{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# YingAdaptAge"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:54.956426Z",
     "iopub.status.busy": "2024-03-05T21:23:54.955834Z",
     "iopub.status.idle": "2024-03-05T21:23:56.285160Z",
     "shell.execute_reply": "2024-03-05T21:23:56.284843Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.287032Z",
     "iopub.status.busy": "2024-03-05T21:23:56.286869Z",
     "iopub.status.idle": "2024-03-05T21:23:56.296598Z",
     "shell.execute_reply": "2024-03-05T21:23:56.296366Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class YingAdaptAge(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.YingAdaptAge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.298103Z",
     "iopub.status.busy": "2024-03-05T21:23:56.298016Z",
     "iopub.status.idle": "2024-03-05T21:23:56.299727Z",
     "shell.execute_reply": "2024-03-05T21:23:56.299500Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.YingAdaptAge()"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.301224Z",
     "iopub.status.busy": "2024-03-05T21:23:56.301148Z",
     "iopub.status.idle": "2024-03-05T21:23:56.303222Z",
     "shell.execute_reply": "2024-03-05T21:23:56.302963Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"yingadaptage\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: All three clocks use whole-blood DNA-methylation beta values.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: The models were trained in human Generation Scotland blood samples.\n",
    "model.metadata[\"year\"] = 2024\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Ying, K., Liu, H., Tarkhov, A.E. et al. Causality-enriched epigenetic age uncouples damage and adaptation. Nature Aging 4, 231–246 (2024).\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s43587-023-00557-0\"\n",
    "model.metadata[\"notes\"] = \"Causality-enriched age predictor restricted to adaptive/protective age-related CpGs, with feature penalties weighted by EWMR causality scores.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"whole blood\"]  # Paper: The model was trained in whole-blood methylation.\n",
    "model.metadata[\"predicts\"] = [\"adaptive epigenetic age\"]  # Paper: AdaptAge is designed to track protective adaptations accumulated during aging.\n",
    "model.metadata[\"training_target\"] = [\"chronological age\"]  # Paper: All three causality-enriched elastic-net models were trained to predict chronological age.\n",
    "model.metadata[\"unit\"] = [\"years\"]  # Paper: Model selection used mean absolute error in years against chronological age.\n",
    "model.metadata[\"model_type\"] = \"causality-weighted elastic net regression\"  # Paper: Feature-specific elastic-net penalty factors were assigned from each CpG's causality score.\n",
    "model.metadata[\"platform\"] = [\"Illumina 450K\"]  # Paper: Training used Generation Scotland whole-blood methylation at CpGs available to the 450K-based analysis.\n",
    "model.metadata[\"population\"] = \"adults\"  # Paper: Age-related methylation was estimated in 7,036 Generation Scotland participants aged 18–93, and 2,664 blood samples were used for clock training.\n",
    "model.metadata[\"journal\"] = \"Nature Aging\"\n",
    "model.metadata[\"last_author\"] = \"Vadim N. Gladyshev\"\n",
    "model.metadata[\"n_features\"] = 999\n",
    "model.metadata[\"citations\"] = 183\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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.304731Z",
     "iopub.status.busy": "2024-03-05T21:23:56.304652Z",
     "iopub.status.idle": "2024-03-05T21:23:56.722696Z",
     "shell.execute_reply": "2024-03-05T21:23:56.722101Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_url = \"https://static-content.springer.com/esm/art%3A10.1038%2Fs43587-023-00557-0/MediaObjects/43587_2023_557_MOESM6_ESM.zip\"\n",
    "supplementary_file_name = \"43587_2023_557_MOESM6_ESM.zip\"\n",
    "os.system(f\"curl -o {supplementary_file_name} {supplementary_url}\")\n",
    "os.system(f'unzip {supplementary_file_name}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5035b180-3d1b-4432-8ebe-b9c92bd93a7f",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c756bdc-1646-4915-b91d-4da228a02fbc",
   "metadata": {},
   "source": [
    "#### From CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "388283a9-923f-4219-b018-e59cb951ffae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.726250Z",
     "iopub.status.busy": "2024-03-05T21:23:56.725991Z",
     "iopub.status.idle": "2024-03-05T21:23:56.733453Z",
     "shell.execute_reply": "2024-03-05T21:23:56.732994Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('YingAdaptAge.csv')\n",
    "df['feature'] = df['term']\n",
    "df['coefficient'] = df['estimate']\n",
    "model.features = df['feature'][1:].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e3e30f8-4cae-4f82-98cf-927c55eea9df",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "051acf76-075a-44e9-91b4-7fd3a28cfbdf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.736209Z",
     "iopub.status.busy": "2024-03-05T21:23:56.736015Z",
     "iopub.status.idle": "2024-03-05T21:23:56.739489Z",
     "shell.execute_reply": "2024-03-05T21:23:56.739020Z"
    }
   },
   "outputs": [],
   "source": [
    "weights = torch.tensor(df['coefficient'][1:].tolist()).unsqueeze(0)\n",
    "intercept = torch.tensor([df['coefficient'][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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.742082Z",
     "iopub.status.busy": "2024-03-05T21:23:56.741921Z",
     "iopub.status.idle": "2024-03-05T21:23:56.745033Z",
     "shell.execute_reply": "2024-03-05T21:23:56.744670Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.747328Z",
     "iopub.status.busy": "2024-03-05T21:23:56.747177Z",
     "iopub.status.idle": "2024-03-05T21:23:56.749282Z",
     "shell.execute_reply": "2024-03-05T21:23:56.748898Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.751463Z",
     "iopub.status.busy": "2024-03-05T21:23:56.751321Z",
     "iopub.status.idle": "2024-03-05T21:23:56.753270Z",
     "shell.execute_reply": "2024-03-05T21:23:56.752925Z"
    }
   },
   "outputs": [],
   "source": [
    "model.preprocess_name = None\n",
    "model.preprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.755318Z",
     "iopub.status.busy": "2024-03-05T21:23:56.755159Z",
     "iopub.status.idle": "2024-03-05T21:23:56.756902Z",
     "shell.execute_reply": "2024-03-05T21:23:56.756591Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.758897Z",
     "iopub.status.busy": "2024-03-05T21:23:56.758765Z",
     "iopub.status.idle": "2024-03-05T21:23:56.762654Z",
     "shell.execute_reply": "2024-03-05T21:23:56.762291Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '⌛',\n",
      " 'citation': ('Ying, Kejun, et al. \"Causality-enriched epigenetic age '\n",
      "              'uncouples damage and adaptation.\" Nature Aging (2024): 1-16.',),\n",
      " 'clock_name': 'yingadaptage',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1038/s43587-023-00557-0',\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: ['cg00008671', 'cg00017970', 'cg00048759', 'cg00050402', 'cg00089550', 'cg00099240', 'cg00108164', 'cg00131893', 'cg00158122', 'cg00223715', 'cg00229508', 'cg00277334', 'cg00290758', 'cg00295744', 'cg00316485', 'cg00335735', 'cg00342891', 'cg00344422', 'cg00346145', 'cg00388262', 'cg00492070', 'cg00505045', 'cg00513984', 'cg00539174', 'cg00544337', 'cg00552753', 'cg00561903', 'cg00577578', 'cg00589581', 'cg00638945']... [Total elements: 999]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=999, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [-0.013709170743823051, -0.00010836099681910127, 33.802955627441406, -0.0494624599814415, -0.026015829294919968, -0.00015495899424422532, 16.04678726196289, 1.9626444578170776, -0.0002919970138464123, 0.582930862903595, -7.09550004103221e-05, -0.0010870120022445917, -6.861089786980301e-05, 1.4208452701568604, -0.0016144789988175035, -5.526280074263923e-05, -0.003765339031815529, -0.00011124699813080952, 3.404261589050293, -0.000265667011262849, -0.00037762499414384365, 7.503885746002197, -9.961259638657793e-05, 0.08913462609052658, -0.007147847209125757, -0.0005405130214057863, -0.00011664799967547879, -0.00048649401287548244, -0.01120647694915533, -0.015965329483151436]... [Tensor of shape torch.Size([1, 999])]\n",
      "base_model.linear.bias: tensor([-511.9743])\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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.764484Z",
     "iopub.status.busy": "2024-03-05T21:23:56.764379Z",
     "iopub.status.idle": "2024-03-05T21:23:56.768373Z",
     "shell.execute_reply": "2024-03-05T21:23:56.768105Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-687.2641],\n",
       "        [-498.8554],\n",
       "        [-377.1122],\n",
       "        [-518.4170],\n",
       "        [-423.5536],\n",
       "        [-665.6058],\n",
       "        [-152.4022],\n",
       "        [-335.6033],\n",
       "        [-560.6229],\n",
       "        [-359.5644]], 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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.770181Z",
     "iopub.status.busy": "2024-03-05T21:23:56.770067Z",
     "iopub.status.idle": "2024-03-05T21:23:56.773699Z",
     "shell.execute_reply": "2024-03-05T21:23:56.773381Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:56.775336Z",
     "iopub.status.busy": "2024-03-05T21:23:56.775243Z",
     "iopub.status.idle": "2024-03-05T21:23:56.778906Z",
     "shell.execute_reply": "2024-03-05T21:23:56.778640Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: 43587_2023_557_MOESM6_ESM.zip\n",
      "Deleted file: YingCausAge.csv\n",
      "Deleted file: YingDamAge.csv\n",
      "Deleted file: YingAdaptAge.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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