def init_start_urls(self):
session = loadSession()
# matches = session.query(Match500.id).all()
matchids = session.query(
distinct(Match500RecentModel.match_id).label('match_id')).group_by(
Match500RecentModel.match_id).having(
func.count(1) != 40).all()
for matchid in matchids:
# print matchid[0]
# print dir(matchid)
url = self.gen_match_urls_with_id(str(matchid[0]))
self.start_urls.append(url)
print classification_report(y_test,lr_y_predict)
# print lr.coef_.T
# print lr_y_predict_prob
#
# print 'Accuracy of LR Classifier:',lr.score(X_test,y_test)
#2、对现有数据进行回测 "伪"
from adagu_bodan.model import loadSession
from adagu_bodan.model.bodan_500 import Bodan500
session = loadSession()
bodans = session.query(Bodan500).filter(Bodan500.away_goal != None, Bodan500.zero_one != None).all()
def predict_win_no_win(bodan):
x_column_names = column_names[0:25]
X = np.array([bodan.__dict__[name] for name in x_column_names])
X = ss.transform(X)
y_predict_prob = lr.predict_proba(X)
y_predict = lr.classes_[np.argmax(y_predict_prob > threshold, axis=1)]
bodan.predict_win_no_win = y_predict[0]
print y_predict[0]
for bodan in bodans:
predict_win_no_win(bodan)
def save_errs(self):
session = loadSession()
for err in self.spider_errs:
session.add(err)
session.commit()
