From 45e1b53aed82108aeac8b4997d98d5a4f89da9ed Mon Sep 17 00:00:00 2001
From: Htun Teza <55363976+finerbrighterlighter@users.noreply.github.com>
Date: Tue, 24 Feb 2026 16:00:24 +0700
Subject: [PATCH] DSM library change

---
 README.md      | 9 +++++----
 ddh/ddh_api.py | 4 ++--
 ddh/utils.py   | 2 +-
 3 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 1c0b628..8677bf0 100644
--- a/README.md
+++ b/README.md
@@ -4,12 +4,13 @@ This repository is a Pytorch implementation of the paper :  [Dynamic Deep Hit](h
 
 The code is based on the [Deep Survival Machines repository](https://github.com/autonlab/DeepSurvivalMachines) and use a similar module structure.
 
-
 ### Code Description
 All the code related to Dynamic Deep Hit is loacted in `ddh`. Only API should be used for application.
 
 An example is available in the `examples` folder.
 
-## TODOs
-- Full documentation
-- Further testing to ensure the model is reproducing the original paper's performance
+## Note
+- Deep Survival Machines library has been merged into auton_survival.
+```
+pip install auton_survival 
+```
diff --git a/ddh/ddh_api.py b/ddh/ddh_api.py
index 8216cab..7824d25 100644
--- a/ddh/ddh_api.py
+++ b/ddh/ddh_api.py
@@ -1,5 +1,5 @@
-from DeepSurvivalMachines.dsm.dsm_api import DeepRecurrentSurvivalMachines
-from dsm.utilities import _reshape_tensor_with_nans, _get_padded_features, _get_padded_targets
+from auton_survival.models.dsm import DeepRecurrentSurvivalMachines
+from auton_survival.models.dsm.utilities import _reshape_tensor_with_nans, _get_padded_features, _get_padded_targets
 from ddh.ddh_torch import DynamicDeepHitTorch
 from ddh.utils import train_ddh
 from ddh.losses import total_loss
diff --git a/ddh/utils.py b/ddh/utils.py
index d6daf57..d1320bb 100644
--- a/ddh/utils.py
+++ b/ddh/utils.py
@@ -1,5 +1,5 @@
 from ddh.losses import total_loss
-from dsm.utilities import get_optimizer, _reshape_tensor_with_nans
+from auton_survival.models.dsm.utilities import get_optimizer, _reshape_tensor_with_nans
 
 import torch
 import torch.nn as nn