Update test_small_run.py

tests/test_small_run.py

@@ -28,21 +28,22 @@ def _run(
 
     # step 0  Data loading
     D, X = load_data(data_dir, n_sample)
+    ninduced
     listDtrain = process_multiSample.get_listDtrain(D)
-
-    for ksample in range(0, len(D)):
+    list_D_chunked = process_multiSample.get_listD_chunked(D)
+    for ksample in range(0, len(list_D_chunked)):
         random.seed(10)
-        ninduced = round(D[ksample]["X"].shape[0] * 0.35)
+        ninduced = round(list_D_chunked[ksample]["X"].shape[0] * 0.35)
         D_tmp = D[ksample]
         D[ksample]["Z"] = D_tmp["X"][random.sample(range(0, D_tmp["X"].shape[0] - 1), ninduced), :]
 
     # step 1 initialize model
-    fit = process_multiSample.ini_multiSample(D, n_loadings, "nb", chol=False)
-
+    fit = process_multiSample.ini_multiSample(list_D_chunked, n_loadings, "nb", chol=False)
+    
     # step 2 fit model
 
     (pp := (output_dir / "models" / "pp")).mkdir(parents=True, exist_ok=True)
-    fit = training_multiSample.train_model_mNSF(fit, pp, listDtrain, D, legacy=legacy, num_epochs=epochs)
+    fit = training_multiSample.train_model_mNSF(fit, pp, listDtrain, list_D_chunked, legacy=legacy, num_epochs=epochs)
     (output_dir / "list_fit_smallData.pkl").write_bytes(pickle.dumps(fit))
 
     # step 3 save results