f = open(fname, 'rb')
predictors = pickle.load(f)
f.close()
+ return True;
def dump():
global predictors
+ ss = '\n'
for i in predictors:
- print i
- print i.coef_
+ ss += str(i.__dict__)
+ ss += '\n'
+ return ss
def predict(index, features):
pp = predictors[index]
true_class = int(pp.classes_[1] == 1)
prob = pp.predict_proba([features])[0, true_class]
+ print prob
return prob