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()