictionaryFile.close()
models = []
for mFile in modelFiles:
models.append(AZBaseClasses.modelRead(mFile))
for k, v in classifiers.iteritems():
classifiers[k] = models[v]
#Try to load the imputeData, basicStat and NTrainEx from a model that saved it!
if hasattr(classifiers.itervalues().next(), "basicStat") and classifiers.itervalues().next().basicStat and not basicStat:
basicStat = classifiers.itervalues().next().basicStat
if hasattr(classifiers.itervalues().next(), "NTrainEx") and classifiers.itervalues().next().NTrainEx and not NTrainEx:
NTrainEx = classifiers.itervalues().next().NTrainEx
if hasattr(classifiers.itervalues().next(), "imputeData") and classifiers.itervalues().next().imputeData and not imputeData:
imputeData = classifiers.itervalues().next().imputeData
domainFile = imputeData #This is needed for domain compatibility between imputer and domain var
if os.path.exists(expressionListFilename):
file = open(expressionListFilename)
expression = pickle.load(file)
file.close()
else:
file = open(expressionFilename)
expression = pickle.load(file)
file.close()
if os.path.exists(weightsFilename):
file = open(weightsFilename)
weights = pickle.load(file)
file.close()
else:
#
# Default expression to read
#
classifiers = []
for mFile in modelFiles:
classifiers.append(AZBaseClasses.modelRead(mFile))
if not classifiers[-1]:
if verbose > 0:
print "ERROR: Could not load the model ",mFile
return None
else:
#Try to load the imputeData, basicStat and NTrainEx from a model that saved it!
if hasattr(classifiers[-1], "basicStat") and classifiers[-1].basicStat and not basicStat:
basicStat = classifiers[-1].basicStat
if hasattr(classifiers[-1], "NTrainEx") and classifiers[-1].NTrainEx and not NTrainEx:
NTrainEx = classifiers[-1].NTrainEx
if hasattr(classifiers[-1], "imputeData") and classifiers[-1].imputeData and not imputeData:
imputeData = classifiers[-1].imputeData
domainFile = imputeData #This is needed for domain compatibilitu betwene imputer and domain var
except:
if verbose > 0: print "ERROR: It was not possible to load the Consensus model"
return None
return ConsensusClassifier(classifiers=classifiers,
expression=expression,
weights=weights,
varNames = [attr.name for attr in domainFile.domain.attributes],
classVar = domainFile.domain.classVar,
verbose = verbose,
domain = domainFile.domain,
basicStat = basicStat,
NTrainEx = NTrainEx,
imputeData = imputeData)
