{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# HypoClock"
   ]
  },
  {
   "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",
    "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": "d95fafdc-643a-40ea-a689-200bd132e90c",
   "metadata": {},
   "source": [
    "Let's first import some packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "id": "8aa77372-7ed3-4da7-abc9-d30372106139",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class HypoClock(pyagingModel):\n",
      "    def __init__(self):\n",
      "        super().__init__()\n",
      "\n",
      "    def preprocess(self, x):\n",
      "        \"\"\"\n",
      "        Compute mean beta per sample, excluding missing (-1) values.\n",
      "        \"\"\"\n",
      "        means = []\n",
      "        for row in x:\n",
      "            filtered_row = row[row != -1]\n",
      "            if len(filtered_row) > 0:\n",
      "                mean = torch.mean(filtered_row)\n",
      "            else:\n",
      "                mean = torch.tensor(float(\"nan\"), device=x.device, dtype=x.dtype)\n",
      "            means.append(mean)\n",
      "        return torch.vstack(means)\n",
      "\n",
      "    def postprocess(self, x):\n",
      "        return 1 - x\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_entire_class(cls):\n",
    "    source = inspect.getsource(cls)\n",
    "    print(source)\n",
    "\n",
    "print_entire_class(pya.models.HypoClock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pya.models.HypoClock()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c",
   "metadata": {},
   "source": [
    "## Define clock metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6601da9e-8adc-44ee-9308-75e3cd31b816",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"hypoclock\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: HypoClock is based on DNA methylation at solo-WCGW sites.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: The HypoClock analysis uses human Illumina 450K solo-WCGW probes.\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\"] = \"Pyaging returns an inverted HypoClock burden score, 1 minus the mean beta value across 678 solo-WCGW CpGs; higher values therefore indicate deeper PMD hypomethylation. The assigned 2018 paper is the biological precursor, while the named 678-site implementation is from 2020.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"multi-tissue\"]  # Paper: Solo-WCGW PMD hypomethylation was evaluated across diverse normal tissues, blood lineages and primary tumors.\n",
    "model.metadata[\"predicts\"] = [\"mitotic age\"]  # Paper: HypoClock is a mitotic-like model based on hypomethylation at solo-WCGW sites.\n",
    "model.metadata[\"training_target\"] = [\"not applicable\"]  # Paper: HypoClock is calculated as an average methylation score rather than fit to an outcome.\n",
    "model.metadata[\"unit\"] = [\"beta value\"]  # Paper: The packaged output is one minus mean methylation across the selected CpGs, retaining a methylation beta-derived scale.\n",
    "model.metadata[\"model_type\"] = \"mean aggregation\"  # Paper: The score is the average DNAm over selected solo-WCGW probes.\n",
    "model.metadata[\"platform\"] = [\"Illumina 450K\"]  # Paper: The 678-site implementation uses solo-WCGWs represented on Illumina 450K arrays.\n",
    "model.metadata[\"population\"] = \"human, age unspecified\"  # Paper: HypoClock was assessed across normal adult tissues, premalignant lesions and cancers.\n",
    "model.metadata[\"journal\"] = \"Genome Medicine\"\n",
    "model.metadata[\"last_author\"] = \"Andrew E. Teschendorff\"\n",
    "model.metadata[\"n_features\"] = 678\n",
    "model.metadata[\"citations\"] = 452\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 directly with curl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b57cb84-b940-4cfe-8933-3955cab9dfe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 30851  100 30851    0     0   425k      0 --:--:-- --:--:-- --:--:--  430k\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_url = \"https://raw.githubusercontent.com/aet21/EpiMitClocks/master/data/dataETOC3.rda\"\n",
    "supplementary_file_name = \"dataETOC3.rda\"\n",
    "os.system(f\"curl -L -o {supplementary_file_name} {supplementary_url}\")"
   ]
  },
  {
   "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 R data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "r_cmd = (\n",
    "    \"load('dataETOC3.rda'); \"\n",
    "    \"df <- data.frame(cpg=dataETOC3.l[[4]]); \"\n",
    "    \"write.csv(df, 'hypoclock_cpgs.csv', row.names=FALSE)\"\n",
    ")\n",
    "os.system(f\"Rscript -e \\\"{r_cmd}\\\"\")\n",
    "\n",
    "features_df = pd.read_csv('hypoclock_cpgs.csv')\n",
    "model.features = features_df['cpg'].tolist()\n",
    "\n",
    "assert len(model.features) == 678, f\"Expected 678 CpGs, got {len(model.features)}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = torch.tensor([1.0]).unsqueeze(0)\n",
    "intercept = torch.tensor([0.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad261636-5b00-4979-bb1d-67a851f7aa19",
   "metadata": {},
   "source": [
    "#### Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d7f43b99-26f2-4622-9a76-316712058877",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = pya.models.LinearModel(input_dim=1)\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": "ad8b4c1d-9d57-48b7-9a30-bcfea7b747b1",
   "metadata": {},
   "source": [
    "## Load reference values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "## Load preprocess and postprocess objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a22fb20-c605-424d-8efb-7620c2c0755c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.preprocess_name = \"mean\"\n",
    "model.preprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.postprocess_name = \"one_minus\"\n",
    "model.postprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e3d6b1-e67e-4f3d-bd39-0ebec5726c3c",
   "metadata": {},
   "source": [
    "## Check all clock parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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': 'Zhou, Wanding, et al. \"DNA methylation loss in late-replicating '\n",
      "             'domains is linked to mitotic cell division.\" Nature Genetics 50, '\n",
      "             'no. 4 (2018): 591-602.',\n",
      " 'clock_name': 'hypoclock',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1038/s41588-018-0073-4',\n",
      " 'notes': 'HypoClock score is 1 - mean beta across 678 solo-WCGW CpGs; '\n",
      "          'reference values are -1 to ignore missing.',\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2018}\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: 678]\n",
      "preprocess_name: 'mean'\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: 'one_minus'\n",
      "postprocess_dependencies: None\n",
      "features: ['cg01689796', 'cg17753162', 'cg26150621', 'cg11684734', 'cg05812697', 'cg24052817', 'cg01827202', 'cg12277524', 'cg06160606', 'cg12758960', 'cg12270633', 'cg25939869', 'cg16823292', 'cg17041296', 'cg17325792', 'cg05347985', 'cg04789392', 'cg16320208', 'cg01779525', 'cg08009265', 'cg08244156', 'cg14711592', 'cg16577588', 'cg03114253', 'cg02245566', 'cg22601108', 'cg01068621', 'cg21291134', 'cg20491963', 'cg07725889']... [Total elements: 678]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=1, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: tensor([[1.]])\n",
      "base_model.linear.bias: tensor([0.])\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": 14,
   "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.9638],\n",
       "        [0.9622],\n",
       "        [1.0423],\n",
       "        [1.0675],\n",
       "        [1.0330],\n",
       "        [0.9733],\n",
       "        [0.9686],\n",
       "        [1.0028],\n",
       "        [0.9955],\n",
       "        [0.9212]], dtype=torch.float64, grad_fn=<RsubBackward1>)"
      ]
     },
     "execution_count": 14,
     "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": "fe8299d7-9285-4e22-82fd-b664434b4369",
   "metadata": {},
   "source": [
    "## Save torch model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac6257b-8d08-4a90-8d0b-7f745dc11ac1",
   "metadata": {},
   "source": [
    "## Clear directory\n",
    "<a id=\"10\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: dataETOC3.rda\n",
      "Deleted file: hypoclock_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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