diff --git "a/ro4_d2.ipynb" "b/ro4_d2.ipynb"
new file mode 100644--- /dev/null
+++ "b/ro4_d2.ipynb"
@@ -0,0 +1,1125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "69594371-53cc-42c0-aa34-7c850aed7356",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: datasets in /home/vmsavla/.local/lib/python3.11/site-packages (3.5.0)\n",
+      "Requirement already satisfied: filelock in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from datasets) (3.15.4)\n",
+      "Requirement already satisfied: numpy>=1.17 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from datasets) (2.0.1)\n",
+      "Requirement already satisfied: pyarrow>=15.0.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from datasets) (19.0.1)\n",
+      "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from datasets) (0.3.8)\n",
+      "Requirement already satisfied: pandas in /home/vmsavla/.local/lib/python3.11/site-packages (from datasets) (2.2.3)\n",
+      "Requirement already satisfied: requests>=2.32.2 in /home/vmsavla/.local/lib/python3.11/site-packages (from datasets) (2.32.3)\n",
+      "Requirement already satisfied: tqdm>=4.66.3 in /home/vmsavla/.local/lib/python3.11/site-packages (from datasets) (4.67.1)\n",
+      "Requirement already satisfied: xxhash in /home/vmsavla/.local/lib/python3.11/site-packages (from datasets) (3.5.0)\n",
+      "Requirement already satisfied: multiprocess<0.70.17 in /home/vmsavla/.local/lib/python3.11/site-packages (from datasets) (0.70.16)\n",
+      "Requirement already satisfied: fsspec<=2024.12.0,>=2023.1.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets) (2024.12.0)\n",
+      "Requirement already satisfied: aiohttp in /home/vmsavla/.local/lib/python3.11/site-packages (from datasets) (3.11.18)\n",
+      "Requirement already satisfied: huggingface-hub>=0.24.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from datasets) (0.30.2)\n",
+      "Requirement already satisfied: packaging in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from datasets) (24.0)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from datasets) (6.0.2)\n",
+      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp->datasets) (2.6.1)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp->datasets) (1.3.2)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from aiohttp->datasets) (24.2.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp->datasets) (1.6.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp->datasets) (6.4.3)\n",
+      "Requirement already satisfied: propcache>=0.2.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp->datasets) (0.3.1)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp->datasets) (1.20.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from huggingface-hub>=0.24.0->datasets) (4.12.2)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from requests>=2.32.2->datasets) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from requests>=2.32.2->datasets) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from requests>=2.32.2->datasets) (2.2.1)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from requests>=2.32.2->datasets) (2024.7.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from pandas->datasets) (2.9.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from pandas->datasets) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /home/vmsavla/.local/lib/python3.11/site-packages (from pandas->datasets) (2025.1)\n",
+      "Requirement already satisfied: six>=1.5 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7c3d244c-044c-495e-95a5-d72c201f0555",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: molfeat in /home/vmsavla/.local/lib/python3.11/site-packages (0.10.1)\n",
+      "Requirement already satisfied: tqdm in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (4.67.1)\n",
+      "Requirement already satisfied: loguru in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (0.7.3)\n",
+      "Requirement already satisfied: joblib in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (1.4.2)\n",
+      "Requirement already satisfied: filelock in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from molfeat) (3.15.4)\n",
+      "Requirement already satisfied: fsspec>=2021.9 in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (2025.3.2)\n",
+      "Requirement already satisfied: s3fs>=2021.9 in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (2025.3.2)\n",
+      "Requirement already satisfied: gcsfs>=2021.9 in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (2025.3.2)\n",
+      "Requirement already satisfied: pandas in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (2.2.3)\n",
+      "Requirement already satisfied: numpy in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from molfeat) (2.0.1)\n",
+      "Requirement already satisfied: scipy in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (1.15.2)\n",
+      "Requirement already satisfied: torch>=1.13 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from molfeat) (2.4.0)\n",
+      "Requirement already satisfied: datamol>=0.8.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (0.12.5)\n",
+      "Requirement already satisfied: pyyaml in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from molfeat) (6.0.2)\n",
+      "Requirement already satisfied: platformdirs in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from molfeat) (4.2.0)\n",
+      "Requirement already satisfied: matplotlib in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (3.10.1)\n",
+      "Requirement already satisfied: h5py in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (3.13.0)\n",
+      "Requirement already satisfied: pyarrow in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (19.0.1)\n",
+      "Requirement already satisfied: pydantic>=2.0.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (2.11.3)\n",
+      "Requirement already satisfied: scikit-learn in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (1.6.1)\n",
+      "Requirement already satisfied: packaging in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from molfeat) (24.0)\n",
+      "Requirement already satisfied: pmapper in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (1.1.2)\n",
+      "Requirement already satisfied: mordredcommunity in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (2.0.6)\n",
+      "Requirement already satisfied: python-dotenv in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (1.1.0)\n",
+      "Requirement already satisfied: rdkit in /home/vmsavla/.local/lib/python3.11/site-packages (from molfeat) (2024.9.6)\n",
+      "Requirement already satisfied: pillow in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from datamol>=0.8.0->molfeat) (9.4.0)\n",
+      "Requirement already satisfied: selfies in /home/vmsavla/.local/lib/python3.11/site-packages (from datamol>=0.8.0->molfeat) (2.2.0)\n",
+      "Requirement already satisfied: typing-extensions in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from datamol>=0.8.0->molfeat) (4.12.2)\n",
+      "Requirement already satisfied: importlib-resources in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from datamol>=0.8.0->molfeat) (6.4.2)\n",
+      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/vmsavla/.local/lib/python3.11/site-packages (from gcsfs>=2021.9->molfeat) (3.11.18)\n",
+      "Requirement already satisfied: decorator>4.1.2 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from gcsfs>=2021.9->molfeat) (5.1.1)\n",
+      "Requirement already satisfied: google-auth>=1.2 in /home/vmsavla/.local/lib/python3.11/site-packages (from gcsfs>=2021.9->molfeat) (2.39.0)\n",
+      "Requirement already satisfied: google-auth-oauthlib in /home/vmsavla/.local/lib/python3.11/site-packages (from gcsfs>=2021.9->molfeat) (1.2.1)\n",
+      "Requirement already satisfied: google-cloud-storage in /home/vmsavla/.local/lib/python3.11/site-packages (from gcsfs>=2021.9->molfeat) (3.1.0)\n",
+      "Requirement already satisfied: requests in /home/vmsavla/.local/lib/python3.11/site-packages (from gcsfs>=2021.9->molfeat) (2.32.3)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from pydantic>=2.0.0->molfeat) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.33.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from pydantic>=2.0.0->molfeat) (2.33.1)\n",
+      "Requirement already satisfied: typing-inspection>=0.4.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from pydantic>=2.0.0->molfeat) (0.4.0)\n",
+      "Requirement already satisfied: aiobotocore<3.0.0,>=2.5.4 in /home/vmsavla/.local/lib/python3.11/site-packages (from s3fs>=2021.9->molfeat) (2.21.1)\n",
+      "Requirement already satisfied: sympy in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from torch>=1.13->molfeat) (1.13.2)\n",
+      "Requirement already satisfied: networkx in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from torch>=1.13->molfeat) (3.3)\n",
+      "Requirement already satisfied: jinja2 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from torch>=1.13->molfeat) (3.1.4)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib->molfeat) (1.3.2)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib->molfeat) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib->molfeat) (4.57.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib->molfeat) (1.4.8)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib->molfeat) (3.2.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from matplotlib->molfeat) (2.9.0)\n",
+      "Requirement already satisfied: six in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from mordredcommunity->molfeat) (1.16.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from pandas->molfeat) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /home/vmsavla/.local/lib/python3.11/site-packages (from pandas->molfeat) (2025.1)\n",
+      "Requirement already satisfied: threadpoolctl>=3.1.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from scikit-learn->molfeat) (3.6.0)\n",
+      "Requirement already satisfied: aioitertools<1.0.0,>=0.5.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2021.9->molfeat) (0.12.0)\n",
+      "Requirement already satisfied: botocore<1.37.2,>=1.37.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2021.9->molfeat) (1.37.1)\n",
+      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2021.9->molfeat) (1.0.1)\n",
+      "Requirement already satisfied: multidict<7.0.0,>=6.0.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2021.9->molfeat) (6.4.3)\n",
+      "Requirement already satisfied: wrapt<2.0.0,>=1.10.10 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiobotocore<3.0.0,>=2.5.4->s3fs>=2021.9->molfeat) (1.17.2)\n",
+      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->gcsfs>=2021.9->molfeat) (2.6.1)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->gcsfs>=2021.9->molfeat) (1.3.2)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->gcsfs>=2021.9->molfeat) (24.2.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->gcsfs>=2021.9->molfeat) (1.6.0)\n",
+      "Requirement already satisfied: propcache>=0.2.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->gcsfs>=2021.9->molfeat) (0.3.1)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->gcsfs>=2021.9->molfeat) (1.20.0)\n",
+      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-auth>=1.2->gcsfs>=2021.9->molfeat) (5.5.2)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-auth>=1.2->gcsfs>=2021.9->molfeat) (0.4.2)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-auth>=1.2->gcsfs>=2021.9->molfeat) (4.9.1)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-auth-oauthlib->gcsfs>=2021.9->molfeat) (2.0.0)\n",
+      "Requirement already satisfied: google-api-core<3.0.0dev,>=2.15.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-cloud-storage->gcsfs>=2021.9->molfeat) (2.24.2)\n",
+      "Requirement already satisfied: google-cloud-core<3.0dev,>=2.4.2 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-cloud-storage->gcsfs>=2021.9->molfeat) (2.4.3)\n",
+      "Requirement already satisfied: google-resumable-media>=2.7.2 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-cloud-storage->gcsfs>=2021.9->molfeat) (2.7.2)\n",
+      "Requirement already satisfied: google-crc32c<2.0dev,>=1.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-cloud-storage->gcsfs>=2021.9->molfeat) (1.7.1)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from requests->gcsfs>=2021.9->molfeat) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from requests->gcsfs>=2021.9->molfeat) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from requests->gcsfs>=2021.9->molfeat) (2.2.1)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from requests->gcsfs>=2021.9->molfeat) (2024.7.4)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from jinja2->torch>=1.13->molfeat) (2.1.5)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from sympy->torch>=1.13->molfeat) (1.3.0)\n",
+      "Requirement already satisfied: googleapis-common-protos<2.0.0,>=1.56.2 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-api-core<3.0.0dev,>=2.15.0->google-cloud-storage->gcsfs>=2021.9->molfeat) (1.70.0)\n",
+      "Requirement already satisfied: protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<7.0.0,>=3.19.5 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-api-core<3.0.0dev,>=2.15.0->google-cloud-storage->gcsfs>=2021.9->molfeat) (6.30.2)\n",
+      "Requirement already satisfied: proto-plus<2.0.0,>=1.22.3 in /home/vmsavla/.local/lib/python3.11/site-packages (from google-api-core<3.0.0dev,>=2.15.0->google-cloud-storage->gcsfs>=2021.9->molfeat) (1.26.1)\n",
+      "Requirement already satisfied: pyasn1<0.7.0,>=0.6.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from pyasn1-modules>=0.2.1->google-auth>=1.2->gcsfs>=2021.9->molfeat) (0.6.1)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib->gcsfs>=2021.9->molfeat) (3.2.2)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install -U molfeat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cd520301-942e-4f78-ba2b-00bcb703b89c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Collecting xgboost\n",
+      "  Using cached xgboost-3.0.0-py3-none-manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n",
+      "Requirement already satisfied: numpy in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from xgboost) (2.0.1)\n",
+      "Collecting nvidia-nccl-cu12 (from xgboost)\n",
+      "  Using cached nvidia_nccl_cu12-2.26.2.post1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.0 kB)\n",
+      "Requirement already satisfied: scipy in /home/vmsavla/.local/lib/python3.11/site-packages (from xgboost) (1.15.2)\n",
+      "Using cached xgboost-3.0.0-py3-none-manylinux_2_28_x86_64.whl (253.9 MB)\n",
+      "Using cached nvidia_nccl_cu12-2.26.2.post1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (291.7 MB)\n",
+      "Installing collected packages: nvidia-nccl-cu12, xgboost\n",
+      "Successfully installed nvidia-nccl-cu12-2.26.2.post1 xgboost-3.0.0\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install xgboost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2b397db7-07fa-4b64-8b0d-9535ad22891f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Collecting seaborn\n",
+      "  Using cached seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from seaborn) (2.0.1)\n",
+      "Requirement already satisfied: pandas>=1.2 in /home/vmsavla/.local/lib/python3.11/site-packages (from seaborn) (2.2.3)\n",
+      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/vmsavla/.local/lib/python3.11/site-packages (from seaborn) (3.10.1)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.57.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.8)\n",
+      "Requirement already satisfied: packaging>=20.0 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.0)\n",
+      "Requirement already satisfied: pillow>=8 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (9.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /home/vmsavla/.local/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /home/vmsavla/.local/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.1)\n",
+      "Requirement already satisfied: six>=1.5 in /sw/pkgs/arc/mamba/py3.11/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
+      "Using cached seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
+      "Installing collected packages: seaborn\n",
+      "Successfully installed seaborn-0.13.2\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f852b57-d77b-49db-a1dd-200ccac9b664",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c6456eee-0ed2-4d13-9e94-0fd393c9862c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Redirect Hugging Face cache to scratch or job-specific temp directory\n",
+    "os.environ['HF_HOME'] = '/home/vmsavla/turbo/vmsavla/opt/huggingface_cache'  # adjust if needed\n",
+    "os.environ['HF_DATASETS_CACHE'] = '/home/vmsavla/turbo/vmsavla/opt/huggingface_datasets'\n",
+    "os.environ['TRANSFORMERS_CACHE'] = '/home/vmsavla/turbo/vmsavla/opt/huggingface_transformers'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1512b194-8b1f-4940-a12c-90828df10675",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vmsavla/.local/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "abb77f88-e9ff-4f04-a141-64aeeea9a359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d2_data = load_dataset(\"vmsavla/ro4_vs_d2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e58ba525-1966-4f63-a6ec-1e5f60c98113",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = d2_data[\"train\"]\n",
+    "val_data = d2_data[\"validation\"]\n",
+    "test_data = d2_data[\"test\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d345d7ad-d57c-4559-8180-b320694c3cf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df = d2_data[\"train\"].to_pandas()\n",
+    "val_df = d2_data[\"validation\"].to_pandas()\n",
+    "test_df = d2_data[\"test\"].to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "32ce5191-d675-481f-a4f4-8d4547096551",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from molfeat.calc import FPCalculator\n",
+    "from molfeat.trans import MoleculeTransformer\n",
+    "\n",
+    "# Initialize transformer\n",
+    "calc = FPCalculator(\"ecfp\")\n",
+    "mol_transf = MoleculeTransformer(calc, n_jobs=30)\n",
+    "\n",
+    "def transform_to_features(df, split_name=\"\", batch_size=100000):\n",
+    "    start = time.time()\n",
+    "    print(f\"\\nStarting transformation for {split_name}...\")\n",
+    "\n",
+    "    smiles = df['clean_smiles'].values\n",
+    "    values = df['value'].values\n",
+    "    all_features = []\n",
+    "\n",
+    "    # Loop over the dataset in batches\n",
+    "    for i in tqdm(range(0, len(smiles), batch_size), desc=f\"{split_name} batches\"):\n",
+    "        batch_smiles = smiles[i:i + batch_size]\n",
+    "        batch_fps = mol_transf(batch_smiles)  # Generate fingerprints\n",
+    "        batch_fps = np.stack(batch_fps)  # Stack into a numpy array\n",
+    "        all_features.append(batch_fps)\n",
+    "\n",
+    "    # Stack all features into a single matrix\n",
+    "    features = np.vstack(all_features)\n",
+    "    \n",
+    "    # Return features (fingerprints), and values (target variable)\n",
+    "    print(f\"Finished {split_name} in {time.time() - start:.2f} seconds.\")\n",
+    "    return smiles, features, values \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8fe2b40d-bd85-4d6c-b7bd-d9f5397a4e40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Starting transformation for train...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "train batches:   0%|          | 0/124 [00:00<?, ?it/s]/home/vmsavla/.local/lib/python3.11/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
+      "  warnings.warn(\n",
+      "train batches: 100%|██████████| 124/124 [30:52<00:00, 14.94s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished train in 1872.79 seconds.\n",
+      "\n",
+      "Starting transformation for validation...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "validation batches: 100%|██████████| 16/16 [03:52<00:00, 14.55s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished validation in 235.71 seconds.\n",
+      "\n",
+      "Starting transformation for test...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "test batches: 100%|██████████| 16/16 [03:46<00:00, 14.13s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished test in 228.47 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "smiles_train, X_train, y_train = transform_to_features(train_df, \"train\")\n",
+    "smiles_val, X_val, y_val = transform_to_features(val_df, \"validation\")\n",
+    "smiles_test, X_test, y_test = transform_to_features(test_df, \"test\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a07da706-afbb-45d3-90f9-d2b6c066db09",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12378792, 2048)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "44fbe4a9-cd36-45ac-8db4-5de1c5c79546",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12378792,)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8ebf5532-6849-438f-83cb-fc69b52ba988",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12378792,)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "smiles_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "598040c6-d193-4b02-b9ea-3df05ec73f5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def clean_features_labels(X, y):\n",
+    "    mask = np.isfinite(y)  # Filters out NaN and Inf\n",
+    "    return X[mask], y[mask]\n",
+    "\n",
+    "X_train, y_train = clean_features_labels(X_train, y_train)\n",
+    "X_val, y_val = clean_features_labels(X_val, y_val)\n",
+    "X_test, y_test = clean_features_labels(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a92f5cc3-b16f-4736-98f4-e671b4f267d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "56a9d871-2020-4b5c-83c5-9415124a53ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the DMatrix (only features and target values)\n",
+    "dtrain = xgb.QuantileDMatrix(X_train, label=y_train)\n",
+    "dval = xgb.QuantileDMatrix(X_val, label=y_val, ref=dtrain)\n",
+    "dtest = xgb.QuantileDMatrix(X_test, label=y_test, ref=dtrain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9d43701d-32b9-4fc8-a1ac-24b91f98134c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vmsavla/.local/lib/python3.11/site-packages/xgboost/core.py:726: FutureWarning: Pass `evals` as keyword args.\n",
+      "  warnings.warn(msg, FutureWarning)\n",
+      "/home/vmsavla/.local/lib/python3.11/site-packages/xgboost/callback.py:386: UserWarning: [22:40:52] WARNING: /workspace/src/context.cc:49: No visible GPU is found, setting device to CPU.\n",
+      "  self.starting_round = model.num_boosted_rounds()\n",
+      "/home/vmsavla/.local/lib/python3.11/site-packages/xgboost/callback.py:386: UserWarning: [22:40:52] WARNING: /workspace/src/context.cc:203: XGBoost is not compiled with CUDA support.\n",
+      "  self.starting_round = model.num_boosted_rounds()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0]\ttrain-rmse:7.30350\teval-rmse:7.25729\n",
+      "[50]\ttrain-rmse:5.77353\teval-rmse:5.76242\n",
+      "[100]\ttrain-rmse:5.39339\teval-rmse:5.38679\n",
+      "[150]\ttrain-rmse:5.18316\teval-rmse:5.17715\n",
+      "[200]\ttrain-rmse:5.03793\teval-rmse:5.03449\n",
+      "[250]\ttrain-rmse:4.94044\teval-rmse:4.93774\n",
+      "[300]\ttrain-rmse:4.86656\teval-rmse:4.86505\n",
+      "[350]\ttrain-rmse:4.80352\teval-rmse:4.80343\n",
+      "[400]\ttrain-rmse:4.75185\teval-rmse:4.75253\n",
+      "[450]\ttrain-rmse:4.70762\teval-rmse:4.70931\n",
+      "[499]\ttrain-rmse:4.67033\teval-rmse:4.67373\n",
+      "Test RMSE: 4.7336\n",
+      "Test R^2: 0.5824\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'Booster' object has no attribute 'eval_metrics'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[20], line 39\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest R^2: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mr2\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     38\u001b[0m \u001b[38;5;66;03m# Step 6: Plot evaluation curves (training/validation RMSE)\u001b[39;00m\n\u001b[0;32m---> 39\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mbst\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval_metrics\u001b[49m\n\u001b[1;32m     40\u001b[0m epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(results[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrmse\u001b[39m\u001b[38;5;124m'\u001b[39m])  \u001b[38;5;66;03m# Number of epochs\u001b[39;00m\n\u001b[1;32m     41\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Booster' object has no attribute 'eval_metrics'"
+     ]
+    }
+   ],
+   "source": [
+    "params = {\n",
+    "    'objective': 'reg:squarederror',\n",
+    "    \"tree_method\": \"hist\", \"device\": \"cuda\",# Regression task\n",
+    "    #\"tree_method\": \"gpu_hist\", # or \"hist\" for cpu\n",
+    "    'eval_metric': 'rmse',  # Root Mean Squared Error\n",
+    "    'max_depth': 6,  # Depth of trees\n",
+    "    'learning_rate': 0.1,  # Learning rate\n",
+    "    'colsample_bytree': 0.8,  # Column sampling rate\n",
+    "    'subsample': 0.8,  # Subsample ratio\n",
+    "    'seed': 42  # For reproducibility\n",
+    "}\n",
+    "\n",
+    "# Step 3: Train the model with early stopping on validation set\n",
+    "num_round = 500  # Specify the number of boosting rounds\n",
+    "evals = [(dtrain, 'train'), (dval, 'eval')]  # Track both training and validation performance\n",
+    "evals_result = {}\n",
+    "# Train the model\n",
+    "bst = xgb.train(\n",
+    "    params,\n",
+    "    dtrain,\n",
+    "    num_round,\n",
+    "    evals,\n",
+    "    early_stopping_rounds=10,  # Stop early if validation metric doesn't improve\n",
+    "    verbose_eval=50,\n",
+    "    evals_result=evals_result# Print evaluation results every 10 rounds\n",
+    ")\n",
+    "\n",
+    "# Step 4: Predict on the test set\n",
+    "y_pred = bst.predict(dtest)\n",
+    "\n",
+    "# Step 5: Evaluate the model performance\n",
+    "# RMSE and R^2\n",
+    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(f\"Test RMSE: {rmse:.4f}\")\n",
+    "print(f\"Test R^2: {r2:.4f}\")\n",
+    "\n",
+    "# Step 6: Plot evaluation curves (training/validation RMSE)\n",
+    "#results = bst.eval_metrics\n",
+    "train_rmse = evals_result['train']['rmse']\n",
+    "val_rmse = evals_result['eval']['rmse']\n",
+    "epochs = range(1, len(train_rmse) + 1)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(epochs, train_rmse, label='Train RMSE')\n",
+    "plt.plot(epochs, val_rmse, label='Validation RMSE')\n",
+    "plt.xlabel('Boosting Round')\n",
+    "plt.ylabel('RMSE')\n",
+    "plt.title('XGBoost RMSE per Boosting Round')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Step 7: Plot feature importance\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "xgb.plot_importance(bst, importance_type=\"weight\", max_num_features=10, height=0.8)\n",
+    "plt.title('Top 10 Feature Importance')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "56763743-a19c-4fcc-bce5-d4981fc37cb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vmsavla/.local/lib/python3.11/site-packages/xgboost/core.py:726: FutureWarning: Pass `evals` as keyword args.\n",
+      "  warnings.warn(msg, FutureWarning)\n",
+      "/home/vmsavla/.local/lib/python3.11/site-packages/xgboost/callback.py:386: UserWarning: [22:51:31] WARNING: /workspace/src/context.cc:49: No visible GPU is found, setting device to CPU.\n",
+      "  self.starting_round = model.num_boosted_rounds()\n",
+      "/home/vmsavla/.local/lib/python3.11/site-packages/xgboost/callback.py:386: UserWarning: [22:51:31] WARNING: /workspace/src/context.cc:203: XGBoost is not compiled with CUDA support.\n",
+      "  self.starting_round = model.num_boosted_rounds()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0]\ttrain-rmse:7.27685\teval-rmse:7.23215\n",
+      "[50]\ttrain-rmse:5.44397\teval-rmse:5.44554\n",
+      "[100]\ttrain-rmse:5.04288\teval-rmse:5.05111\n",
+      "[150]\ttrain-rmse:4.84062\teval-rmse:4.85291\n",
+      "[200]\ttrain-rmse:4.71714\teval-rmse:4.73322\n",
+      "[250]\ttrain-rmse:4.63345\teval-rmse:4.65308\n",
+      "[300]\ttrain-rmse:4.56989\teval-rmse:4.59395\n",
+      "[350]\ttrain-rmse:4.51755\teval-rmse:4.54636\n",
+      "[400]\ttrain-rmse:4.47385\teval-rmse:4.50751\n",
+      "[450]\ttrain-rmse:4.43351\teval-rmse:4.47145\n",
+      "[500]\ttrain-rmse:4.39800\teval-rmse:4.44092\n",
+      "[550]\ttrain-rmse:4.37058\teval-rmse:4.41721\n",
+      "[600]\ttrain-rmse:4.34387\teval-rmse:4.39414\n",
+      "[650]\ttrain-rmse:4.31775\teval-rmse:4.37296\n",
+      "[700]\ttrain-rmse:4.29368\teval-rmse:4.35331\n",
+      "[750]\ttrain-rmse:4.27426\teval-rmse:4.33767\n",
+      "[800]\ttrain-rmse:4.25247\teval-rmse:4.32011\n",
+      "[850]\ttrain-rmse:4.23139\teval-rmse:4.30346\n",
+      "[900]\ttrain-rmse:4.21413\teval-rmse:4.29017\n",
+      "[950]\ttrain-rmse:4.19668\teval-rmse:4.27678\n",
+      "[999]\ttrain-rmse:4.18197\teval-rmse:4.26579\n",
+      "Test RMSE: 4.3370\n",
+      "Test R^2: 0.6495\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "params = {\n",
+    "    'objective': 'reg:squarederror',\n",
+    "    \"tree_method\": \"hist\", \"device\": \"cuda\",# Regression task\n",
+    "    #\"tree_method\": \"gpu_hist\", # or \"hist\" for cpu\n",
+    "    'eval_metric': 'rmse',  # Root Mean Squared Error\n",
+    "    'max_depth': 8,  # Depth of trees\n",
+    "    'learning_rate': 0.1,  # Learning rate\n",
+    "    'colsample_bytree': 0.8,  # Column sampling rate\n",
+    "    'subsample': 0.8,  # Subsample ratio\n",
+    "    'seed': 42  # For reproducibility\n",
+    "}\n",
+    "\n",
+    "# Step 3: Train the model with early stopping on validation set\n",
+    "num_round = 1000  # Specify the number of boosting rounds\n",
+    "evals = [(dtrain, 'train'), (dval, 'eval')]  # Track both training and validation performance\n",
+    "evals_result = {}\n",
+    "# Train the model\n",
+    "bst = xgb.train(\n",
+    "    params,\n",
+    "    dtrain,\n",
+    "    num_round,\n",
+    "    evals,\n",
+    "    early_stopping_rounds=10,  # Stop early if validation metric doesn't improve\n",
+    "    verbose_eval=50,\n",
+    "    evals_result=evals_result# Print evaluation results every 10 rounds\n",
+    ")\n",
+    "\n",
+    "# Step 4: Predict on the test set\n",
+    "y_pred = bst.predict(dtest)\n",
+    "\n",
+    "# Step 5: Evaluate the model performance\n",
+    "# RMSE and R^2\n",
+    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(f\"Test RMSE: {rmse:.4f}\")\n",
+    "print(f\"Test R^2: {r2:.4f}\")\n",
+    "\n",
+    "# Step 6: Plot evaluation curves (training/validation RMSE)\n",
+    "#results = bst.eval_metrics\n",
+    "train_rmse = evals_result['train']['rmse']\n",
+    "val_rmse = evals_result['eval']['rmse']\n",
+    "epochs = range(1, len(train_rmse) + 1)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(epochs, train_rmse, label='Train RMSE')\n",
+    "plt.plot(epochs, val_rmse, label='Validation RMSE')\n",
+    "plt.xlabel('Boosting Round')\n",
+    "plt.ylabel('RMSE')\n",
+    "plt.title('XGBoost RMSE per Boosting Round')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Step 7: Plot feature importance\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "xgb.plot_importance(bst, importance_type=\"weight\", max_num_features=10, height=0.8)\n",
+    "plt.title('Top 10 Feature Importance')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c6a0938d-473b-4f48-8f9f-0367b4ceb9af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epochs = len(evals_result[\"train\"][\"rmse\"])\n",
+    "x_axis = range(epochs)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(x_axis, evals_result[\"train\"][\"rmse\"], label=\"Train\")\n",
+    "plt.plot(x_axis, evals_result[\"eval\"][\"rmse\"], label=\"Validation\")\n",
+    "plt.xlabel(\"Boosting Round\")\n",
+    "plt.ylabel(\"RMSE\")\n",
+    "plt.title(\"XGBoost Training vs Validation RMSE\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "444e1d32-393f-4f1d-a3b2-ee1bea2a5aa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vmsavla/.local/lib/python3.11/site-packages/xgboost/core.py:726: FutureWarning: Pass `evals` as keyword args.\n",
+      "  warnings.warn(msg, FutureWarning)\n",
+      "/home/vmsavla/.local/lib/python3.11/site-packages/xgboost/callback.py:386: UserWarning: [23:13:11] WARNING: /workspace/src/context.cc:49: No visible GPU is found, setting device to CPU.\n",
+      "  self.starting_round = model.num_boosted_rounds()\n",
+      "/home/vmsavla/.local/lib/python3.11/site-packages/xgboost/callback.py:386: UserWarning: [23:13:11] WARNING: /workspace/src/context.cc:203: XGBoost is not compiled with CUDA support.\n",
+      "  self.starting_round = model.num_boosted_rounds()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0]\ttrain-rmse:7.36484\teval-rmse:7.31502\n",
+      "[50]\ttrain-rmse:5.89925\teval-rmse:5.89683\n",
+      "[100]\ttrain-rmse:5.45960\teval-rmse:5.46167\n",
+      "[150]\ttrain-rmse:5.21318\teval-rmse:5.21958\n",
+      "[200]\ttrain-rmse:5.05350\teval-rmse:5.06312\n",
+      "[250]\ttrain-rmse:4.93777\teval-rmse:4.94939\n",
+      "[300]\ttrain-rmse:4.84910\teval-rmse:4.86314\n",
+      "[350]\ttrain-rmse:4.78038\teval-rmse:4.79525\n",
+      "[400]\ttrain-rmse:4.72377\teval-rmse:4.74015\n",
+      "[450]\ttrain-rmse:4.67814\teval-rmse:4.69635\n",
+      "[500]\ttrain-rmse:4.63623\teval-rmse:4.65604\n",
+      "[550]\ttrain-rmse:4.60030\teval-rmse:4.62275\n",
+      "[600]\ttrain-rmse:4.56820\teval-rmse:4.59273\n",
+      "[650]\ttrain-rmse:4.54049\teval-rmse:4.56735\n",
+      "[700]\ttrain-rmse:4.51488\teval-rmse:4.54405\n",
+      "[750]\ttrain-rmse:4.49093\teval-rmse:4.52243\n",
+      "[800]\ttrain-rmse:4.46854\teval-rmse:4.50244\n",
+      "[850]\ttrain-rmse:4.44800\teval-rmse:4.48398\n",
+      "[900]\ttrain-rmse:4.42854\teval-rmse:4.46680\n",
+      "[950]\ttrain-rmse:4.41032\teval-rmse:4.45079\n",
+      "[999]\ttrain-rmse:4.39300\teval-rmse:4.43575\n",
+      "Test RMSE: 4.5048\n",
+      "Test R^2: 0.6218\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "params = {\n",
+    "    'objective': 'reg:squarederror',\n",
+    "    \"tree_method\": \"hist\", \"device\": \"cuda\",# Regression task\n",
+    "    #\"tree_method\": \"gpu_hist\", # or \"hist\" for cpu\n",
+    "    'eval_metric': 'rmse',  # Root Mean Squared Error\n",
+    "    'max_depth': 8,  # Depth of trees\n",
+    "    'learning_rate': 0.05,  # Learning rate\n",
+    "    'colsample_bytree': 0.8,  # Column sampling rate\n",
+    "    'subsample': 0.8,  # Subsample ratio\n",
+    "    'seed': 42  # For reproducibility\n",
+    "}\n",
+    "\n",
+    "# Step 3: Train the model with early stopping on validation set\n",
+    "num_round = 1000  # Specify the number of boosting rounds\n",
+    "evals = [(dtrain, 'train'), (dval, 'eval')]  # Track both training and validation performance\n",
+    "evals_result = {}\n",
+    "# Train the model\n",
+    "bst = xgb.train(\n",
+    "    params,\n",
+    "    dtrain,\n",
+    "    num_round,\n",
+    "    evals,\n",
+    "    early_stopping_rounds=10,  # Stop early if validation metric doesn't improve\n",
+    "    verbose_eval=50,\n",
+    "    evals_result=evals_result# Print evaluation results every 10 rounds\n",
+    ")\n",
+    "\n",
+    "# Step 4: Predict on the test set\n",
+    "y_pred = bst.predict(dtest)\n",
+    "\n",
+    "# Step 5: Evaluate the model performance\n",
+    "# RMSE and R^2\n",
+    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(f\"Test RMSE: {rmse:.4f}\")\n",
+    "print(f\"Test R^2: {r2:.4f}\")\n",
+    "\n",
+    "# Step 6: Plot evaluation curves (training/validation RMSE)\n",
+    "#results = bst.eval_metrics\n",
+    "train_rmse = evals_result['train']['rmse']\n",
+    "val_rmse = evals_result['eval']['rmse']\n",
+    "epochs = range(1, len(train_rmse) + 1)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(epochs, train_rmse, label='Train RMSE')\n",
+    "plt.plot(epochs, val_rmse, label='Validation RMSE')\n",
+    "plt.xlabel('Boosting Round')\n",
+    "plt.ylabel('RMSE')\n",
+    "plt.title('XGBoost Training vs Validation RMSE')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Step 7: Plot feature importance\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "xgb.plot_importance(bst, importance_type=\"weight\", max_num_features=10, height=0.8)\n",
+    "plt.title('Top 10 Feature Importance')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42a7d3ba-9550-4f80-af66-eb4c3fc9054b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "import numpy as np\n",
+    "\n",
+    "seeds = [42, 52, 62]\n",
+    "models = []\n",
+    "preds = []\n",
+    "\n",
+    "for seed in seeds:\n",
+    "    model = xgb.train(\n",
+    "        params={\n",
+    "            'objective': 'reg:squarederror',\n",
+    "            'tree_method': 'hist',\n",
+    "            'nthread': 10,\n",
+    "            'max_depth': 8,\n",
+    "            'eta': 0.1,\n",
+    "            'seed': seed,\n",
+    "        },\n",
+    "        dtrain=dtrain,\n",
+    "        num_boost_round=1000,\n",
+    "        evals=[(dtrain, 'train'), (dval, 'eval')],\n",
+    "        early_stopping_rounds=50,\n",
+    "        verbose_eval=100\n",
+    "    )\n",
+    "    models.append(model)\n",
+    "    preds.append(model.predict(dtest))\n",
+    "\n",
+    "# Average predictions\n",
+    "final_preds = np.mean(preds, axis=0)\n",
+    "\n",
+    "# Evaluate\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "rmse = mean_squared_error(y_test, final_preds, squared=False)\n",
+    "r2 = r2_score(y_test, final_preds)\n",
+    "\n",
+    "print(f\"Ensemble Test RMSE: {rmse:.4f}\")\n",
+    "print(f\"Ensemble Test R^2: {r2:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05b74751-715f-47e8-87ba-8ad3e83fb685",
+   "metadata": {},
+   "source": [
+    "[0]\ttrain-rmse:7.27324\teval-rmse:7.22787\n",
+    "[100]\ttrain-rmse:5.03044\teval-rmse:5.03980\n",
+    "[200]\ttrain-rmse:4.70678\teval-rmse:4.72382\n",
+    "[300]\ttrain-rmse:4.56066\teval-rmse:4.58728\n",
+    "[400]\ttrain-rmse:4.46887\teval-rmse:4.50489\n",
+    "[500]\ttrain-rmse:4.39386\teval-rmse:4.43878\n",
+    "[600]\ttrain-rmse:4.33720\teval-rmse:4.39059\n",
+    "[700]\ttrain-rmse:4.28671\teval-rmse:4.34790\n",
+    "[800]\ttrain-rmse:4.24495\teval-rmse:4.31521\n",
+    "[900]\ttrain-rmse:4.21020\teval-rmse:4.28853\n",
+    "[999]\ttrain-rmse:4.17586\teval-rmse:4.26278\n",
+    "[0]\ttrain-rmse:7.27324\teval-rmse:7.22787\n",
+    "[100]\ttrain-rmse:5.03044\teval-rmse:5.03980\n",
+    "[200]\ttrain-rmse:4.70678\teval-rmse:4.72382\n",
+    "[300]\ttrain-rmse:4.56066\teval-rmse:4.58728\n",
+    "[400]\ttrain-rmse:4.46887\teval-rmse:4.50489\n",
+    "[500]\ttrain-rmse:4.39386\teval-rmse:4.43878\n",
+    "[600]\ttrain-rmse:4.33720\teval-rmse:4.39059\n",
+    "[700]\ttrain-rmse:4.28671\teval-rmse:4.34790"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae37ea9d-e0bc-4d29-8ec9-d341ee5969b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install optuna"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2041f70c-3e3a-43b7-a64a-b1022ce66da9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "import optuna\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import numpy as np\n",
+    "\n",
+    "# Prepare DMatrix (assuming you already have these)\n",
+    "# dtrain = xgb.DMatrix(X_train_final, label=y_train_final)\n",
+    "\n",
+    "def objective(trial):\n",
+    "    param = {\n",
+    "        'objective': 'reg:squarederror',\n",
+    "        'eval_metric': 'rmse',\n",
+    "        'tree_method': 'gpu_hist',  # or \"hist\" if running on CPU\n",
+    "        'device': 'cuda',\n",
+    "        'max_depth': trial.suggest_int('max_depth', 4, 10),\n",
+    "        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3),\n",
+    "        'subsample': trial.suggest_float('subsample', 0.6, 1.0),\n",
+    "        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.6, 1.0),\n",
+    "        'lambda': trial.suggest_float('lambda', 1e-8, 10.0, log=True),  # L2 regularization\n",
+    "        'alpha': trial.suggest_float('alpha', 1e-8, 10.0, log=True),    # L1 regularization\n",
+    "        'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),\n",
+    "    }\n",
+    "\n",
+    "    cv_results = xgb.cv(\n",
+    "        param,\n",
+    "        dtrain,\n",
+    "        num_boost_round=800,\n",
+    "        nfold=3,\n",
+    "        early_stopping_rounds=10,\n",
+    "        stratified=False,\n",
+    "        seed=42,\n",
+    "        verbose_eval=100\n",
+    "    )\n",
+    "\n",
+    "    return cv_results['test-rmse-mean'].min()\n",
+    "\n",
+    "# Run Optuna study\n",
+    "study = optuna.create_study(direction='minimize')\n",
+    "study.optimize(objective, n_trials=30)\n",
+    "\n",
+    "# Print best params\n",
+    "print(\"Best trial:\")\n",
+    "print(f\"  Value (best RMSE): {study.best_value:.4f}\")\n",
+    "print(\"  Params:\")\n",
+    "for key, value in study.best_params.items():\n",
+    "    print(f\"    {key}: {value}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e0d8ff7-5424-4e2e-b768-af773df5e25d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "b595lab",
+   "language": "python",
+   "name": "b595lab"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}