def trainAndClassify(params):
logging.getLogger("trainAndClassify").info("~~~~~~~~~~~~~BEGIN")
classifierPolicy = getClassifierPolicy(params.classifierType)
# Load raw CSV reviews
rawCsvReviews = csv.DictReader(open(params.csvReviewsPath))
rawCsvReviews = [review for review in rawCsvReviews]
# TRAIN: Load raw CSV comments
rawCsvCommentsTrain = csv.DictReader(open(params.csvCommentsPathTrain))
rawCsvCommentsTrain = [comment for comment in rawCsvCommentsTrain]
# TRAIN: Create context
logging.getLogger("trainAndClassify").info(
"~~~~~~~~~~~~~CREATING TRAINING CONTEXT")
ctxTrain = MinerContext.loadContext(params.ctxCacheTrainFileName,
rawCsvCommentsTrain, rawCsvReviews,
params.supportThresh)
# TRAIN: Create features sets
logging.getLogger("trainAndClassify").info(
"~~~~~~~~~~~~~PREPARING TRAINING FEATURES")
featuresMapsTrain = []
classifierPolicy[eClassifierCB.PrepareFeatures](ctxTrain,
featuresMapsTrain,
params.featuresBitMask)
# TRAIN: Add CAR if desired
if (params.featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.CAR):
MinerFeaturesUtils.addFeaturesCAR(ctxTrain, featuresMapsTrain,
params.CARMinSup, params.CARMinConf,
params.CARCacheFileName)
# TRAIN: Convert features set to classifier specific input
logging.getLogger("trainAndClassify").info(
"~~~~~~~~~~~~~CONVERTING TRAINING INPUTS")
classifierInputsTrain = []
classifierPolicy[eClassifierCB.ClassifierInputs](ctxTrain,
featuresMapsTrain,
classifierInputsTrain,
True)
# TEST: Load raw CSV comments
rawCsvCommentsTest = csv.DictReader(open(params.csvCommentsPathTest))
rawCsvCommentsTest = [comment for comment in rawCsvCommentsTest]
# TEST: Create context
logging.getLogger("trainAndClassify").info(
"~~~~~~~~~~~~~CREATING TESTING CONTEXT")
ctxTest = MinerContext.loadContext(params.ctxCacheTestFileName,
rawCsvCommentsTest, rawCsvReviews,
params.supportThresh)
# HACK - replace filtered words with those of training context
ctxTest.mFilteredWords = ctxTrain.mFilteredWords
# TEST: Create features sets
logging.getLogger("trainAndClassify").info(
"~~~~~~~~~~~~~PREPARING TESTING FEATURES")
featuresMapsTest = []
classifierPolicy[eClassifierCB.PrepareFeatures](ctxTest, featuresMapsTest,
params.featuresBitMask)
# TEST: Add CAR if desired
if (params.featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.CAR):
MinerFeaturesUtils.addFeaturesCAR(ctxTest, featuresMapsTest,
params.CARMinSup, params.CARMinConf,
params.CARCacheFileName)
# TEST: Convert features set to classifier specific input
logging.getLogger("trainAndClassify").info(
"~~~~~~~~~~~~~CONVERTING TESTING INPUTS")
classifierInputsTest = []
classifierPolicy[eClassifierCB.ClassifierInputs](ctxTest, featuresMapsTest,
classifierInputsTest,
True)
logging.getLogger("trainAndClassify").info("~~~~~~~~~~~~~CLASSIFYING")
classifier = classifierPolicy[eClassifierCB.Classify](
classifierInputsTrain, classifierInputsTest, params.bDebug,
params.outDebugFileName, params.outDebugLabel)
logging.getLogger("trainAndClassify").info("~~~~~~~~~~~~~WRITING OUTPUTS")
writeOutput(ctxTest, featuresMapsTest, params.classifierType, classifier)
logging.getLogger("trainAndClassify").info("~~~~~~~~~~~~~END")
def SvmPrepareFeatures( ctx, outFeaturesMaps ):
logging.getLogger("Svm").info( "prepare features" )
MinerFeaturesUtils.initFeatures( ctx, outFeaturesMaps )
MinerFeaturesUtils.addFeaturesCommentLength( ctx, outFeaturesMaps )
MinerFeaturesUtils.addFeaturesHelpfulnessRatio( ctx, outFeaturesMaps )
MinerFeaturesUtils.addFeaturesPhrases( ctx, outFeaturesMaps )
MinerFeaturesUtils.addFeaturesWordExists( ctx, outFeaturesMaps )
MinerFeaturesUtils.addFeaturesAuthorFreqInReview(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesReviewAuthorMentioned(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesCommentAuthorMentioned( ctx, outFeaturesMaps )
MinerFeaturesUtils.addFeaturesCAR( ctx, outFeaturesMaps )
def trainAndClassify( params ):
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~BEGIN" )
classifierPolicy = getClassifierPolicy(params.classifierType)
# Load raw CSV reviews
rawCsvReviews = csv.DictReader(open(params.csvReviewsPath))
rawCsvReviews = [review for review in rawCsvReviews]
# TRAIN: Load raw CSV comments
rawCsvCommentsTrain = csv.DictReader(open(params.csvCommentsPathTrain))
rawCsvCommentsTrain = [comment for comment in rawCsvCommentsTrain]
# TRAIN: Create context
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~CREATING TRAINING CONTEXT" )
ctxTrain = MinerContext.loadContext(params.ctxCacheTrainFileName, rawCsvCommentsTrain, rawCsvReviews, params.supportThresh)
# TRAIN: Create features sets
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~PREPARING TRAINING FEATURES" )
featuresMapsTrain = []
classifierPolicy[ eClassifierCB.PrepareFeatures ](ctxTrain, featuresMapsTrain, params.featuresBitMask)
# TRAIN: Add CAR if desired
if ( params.featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.CAR ):
MinerFeaturesUtils.addFeaturesCAR( ctxTrain, featuresMapsTrain, params.CARMinSup, params.CARMinConf, params.CARCacheFileName )
# TRAIN: Convert features set to classifier specific input
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~CONVERTING TRAINING INPUTS" )
classifierInputsTrain = []
classifierPolicy[ eClassifierCB.ClassifierInputs ](ctxTrain, featuresMapsTrain, classifierInputsTrain, True)
# TEST: Load raw CSV comments
rawCsvCommentsTest = csv.DictReader(open(params.csvCommentsPathTest))
rawCsvCommentsTest = [comment for comment in rawCsvCommentsTest]
# TEST: Create context
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~CREATING TESTING CONTEXT" )
ctxTest = MinerContext.loadContext(params.ctxCacheTestFileName, rawCsvCommentsTest, rawCsvReviews, params.supportThresh)
# HACK - replace filtered words with those of training context
ctxTest.mFilteredWords = ctxTrain.mFilteredWords
# TEST: Create features sets
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~PREPARING TESTING FEATURES" )
featuresMapsTest = []
classifierPolicy[ eClassifierCB.PrepareFeatures ](ctxTest, featuresMapsTest, params.featuresBitMask )
# TEST: Add CAR if desired
if ( params.featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.CAR ):
MinerFeaturesUtils.addFeaturesCAR( ctxTest, featuresMapsTest, params.CARMinSup, params.CARMinConf, params.CARCacheFileName )
# TEST: Convert features set to classifier specific input
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~CONVERTING TESTING INPUTS" )
classifierInputsTest = []
classifierPolicy[ eClassifierCB.ClassifierInputs ](ctxTest, featuresMapsTest, classifierInputsTest, True)
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~CLASSIFYING" )
classifier = classifierPolicy[ eClassifierCB.Classify ]( classifierInputsTrain, classifierInputsTest, params.bDebug, params.outDebugFileName, params.outDebugLabel )
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~WRITING OUTPUTS" )
writeOutput(ctxTest, featuresMapsTest, params.classifierType, classifier)
logging.getLogger( "trainAndClassify" ).info( "~~~~~~~~~~~~~END" )
def SvmPrepareFeatures(ctx, outFeaturesMaps):
logging.getLogger("Svm").info("prepare features")
MinerFeaturesUtils.initFeatures(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesCommentLength(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesHelpfulnessRatio(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesPhrases(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesWordExists(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesAuthorFreqInReview(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesReviewAuthorMentioned(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesCommentAuthorMentioned(ctx, outFeaturesMaps)
MinerFeaturesUtils.addFeaturesCAR(ctx, outFeaturesMaps)
def NaiveBayesPrepareFeatures( ctx, outFeaturesMaps, featuresBitMask ):
logging.getLogger("NaiveBayes").info( "prepare features" )
MinerFeaturesUtils.initFeatures( ctx, outFeaturesMaps )
if ( featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.wordExists ):
MinerFeaturesUtils.addFeaturesWordExists( ctx, outFeaturesMaps )
if ( featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.commentLength ):
MinerFeaturesUtils.addFeaturesCommentLength( ctx, outFeaturesMaps )
if ( featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.helpfullnessRatio ):
MinerFeaturesUtils.addFeaturesHelpfulnessRatio( ctx, outFeaturesMaps )
if ( featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.authorFreqInReview ):
MinerFeaturesUtils.addFeaturesAuthorFreqInReview(ctx, outFeaturesMaps)
if ( featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.reviewAuthorMentioned ):
MinerFeaturesUtils.addFeaturesReviewAuthorMentioned(ctx, outFeaturesMaps)
if ( featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.commentAuthorMentioned ):
MinerFeaturesUtils.addFeaturesCommentAuthorMentioned( ctx, outFeaturesMaps )
if ( featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.dist ):
MinerFeaturesUtils.addFeaturesDist( ctx, outFeaturesMaps )
if ( featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.phrases ):
MinerFeaturesUtils.addFeaturesPhrases( ctx, outFeaturesMaps )
def NaiveBayesPrepareFeatures(ctx, outFeaturesMaps, featuresBitMask):
logging.getLogger("NaiveBayes").info("prepare features")
MinerFeaturesUtils.initFeatures(ctx, outFeaturesMaps)
if (featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.wordExists):
MinerFeaturesUtils.addFeaturesWordExists(ctx, outFeaturesMaps)
if (featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.commentLength):
MinerFeaturesUtils.addFeaturesCommentLength(ctx, outFeaturesMaps)
if (featuresBitMask
& MinerFeaturesUtils.eFeaturesMaskBits.helpfullnessRatio):
MinerFeaturesUtils.addFeaturesHelpfulnessRatio(ctx, outFeaturesMaps)
if (featuresBitMask
& MinerFeaturesUtils.eFeaturesMaskBits.authorFreqInReview):
MinerFeaturesUtils.addFeaturesAuthorFreqInReview(ctx, outFeaturesMaps)
if (featuresBitMask
& MinerFeaturesUtils.eFeaturesMaskBits.reviewAuthorMentioned):
MinerFeaturesUtils.addFeaturesReviewAuthorMentioned(
ctx, outFeaturesMaps)
if (featuresBitMask
& MinerFeaturesUtils.eFeaturesMaskBits.commentAuthorMentioned):
MinerFeaturesUtils.addFeaturesCommentAuthorMentioned(
ctx, outFeaturesMaps)
if (featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.dist):
MinerFeaturesUtils.addFeaturesDist(ctx, outFeaturesMaps)
if (featuresBitMask & MinerFeaturesUtils.eFeaturesMaskBits.phrases):
MinerFeaturesUtils.addFeaturesPhrases(ctx, outFeaturesMaps)
