{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# DeconvoluteBloodEPIC"
   ]
  },
  {
   "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 DeconvoluteBloodEPIC(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.DeconvoluteBloodEPIC)\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.DeconvoluteBloodEPIC()"
   ]
  },
  {
   "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\"] = \"deconvolutebloodepicneutrophil\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: The deconvolution models consume bulk DNA-methylation measurements.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: The EPIC reference represents major human blood-cell types.\n",
    "model.metadata[\"year\"] = 2018\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Salas, L.A., Koestler, D.C., Butler, R.A. et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biology 19, 64 (2018).\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1186/s13059-018-1448-7\"\n",
    "model.metadata[\"notes\"] = \"Reference-based constrained deconvolution returning the neutrophil proportion from EPIC-array blood methylation. Pyaging uses the paper's automatic 600-CpG top-hypermethylated/top-hypomethylated reference, not the paper's preferred 450-CpG EPIC IDOL library.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"purified blood leukocytes\"]  # Paper: Reference methylation profiles represent six abundant blood-cell types; CpGs are selected for cell-type-specific methylation.\n",
    "model.metadata[\"predicts\"] = [\"neutrophil proportion\"]  # Paper: The EPIC mode estimates proportions of six blood-cell types, including the assigned cell type.\n",
    "model.metadata[\"training_target\"] = [\"cell-type-specific methylation contrast\"]  # Paper: The automatic reference selects the most hypermethylated and hypomethylated CpGs for each purified leukocyte type.\n",
    "model.metadata[\"unit\"] = [\"proportion\"]  # Paper: Estimated proportions are constrained between zero and one and to sum to one.\n",
    "model.metadata[\"model_type\"] = \"reference-based constrained deconvolution\"  # Paper: The model solves B = X × P for P by constrained quadratic programming.\n",
    "model.metadata[\"platform\"] = [\"Illumina EPIC\"]  # Paper: DeconvoluteBloodEPIC is the variant tailored to the EPIC methylation platform.\n",
    "model.metadata[\"population\"] = \"adults\"  # Paper: The reference cells came from 37 anonymous healthy adults; donor ages ranged from 19 to 59 years.\n",
    "model.metadata[\"journal\"] = \"Genome Biology\"\n",
    "model.metadata[\"last_author\"] = \"Brock C. Christensen\"\n",
    "model.metadata[\"n_features\"] = 600\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/EPIC_salas_18_reference.csv\"\n",
    "os.system(f\"curl -L {coeff_url} -o EPIC_salas_18_reference.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('EPIC_salas_18_reference.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 = 0\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': 'deconvolutebloodepicneutrophil',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1038/s43587-025-00987-y',\n",
      " 'notes': 'Estimated proportion of neutrophil from EPIC reference.',\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2024}\n",
      "reference_values: [0.20518415451984126, 0.19970558314682532, 0.1024157725119047, 0.2008561107023811, 0.14881559691666668, 0.18691947926984118, 0.09904868163095237, 0.11866279766269845, 0.11413279887698413, 0.07614163463888889, 0.11287183193253969, 0.09002383478571424, 0.15332935412698398, 0.10906152481746033, 0.12821537873412694, 0.11714246567063487, 0.1349594052698413, 0.21058724882142854, 0.23903708456349196, 0.09275976726984132, 0.08500618109523812, 0.17428245038095244, 0.24038344935714295, 0.24988523996428577, 0.1389251376904763, 0.11076903522222221, 0.10888998828968255, 0.09822708860317463, 0.09986797370634924, 0.11328396424206344]... [Tensor of shape torch.Size([600])]\n",
      "preprocess_name: 'fill_with_reference_means'\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: None\n",
      "postprocess_dependencies: None\n",
      "features: ['cg02091275', 'cg10613215', 'cg04738410', 'cg23954655', 'cg10825315', 'cg12093275', 'cg05915362', 'cg15472145', 'cg14781394', 'cg03753191', 'cg04554131', 'cg08708961', 'cg05366453', 'cg23889684', 'cg09936080', 'cg11612354', 'cg13525276', 'cg21264165', 'cg24197536', 'cg19935718', 'cg24756631', 'cg14637146', 'cg11311865', 'cg04449166', 'cg08311647', 'cg21870144', 'cg06516865', 'cg03172796', 'cg25446602', 'cg24102319']... [Total elements: 600]\n",
      "base_model_features: None\n",
      "base_model: None\n",
      "pseudo_inv: [0.018423516597868533, 0.02076496108902344, 0.006246045739563567, 0.017322627415437906, 0.012596462279237032, 0.014680027443715469, 0.005387403052654981, 0.00826322984364358, 0.006720451696782447, 0.003520144956793594, 0.007097605150112215, 0.006828305361872053, 0.009242538044463792, 0.005067549321932598, 0.00998902642967594, 0.009749104467339021, 0.010083925381389934, 0.01566659748873227, 0.017857728483860698, 0.005648755947299582, 0.003880839808950153, 0.010944187408221806, 0.014789742065456597, 0.01823321395305361, 0.009616296123845524, 0.005330229811626641, 0.007168874993235975, 0.003581639488212511, 0.004981727200100809, 0.006052278008950252]... [Tensor of shape torch.Size([6, 600])]\n",
      "cell_index: 0\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": "4d941337",
   "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([[0.3807],\n",
       "        [0.0000],\n",
       "        [0.0779],\n",
       "        [0.4891],\n",
       "        [0.1699],\n",
       "        [0.1325],\n",
       "        [0.1939],\n",
       "        [0.0689],\n",
       "        [0.0000],\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: EPIC_salas_18_reference.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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