def __init__(self): self.cm = cmClass.sq_cv_meta() self.cq = cqClass.sq_cv_q() self.cqm = cqmClass.sq_cv_q_meta() self.cqp = cqpClass.cv_q_predict() self.dtp = dtpClass.sq_dt_prediction() self.cfm = cfmClass.csv_file_manage() self.tm = tmClass.text_modification() self.vacMatching = vacancyMatching.VacancyMatching()
def __init__(self):
self.fileName = ""
self.tempFileOb = ''
self.tm = tmClass.text_modification()
self.fmArff = fmClass.file_manage('arff')
self.fmPkl = fmClass.file_manage('pkl')
self.features = {}
self.featureNameList = []
self.featureCounts = defaultdict(lambda :1)
self.featureVectors = []
self.labelCounts = defaultdict(lambda :0)
def __init__(self):
self.cm = cmClass.sq_cv_meta()
self.cqm = cqmClass.sq_cv_q_meta()
self.cq = cqClass.sq_cv_q()
self.cqp = cqpClass.cv_q_predict()
self.nbp = nbpClass.sq_nb_prediction()
self.predicDT = predictionDT.predictionDT()
self.tm = tmClass.text_modification()
self.fm = fmClass.file_manage('arff')
self.fmPkl = fmClass.file_manage('pkl')
self.fileName = ""
self.featureNameList = []
self.features = {}
self.featureCounts = defaultdict(lambda :1)
self.featureVectors = []
self.labelCounts = defaultdict(lambda :0)
def __init__(self):
self.cqm = cqmClass.sq_cv_q_meta()
self.tm = tmClass.text_modification()
st = self.tm.stringLowercase(st)
index = keyValues.index(st)
val = val + index
# If there are more than one values, then it multiplies by 10000 to get unique value
if len(str) > 1:
val = val * 10000
return val
if __name__ == "__main__":
cm = cmClass.sq_cv_meta()
cq = cqClass.sq_cv_q()
tm = tmClass.text_modification()
cfm = cfmClass.csv_file_manage()
td = TrainDataset()
"""
Functions Related to Save data in CSV files predictionDT.py
"""
types = cm.getMetaValue('predict_cat')
for idx, type in enumerate(types):
cfm.openCsv('predictionDTDataset/' + tm.stringUppercase(type), 'a')
dataSet = cq.getDataForDtDataset()
for data in dataSet:
elements = []
if type == 'job':