{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# RetroelementAgeV1"
   ]
  },
  {
   "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 RetroelementAgeV1(pyagingModel):\n",
      "    def __init__(self):\n",
      "        super().__init__()\n",
      "\n",
      "    def preprocess(self, x):\n",
      "        return x\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.RetroelementAgeV1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pya.models.RetroelementAgeV1()"
   ]
  },
  {
   "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\"] = \"retroelementagev1\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: blood DNA methylation\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: 12,670 people\n",
    "model.metadata[\"year\"] = 2024\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Ndhlovu, Lishomwa C., et al. \\\"Retro-age: A unique epigenetic biomarker of aging captured by DNA methylation states of retroelements.\\\" Aging Cell 23 (2024): e14288.\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1111/acel.14288\"\n",
    "model.metadata[\"notes\"] = \"Whole-blood Retroelement-Age V1, trained by 10-fold-cross-validated elastic net on EPIC v1.0 CpGs annotated to HERV and active LINE elements.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"whole blood\"]  # Paper: DNA methylation data generated from whole blood\n",
    "model.metadata[\"predicts\"] = [\"chronological age\"]  # Paper: CpG sites predictive of chronological age\n",
    "model.metadata[\"training_target\"] = [\"chronological age\"]  # Paper: chronological ages ranging from 12 to 100 years old\n",
    "model.metadata[\"unit\"] = [\"years\"]  # Paper: chronological ages ranging from 12 to 100 years\n",
    "model.metadata[\"model_type\"] = \"elastic net regression\"  # Paper: generalized linear elastic net model ... 10-fold cross-validation\n",
    "model.metadata[\"platform\"] = [\"Illumina EPIC\"]  # Paper: EPIC v.1.0 dataset\n",
    "model.metadata[\"population\"] = \"all ages\"  # Paper: 12,670 people ... ages ranging from 12 to 100 years old\n",
    "model.metadata[\"journal\"] = \"Aging Cell\"\n",
    "model.metadata[\"last_author\"] = \"Michael J. Corley\"\n",
    "model.metadata[\"n_features\"] = 1317\n",
    "model.metadata[\"citations\"] = 26\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": "fbacbbc5-7cf1-41bd-81c4-45adde992de6",
   "metadata": {},
   "source": [
    "#### Download directly with curl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "348e113d-a00a-481d-84ac-e8459a4a5050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_url = \"https://zenodo.org/records/11099870/files/Retroelement_AgeV1coefficients.csv?download=1\"\n",
    "supplementary_file_name = \"coefficients.csv\"\n",
    "os.system(f\"curl -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 CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('coefficients.csv', index_col=0)\n",
    "df['feature'] = df['name']\n",
    "model.features = df['feature'].tolist()[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = torch.tensor(df['coefficient'].tolist()[1:]).unsqueeze(0)\n",
    "intercept = torch.tensor([df['coefficient'].tolist()[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad261636-5b00-4979-bb1d-67a851f7aa19",
   "metadata": {},
   "source": [
    "#### Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d7f43b99-26f2-4622-9a76-316712058877",
   "metadata": {},
   "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": "ad8b4c1d-9d57-48b7-9a30-bcfea7b747b1",
   "metadata": {},
   "source": [
    "## Load reference values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.reference_values = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3bcf7b-74a8-4d21-9ccb-4de0c2b0516b",
   "metadata": {},
   "source": [
    "## Load preprocess and postprocess objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a22fb20-c605-424d-8efb-7620c2c0755c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.preprocess_name = None\n",
    "model.preprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.postprocess_name = None\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": 12,
   "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': ('Ndhlovu, Lishomwa C., et al. \"Retro‐age: A unique epigenetic '\n",
      "              'biomarker of aging captured by DNA methylation states of '\n",
      "              'retroelements.\" Aging Cell (2024): e14288.',),\n",
      " 'clock_name': 'retroelementagev1',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1111/acel.14288',\n",
      " 'notes': None,\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2024}\n",
      "reference_values: None\n",
      "preprocess_name: None\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: None\n",
      "postprocess_dependencies: None\n",
      "features: ['cg22006408', 'cg25231957', 'cg18158970', 'cg19246944', 'cg18515940', 'cg02688359', 'cg12451887', 'cg10621809', 'cg25811448', 'cg06655097', 'cg12786023', 'cg06922212', 'cg16905434', 'cg15851164', 'cg06059332', 'cg04134193', 'cg24634009', 'cg06159896', 'cg21814996', 'cg27433764', 'cg07416187', 'cg00995689', 'cg13292984', 'cg12121166', 'cg11544647', 'cg25292140', 'cg10960147', 'cg10136168', 'cg16912512', 'cg15366524']... [Total elements: 1317]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=1317, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [2.552152395248413, 1.2862745523452759, 3.9165291786193848, 7.276383876800537, -0.7971858978271484, -0.7952958345413208, 2.0488688945770264, 0.4890359044075012, 0.3316255509853363, 0.11408211290836334, -0.19781948626041412, 0.08929739892482758, 0.6588783264160156, 0.10985901951789856, -0.2538025975227356, -0.11299397051334381, -5.241518020629883, 0.6423152089118958, -10.09760856628418, -0.17259803414344788, -3.927119731903076, 0.06528078764677048, 0.03197569400072098, -2.1564865112304688, 0.19085198640823364, -6.1964287757873535, 1.167095422744751, 3.6051061153411865, -1.0168722867965698, -0.029270412400364876]... [Tensor of shape torch.Size([1, 1317])]\n",
      "base_model.linear.bias: tensor([78.1362])\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": 13,
   "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[159.9747],\n",
       "        [ -3.6259],\n",
       "        [142.4391],\n",
       "        [  5.3667],\n",
       "        [114.5559],\n",
       "        [ 30.8754],\n",
       "        [-16.2213],\n",
       "        [230.4298],\n",
       "        [ 50.1820],\n",
       "        [113.8849]], 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": "fe8299d7-9285-4e22-82fd-b664434b4369",
   "metadata": {},
   "source": [
    "## Save torch model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: coefficients.csv\n",
      "Deleted folder: .ipynb_checkpoints\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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    "name": "ipython",
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