def __init__(self, cus, accounts):
self.cus = util_data.ProjectData(cus).data[[
self.number, self.name, self.city, self.state, self.site, self.main
]]
self.accounts = util_data.ProjectData(accounts).data[[
self.number, self.assets
]]
self.merge()
def __init__(self, project, kernel='linear', C=1, gamma=0.1):
self.project = util_data.ProjectData(project)
self.kernel = kernel
self.C = C
self.gamma = gamma
self.createClassifier()
self.fitData()
self.predict()
self.getAccuracy()
def __init__(self, banks):
self.banks = util_data.ProjectData(banks).data[[
self.number, self.name, self.site, self.main, self.branches,
self.assets
]]
# self.banks.columns = ['bankID','name','charter','location','branches', 'assets']
self.banks.columns = [
'name', 'rank', 'id', 'location', 'charter', 'assets',
'assets_dom', 'assets_dom_pct', 'assets_cum_pct', 'branches',
'branches_for', 'ibf', 'owned_for_pct'
]
self.banks.apply(lambda x: pd.to_numeric(x, errors='ignore'))
def __init__(self,
project,
split=False,
score=False,
silhouette=False,
results=False,
params={}):
self.DF = util_data.ProjectData(project).DF # get datasets
self.preprocessData()
logger.info('params: ' + str(params))
if silhouette: self.fitSilhouette()
if score: self.fitNscore(params)
if results: print(self.__repr__())
def __init__(self, project, split=False, score=False):
self.project = util_data.ProjectData(project)
# self.preprocessData()
if split: self.splitTrainTest()
if score: self.fitNscore()
def __init__(self, project):
self.bag = util_data.ProjectData(project)