{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# YingCausAge"
   ]
  },
  {
   "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": "2024-03-05T21:23:58.758380Z",
     "iopub.status.busy": "2024-03-05T21:23:58.757796Z",
     "iopub.status.idle": "2024-03-05T21:24:00.073542Z",
     "shell.execute_reply": "2024-03-05T21:24:00.073242Z"
    }
   },
   "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": "2024-03-05T21:24:00.075336Z",
     "iopub.status.busy": "2024-03-05T21:24:00.075168Z",
     "iopub.status.idle": "2024-03-05T21:24:00.085030Z",
     "shell.execute_reply": "2024-03-05T21:24:00.084775Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class YingCausAge(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.YingCausAge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:24:00.086436Z",
     "iopub.status.busy": "2024-03-05T21:24:00.086352Z",
     "iopub.status.idle": "2024-03-05T21:24:00.087854Z",
     "shell.execute_reply": "2024-03-05T21:24:00.087634Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.YingCausAge()"
   ]
  },
  {
   "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": "2024-03-05T21:24:00.089255Z",
     "iopub.status.busy": "2024-03-05T21:24:00.089178Z",
     "iopub.status.idle": "2024-03-05T21:24:00.091166Z",
     "shell.execute_reply": "2024-03-05T21:24:00.090949Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"yingcausage\"\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 chronological-age clock using EWMR-prioritized CpGs and feature-specific penalties derived from 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\"] = [\"chronological age\"]  # Paper: CausAge is the causality-enriched chronological-age predictor.\n",
    "model.metadata[\"training_target\"] = [\"chronological age\"]  # Paper: The elastic-net outcome was chronological age.\n",
    "model.metadata[\"unit\"] = [\"years\"]  # Paper: Prediction accuracy is evaluated as mean absolute error in years.\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\"] = 585\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": "2024-03-05T21:24:00.092676Z",
     "iopub.status.busy": "2024-03-05T21:24:00.092596Z",
     "iopub.status.idle": "2024-03-05T21:24:00.176279Z",
     "shell.execute_reply": "2024-03-05T21:24:00.175983Z"
    }
   },
   "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": "2024-03-05T21:24:00.178083Z",
     "iopub.status.busy": "2024-03-05T21:24:00.177971Z",
     "iopub.status.idle": "2024-03-05T21:24:00.181716Z",
     "shell.execute_reply": "2024-03-05T21:24:00.181466Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('YingCausAge.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": "2024-03-05T21:24:00.183214Z",
     "iopub.status.busy": "2024-03-05T21:24:00.183134Z",
     "iopub.status.idle": "2024-03-05T21:24:00.185174Z",
     "shell.execute_reply": "2024-03-05T21:24:00.184928Z"
    }
   },
   "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": "2024-03-05T21:24:00.186672Z",
     "iopub.status.busy": "2024-03-05T21:24:00.186582Z",
     "iopub.status.idle": "2024-03-05T21:24:00.188819Z",
     "shell.execute_reply": "2024-03-05T21:24:00.188549Z"
    }
   },
   "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": "2024-03-05T21:24:00.190298Z",
     "iopub.status.busy": "2024-03-05T21:24:00.190219Z",
     "iopub.status.idle": "2024-03-05T21:24:00.191842Z",
     "shell.execute_reply": "2024-03-05T21:24:00.191543Z"
    }
   },
   "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": "2024-03-05T21:24:00.193245Z",
     "iopub.status.busy": "2024-03-05T21:24:00.193163Z",
     "iopub.status.idle": "2024-03-05T21:24:00.194736Z",
     "shell.execute_reply": "2024-03-05T21:24:00.194486Z"
    }
   },
   "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": "2024-03-05T21:24:00.196107Z",
     "iopub.status.busy": "2024-03-05T21:24:00.196033Z",
     "iopub.status.idle": "2024-03-05T21:24:00.197586Z",
     "shell.execute_reply": "2024-03-05T21:24:00.197331Z"
    }
   },
   "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": "2024-03-05T21:24:00.198997Z",
     "iopub.status.busy": "2024-03-05T21:24:00.198910Z",
     "iopub.status.idle": "2024-03-05T21:24:00.201284Z",
     "shell.execute_reply": "2024-03-05T21:24:00.201055Z"
    }
   },
   "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': 'yingcausage',\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: ['cg00027162', 'cg00048759', 'cg00200653', 'cg00347863', 'cg00505045', 'cg00563845', 'cg00603274', 'cg00614360', 'cg00655552', 'cg00663739', 'cg00715290', 'cg00879155', 'cg00910168', 'cg00962755', 'cg01035616', 'cg01048752', 'cg01105058', 'cg01274524', 'cg01321673', 'cg01329511', 'cg01334432', 'cg01399860', 'cg01421252', 'cg01454752', 'cg01503516', 'cg01538166', 'cg01557754', 'cg01579218', 'cg01597480', 'cg01762785']... [Total elements: 585]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=585, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [1.6678526401519775, 5.419585227966309, -0.2697699964046478, 4.103872299194336, 12.006643295288086, -0.5450900197029114, 0.14688290655612946, 1.1880619525909424, -0.965871274471283, 3.5483784675598145, -10.219189643859863, 0.6130169034004211, -1.1934856176376343, 1.0630154609680176, 1.8167389631271362, -1.1464102268218994, 5.109470367431641, 0.2968243360519409, 0.8408879041671753, 3.6986474990844727, -1.898300051689148, -0.3860916793346405, -0.8981965780258179, 5.881424903869629, 1.8310381174087524, -4.833215236663818, -4.612349987030029, -2.4021832942962646, -2.8061323165893555, 0.24915219843387604]... [Tensor of shape torch.Size([1, 585])]\n",
      "base_model.linear.bias: tensor([86.8082])\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": "2024-03-05T21:24:00.203045Z",
     "iopub.status.busy": "2024-03-05T21:24:00.202925Z",
     "iopub.status.idle": "2024-03-05T21:24:00.207096Z",
     "shell.execute_reply": "2024-03-05T21:24:00.206850Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[177.3734],\n",
       "        [327.3683],\n",
       "        [158.4421],\n",
       "        [157.5365],\n",
       "        [131.7594],\n",
       "        [  7.4059],\n",
       "        [117.2152],\n",
       "        [168.9700],\n",
       "        [123.9884],\n",
       "        [257.4784]], 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": "2024-03-05T21:24:00.208597Z",
     "iopub.status.busy": "2024-03-05T21:24:00.208513Z",
     "iopub.status.idle": "2024-03-05T21:24:00.210841Z",
     "shell.execute_reply": "2024-03-05T21:24:00.210570Z"
    }
   },
   "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": "2024-03-05T21:24:00.212243Z",
     "iopub.status.busy": "2024-03-05T21:24:00.212164Z",
     "iopub.status.idle": "2024-03-05T21:24:00.215584Z",
     "shell.execute_reply": "2024-03-05T21:24:00.215332Z"
    }
   },
   "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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