fix mgcd bug

edustudio/traintpl/__init__.py

@@ -0,0 +1,11 @@
+# -*- coding: utf-8 -*-
+# @Author : Xiangzhi Chen
+# @Github : kervias
+
+from .base_traintpl import BaseTrainTPL
+from .gd_traintpl import GDTrainTPL
+from .general_traintpl import GeneralTrainTPL
+from .atkt_traintpl import AtktTrainTPL
+from .dcd_traintpl import DCDTrainTPL
+from .adversarial_traintpl import AdversarialTrainTPL
+from .group_traintpl import GroupTrainTPL

edustudio/traintpl/group_traintpl.py

@@ -0,0 +1,89 @@
+from edustudio.traintpl import GeneralTrainTPL
+from edustudio.utils.common import tensor2npy
+import torch
+from tqdm import tqdm
+
+
+class GroupTrainTPL(GeneralTrainTPL):
+
+    @torch.no_grad()
+    def evaluate(self, loader):
+        self.model.eval()
+        stu_id_list = list(range(len(loader)))
+        pd_list = list(range(len(loader)))
+        gt_list = list(range(len(loader)))
+        for idx, batch_dict in enumerate(tqdm(loader, ncols=self.frame_cfg['TQDM_NCOLS'], desc="[PREDICT]")):
+            batch_dict = self.batch_dict2device(batch_dict)
+            eval_dict = self.model.predict(**batch_dict)
+            stu_id_list[idx] = batch_dict['group_id']
+            pd_list[idx] = eval_dict['y_pd']
+            gt_list[idx] = eval_dict['y_gt'] if 'y_gt' in eval_dict else batch_dict['label']
+        y_pd = torch.hstack(pd_list)
+        y_gt = torch.hstack(gt_list)
+        group_id = torch.hstack(stu_id_list)
+
+        eval_data_dict = {
+            'group_id': group_id,
+            'y_pd': y_pd,
+            'y_gt': y_gt,
+        }
+        if hasattr(self.model, 'get_stu_status'):
+            stu_stats_list = []
+            idx = torch.arange(0, self.datatpl_cfg['dt_info']['stu_count']).to(self.traintpl_cfg['device'])
+            for i in range(0,self.datatpl_cfg['dt_info']['stu_count'], self.traintpl_cfg['eval_batch_size']):
+                batch_stu_id = idx[i:i+self.traintpl_cfg['eval_batch_size']]
+                batch = self.model.get_stu_status(batch_stu_id)
+                stu_stats_list.append(batch)
+            stu_stats = torch.vstack(stu_stats_list)
+            eval_data_dict.update({
+                'stu_stats': tensor2npy(stu_stats),
+            })
+        if hasattr(self.datatpl, 'Q_mat'):
+            eval_data_dict.update({
+                'Q_mat': tensor2npy(self.datatpl.Q_mat)
+            })
+        eval_result = {}
+        for evaltpl in self.evaltpls: eval_result.update(
+                evaltpl.eval(ignore_metrics=self.traintpl_cfg['ignore_metrics_in_train'], **eval_data_dict)
+            )
+        return eval_result
+
+    @torch.no_grad()
+    def inference(self, loader):
+        self.model.eval()
+        stu_id_list = list(range(len(loader)))
+        pd_list = list(range(len(loader)))
+        gt_list = list(range(len(loader)))
+        for idx, batch_dict in enumerate(tqdm(loader, ncols=self.frame_cfg['TQDM_NCOLS'], desc="[PREDICT]")):
+            batch_dict = self.batch_dict2device(batch_dict)
+            eval_dict = self.model.predict(**batch_dict)
+            stu_id_list[idx] = batch_dict['group_id']
+            pd_list[idx] = eval_dict['y_pd']
+            gt_list[idx] = eval_dict['y_gt'] if 'y_gt' in eval_dict else batch_dict['label']
+        y_pd = torch.hstack(pd_list)
+        y_gt = torch.hstack(gt_list)
+        group_id = torch.hstack(stu_id_list)
+
+        eval_data_dict = {
+            'group_id': group_id,
+            'y_pd': y_pd,
+            'y_gt': y_gt,
+        }
+        if hasattr(self.model, 'get_stu_status'):
+            stu_stats_list = []
+            idx = torch.arange(0, self.datatpl_cfg['dt_info']['stu_count']).to(self.traintpl_cfg['device'])
+            for i in range(0,self.datatpl_cfg['dt_info']['stu_count'], self.traintpl_cfg['eval_batch_size']):
+                batch_stu_id = idx[i:i+self.traintpl_cfg['eval_batch_size']]
+                batch = self.model.get_stu_status(batch_stu_id)
+                stu_stats_list.append(batch)
+            stu_stats = torch.vstack(stu_stats_list)
+            eval_data_dict.update({
+                'stu_stats': tensor2npy(stu_stats),
+            })
+        if hasattr(self.datatpl, 'Q_mat'):
+            eval_data_dict.update({
+                'Q_mat': tensor2npy(self.datatpl.Q_mat)
+            })
+        eval_result = {}
+        for evaltpl in self.evaltpls: eval_result.update(evaltpl.eval(**eval_data_dict))
+        return eval_result

examples/single_model/run_mgcd_demo.py

@@ -0,0 +1,32 @@
+import sys
+import os
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../")
+os.chdir(os.path.dirname(os.path.abspath(__file__)))
+
+from edustudio.quickstart import run_edustudio
+
+run_edustudio(
+    dataset='ASSIST_0910',
+    cfg_file_name=None,
+    traintpl_cfg_dict={
+        'cls': 'GroupTrainTPL',
+        'early_stop_metrics': [('rmse','min')],
+        'best_epoch_metric': 'rmse',
+        'batch_size': 512
+    },
+    datatpl_cfg_dict={
+        'cls': 'MGCDDataTPL',
+        # 'load_data_from': 'rawdata',
+        # 'raw2mid_op': 'R2M_ASSIST_1213'
+    },
+    modeltpl_cfg_dict={
+        'cls': 'MGCD',
+    },
+    evaltpl_cfg_dict={
+        'clses': ['PredictionEvalTPL'],
+        'PredictionEvalTPL': {
+            'use_metrics': ['rmse']
+        }
+    }
+)