Esempio n. 1
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 def create_stan_data(self, n_users, pct_items, err_rates, difficulty_dict):
     self.err_rates = err_rates
     self.difficulty_dict = difficulty_dict
     self.sim_df = simulation.create_sim_df(create_user_data, self.df, n_users, pct_items,
                                                     err_rates, difficulty_dict, extraarg=self.parsers)
     stan_data = utils.calc_distances(self.sim_df, (lambda x,y: 1 - evalb(x, y)), label_colname="parse", item_colname="sentenceId")
     return stan_data
Esempio n. 2
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 def create_stan_data(self, n_users, pct_items, err_rates, difficulty_dict):
     self.err_rates = err_rates
     self.difficulty_dict = difficulty_dict
     self.sim_df = simulation.create_sim_df(create_user_data, self.df,
                                            n_users, pct_items, err_rates,
                                            difficulty_dict)
     stan_data = utils.calc_distances(
         self.sim_df, (lambda x, y: 1 - kendaltauscore(x, y)),
         label_colname="rankings",
         item_colname="topic_item")
     return stan_data
 def create_stan_data(self, n_users, pct_items, err_rates, difficulty_dict):
     self.err_rates = err_rates
     self.difficulty_dict = difficulty_dict
     self.sim_df = simulation.create_sim_df(create_user_data, self.df,
                                            n_users, pct_items, err_rates,
                                            difficulty_dict)
     stan_data = utils.calc_distances(
         self.sim_df, (lambda x, y: 1 - oks_score_multi(x, y)),
         label_colname="annotation",
         item_colname="item")
     return stan_data
Esempio n. 4
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 def produce_stan_data(self):
     self.stan_data = utils.calc_distances(self.annodf,
                                           self.distance_fn,
                                           label_colname=self.label_colname,
                                           item_colname=self.item_colname,
                                           uid_colname=self.uid_colname)
Esempio n. 5
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 def produce_stan_data(self):
     ''' use distance function to create distance matrices and other data in its final form before training '''
     self.stan_data = utils.calc_distances(self.annodf, self.distance_fn, label_colname=self.label_colname, item_colname=self.item_colname, uid_colname=self.uid_colname)
 def create_stan_data(self, n_users, pct_items, err_rates, difficulty_dict):
     self.err_rates = err_rates
     self.difficulty_dict = difficulty_dict
     self.sim_df = simulation.create_sim_df(create_user_data, self.df, n_users, pct_items, err_rates, difficulty_dict)
     stan_data = utils.calc_distances(self.sim_df, (lambda x,y: 1 - bb_intersection_over_union(x, y)))
     return stan_data