def __init__(self, config=dict()):
GenericRanking.__init__(self, config)
self.logger = logging.getLogger(__name__)
self.name = 'svm'
for data_tier in self.data_tiers:
try:
self.clf_trend[data_tier] = joblib.load(self.data_path + '/' + self.name + '_trend_' + data_tier + '.pkl')
self.clf_avg[data_tier] = joblib.load(self.data_path + '/' + self.name + '_avg_' + data_tier + '.pkl')
except:
self.logger.info('%s classifier and regressor for data tier %s need to be trained', self.name, data_tier)
self.clf_trend[data_tier] = SVC(kernel='poly', probability=True, C=0.5)
self.clf_avg[data_tier] = SVR()
self.train()
self.test()
def __init__(self, config=dict()):
GenericRanking.__init__(self, config)
self.logger = logging.getLogger(__name__)
self.name = 'bayesian'
for data_tier in self.data_tiers:
try:
self.clf_trend[data_tier] = joblib.load(self.data_path + '/' + self.name + '_trend_' + data_tier + '.pkl')
self.clf_avg[data_tier] = joblib.load(self.data_path + '/' + self.name + '_avg_' + data_tier + '.pkl')
except:
self.logger.info('%s classifier and regressor for data tier %s need to be trained', self.name, data_tier)
self.clf_trend[data_tier] = GaussianNB()
self.clf_avg[data_tier] = BayesianRidge()
self.train()
self.test()
def __init__(self, config=dict()):
GenericRanking.__init__(self, config)
self.logger = logging.getLogger(__name__)
self.name = 'bayesian'
for data_tier in self.data_tiers:
try:
self.clf_trend[data_tier] = joblib.load(self.data_path + '/' +
self.name + '_trend_' +
data_tier + '.pkl')
self.clf_avg[data_tier] = joblib.load(self.data_path + '/' +
self.name + '_avg_' +
data_tier + '.pkl')
except:
self.logger.info(
'%s classifier and regressor for data tier %s need to be trained',
self.name, data_tier)
self.clf_trend[data_tier] = GaussianNB()
self.clf_avg[data_tier] = BayesianRidge()
self.train()
self.test()
def __init__(self, config=dict()):
GenericRanking.__init__(self, config)
self.logger = logging.getLogger(__name__)
self.name = 'svm'
for data_tier in self.data_tiers:
try:
self.clf_trend[data_tier] = joblib.load(self.data_path + '/' +
self.name + '_trend_' +
data_tier + '.pkl')
self.clf_avg[data_tier] = joblib.load(self.data_path + '/' +
self.name + '_avg_' +
data_tier + '.pkl')
except:
self.logger.info(
'%s classifier and regressor for data tier %s need to be trained',
self.name, data_tier)
self.clf_trend[data_tier] = SVC(kernel='poly',
probability=True,
C=0.5)
self.clf_avg[data_tier] = SVR()
self.train()
self.test()
