{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9e72fe82",
   "metadata": {},
   "source": [
    "# SenMortalityAge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c38243a",
   "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": "0fc4495f",
   "metadata": {},
   "source": [
    "Let's first import some packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c8b1d1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:11.502413Z",
     "iopub.status.busy": "2026-07-04T19:46:11.502320Z",
     "iopub.status.idle": "2026-07-04T19:46:13.721725Z",
     "shell.execute_reply": "2026-07-04T19:46:13.721159Z"
    }
   },
   "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": "1658388d",
   "metadata": {},
   "source": [
    "## Instantiate model class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "788f3fe0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:13.723500Z",
     "iopub.status.busy": "2026-07-04T19:46:13.723271Z",
     "iopub.status.idle": "2026-07-04T19:46:13.726166Z",
     "shell.execute_reply": "2026-07-04T19:46:13.725805Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class SenMortalityAge(LinearReferenceClock):\n",
      "    pass\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_entire_class(cls):\n",
    "    source = inspect.getsource(cls)\n",
    "    print(source)\n",
    "\n",
    "print_entire_class(pya.models.SenMortalityAge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3f638d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:13.727415Z",
     "iopub.status.busy": "2026-07-04T19:46:13.727326Z",
     "iopub.status.idle": "2026-07-04T19:46:13.728917Z",
     "shell.execute_reply": "2026-07-04T19:46:13.728631Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.SenMortalityAge()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d182a40",
   "metadata": {},
   "source": [
    "## Define clock metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c190867",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:13.730569Z",
     "iopub.status.busy": "2026-07-04T19:46:13.730445Z",
     "iopub.status.idle": "2026-07-04T19:46:13.732822Z",
     "shell.execute_reply": "2026-07-04T19:46:13.732421Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"senmortalityage\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: The predictors use CpG DNA-methylation beta values.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: All three assigned predictors were developed from human cell or human whole-blood datasets.\n",
    "model.metadata[\"year\"] = 2026\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Kasamoto, K., Gibson, J., Moqri, M., Smith, R. & Higgins-Chen, A.T. DNA methylation signatures of cellular senescence are not reversed by senolytic treatment. Aging Cell 25, e70430 (2026).\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1111/acel.70430\"\n",
    "model.metadata[\"notes\"] = \"Senescence-enriched elastic-net Cox predictor of mortality, restricted to direction-concordant senescence/age/mortality CpGs and trained in the Framingham Heart Study.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"whole blood\"]  # Paper: FHS whole-blood methylation was used for model training.\n",
    "model.metadata[\"predicts\"] = [\"mortality risk\"]  # Paper: SenMortalityAge is the mortality predictor among the three clocks.\n",
    "model.metadata[\"training_target\"] = [\"mortality\"]  # Paper: Elastic-net Cox regression was fitted to time-to-death.\n",
    "model.metadata[\"unit\"] = [\"log hazard\"]  # Paper: Pyaging returns the Cox linear predictor directly without exponentiation or another postprocess.\n",
    "model.metadata[\"model_type\"] = \"elastic net Cox regression\"  # Paper: The mortality model used elastic-net Cox regression with cross-validated lambda.\n",
    "model.metadata[\"platform\"] = [\"Illumina 450K\", \"Illumina EPIC\"]  # Paper: Feature discovery and predictor training used human 450K and EPIC methylation datasets.\n",
    "model.metadata[\"population\"] = \"adults\"  # Paper: The FHS cohorts comprised 2,748 Offspring and 1,457 Third Generation participants; the model used a 70/30 split.\n",
    "model.metadata[\"journal\"] = \"Aging Cell\"\n",
    "model.metadata[\"last_author\"] = \"Albert T. Higgins-Chen\"\n",
    "model.metadata[\"n_features\"] = 91\n",
    "model.metadata[\"citations\"] = 0\n",
    "model.metadata[\"citations_date\"] = \"2026-07-05\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a7de29",
   "metadata": {},
   "source": [
    "## Download clock dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2aab6355",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:13.734491Z",
     "iopub.status.busy": "2026-07-04T19:46:13.734385Z",
     "iopub.status.idle": "2026-07-04T19:46:14.019130Z",
     "shell.execute_reply": "2026-07-04T19:46:14.018656Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system(f\"curl -sL -o SenMortalityAge_CpGs.csv https://raw.githubusercontent.com/HigginsChenLab/methylCIPHER/19b12296b0d7eb7055a97d068064df635f44ce3e/data-raw/SenescenceAge/SenMortalityAge_CpGs.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7abb5d9f",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "305df9de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.020598Z",
     "iopub.status.busy": "2026-07-04T19:46:14.020497Z",
     "iopub.status.idle": "2026-07-04T19:46:14.024178Z",
     "shell.execute_reply": "2026-07-04T19:46:14.023715Z"
    }
   },
   "outputs": [],
   "source": [
    "coef_df = pd.read_csv('SenMortalityAge_CpGs.csv')\n",
    "model.features = coef_df['CpG'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499392c1",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86fb7f12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.025573Z",
     "iopub.status.busy": "2026-07-04T19:46:14.025491Z",
     "iopub.status.idle": "2026-07-04T19:46:14.027677Z",
     "shell.execute_reply": "2026-07-04T19:46:14.027199Z"
    }
   },
   "outputs": [],
   "source": [
    "weights = torch.tensor(coef_df['Coefficient'].tolist()).unsqueeze(0).float()\n",
    "intercept = torch.tensor([0.0]).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a389a978",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.028774Z",
     "iopub.status.busy": "2026-07-04T19:46:14.028696Z",
     "iopub.status.idle": "2026-07-04T19:46:14.031095Z",
     "shell.execute_reply": "2026-07-04T19:46:14.030519Z"
    }
   },
   "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": "654dfcb1",
   "metadata": {},
   "source": [
    "## Load reference values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c7afa51d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.032685Z",
     "iopub.status.busy": "2026-07-04T19:46:14.032571Z",
     "iopub.status.idle": "2026-07-04T19:46:14.034395Z",
     "shell.execute_reply": "2026-07-04T19:46:14.033938Z"
    }
   },
   "outputs": [],
   "source": [
    "model.reference_values = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de7e5b2",
   "metadata": {},
   "source": [
    "## Load preprocess and postprocess objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d533085",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.035724Z",
     "iopub.status.busy": "2026-07-04T19:46:14.035639Z",
     "iopub.status.idle": "2026-07-04T19:46:14.037428Z",
     "shell.execute_reply": "2026-07-04T19:46:14.036896Z"
    }
   },
   "outputs": [],
   "source": [
    "model.preprocess_name = None\n",
    "model.preprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aa76d691",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.039206Z",
     "iopub.status.busy": "2026-07-04T19:46:14.039070Z",
     "iopub.status.idle": "2026-07-04T19:46:14.041298Z",
     "shell.execute_reply": "2026-07-04T19:46:14.040716Z"
    }
   },
   "outputs": [],
   "source": [
    "model.postprocess_name = None\n",
    "model.postprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "972fd395",
   "metadata": {},
   "source": [
    "## Check all clock parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5fb0410a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.042850Z",
     "iopub.status.busy": "2026-07-04T19:46:14.042767Z",
     "iopub.status.idle": "2026-07-04T19:46:14.048290Z",
     "shell.execute_reply": "2026-07-04T19:46:14.046482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '⌛',\n",
      " 'citation': 'Kasamoto, Kotaro, et al. \"DNA methylation clocks for estimating '\n",
      "             'replicative senescence in human cells.\" Aging Cell (2026): '\n",
      "             'e70430.',\n",
      " 'clock_name': 'senmortalityage',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1111/acel.70430',\n",
      " 'notes': None,\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2026}\n",
      "reference_values: None\n",
      "preprocess_name: None\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: None\n",
      "postprocess_dependencies: None\n",
      "features: ['cg00006787', 'cg00054496', 'cg00458878', 'cg00459119', 'cg03175417', 'cg03366574', 'cg04094193', 'cg04234014', 'cg05396212', 'cg06015525', 'cg06507987', 'cg06542681', 'cg07458308', 'cg08332990', 'cg08726900', 'cg08770961', 'cg10682299', 'cg11974796', 'cg12132563', 'cg12506165', 'cg13029847', 'cg13315970', 'cg13714749', 'cg14205663', 'cg15163603', 'cg16086130', 'cg16181396', 'cg16374999', 'cg16552271', 'cg17340519']... [Total elements: 91]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=91, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [0.1905025690793991, 2.4848039150238037, 0.8850484490394592, 0.7116715908050537, 2.5755298137664795, 3.6517186164855957, 1.0995451211929321, 0.43131953477859497, 1.3369412422180176, 1.197675108909607, 0.7618207931518555, 0.18213650584220886, 3.231579303741455, 0.2401280403137207, 0.8895606994628906, 0.3462097942829132, 2.011119842529297, 0.7437288761138916, 0.5966653823852539, 0.6629067659378052, 0.5791985988616943, 1.4628826379776, 0.20292864739894867, 1.2046616077423096, -0.05603138357400894, 1.015661358833313, 0.6152376532554626, 0.8949704766273499, 0.13805358111858368, 0.4781338572502136]... [Tensor of shape torch.Size([1, 91])]\n",
      "base_model.linear.bias: tensor([0.])\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pya.utils.print_model_details(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89d9d61",
   "metadata": {},
   "source": [
    "## Basic test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a28245c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.050565Z",
     "iopub.status.busy": "2026-07-04T19:46:14.050406Z",
     "iopub.status.idle": "2026-07-04T19:46:14.055127Z",
     "shell.execute_reply": "2026-07-04T19:46:14.054736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-32.2390],\n",
       "        [ 10.5977],\n",
       "        [ 19.5190],\n",
       "        [ -3.3155],\n",
       "        [ 12.2869],\n",
       "        [ 26.4824],\n",
       "        [  3.7673],\n",
       "        [-23.1682],\n",
       "        [-18.8041],\n",
       "        [ -3.3204]], dtype=torch.float64, grad_fn=<AddmmBackward0>)"
      ]
     },
     "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": "91cb23c0",
   "metadata": {},
   "source": [
    "## Save torch model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46a099de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.056716Z",
     "iopub.status.busy": "2026-07-04T19:46:14.056614Z",
     "iopub.status.idle": "2026-07-04T19:46:14.059979Z",
     "shell.execute_reply": "2026-07-04T19:46:14.059538Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39acf362",
   "metadata": {},
   "source": [
    "## Clear directory\n",
    "<a id=\"10\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3b401c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-04T19:46:14.061181Z",
     "iopub.status.busy": "2026-07-04T19:46:14.061113Z",
     "iopub.status.idle": "2026-07-04T19:46:14.064477Z",
     "shell.execute_reply": "2026-07-04T19:46:14.064068Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: SenMortalityAge_CpGs.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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