{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf58bcdf",
   "metadata": {},
   "source": [
    "# CVDWesterman"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e44a46b8",
   "metadata": {},
   "source": [
    "## Index\n",
    "1. [Instantiate model class](#Instantiate-model-class)\n",
    "2. [Define clock metadata](#Define-clock-metadata)\n",
    "3. [Download clock dependencies](#Download-clock-dependencies)\n",
    "5. [Load features](#Load-features)\n",
    "6. [Load weights into base model](#Load-weights-into-base-model)\n",
    "7. [Load reference values](#Load-reference-values)\n",
    "8. [Load preprocess and postprocess objects](#Load-preprocess-and-postprocess-objects)\n",
    "10. [Check all clock parameters](#Check-all-clock-parameters)\n",
    "10. [Basic test](#Basic-test)\n",
    "11. [Save torch model](#Save-torch-model)\n",
    "12. [Clear directory](#Clear-directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d42bd7e5",
   "metadata": {},
   "source": [
    "Let's first import some packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "714014f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:18.783639Z",
     "iopub.status.busy": "2026-07-04T20:25:18.783340Z",
     "iopub.status.idle": "2026-07-04T20:25:20.933337Z",
     "shell.execute_reply": "2026-07-04T20:25:20.932817Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import inspect\n",
    "import shutil\n",
    "import json\n",
    "import torch\n",
    "import pandas as pd\n",
    "import pyaging as pya"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f8302e",
   "metadata": {},
   "source": [
    "## Instantiate model class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf2f68fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:20.935292Z",
     "iopub.status.busy": "2026-07-04T20:25:20.935076Z",
     "iopub.status.idle": "2026-07-04T20:25:20.937956Z",
     "shell.execute_reply": "2026-07-04T20:25:20.937585Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class CVDWesterman(LinearReferenceClock):\n",
      "    def postprocess(self, x):\n",
      "        \"\"\"Logistic transform to a CVD risk probability.\"\"\"\n",
      "        return torch.sigmoid(x)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_entire_class(cls):\n",
    "    source = inspect.getsource(cls)\n",
    "    print(source)\n",
    "\n",
    "print_entire_class(pya.models.CVDWesterman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac4e326f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:20.939157Z",
     "iopub.status.busy": "2026-07-04T20:25:20.939070Z",
     "iopub.status.idle": "2026-07-04T20:25:20.940546Z",
     "shell.execute_reply": "2026-07-04T20:25:20.940210Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.CVDWesterman()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e29493d",
   "metadata": {},
   "source": [
    "## Define clock metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d49a8755",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:20.941831Z",
     "iopub.status.busy": "2026-07-04T20:25:20.941755Z",
     "iopub.status.idle": "2026-07-04T20:25:20.943834Z",
     "shell.execute_reply": "2026-07-04T20:25:20.943444Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"cvdwesterman\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: DNA methylation cardiovascular risk score from blood cohorts.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: DNA methylation cardiovascular risk score from blood cohorts.\n",
    "model.metadata[\"year\"] = 2020\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Westerman, K. et al. Epigenomic assessment of cardiovascular disease risk and interactions with traditional risk metrics. Journal of the American Heart Association 9, e015299 (2020).\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1161/jaha.119.015299\"\n",
    "model.metadata[\"notes\"] = \"Whole-blood DNA-methylation score for cardiovascular risk. The paper's final cross-study learner stacks cohort-specific elastic-net Cox models; the packaged pyaging implementation is a 235-CpG linear score followed by a sigmoid.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"whole blood\"]  # Paper: DNA methylation data ... collected using the HumanMethylation450 platform.\n",
    "model.metadata[\"predicts\"] = [\"cardiovascular disease risk\"]  # Paper: postprocess_name = 'sigmoid'\n",
    "model.metadata[\"training_target\"] = [\"cardiovascular disease\"]  # Paper: CVD events included coronary heart disease, stroke, and death from CVD.\n",
    "model.metadata[\"unit\"] = [\"probability\"]  # Paper: Logistic transform to a CVD risk probability.\n",
    "model.metadata[\"model_type\"] = \"elastic net Cox ensemble\"  # Paper: Penalized Cox proportional hazards regressions with the elastic net penalty.\n",
    "model.metadata[\"platform\"] = [\"Illumina 450K\"]  # Paper: Illumina HumanMethylation450 microarray platform\n",
    "model.metadata[\"population\"] = \"adults\"  # Paper: WHI, FHS-JHU, and LBC were the initial training cohorts.\n",
    "model.metadata[\"journal\"] = \"Journal of the American Heart Association\"\n",
    "model.metadata[\"last_author\"] = \"José M. Ordovás\"\n",
    "model.metadata[\"n_features\"] = 235\n",
    "model.metadata[\"citations\"] = 53\n",
    "model.metadata[\"citations_date\"] = \"2026-07-05\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9091c3",
   "metadata": {},
   "source": [
    "## Download clock dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11138ae8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:20.944957Z",
     "iopub.status.busy": "2026-07-04T20:25:20.944892Z",
     "iopub.status.idle": "2026-07-04T20:25:21.274961Z",
     "shell.execute_reply": "2026-07-04T20:25:21.274139Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system(f\"curl -sL -o coefficients.csv https://raw.githubusercontent.com/bio-learn/biolearn/180852e2bab473303cb85da627178b1695ee9d86/biolearn/data/CVD_Westermann.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e705eb35",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78ac6cd0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.280151Z",
     "iopub.status.busy": "2026-07-04T20:25:21.279312Z",
     "iopub.status.idle": "2026-07-04T20:25:21.289633Z",
     "shell.execute_reply": "2026-07-04T20:25:21.288258Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('coefficients.csv')\n",
    "mask = df['CpGmarker'].astype(str).str.lower().isin(['intercept', '(intercept)'])\n",
    "intercept_value = float(df.loc[mask, 'CoefficientTraining'].iloc[0]) if mask.any() else 0.0\n",
    "coef_df = df.loc[~mask].reset_index(drop=True)\n",
    "model.features = coef_df['CpGmarker'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48174ee5",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2e98f1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.292689Z",
     "iopub.status.busy": "2026-07-04T20:25:21.292424Z",
     "iopub.status.idle": "2026-07-04T20:25:21.297083Z",
     "shell.execute_reply": "2026-07-04T20:25:21.296582Z"
    }
   },
   "outputs": [],
   "source": [
    "weights = torch.tensor(coef_df['CoefficientTraining'].tolist()).unsqueeze(0).float()\n",
    "intercept = torch.tensor([intercept_value]).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1b2de96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.298716Z",
     "iopub.status.busy": "2026-07-04T20:25:21.298589Z",
     "iopub.status.idle": "2026-07-04T20:25:21.301406Z",
     "shell.execute_reply": "2026-07-04T20:25:21.300753Z"
    }
   },
   "outputs": [],
   "source": [
    "base_model = pya.models.LinearModel(input_dim=len(model.features))\n",
    "\n",
    "base_model.linear.weight.data = weights.float()\n",
    "base_model.linear.bias.data = intercept.float()\n",
    "\n",
    "model.base_model = base_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785a8ea5",
   "metadata": {},
   "source": [
    "## Load reference values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e81598e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.303014Z",
     "iopub.status.busy": "2026-07-04T20:25:21.302910Z",
     "iopub.status.idle": "2026-07-04T20:25:21.304561Z",
     "shell.execute_reply": "2026-07-04T20:25:21.304171Z"
    }
   },
   "outputs": [],
   "source": [
    "model.reference_values = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c050dfc",
   "metadata": {},
   "source": [
    "## Load preprocess and postprocess objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3bcf4df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.305858Z",
     "iopub.status.busy": "2026-07-04T20:25:21.305776Z",
     "iopub.status.idle": "2026-07-04T20:25:21.307465Z",
     "shell.execute_reply": "2026-07-04T20:25:21.307136Z"
    }
   },
   "outputs": [],
   "source": [
    "model.preprocess_name = None\n",
    "model.preprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "719a7a51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.308823Z",
     "iopub.status.busy": "2026-07-04T20:25:21.308735Z",
     "iopub.status.idle": "2026-07-04T20:25:21.311070Z",
     "shell.execute_reply": "2026-07-04T20:25:21.310220Z"
    }
   },
   "outputs": [],
   "source": [
    "model.postprocess_name = 'sigmoid'\n",
    "model.postprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e5021f",
   "metadata": {},
   "source": [
    "## Check all clock parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6bb92439",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.313068Z",
     "iopub.status.busy": "2026-07-04T20:25:21.312943Z",
     "iopub.status.idle": "2026-07-04T20:25:21.317159Z",
     "shell.execute_reply": "2026-07-04T20:25:21.316616Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '⌛',\n",
      " 'citation': 'Westerman, Kenneth, et al. \"Epigenomic assessment of '\n",
      "             'cardiovascular disease risk and interactions with traditional '\n",
      "             'risk metrics.\" Journal of the American Heart Association 9.8 '\n",
      "             '(2020): e015299.',\n",
      " 'clock_name': 'cvdwesterman',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1161/JAHA.119.015299',\n",
      " 'notes': None,\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2020}\n",
      "reference_values: None\n",
      "preprocess_name: None\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: 'sigmoid'\n",
      "postprocess_dependencies: None\n",
      "features: ['cg13077366', 'cg13679303', 'cg19214707', 'cg18593317', 'cg03463778', 'cg26626449', 'cg05630272', 'cg23522872', 'cg12588880', 'cg13074055', 'cg02655711', 'cg12624197', 'cg05843457', 'cg10963061', 'cg12121643', 'cg23613051', 'cg26504835', 'cg23063825', 'cg25188006', 'cg00887153', 'cg16574737', 'cg25283121', 'cg23239574', 'cg25564433', 'cg10612096', 'cg13903421', 'cg16268165', 'cg00600477', 'cg10516993', 'cg03930209']... [Total elements: 235]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=235, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [0.0023171070497483015, 0.0011196619598194957, -0.013891435228288174, -0.002025559078902006, -0.009562727063894272, 0.06509598344564438, -0.020258523523807526, 0.0033463360741734505, -0.007966549135744572, -0.010243932716548443, -0.01568872109055519, 0.028809623792767525, 0.002753776963800192, 0.019885685294866562, 0.047365110367536545, 0.037816889584064484, 0.0031414860859513283, -0.004254682920873165, 0.002840817905962467, 0.002047969028353691, 0.0016441689804196358, 0.010059863328933716, 0.013813807629048824, 0.002602970926091075, 0.0023500178940594196, 0.004787160083651543, -0.006558055058121681, 0.01796160265803337, 0.016836676746606827, 0.0022038749884814024]... [Tensor of shape torch.Size([1, 235])]\n",
      "base_model.linear.bias: tensor([0.])\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pya.utils.print_model_details(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5bdf248",
   "metadata": {},
   "source": [
    "## Basic test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "22cd81c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.318602Z",
     "iopub.status.busy": "2026-07-04T20:25:21.318504Z",
     "iopub.status.idle": "2026-07-04T20:25:21.324451Z",
     "shell.execute_reply": "2026-07-04T20:25:21.323983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.3971],\n",
       "        [0.6197],\n",
       "        [0.7023],\n",
       "        [0.5075],\n",
       "        [0.5564],\n",
       "        [0.5077],\n",
       "        [0.5431],\n",
       "        [0.4493],\n",
       "        [0.6137],\n",
       "        [0.3952]], dtype=torch.float64, grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "input = torch.randn(10, len(model.features), dtype=float)\n",
    "model.eval()\n",
    "model.to(float)\n",
    "pred = model(input)\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c01c9d92",
   "metadata": {},
   "source": [
    "## Save torch model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b22b6e10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.325854Z",
     "iopub.status.busy": "2026-07-04T20:25:21.325765Z",
     "iopub.status.idle": "2026-07-04T20:25:21.329091Z",
     "shell.execute_reply": "2026-07-04T20:25:21.328768Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a96389",
   "metadata": {},
   "source": [
    "## Clear directory\n",
    "<a id=\"10\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7b55163",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T20:25:21.330901Z",
     "iopub.status.busy": "2026-07-04T20:25:21.330792Z",
     "iopub.status.idle": "2026-07-04T20:25:21.334988Z",
     "shell.execute_reply": "2026-07-04T20:25:21.334588Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: coefficients.csv\n"
     ]
    }
   ],
   "source": [
    "# Function to remove a folder and all its contents\n",
    "def remove_folder(path):\n",
    "    try:\n",
    "        shutil.rmtree(path)\n",
    "        print(f\"Deleted folder: {path}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error deleting folder {path}: {e}\")\n",
    "\n",
    "# Get a list of all files and folders in the current directory\n",
    "all_items = os.listdir('.')\n",
    "\n",
    "# Loop through the items\n",
    "for item in all_items:\n",
    "    # Check if it's a file and does not end with .ipynb\n",
    "    if os.path.isfile(item) and not item.endswith('.ipynb'):\n",
    "        os.remove(item)\n",
    "        print(f\"Deleted file: {item}\")\n",
    "    # Check if it's a folder\n",
    "    elif os.path.isdir(item):\n",
    "        remove_folder(item)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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