{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# TwelveCellDeconvoluteBloodEPIC"
   ]
  },
  {
   "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)\n"
   ]
  },
  {
   "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": "2025-04-07T17:47:34.133350Z",
     "iopub.status.busy": "2025-04-07T17:47:34.132902Z",
     "iopub.status.idle": "2025-04-07T17:47:35.476506Z",
     "shell.execute_reply": "2025-04-07T17:47:35.476150Z"
    }
   },
   "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": "2025-04-07T17:47:35.478214Z",
     "iopub.status.busy": "2025-04-07T17:47:35.477997Z",
     "iopub.status.idle": "2025-04-07T17:47:35.484695Z",
     "shell.execute_reply": "2025-04-07T17:47:35.484415Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class TwelveCellDeconvoluteBloodEPIC(DeconvolutionSingleCell):\n",
      "    def __init__(self):\n",
      "        super().__init__()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_entire_class(cls):\n",
    "    source = inspect.getsource(cls)\n",
    "    print(source)\n",
    "\n",
    "print_entire_class(pya.models.TwelveCellDeconvoluteBloodEPIC)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:35.485963Z",
     "iopub.status.busy": "2025-04-07T17:47:35.485866Z",
     "iopub.status.idle": "2025-04-07T17:47:35.487577Z",
     "shell.execute_reply": "2025-04-07T17:47:35.487295Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.TwelveCellDeconvoluteBloodEPIC()"
   ]
  },
  {
   "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": "2025-04-07T17:47:35.488812Z",
     "iopub.status.busy": "2025-04-07T17:47:35.488727Z",
     "iopub.status.idle": "2025-04-07T17:47:35.490718Z",
     "shell.execute_reply": "2025-04-07T17:47:35.490459Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"twelvecelldeconvolutebloodepicneu\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: The TwelveCell model is a DeconvolutionModel whose reference rows are CpG loci.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: The registry declares species Human for TwelveCellDeconvoluteBloodEPIC.\n",
    "model.metadata[\"year\"] = 2022\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Salas, L.A., Zhang, Z., Koestler, D.C. et al. Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nature Communications 13, 761 (2022).\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s41467-021-27864-7\"\n",
    "model.metadata[\"notes\"] = \"Reference-based constrained deconvolution returning the neutrophil proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"purified blood leukocytes\"]  # Paper: The reference-matrix columns are twelve leukocyte subtypes used to deconvolve peripheral blood.\n",
    "model.metadata[\"predicts\"] = [\"neutrophil proportion\"]  # Paper: The model output is Cell Proportions and Neu is one of the twelve returned columns.\n",
    "model.metadata[\"training_target\"] = [\"cell-type proportions\"]  # Paper: IDOL used artificial-mixture ground truth with known, prespecified proportions of the 12 cell types and optimized libraries by comparing deconvolution estimates with those known proportions.\n",
    "model.metadata[\"unit\"] = [\"proportion\"]  # Paper: The solver constrains each estimated cell proportion to 0–1 and all proportions to sum to one.\n",
    "model.metadata[\"model_type\"] = \"reference-based constrained deconvolution\"  # Paper: The implementation minimizes reference-matrix reconstruction error by constrained quadratic programming.\n",
    "model.metadata[\"platform\"] = [\"Illumina EPIC\"]  # Paper: The TwelveCell model registry sets platform to EPIC.\n",
    "model.metadata[\"population\"] = \"adults\"  # Paper: Reference cells were isolated from 41 male and 15 female anonymous healthy donors with mean age 32.2 years and range 19–58 years, spanning multiple self-identified ancestries.\n",
    "model.metadata[\"journal\"] = \"Nature Communications\"\n",
    "model.metadata[\"last_author\"] = \"Brock C. Christensen\"\n",
    "model.metadata[\"n_features\"] = 240\n",
    "model.metadata[\"citations\"] = 13\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": "7bec474f-80ce-4884-9472-30c193327117",
   "metadata": {},
   "source": [
    "#### Download reference file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa4a1b59-dda3-4ea8-8f34-b3c53ecbc310",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:35.492080Z",
     "iopub.status.busy": "2025-04-07T17:47:35.491992Z",
     "iopub.status.idle": "2025-04-07T17:47:36.204837Z",
     "shell.execute_reply": "2025-04-07T17:47:36.204380Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_url = \"https://raw.githubusercontent.com/bio-learn/biolearn/master/biolearn/data/twelve_cell_deconv.csv\"\n",
    "os.system(f\"curl -L {coeff_url} -o twelve_cell_deconv.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5035b180-3d1b-4432-8ebe-b9c92bd93a7f",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f4af76-b93c-438c-b57f-f129d6e9ec99",
   "metadata": {},
   "source": [
    "#### From CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f26a49e3-7389-416c-9080-539f50e9abd0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:36.206985Z",
     "iopub.status.busy": "2025-04-07T17:47:36.206832Z",
     "iopub.status.idle": "2025-04-07T17:47:36.211174Z",
     "shell.execute_reply": "2025-04-07T17:47:36.210783Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "ref = pd.read_csv('twelve_cell_deconv.csv', index_col=0)\n",
    "model.features = ref.index.astype(str).tolist()\n",
    "\n",
    "ref_matrix = torch.tensor(ref.values, dtype=torch.float64)\n",
    "pseudo_inv = torch.linalg.pinv(ref_matrix)\n",
    "\n",
    "model.pseudo_inv = pseudo_inv\n",
    "model.cell_index = 10\n",
    "model.reference_values = torch.nanmean(ref_matrix, dim=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79e5690-e284-4de6-8460-d3545a8192af",
   "metadata": {},
   "source": [
    "#### From CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f6187ed-fcff-4ff2-bcb1-b5bcef8190e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:36.213121Z",
     "iopub.status.busy": "2025-04-07T17:47:36.212960Z",
     "iopub.status.idle": "2025-04-07T17:47:36.216800Z",
     "shell.execute_reply": "2025-04-07T17:47:36.216396Z"
    }
   },
   "outputs": [],
   "source": [
    "# No linear base model; deconvolution logic in model forward\n"
   ]
  },
  {
   "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": "2025-04-07T17:47:36.218646Z",
     "iopub.status.busy": "2025-04-07T17:47:36.218507Z",
     "iopub.status.idle": "2025-04-07T17:47:36.221310Z",
     "shell.execute_reply": "2025-04-07T17:47:36.220958Z"
    }
   },
   "outputs": [],
   "source": [
    "model.base_model = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad8b4c1d-9d57-48b7-9a30-bcfea7b747b1",
   "metadata": {},
   "source": [
    "## Load reference values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90d45266-962d-41b6-927c-6a147ed41305",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:36.222862Z",
     "iopub.status.busy": "2025-04-07T17:47:36.222749Z",
     "iopub.status.idle": "2025-04-07T17:47:36.224649Z",
     "shell.execute_reply": "2025-04-07T17:47:36.224309Z"
    }
   },
   "outputs": [],
   "source": [
    "# reference_values already set above"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3bcf7b-74a8-4d21-9ccb-4de0c2b0516b",
   "metadata": {},
   "source": [
    "## Load preprocess and postprocess objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7d32b69-e20e-42ff-aba9-d07b9b44dbd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:36.226270Z",
     "iopub.status.busy": "2025-04-07T17:47:36.226156Z",
     "iopub.status.idle": "2025-04-07T17:47:36.228064Z",
     "shell.execute_reply": "2025-04-07T17:47:36.227750Z"
    }
   },
   "outputs": [],
   "source": [
    "model.preprocess_name = \"fill_with_reference_means\"\n",
    "model.preprocess_dependencies = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a22fb20-c605-424d-8efb-7620c2c0755c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:36.229604Z",
     "iopub.status.busy": "2025-04-07T17:47:36.229468Z",
     "iopub.status.idle": "2025-04-07T17:47:36.231233Z",
     "shell.execute_reply": "2025-04-07T17:47:36.230916Z"
    }
   },
   "outputs": [],
   "source": [
    "model.postprocess_name = None\n",
    "model.postprocess_dependencies = None\n"
   ]
  },
  {
   "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": "2025-04-07T17:47:36.232808Z",
     "iopub.status.busy": "2025-04-07T17:47:36.232688Z",
     "iopub.status.idle": "2025-04-07T17:47:36.236913Z",
     "shell.execute_reply": "2025-04-07T17:47:36.236596Z"
    }
   },
   "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. \"A unified framework for systematic curation '\n",
      "             'and evaluation of aging biomarkers.\" Nature Aging (2025): 1-17.',\n",
      " 'clock_name': 'twelvecelldeconvolutebloodepicneu',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1038/s43587-025-00987-y',\n",
      " 'notes': 'Estimated proportion of Neu from 12-cell EPIC reference.',\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2024}\n",
      "reference_values: [0.9106685833333333, 0.8335768333333333, 0.8720161666666666, 0.8286470833333333, 0.92628525, 0.9155413333333334, 0.8792788333333332, 0.9127326666666665, 0.9067565833333332, 0.84800775, 0.10073516666666667, 0.14440299999999998, 0.049399916666666675, 0.08766050000000002, 0.07896441666666666, 0.12878258333333334, 0.16374524999999998, 0.13815541666666667, 0.17540216666666666, 0.1698345, 0.9202164166666665, 0.8607305833333334, 0.9136148333333334, 0.9245049999999999, 0.8901303333333334, 0.9271245, 0.8883705833333333, 0.8855881666666668, 0.9244469166666666, 0.92171125]... [Tensor of shape torch.Size([240])]\n",
      "preprocess_name: 'fill_with_reference_means'\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: None\n",
      "postprocess_dependencies: None\n",
      "features: ['cg11778706', 'cg13745692', 'cg15163862', 'cg15667493', 'cg09590647', 'cg24160371', 'cg04200580', 'cg02177087', 'cg02690807', 'cg08486432', 'cg08674813', 'cg17565444', 'cg20208538', 'cg08747761', 'cg16576285', 'cg08269714', 'cg10931172', 'cg18421366', 'cg06805218', 'cg21268578', 'cg23635663', 'cg10712263', 'cg03984633', 'cg27576286', 'cg00789615', 'cg20591136', 'cg08012294', 'cg19811231', 'cg13335048', 'cg27109907']... [Total elements: 240]\n",
      "base_model_features: None\n",
      "base_model: None\n",
      "pseudo_inv: [0.0062597217235298664, 0.01539477951995796, 0.003836934604777173, 0.005564101437478809, 0.002277140363479023, 0.006073633184626901, 0.008477183290511526, -0.0001975656527457585, 0.005601139656499912, 0.007094362071883866, -0.006240708820602573, -0.009260375574434002, -0.0010103501546960773, -2.0931822748843616e-05, -0.003443105142629081, -0.006973754074381415, -0.014081560765767536, -0.01165024465832652, -0.017254358157385866, -0.018101141218108914, 0.016011891819700665, 0.007716146910953028, 0.01145146135203884, 0.007735843885753704, 0.014959580407076114, 0.011932867918461359, 0.014137094238940516, 0.016570818459337337, 0.006383014308174419, 0.007057464604785132]... [Tensor of shape torch.Size([12, 240])]\n",
      "cell_index: 10\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": "2f0a4df8",
   "metadata": {},
   "source": [
    "## Basic Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:36.238465Z",
     "iopub.status.busy": "2025-04-07T17:47:36.238354Z",
     "iopub.status.idle": "2025-04-07T17:47:36.242868Z",
     "shell.execute_reply": "2025-04-07T17:47:36.242565Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.0000],\n",
       "        [0.0121],\n",
       "        [0.0000],\n",
       "        [0.0000],\n",
       "        [0.0000],\n",
       "        [0.0000],\n",
       "        [0.2412],\n",
       "        [0.1009],\n",
       "        [0.2960],\n",
       "        [0.0000]], dtype=torch.float64)"
      ]
     },
     "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:36.244263Z",
     "iopub.status.busy": "2025-04-07T17:47:36.244159Z",
     "iopub.status.idle": "2025-04-07T17:47:36.246682Z",
     "shell.execute_reply": "2025-04-07T17:47:36.246395Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac6257b-8d08-4a90-8d0b-7f745dc11ac1",
   "metadata": {},
   "source": [
    "\n",
    "## Clear directory\n",
    "<a id=\"10\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:47:36.248184Z",
     "iopub.status.busy": "2025-04-07T17:47:36.248077Z",
     "iopub.status.idle": "2025-04-07T17:47:36.255709Z",
     "shell.execute_reply": "2025-04-07T17:47:36.255449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: twelve_cell_deconv.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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