def evaluate(self): eers = [] for subject in subjects: genuine_user_data = data.loc[data.subject == "vaibhav", \ "H.period":"UD.l.Return"] imposter_data = data.loc[data.subject != "vaibhav", :] self.train = genuine_user_data[-1:] self.test_genuine = genuine_user_data[:20] self.test_imposter = imposter_data.groupby("subject"). \ head(20).loc[:, "H.period":"UD.l.Return"] #total - selected genuine ,,,,, first five of every imposter displayed #print(genuine_user_data[:200]) self.training() self.testing() eers.append(evaluateEER(self.u_scores, \ self.i_scores)) #print(evaluateEER(self.u_scores, \ # self.i_scores)) #print(np.mean(eers)) break return np.mean(eers)
def evaluate(self): eers = [] for subject in subjects: genuine_user_data = data.loc[data.subject == subject, \ "H.period":"H.Return"] imposter_data = data.loc[data.subject != subject, :] self.train = genuine_user_data[:200] self.test_genuine = genuine_user_data[200:] self.test_imposter = imposter_data.groupby("subject"). \ head(5).loc[:, "H.period":"H.Return"] self.training() self.testing() eers.append(evaluateEER(self.user_scores, \ self.imposter_scores)) return np.mean(eers)