{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# MammalianBlood2"
   ]
  },
  {
   "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": "2025-04-07T17:58:10.196194Z",
     "iopub.status.busy": "2025-04-07T17:58:10.195788Z",
     "iopub.status.idle": "2025-04-07T17:58:11.671668Z",
     "shell.execute_reply": "2025-04-07T17:58:11.671349Z"
    }
   },
   "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\n",
    "import numpy as np"
   ]
  },
  {
   "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": "2025-04-07T17:58:11.673404Z",
     "iopub.status.busy": "2025-04-07T17:58:11.673170Z",
     "iopub.status.idle": "2025-04-07T17:58:11.681231Z",
     "shell.execute_reply": "2025-04-07T17:58:11.680976Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class MammalianBlood2(pyagingModel):\n",
      "    def __init__(self):\n",
      "        super().__init__()\n",
      "\n",
      "    def forward(self, x):\n",
      "        x_cpg = x[:, :-1756]  # number of species in lookup table\n",
      "        x_species = x[:, -1756:]  # number of species in lookup table\n",
      "        x = self.base_model(x_cpg)\n",
      "        x = self.postprocess(x, x_species)\n",
      "        return x\n",
      "\n",
      "    def preprocess(self, x):\n",
      "        return x\n",
      "\n",
      "    def postprocess(self, x, x_species):\n",
      "        \"\"\"\n",
      "        Converts output of relative age to age in units of years.\n",
      "        \"\"\"\n",
      "        indices = torch.argmax(x_species, dim=1)\n",
      "        anage_array = self.postprocess_dependencies[0]\n",
      "        anage_tensor = torch.tensor(anage_array, dtype=x.dtype, device=x.device)\n",
      "        gestation_time = anage_tensor[indices, 0].unsqueeze(1)\n",
      "        max_age = anage_tensor[indices, 3].unsqueeze(1)\n",
      "\n",
      "        x = torch.exp(-torch.exp(-x))\n",
      "        x = x * (max_age + gestation_time) - gestation_time\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.MammalianBlood2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:58:11.682439Z",
     "iopub.status.busy": "2025-04-07T17:58:11.682349Z",
     "iopub.status.idle": "2025-04-07T17:58:11.683957Z",
     "shell.execute_reply": "2025-04-07T17:58:11.683718Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.MammalianBlood2()"
   ]
  },
  {
   "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": "2025-04-07T17:58:11.685197Z",
     "iopub.status.busy": "2025-04-07T17:58:11.685105Z",
     "iopub.status.idle": "2025-04-07T17:58:11.687127Z",
     "shell.execute_reply": "2025-04-07T17:58:11.686874Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"mammalianblood2\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: methylation\n",
    "model.metadata[\"species\"] = \"multiple species\"  # Paper: multi\n",
    "model.metadata[\"year\"] = 2023\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Lu, A. T., et al. \\\"Universal DNA methylation age across mammalian tissues.\\\" Nature Aging 3.9 (2023): 1144-1166.\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s43587-023-00462-6\"\n",
    "model.metadata[\"notes\"] = \"Blood-specific universal clock 2 fits relative age and returns species-adjusted chronological age in years after the maximum-lifespan inverse transformation.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"blood\"]  # Paper: blood\n",
    "model.metadata[\"predicts\"] = [\"chronological age\"]  # Paper: The official inverse transformation returns DNAm chronological age in years.\n",
    "model.metadata[\"training_target\"] = [\"relative age\"]  # Paper: relative age\n",
    "model.metadata[\"unit\"] = [\"years\"]  # Paper: The official inverse transformation returns DNAm chronological age in years.\n",
    "model.metadata[\"model_type\"] = \"elastic net regression\"  # Paper: Elastic net\n",
    "model.metadata[\"platform\"] = [\"Horvath MammalMethylChip40\"]  # Paper: HorvathMammalMethylChip40\n",
    "model.metadata[\"population\"] = \"multiple mammalian species\"  # Paper: multi-species mammalian blood samples\n",
    "model.metadata[\"journal\"] = \"Nature Aging\"\n",
    "model.metadata[\"last_author\"] = \"Steve Horvath\"\n",
    "model.metadata[\"n_features\"] = 2257\n",
    "model.metadata[\"citations\"] = 390\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": "5881f33e-0486-45d1-bd87-9c995f47ca62",
   "metadata": {},
   "source": [
    "#### Download GitHub repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e85525f7-6da4-4962-a7d5-0607e76eea33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:58:11.688461Z",
     "iopub.status.busy": "2025-04-07T17:58:11.688384Z",
     "iopub.status.idle": "2025-04-07T18:00:19.988633Z",
     "shell.execute_reply": "2025-04-07T18:00:19.988299Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "github_url = \"https://github.com/shorvath/MammalianMethylationConsortium.git\"\n",
    "github_folder_name = github_url.split('/')[-1].split('.')[0]\n",
    "os.system(f\"git clone {github_url}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "047debfa-2c1e-4f76-bb11-4893ecb3d1e8",
   "metadata": {},
   "source": [
    "#### Download from R package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d81f2c9d-362f-43cb-ad52-012e28217164",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:19.990342Z",
     "iopub.status.busy": "2025-04-07T18:00:19.990222Z",
     "iopub.status.idle": "2025-04-07T18:00:19.992712Z",
     "shell.execute_reply": "2025-04-07T18:00:19.992443Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing download.r\n"
     ]
    }
   ],
   "source": [
    "%%writefile download.r\n",
    "\n",
    "options(repos = c(CRAN = \"https://cloud.r-project.org/\"))\n",
    "\n",
    "myinput.list=readRDS('MammalianMethylationConsortium/UniversalPanMammalianClock/ClockParameters/mydata_GitHub.Rds')\n",
    "anage=myinput.list[[3]]\n",
    "anage=subset(anage,select=c(SpeciesLatinName,GestationTimeInYears, averagedMaturity.yrs,maxAge))\n",
    "anage$HighmaxAge=1.3*anage$maxAge\n",
    "anage$HighmaxAge[anage$SpeciesLatinName=='Homo sapiens']=anage$maxAge[anage$SpeciesLatinName=='Homo sapiens']\n",
    "anage$HighmaxAge[anage$SpeciesLatinName=='Mus musculus']=anage$maxAge[anage$SpeciesLatinName=='Mus musculus']\n",
    "write.csv(anage, \"species_annotation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ce2bac8-dd33-46cb-a7b6-14a1d0976f05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:19.994180Z",
     "iopub.status.busy": "2025-04-07T18:00:19.994056Z",
     "iopub.status.idle": "2025-04-07T18:00:20.630642Z",
     "shell.execute_reply": "2025-04-07T18:00:20.629430Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system(\"Rscript download.r\")"
   ]
  },
  {
   "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": 8,
   "id": "8a3d5de6-6303-487a-8b4d-e6345792f7be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.632552Z",
     "iopub.status.busy": "2025-04-07T18:00:20.632417Z",
     "iopub.status.idle": "2025-04-07T18:00:20.645915Z",
     "shell.execute_reply": "2025-04-07T18:00:20.645138Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('MammalianMethylationConsortium/UniversalPanMammalianClock/ClockParameters/tissue_specific_clock/UniversalBloodClock2_final.csv')\n",
    "df['feature'] = df['var']\n",
    "df['coefficient'] = df['beta']\n",
    "cpg_features = df['feature'][1:].tolist()\n",
    "\n",
    "anage_df = pd.read_csv('species_annotation.csv', index_col=0)\n",
    "anage_df = anage_df[~anage_df['HighmaxAge'].isna()]\n",
    "anage_df = anage_df[~anage_df['GestationTimeInYears'].isna()]\n",
    "anage_df = anage_df.reset_index().drop('index', axis=1)\n",
    "anage_df = anage_df.fillna(0)\n",
    "species_features = anage_df['SpeciesLatinName'].tolist()\n",
    "\n",
    "model.features = cpg_features + species_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.649844Z",
     "iopub.status.busy": "2025-04-07T18:00:20.649494Z",
     "iopub.status.idle": "2025-04-07T18:00:20.655224Z",
     "shell.execute_reply": "2025-04-07T18:00:20.654157Z"
    }
   },
   "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": 10,
   "id": "d7f43b99-26f2-4622-9a76-316712058877",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.659016Z",
     "iopub.status.busy": "2025-04-07T18:00:20.658669Z",
     "iopub.status.idle": "2025-04-07T18:00:20.662630Z",
     "shell.execute_reply": "2025-04-07T18:00:20.662237Z"
    }
   },
   "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": 11,
   "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.664353Z",
     "iopub.status.busy": "2025-04-07T18:00:20.664226Z",
     "iopub.status.idle": "2025-04-07T18:00:20.667663Z",
     "shell.execute_reply": "2025-04-07T18:00:20.666712Z"
    }
   },
   "outputs": [],
   "source": [
    "reference_list = np.array([0] * len(model.features))\n",
    "reference_list[len(cpg_features) + np.where(anage_df.SpeciesLatinName == 'Homo sapiens')[0][0]] = 0.5\n",
    "model.reference_values = reference_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3bcf7b-74a8-4d21-9ccb-4de0c2b0516b",
   "metadata": {},
   "source": [
    "## Load preprocess and postprocess objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a22fb20-c605-424d-8efb-7620c2c0755c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.670694Z",
     "iopub.status.busy": "2025-04-07T18:00:20.670521Z",
     "iopub.status.idle": "2025-04-07T18:00:20.672897Z",
     "shell.execute_reply": "2025-04-07T18:00:20.672525Z"
    }
   },
   "outputs": [],
   "source": [
    "model.preprocess_name = None\n",
    "model.preprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.675491Z",
     "iopub.status.busy": "2025-04-07T18:00:20.675165Z",
     "iopub.status.idle": "2025-04-07T18:00:20.680689Z",
     "shell.execute_reply": "2025-04-07T18:00:20.679824Z"
    }
   },
   "outputs": [],
   "source": [
    "model.postprocess_name = 'mammalian2'\n",
    "anage_df = anage_df.loc[:, ['GestationTimeInYears','averagedMaturity.yrs', 'maxAge','HighmaxAge']]\n",
    "anage_array = np.array(anage_df)\n",
    "model.postprocess_dependencies = [anage_array]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e3d6b1-e67e-4f3d-bd39-0ebec5726c3c",
   "metadata": {},
   "source": [
    "## Check all clock parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2168355c-47d9-475d-b816-49f65e74887c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.684173Z",
     "iopub.status.busy": "2025-04-07T18:00:20.683837Z",
     "iopub.status.idle": "2025-04-07T18:00:20.691071Z",
     "shell.execute_reply": "2025-04-07T18:00:20.690792Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '⌛',\n",
      " 'citation': 'Lu, A. T., et al. \"Universal DNA methylation age across '\n",
      "             'mammalian tissues.\" Nature aging 3.9 (2023): 1144-1166.',\n",
      " 'clock_name': 'mammalianblood2',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1038/s43587-023-00462-6',\n",
      " 'notes': None,\n",
      " 'research_only': None,\n",
      " 'species': 'multi',\n",
      " 'version': None,\n",
      " 'year': 2023}\n",
      "reference_values: array([0, 0, 0, ..., 0, 0, 0])\n",
      "preprocess_name: None\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: 'mammalian2'\n",
      "postprocess_dependencies: [array([[2.19178082e-02, 2.49315068e+00, 3.60000000e+01, 4.68000000e+01],\n",
      "       [1.64383562e-02, 3.00000000e+00, 1.15000000e+01, 1.49500000e+01],\n",
      "       [3.01369863e-02, 4.50000000e+00, 1.50000000e+01, 1.95000000e+01],\n",
      "       ...,\n",
      "       [5.60876712e-01, 3.05123288e+00, 2.30000000e+01, 2.99000000e+01],\n",
      "       [9.87671233e-02, 1.01706849e+00, 3.50000000e+00, 4.55000000e+00],\n",
      "       [3.15068493e-01, 4.58334000e-01, 2.70000000e+01, 3.51000000e+01]])]\n",
      "features: ['cg00005271', 'cg00114412', 'cg00227334', 'cg00258262', 'cg00264615', 'cg00370791', 'cg00393367', 'cg00439117', 'cg00458823', 'cg00559067', 'cg00587168', 'cg00756693', 'cg00855890', 'cg00915004', 'cg00953859', 'cg00988788', 'cg01053290', 'cg01137681', 'cg01153166', 'cg01156745', 'cg01342051', 'cg01393939', 'cg01454752', 'cg01468874', 'cg01486146', 'cg01528792', 'cg01585174', 'cg01639959', 'cg01701526', 'cg01787104']... [Total elements: 2257]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=2257, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [0.08963627368211746, -0.001239112694747746, -0.00347901857458055, 0.009207980707287788, -7.227044989122078e-05, -0.055550578981637955, 0.04472345858812332, -0.36105695366859436, -0.018508171662688255, 0.07516830414533615, 0.011418496258556843, 0.008542953990399837, 0.10099010914564133, 0.26528534293174744, 0.013976222835481167, -0.01059863530099392, -0.05601461976766586, 0.12440702319145203, 0.0005542261060327291, -0.06668984144926071, 0.03973907604813576, 0.08093400299549103, 0.018934927880764008, 0.002540371846407652, 0.07929009199142456, -0.14058975875377655, -0.01051307376474142, -0.11011394113302231, -0.06954656541347504, -0.06167547032237053]... [Tensor of shape torch.Size([1, 501])]\n",
      "base_model.linear.bias: tensor([1.0883])\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": 15,
   "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.692532Z",
     "iopub.status.busy": "2025-04-07T18:00:20.692433Z",
     "iopub.status.idle": "2025-04-07T18:00:20.697724Z",
     "shell.execute_reply": "2025-04-07T18:00:20.697432Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 3.8852],\n",
       "        [ 0.5774],\n",
       "        [-0.1589],\n",
       "        [11.6877],\n",
       "        [-0.1233],\n",
       "        [ 5.4960],\n",
       "        [-0.0658],\n",
       "        [-0.5753],\n",
       "        [10.2756],\n",
       "        [42.2815]], dtype=torch.float64, grad_fn=<SubBackward0>)"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.699129Z",
     "iopub.status.busy": "2025-04-07T18:00:20.699034Z",
     "iopub.status.idle": "2025-04-07T18:00:20.703405Z",
     "shell.execute_reply": "2025-04-07T18:00:20.703066Z"
    }
   },
   "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": 17,
   "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T18:00:20.705096Z",
     "iopub.status.busy": "2025-04-07T18:00:20.704976Z",
     "iopub.status.idle": "2025-04-07T18:00:20.854911Z",
     "shell.execute_reply": "2025-04-07T18:00:20.854275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: coefficients.xlsx\n",
      "Deleted file: species_annotation.csv\n",
      "Deleted folder: MammalianMethylationConsortium\n",
      "Deleted file: download.r\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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