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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,213 @@ | ||
| from .base_evaltpl import BaseEvalTPL | ||
| import torch | ||
| import pandas as pd | ||
| import numpy as np | ||
| from edustudio.utils.common import tensor2npy | ||
| from edustudio.utils.callback import ModeState | ||
| from tqdm import tqdm | ||
|
|
||
|
|
||
| """ | ||
| reference code: https://github.com/CSLiJT/ID-CDF/blob/main/tools.py | ||
| """ | ||
|
|
||
| class IdentifiabilityEvalTPL(BaseEvalTPL): | ||
| default_cfg = { | ||
| 'use_metrics': ['doc_all'], # Degree of Consistency | ||
| 'test_only_metrics': ['doc_all'] | ||
| } | ||
|
|
||
|
|
||
| def eval(self, stu_stats:np.ndarray, Q_mat:np.ndarray, **kwargs): | ||
| metric_result = {} | ||
| ignore_metrics = kwargs.get('ignore_metrics', {}) | ||
| for metric_name in self.evaltpl_cfg[self.name]['use_metrics']: | ||
| if metric_name not in ignore_metrics: | ||
| if metric_name in self.evaltpl_cfg[self.name]['test_only_metrics'] and \ | ||
| self.callback_list.mode_state != ModeState.END: | ||
| continue | ||
|
|
||
| """ | ||
| theta_mat: np.array, user_know_hit: np.array, | ||
| log_mat: np.array, Q_mat: np.array, know_list = None): | ||
| """ | ||
| if metric_name == "doc_all": | ||
| metric_result[metric_name] = self._get_metrics(metric_name)( | ||
| stu_stats, | ||
| self._gen_hit_mat( | ||
| self.stu_id_total, | ||
| self.exer_id_total, | ||
| Q_mat | ||
| ), | ||
| self.log_mat_total, | ||
| Q_mat | ||
| ) | ||
| elif metric_name == "doc_train_val": | ||
| metric_result[metric_name] = self._get_metrics(metric_name)( | ||
| stu_stats, | ||
| self._gen_hit_mat( | ||
| self.stu_id_train_val, | ||
| self.exer_id_train_val, | ||
| Q_mat | ||
| ), | ||
| self.log_mat_train_val, | ||
| Q_mat | ||
| ) | ||
| elif metric_name == "doc_test": | ||
| metric_result[metric_name] = self._get_metrics(metric_name)( | ||
| stu_stats, | ||
| self._gen_hit_mat( | ||
| self.test_loader.dataset.dict_main['stu_id'], | ||
| self.test_loader.dataset.dict_main['exer_id'], | ||
| Q_mat | ||
| ), | ||
| self.log_mat_test, | ||
| Q_mat | ||
| ) | ||
| else: | ||
| raise ValueError(f"unknown metric_name: {metric_name}") | ||
| return metric_result | ||
|
|
||
| def _get_metrics(self, metric): | ||
| if metric == "doc_all": | ||
| return self.degree_of_consistency | ||
| elif metric == "doc_test": | ||
| return self.degree_of_consistency | ||
| elif metric == "doc_train_val": | ||
| return self.degree_of_consistency | ||
| else: | ||
| raise NotImplementedError | ||
|
|
||
| def degree_of_consistency(self, theta_mat: np.array, user_know_hit: np.array, \ | ||
| log_mat: np.array, Q_mat: np.array, know_list = None): | ||
| ''' | ||
| theta_mat: (n_user, n_know): the diagnostic result matrix | ||
| user_know_hit: (n_user, n_know): the (i,j) element indicate \ | ||
| the number of hits of the i-th user on the j-th attribute | ||
| log_mat: (n_user, n_exer): the matrix indicating whether the \ | ||
| student has correctly answered the exercise (+1) or not(-1) | ||
| Q_mat: (n_exer, n_know) | ||
| ''' | ||
| n_user, n_know = theta_mat.shape | ||
| n_exer = log_mat.shape[1] | ||
| doa_all = [] | ||
| know_list = list(range(n_know)) if know_list is None else know_list | ||
| for know_id in tqdm(know_list, desc='compute_DOC', ncols=100): | ||
| Z = 1e-9 | ||
| dm = 0 | ||
| exer_list = np.where(Q_mat[:,know_id] > 0)[0] | ||
| user_list = np.where(user_know_hit[:,know_id]>0)[0] | ||
| n_u_k = len(user_list) | ||
| # pbar = tqdm(total = n_u_k * (n_u_k - 1), desc='know_id = %d'%know_id) | ||
| for a in user_list: | ||
| for b in user_list: | ||
| # if m_ak != m_bk, then either m_ak > m_bk or m_bk > m_ak | ||
| if a == b: | ||
| continue | ||
| Z += (theta_mat[a, know_id] > theta_mat[b, know_id]) | ||
| nab = 1e-9 | ||
| dab = 1e-9 | ||
| for exer_id in exer_list: | ||
| Jab = (log_mat[a,exer_id] * log_mat[b,exer_id] != 0) | ||
| nab += Jab * (log_mat[a, exer_id] > log_mat[b, exer_id]) | ||
| dab += Jab * (log_mat[a, exer_id] != log_mat[b, exer_id]) | ||
| dm += (theta_mat[a, know_id] > theta_mat[b, know_id]) * nab / dab | ||
| # pbar.update(1) | ||
|
|
||
| doa = dm / Z | ||
| doa_all.append(doa) | ||
| return np.mean(doa_all) | ||
|
|
||
| def set_dataloaders(self, train_loader, test_loader, valid_loader=None): | ||
| super().set_dataloaders(train_loader, test_loader, valid_loader) | ||
|
|
||
| if self.valid_loader: | ||
|
|
||
| self.stu_id_total = tensor2npy(torch.cat([ | ||
| self.train_loader.dataset.dict_main['stu_id'], | ||
| self.valid_loader.dataset.dict_main['stu_id'], | ||
| self.test_loader.dataset.dict_main['stu_id'], | ||
| ])) | ||
| self.exer_id_total = tensor2npy(torch.cat([ | ||
| self.train_loader.dataset.dict_main['exer_id'], | ||
| self.valid_loader.dataset.dict_main['exer_id'], | ||
| self.test_loader.dataset.dict_main['exer_id'], | ||
| ])) | ||
|
|
||
| self.label_id_total = tensor2npy(torch.cat([ | ||
| self.train_loader.dataset.dict_main['label'], | ||
| self.valid_loader.dataset.dict_main['label'], | ||
| self.test_loader.dataset.dict_main['label'], | ||
| ])) | ||
|
|
||
| self.stu_id_train_val = tensor2npy(torch.cat([ | ||
| self.train_loader.dataset.dict_main['stu_id'], | ||
| self.valid_loader.dataset.dict_main['stu_id'], | ||
| ])) | ||
| self.exer_id_train_val = tensor2npy(torch.cat([ | ||
| self.train_loader.dataset.dict_main['exer_id'], | ||
| self.valid_loader.dataset.dict_main['exer_id'], | ||
| ])) | ||
|
|
||
| self.label_id_train_val = tensor2npy(torch.cat([ | ||
| self.train_loader.dataset.dict_main['label'], | ||
| self.valid_loader.dataset.dict_main['label'], | ||
| ])) | ||
|
|
||
| else: | ||
| self.stu_id_total = tensor2npy(torch.cat([ | ||
| self.train_loader.dataset.dict_main['stu_id'], | ||
| self.test_loader.dataset.dict_main['stu_id'], | ||
| ])) | ||
| self.exer_id_total = tensor2npy(torch.cat([ | ||
| self.train_loader.dataset.dict_main['exer_id'], | ||
| self.test_loader.dataset.dict_main['exer_id'], | ||
| ])) | ||
|
|
||
| self.label_id_total = tensor2npy(torch.cat([ | ||
| self.train_loader.dataset.dict_main['label'], | ||
| self.test_loader.dataset.dict_main['label'], | ||
| ])) | ||
|
|
||
| self.stu_id_train_val = tensor2npy(self.train_loader.dataset.dict_main['stu_id']) | ||
| self.exer_id_train_val = tensor2npy(self.train_loader.dataset.dict_main['exer_id']) | ||
| self.label_id_train_val = tensor2npy(self.train_loader.dataset.dict_main['label']) | ||
|
|
||
| self.log_mat_total = self._gen_log_mat(self.stu_id_total, self.exer_id_total, self.label_id_total) | ||
| self.log_mat_train_val = self._gen_log_mat(self.stu_id_train_val, self.exer_id_train_val, self.label_id_train_val) | ||
| self.log_mat_test = self._gen_log_mat( | ||
| tensor2npy(self.test_loader.dataset.dict_main['stu_id']), | ||
| tensor2npy(self.test_loader.dataset.dict_main['exer_id']), | ||
| tensor2npy(self.test_loader.dataset.dict_main['label']), | ||
| ) | ||
|
|
||
| def _gen_log_mat(self, uid, iid, label): | ||
| n_stu = self.datatpl_cfg['dt_info']['stu_count'] | ||
| n_exer = self.datatpl_cfg['dt_info']['exer_count'] | ||
| label = label.copy() | ||
| label[label == 0] = -1 | ||
| log_mat = np.zeros((n_stu, n_exer), dtype=np.float32) | ||
| log_mat[uid, iid] = label | ||
| return log_mat | ||
|
|
||
|
|
||
| def _gen_hit_mat(self, uid, iid, Q_mat): | ||
| n_stu = self.datatpl_cfg['dt_info']['stu_count'] | ||
| n_exer = self.datatpl_cfg['dt_info']['exer_count'] | ||
| n_cpt = self.datatpl_cfg['dt_info']['cpt_count'] | ||
|
|
||
| tmp_df = pd.DataFrame({ | ||
| 'uid': uid, | ||
| 'iid': iid | ||
| }) | ||
|
|
||
| # assert tmp_df['uid'].nunique() == n_stu | ||
|
|
||
| hit_df = tmp_df.groupby('uid').agg(list).apply(lambda x: list(Q_mat[np.array(x['iid'])].sum(axis=0)), axis=1) | ||
| if tmp_df['uid'].nunique() != n_stu: | ||
| zeros = list(np.zeros((n_cpt, ))) | ||
| new_index = list(range(n_stu)) | ||
| hit_df = hit_df.reindex(new_index, fill_value=zeros) | ||
| hit_df = hit_df.sort_index() | ||
|
|
||
| return np.array(hit_df.to_list()) |
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