{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# epiTOC2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f514a3-772c-4a14-afdf-5a8376851ff4",
   "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",
    "10. [Basic test](#Basic-test)\n",
    "11. [Save torch model](#Save-torch-model)\n",
    "12. [Clear directory](#Clear-directory)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95fafdc-643a-40ea-a689-200bd132e90c",
   "metadata": {},
   "source": [
    "Let's first import some packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4adfb4de-cd79-4913-a1af-9e23e9e236c9",
   "metadata": {},
   "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": "145082e5-ced4-47ae-88c0-cb69773e3c5a",
   "metadata": {},
   "source": [
    "## Instantiate model class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8aa77372-7ed3-4da7-abc9-d30372106139",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class epiTOC2(pyagingModel):\n",
      "    def __init__(self):\n",
      "        super().__init__()\n",
      "        self.delta = None\n",
      "        self.beta0 = None\n",
      "\n",
      "    def preprocess(self, x):\n",
      "        \"\"\"\n",
      "        Replace NaNs with zero; missing features should already be imputed via reference_values.\n",
      "        \"\"\"\n",
      "        return torch.nan_to_num(x, nan=0.0)\n",
      "\n",
      "    def forward(self, x):\n",
      "        x = self.preprocess(x)\n",
      "\n",
      "        device = x.device\n",
      "        dtype = x.dtype\n",
      "\n",
      "        delta = self.delta.to(device=device, dtype=dtype)\n",
      "        beta0 = self.beta0.to(device=device, dtype=dtype)\n",
      "\n",
      "        denom = delta * (1 - beta0)\n",
      "        denom = torch.where(denom == 0, torch.ones_like(denom), denom)\n",
      "\n",
      "        contrib = (x - beta0) / denom\n",
      "        k = contrib.size(1)\n",
      "        vals = 2.0 * torch.sum(contrib, dim=1) / k\n",
      "\n",
      "        return self.postprocess(vals.unsqueeze(1))\n",
      "\n",
      "    def postprocess(self, x):\n",
      "        return x\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_entire_class(cls):\n",
    "    source = inspect.getsource(cls)\n",
    "    print(source)\n",
    "\n",
    "print_entire_class(pya.models.epiTOC2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pya.models.epiTOC2()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c",
   "metadata": {},
   "source": [
    "## Define clock metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6601da9e-8adc-44ee-9308-75e3cd31b816",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"epitoc2\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: The study constructs clocks from DNA methylation measurements.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: The analyzed samples and clock are human.\n",
    "model.metadata[\"year\"] = 2020\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Teschendorff, Andrew E. \\\"A comparison of epigenetic mitotic-like clocks for cancer risk prediction.\\\" Genome Medicine 12 (2020): 56.\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1186/s13073-020-00752-3\"\n",
    "model.metadata[\"notes\"] = \"Dynamic methylation-transmission model returning total cumulative stem-cell divisions per stem cell; an intrinsic rate additionally requires chronological age but is not this implementation's returned value.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"whole blood\"]  # Paper: Parameters were fitted in the Hannum whole-blood cohort.\n",
    "model.metadata[\"predicts\"] = [\"mitotic age\"]  # Paper: The epiTOC2 estimate is total stem-cell divisions per stem cell.\n",
    "model.metadata[\"training_target\"] = [\"chronological age\"]  # Paper: CpG dynamic-model parameters were fitted against age-associated methylation trajectories.\n",
    "model.metadata[\"unit\"] = [\"cell divisions per stem cell\"]  # Paper: The model output is total stem-cell divisions per stem cell.\n",
    "model.metadata[\"model_type\"] = \"dynamic methylation transmission model\"  # Paper: A dynamic model of methylation transmission with stem-cell divisions defines epiTOC2.\n",
    "model.metadata[\"platform\"] = [\"Illumina 450K\"]  # Paper: The Hannum training cohort was measured on the Illumina 450K array.\n",
    "model.metadata[\"population\"] = \"adults\"  # Paper: Model fitting used Hannum whole blood spanning ages 19 to 101.\n",
    "model.metadata[\"journal\"] = \"Genome Medicine\"\n",
    "model.metadata[\"last_author\"] = \"Andrew E. Teschendorff\"\n",
    "model.metadata[\"n_features\"] = 163\n",
    "model.metadata[\"citations\"] = 155\n",
    "model.metadata[\"citations_date\"] = \"2026-07-05\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74492239-5aae-4026-9d90-6bc9c574c110",
   "metadata": {},
   "source": [
    "## Download clock dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c07158bc-19c9-47de-8276-5d1dc5361b22",
   "metadata": {},
   "source": [
    "#### Download coefficient file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6b57cb84-b940-4cfe-8933-3955cab9dfe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_url = \"https://raw.githubusercontent.com/bio-learn/biolearn/master/biolearn/data/EpiTOC2.csv\"\n",
    "os.system(f\"curl -L {coeff_url} -o EpiTOC2.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5035b180-3d1b-4432-8ebe-b9c92bd93a7f",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "793d7b24-b1ef-4dd2-ac26-079f7b67fba7",
   "metadata": {},
   "source": [
    "#### From Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('EpiTOC2.csv', index_col=0)\n",
    "df['feature'] = df.index.astype(str)\n",
    "model.features = df['feature'].tolist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0",
   "metadata": {},
   "source": [
    "## Load weights into base model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### From CSV file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ad261636-5b00-4979-bb1d-67a851f7aa19",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.delta = torch.tensor(df['delta'].values, dtype=torch.float32).unsqueeze(0)\n",
    "model.beta0 = torch.tensor(df['beta0'].values, dtype=torch.float32).unsqueeze(0)\n",
    "model.base_model = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d7f43b99-26f2-4622-9a76-316712058877",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Linear model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad8b4c1d-9d57-48b7-9a30-bcfea7b747b1",
   "metadata": {},
   "source": [
    "# Not used; computation happens in the model forward\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.reference_values = [-1]*len(model.features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3bcf7b-74a8-4d21-9ccb-4de0c2b0516b",
   "metadata": {},
   "source": [
    "model.reference_values = [0.0] * len(model.features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7a22fb20-c605-424d-8efb-7620c2c0755c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.preprocess_name = \"mean\"\n",
    "model.preprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.preprocess_name = \"nan_to_zero\"\n",
    "model.preprocess_dependencies = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e3d6b1-e67e-4f3d-bd39-0ebec5726c3c",
   "metadata": {},
   "source": [
    "model.postprocess_name = None\n",
    "model.postprocess_dependencies = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2168355c-47d9-475d-b816-49f65e74887c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '⌛',\n",
      " 'citation': 'Teschendorff, Andrew E. \"A comparison of epigenetic mitotic-like '\n",
      "             'clocks for cancer risk prediction.\" Genome Medicine 12.1 (2020): '\n",
      "             '56.',\n",
      " 'clock_name': 'epitoc2',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1186/s13073-020-00752-3',\n",
      " 'notes': 'Stem cell division rate estimate using EpiTOC2.',\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2020}\n",
      "reference_values: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]... [Total elements: 163]\n",
      "preprocess_name: 'nan_to_zero'\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: None\n",
      "postprocess_dependencies: None\n",
      "features: ['cg00043095', 'cg00347369', 'cg00397986', 'cg00466268', 'cg00884606', 'cg00916884', 'cg01435574', 'cg01537995', 'cg01587896', 'cg01699217', 'cg01783070', 'cg01830294', 'cg02150988', 'cg02186542', 'cg02266732', 'cg02631468', 'cg02726121', 'cg02796545', 'cg02964724', 'cg03045635', 'cg03111498', 'cg03140968', 'cg03181582', 'cg03430846', 'cg03450948', 'cg03603951', 'cg03874199', 'cg04188273', 'cg04408488', 'cg04431946']... [Total elements: 163]\n",
      "base_model_features: None\n",
      "base_model: None\n",
      "delta: [4.999999873689376e-05, 4.999999873689376e-05, 4.999999873689376e-05, 9.999999747378752e-05, 9.999999747378752e-05, 4.999999873689376e-05, 4.999999873689376e-05, 4.999999873689376e-05, 9.999999747378752e-05, 4.999999873689376e-05, 4.999999873689376e-05, 0.0002500000118743628, 9.999999747378752e-05, 4.999999873689376e-05, 4.999999873689376e-05, 4.999999873689376e-05, 4.999999873689376e-05, 9.999999747378752e-05, 4.999999873689376e-05, 0.0002500000118743628, 9.999999747378752e-05, 4.999999873689376e-05, 4.999999873689376e-05, 4.999999873689376e-05, 9.999999747378752e-05, 4.999999873689376e-05, 4.999999873689376e-05, 9.999999747378752e-06, 4.999999873689376e-05, 4.999999873689376e-05]... [Tensor of shape torch.Size([1, 163])]\n",
      "beta0: [0.019999999552965164, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.009999999776482582, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.029999999329447746, 0.05000000074505806, 0.03999999910593033, 0.0, 0.029999999329447746, 0.05000000074505806, 0.019999999552965164, 0.05000000074505806, 0.029999999329447746, 0.019999999552965164, 0.05000000074505806, 0.029999999329447746, 0.029999999329447746, 0.03999999910593033, 0.05000000074505806, 0.05000000074505806, 0.029999999329447746, 0.009999999776482582, 0.029999999329447746, 0.019999999552965164, 0.019999999552965164, 0.03999999910593033]... [Tensor of shape torch.Size([1, 163])]\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pya.utils.print_model_details(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986d0262-e0c7-4036-b687-dee53ba392fb",
   "metadata": {},
   "source": [
    "## Basic test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1562.6673],\n",
       "        [-3824.6137],\n",
       "        [  645.1144],\n",
       "        [-1810.9748],\n",
       "        [ -142.2699],\n",
       "        [ 1630.9714],\n",
       "        [ 9229.4758],\n",
       "        [ 4666.7934],\n",
       "        [ 5489.9210],\n",
       "        [ 1256.8648]], dtype=torch.float64)"
      ]
     },
     "execution_count": 43,
     "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe8299d7-9285-4e22-82fd-b664434b4369",
   "metadata": {},
   "source": [
    "## Save torch model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5bb2b45d",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e22298",
   "metadata": {},
   "source": [
    "## Clear directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: EpiTOC2.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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