update test files of NCDM

tests/ncdm/conftest.py

@@ -1,41 +1,31 @@
 # coding: utf-8
-# 2021/4/6 @ WangFei
+# 2023/11/17 @ WangFei
 
-import random
 import pytest
-import torch
-import numpy as np
-from torch.utils.data import TensorDataset, DataLoader
+import pandas as pd
+import random
 
 
 @pytest.fixture(scope="package")
-def conf():
-    user_num = 5
-    item_num = 2
-    knowledge_num = 4
-    return user_num, item_num, knowledge_num
+def meta():
+    meta_data = {'userId': ['001', '002', '003'], 'itemId': ['adf', 'w5'], 'skill': ['skill1', 'skill2', 'skill3', 'skill4']}
+    return meta_data
 
 
 @pytest.fixture(scope="package")
 def data(conf):
-    user_num, item_num, knowledge_num = conf
-    knowledge_embs = np.zeros((item_num, knowledge_num))
-    for i in range(item_num):
-        for j in range(knowledge_num):
-            knowledge_embs[i][j] = random.randint(0, 1)
-    log = []
-    for i in range(user_num):
-        for j in range(item_num):
-            score = random.randint(0, 1)
-            log.append((i, j, knowledge_embs[j], score))
-
-    user_id, item_id, knowledge_emb, score = zip(*log)
-    batch_size = 4
+    meta_data = meta
+    item_skills = []
+    skll_n = len(meta_data['skill'])
+    for itemid in meta_data['itemId']:
+        item_skills.append(meta['skill'][random.randint(0, skll_n - 1)])
+    userIds, itemIds, skills, responses = []
+    for user in meta_data['userId']:
+        for i, item in enumerate(meta_data['itemId']):
+            userIds.append(user)
+            itemIds.append(item)
+            skills.append(item_skills[i])
+            responses.append(random.randint(0, 1))
 
-    dataset = TensorDataset(
-        torch.tensor(user_id, dtype=torch.int64),
-        torch.tensor(item_id, dtype=torch.int64),
-        torch.tensor(knowledge_emb, dtype=torch.int64),
-        torch.tensor(score, dtype=torch.float)
-    )
-    return DataLoader(dataset, batch_size=batch_size)
+    df_data = pd.DataFrame({'userId': userIds, 'itemId': itemIds, 'skill': skills, 'response': responses})
+    return df_data

tests/ncdm/test_ncdm.py

@@ -1,12 +1,14 @@
 # coding: utf-8
-# 2021/4/6 @ WangFei
+# 2023/11/17 @ WangFei
 from EduCDM import NCDM
 
 
-def test_train(data, conf, tmp_path):
-    user_num, item_num, knowledge_num = conf
-    cdm = NCDM(knowledge_num, item_num, user_num)
-    cdm.train(data, test_data=data, epoch=2)
+def test_train(data, meta, tmp_path):
+    df_data = data
+    meta_data = meta
+    cdm = NCDM(meta_data)
+    cdm.fit(train_data=df_data, epoch=2, val_data=df_data)
     filepath = tmp_path / "mcd.params"
     cdm.save(filepath)
     cdm.load(filepath)
+    cdm.eval(df_data)