{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# Stubbs"
   ]
  },
  {
   "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": "2024-03-05T21:23:26.035319Z",
     "iopub.status.busy": "2024-03-05T21:23:26.034902Z",
     "iopub.status.idle": "2024-03-05T21:23:27.358337Z",
     "shell.execute_reply": "2024-03-05T21:23:27.358041Z"
    }
   },
   "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": "2024-03-05T21:23:27.360254Z",
     "iopub.status.busy": "2024-03-05T21:23:27.360092Z",
     "iopub.status.idle": "2024-03-05T21:23:27.369620Z",
     "shell.execute_reply": "2024-03-05T21:23:27.369349Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class Stubbs(pyagingModel):\n",
      "    def __init__(self):\n",
      "        super().__init__()\n",
      "\n",
      "    def preprocess(self, x):\n",
      "        \"\"\"\n",
      "        Apply quantile normalization on x using gold standard means\n",
      "        and then scale with the means and standard deviation.\n",
      "        \"\"\"\n",
      "\n",
      "        gold_standard_means = torch.tensor(\n",
      "            self.reference_values, device=x.device, dtype=x.dtype\n",
      "        )\n",
      "\n",
      "        # Ensure gold_standard_means is a 1D tensor and sorted\n",
      "        sorted_gold_standard = torch.sort(gold_standard_means)[0]\n",
      "\n",
      "        # Pre-compute the quantile indices\n",
      "        quantile_indices = torch.linspace(\n",
      "            0, len(sorted_gold_standard) - 1, steps=x.size(1)\n",
      "        ).long()\n",
      "\n",
      "        # Prepare a tensor to hold normalized data\n",
      "        normalized_data = torch.empty_like(x, device=x.device, dtype=x.dtype)\n",
      "\n",
      "        for i in range(x.size(0)):\n",
      "            sorted_indices = torch.argsort(x[i, :])\n",
      "            normalized_data[i, sorted_indices] = sorted_gold_standard[quantile_indices]\n",
      "\n",
      "        gold_standard_stds = torch.tensor(\n",
      "            self.preprocess_dependencies[0], device=x.device, dtype=x.dtype\n",
      "        )\n",
      "\n",
      "        # Avoid division by zero in case of a column with constant value\n",
      "        gold_standard_stds[torch.abs(gold_standard_stds) < 10e-10] = 1.0\n",
      "\n",
      "        normalized_data = (normalized_data - gold_standard_means) / gold_standard_stds\n",
      "\n",
      "        # Return only the subset from x that is used in the base model\n",
      "        return normalized_data[:, self.preprocess_dependencies[1]]\n",
      "\n",
      "    def postprocess(self, x):\n",
      "        \"\"\"\n",
      "        Applies a convertion from the output of an ElasticNet to mouse age in months.\n",
      "        \"\"\"\n",
      "        age = torch.exp(0.1207 * (x**2) + 1.2424 * x + 2.5440) - 3\n",
      "        age = age * (7 / 30.5)  # weeks to months\n",
      "        return age\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_entire_class(cls):\n",
    "    source = inspect.getsource(cls)\n",
    "    print(source)\n",
    "\n",
    "print_entire_class(pya.models.Stubbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:27.371059Z",
     "iopub.status.busy": "2024-03-05T21:23:27.370972Z",
     "iopub.status.idle": "2024-03-05T21:23:27.372670Z",
     "shell.execute_reply": "2024-03-05T21:23:27.372446Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.Stubbs()"
   ]
  },
  {
   "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": "2024-03-05T21:23:27.374161Z",
     "iopub.status.busy": "2024-03-05T21:23:27.374073Z",
     "iopub.status.idle": "2024-03-05T21:23:27.376048Z",
     "shell.execute_reply": "2024-03-05T21:23:27.375821Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"stubbs\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: The predictor uses DNA methylation at mouse CpG sites.\n",
    "model.metadata[\"species\"] = \"Mus musculus\"  # Paper: The study developed an age predictor in mouse.\n",
    "model.metadata[\"year\"] = 2017\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Stubbs, T. M., et al. “Multi-tissue DNA methylation age predictor in mouse.” Genome Biology 18: 68 (2017).\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1186/s13059-017-1203-5\"\n",
    "model.metadata[\"notes\"] = \"Mouse multi-tissue RRBS age predictor: 17,992 common input loci are normalized and reduced to 329 nonzero clock sites, then quadratically calibrated.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"multi-tissue\", \"liver\", \"lung\", \"heart\", \"brain cortex\", \"skeletal muscle\", \"cerebellum\", \"spleen\"]  # Paper: Training used healthy samples from Babraham, Reizel, and Cannon; these covered liver, lung, heart, cortex, muscle, cerebellum, and spleen.\n",
    "model.metadata[\"predicts\"] = [\"chronological age\"]  # Paper: The multi-tissue predictor estimates mouse chronological age.\n",
    "model.metadata[\"training_target\"] = [\"chronological age\"]  # Paper: The model predicted log-transformed mouse age in weeks after adding three weeks.\n",
    "model.metadata[\"unit\"] = [\"months\"]  # Paper: The packaged postprocess converts calibrated weeks to months by multiplying by 7/30.5.\n",
    "model.metadata[\"model_type\"] = \"quadratically calibrated elastic net regression\"  # Paper: GLMNET elastic net selected 329 sites, followed by a fitted quadratic transformation.\n",
    "model.metadata[\"platform\"] = [\"RRBS\"]  # Paper: The methylation libraries and external datasets were RRBS.\n",
    "model.metadata[\"population\"] = \"mice\"  # Paper: The RRBS predictor was trained in Mus musculus samples spanning newborn animals through 41 weeks.\n",
    "model.metadata[\"journal\"] = \"Genome Biology\"\n",
    "model.metadata[\"last_author\"] = \"Wolf Reik\"\n",
    "model.metadata[\"n_features\"] = 17992\n",
    "model.metadata[\"citations\"] = 430\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": "ce913116-6080-408f-8003-a8278d0ce6e7",
   "metadata": {},
   "source": [
    "#### Download directly with curl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef668196-bccb-43c5-bc09-be027074e507",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:27.377519Z",
     "iopub.status.busy": "2024-03-05T21:23:27.377430Z",
     "iopub.status.idle": "2024-03-05T21:23:27.768590Z",
     "shell.execute_reply": "2024-03-05T21:23:27.767803Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_url = \"https://elifesciences.org/download/aHR0cHM6Ly9jZG4uZWxpZmVzY2llbmNlcy5vcmcvYXJ0aWNsZXMvNDA2NzUvZWxpZmUtNDA2NzUtc3VwcDMtdjIueGxzeA--/elife-40675-supp3-v2.xlsx?_hash=qzOMc4yUFACfDFG%2FlgxkFTHWt%2BSXSmP9zz1BM3oOTRM%3D\"\n",
    "supplementary_file_name = \"coefficients.xlsx\"\n",
    "os.system(f\"curl -o {supplementary_file_name} {supplementary_url}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c409d6-5761-4795-a46f-8ed3d8d64d70",
   "metadata": {},
   "source": [
    "#### Download GitHub repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05b81104-e0ba-40f0-9e8d-a6033c434481",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:27.775311Z",
     "iopub.status.busy": "2024-03-05T21:23:27.774904Z",
     "iopub.status.idle": "2024-03-05T21:23:48.266635Z",
     "shell.execute_reply": "2024-03-05T21:23:48.266322Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "github_url = \"https://github.com/EpigenomeClock/MouseEpigeneticClock.git\"\n",
    "github_folder_name = github_url.split('/')[-1].split('.')[0]\n",
    "os.system(f\"git clone {github_url}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5035b180-3d1b-4432-8ebe-b9c92bd93a7f",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "110a5ded-d25f-4cef-8e84-4f51210dfc26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.268356Z",
     "iopub.status.busy": "2024-03-05T21:23:48.268255Z",
     "iopub.status.idle": "2024-03-05T21:23:48.448533Z",
     "shell.execute_reply": "2024-03-05T21:23:48.448246Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('coefficients.xlsx', sheet_name='Young age multi-tissue', nrows=329)\n",
    "df['feature'] = df['Chromosome'].astype(str) + ':' + df['Position'].astype(int).astype(str)\n",
    "df['coefficient'] = df['Weight']\n",
    "\n",
    "reference_feature_values_df = pd.read_table('MouseEpigeneticClock/TrainingMatrix/TrainingData_Babraham_Reizel_Cannon.txt', index_col=0)\n",
    "reference_feature_values_df.index = ['chr' + index for index in reference_feature_values_df.index]\n",
    "reference_feature_values_df = reference_feature_values_df.T\n",
    "\n",
    "model.features = reference_feature_values_df.columns.tolist()\n",
    "model.base_model_features = df['feature'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.450245Z",
     "iopub.status.busy": "2024-03-05T21:23:48.450155Z",
     "iopub.status.idle": "2024-03-05T21:23:48.452178Z",
     "shell.execute_reply": "2024-03-05T21:23:48.451957Z"
    }
   },
   "outputs": [],
   "source": [
    "weights = torch.tensor(df['coefficient'].tolist()).unsqueeze(0)\n",
    "intercept = torch.tensor([0.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad261636-5b00-4979-bb1d-67a851f7aa19",
   "metadata": {},
   "source": [
    "#### Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d7f43b99-26f2-4622-9a76-316712058877",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.453592Z",
     "iopub.status.busy": "2024-03-05T21:23:48.453504Z",
     "iopub.status.idle": "2024-03-05T21:23:48.455532Z",
     "shell.execute_reply": "2024-03-05T21:23:48.455322Z"
    }
   },
   "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": 10,
   "id": "86de757f-fb38-4bcb-b91e-fc3372d22aad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.457055Z",
     "iopub.status.busy": "2024-03-05T21:23:48.456954Z",
     "iopub.status.idle": "2024-03-05T21:23:48.462660Z",
     "shell.execute_reply": "2024-03-05T21:23:48.462368Z"
    }
   },
   "outputs": [],
   "source": [
    "model.reference_values = reference_feature_values_df.mean().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3bcf7b-74a8-4d21-9ccb-4de0c2b0516b",
   "metadata": {},
   "source": [
    "## Load preprocess and postprocess objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a22fb20-c605-424d-8efb-7620c2c0755c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.464322Z",
     "iopub.status.busy": "2024-03-05T21:23:48.464241Z",
     "iopub.status.idle": "2024-03-05T21:23:48.500959Z",
     "shell.execute_reply": "2024-03-05T21:23:48.500678Z"
    }
   },
   "outputs": [],
   "source": [
    "model.preprocess_name = 'quantile_normalization_and_scale_with_gold_standard'\n",
    "gold_standard_stds = reference_feature_values_df.std().tolist()\n",
    "indices = [model.features.index(item) for item in model.base_model_features]\n",
    "model.preprocess_dependencies = [gold_standard_stds, indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.502590Z",
     "iopub.status.busy": "2024-03-05T21:23:48.502505Z",
     "iopub.status.idle": "2024-03-05T21:23:48.504175Z",
     "shell.execute_reply": "2024-03-05T21:23:48.503949Z"
    }
   },
   "outputs": [],
   "source": [
    "model.postprocess_name = 'stubbs'\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": 13,
   "id": "2168355c-47d9-475d-b816-49f65e74887c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.505713Z",
     "iopub.status.busy": "2024-03-05T21:23:48.505639Z",
     "iopub.status.idle": "2024-03-05T21:23:48.541908Z",
     "shell.execute_reply": "2024-03-05T21:23:48.541597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '⌛',\n",
      " 'citation': 'Stubbs, Thomas M., et al. \"Multi-tissue DNA methylation age '\n",
      "             'predictor in mouse.\" Genome biology 18 (2017): 1-14.',\n",
      " 'clock_name': 'stubbs',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1186/s13059-017-1203-5',\n",
      " 'notes': None,\n",
      " 'research_only': None,\n",
      " 'species': 'Mus musculus',\n",
      " 'version': None,\n",
      " 'year': 2017}\n",
      "reference_values: [0.009419308625383253, 0.9454021653384829, 0.9262220310728511, 0.01115736080094597, 0.005828992981096152, 0.015486742789977479, 0.10632124338173232, 0.008327731508385066, 0.9470653205543639, 0.7441407980821007, 0.9443701038672639, 0.7338555884793152, 0.7296056309787987, 0.9211735929020528, 0.012150415029205755, 0.01607368220739321, 0.011272227879684186, 0.012648110923722247, 0.17279958253325498, 0.606354333653314, 0.612341969307046, 0.0016412203744236714, 0.0016069770681773007, 0.0009744614615922172, 0.0031840144988144236, 0.0010236321557703402, 0.00775896971942178, 0.018119994693935132, 0.0023590301620859755, 0.0022931444123898757]... [Total elements: 17992]\n",
      "preprocess_name: 'quantile_normalization_and_scale_with_gold_standard'\n",
      "preprocess_dependencies: [[0.023642310306951884,\n",
      "  0.05435434941621924,\n",
      "  0.06631580380799687,\n",
      "  0.026623454903173593,\n",
      "  0.017584746842308856,\n",
      "  0.024321506482160166,\n",
      "  0.13184542957540177,\n",
      "  0.019301854976409266,\n",
      "  0.047974117327149075,\n",
      "  0.12618319313114076,\n",
      "  0.05987574107783149,\n",
      "  0.11025266086632349,\n",
      "  0.11027369393935178,\n",
      "  0.07440416525300099,\n",
      "  0.022383145141857803,\n",
      "  0.02811285906451366,\n",
      "  0.024896647438452553,\n",
      "  0.023301970483289722,\n",
      "  0.0938029844877144,\n",
      "  0.17987877217020218,\n",
      "  0.17780423833442482,\n",
      "  0.008294108236784602,\n",
      "  0.0075203592490610425,\n",
      "  0.006227194990463238,\n",
      "  0.016346590876057863,\n",
      "  0.0058114748946614175,\n",
      "  0.019747162343303495,\n",
      "  0.027454147651420777,\n",
      "  0.014813583680173594,\n",
      "  0.01026121648600312,\n",
      "  0.03412512329332476,\n",
      "  0.16428930709207673,\n",
      "  0.03486713839379636,\n",
      "  0.025083029584654064,\n",
      "  0.020245287089368864,\n",
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      "  8496]]\n",
      "postprocess_name: 'stubbs'\n",
      "postprocess_dependencies: None\n",
      "features: ['chr1:10038066', 'chr1:101374410', 'chr1:101374418', 'chr1:105356622', 'chr1:105356633', 'chr1:105780925', 'chr1:106173313', 'chr1:106759301', 'chr1:109034542', 'chr1:110429621', 'chr1:110429664', 'chr1:113816340', 'chr1:113816377', 'chr1:118453160', 'chr1:118628354', 'chr1:119047941', 'chr1:119048037', 'chr1:119054228', 'chr1:119054537', 'chr1:119471763', 'chr1:119471798', 'chr1:119836882', 'chr1:119836891', 'chr1:119836906', 'chr1:119836918', 'chr1:119836920', 'chr1:119836928', 'chr1:120073681', 'chr1:120121455', 'chr1:120121465']... [Total elements: 17992]\n",
      "base_model_features: ['chr1:73924900', 'chr1:153890242', 'chr1:180350659', 'chr2:25579878', 'chr2:131180254', 'chr2:162944571', 'chr2:164678867', 'chr2:164833043', 'chr2:168893991', 'chr2:173184335', 'chr2:173184340', 'chr2:180702779', 'chr3:53477767', 'chr3:53477794', 'chr3:94399354', 'chr3:96219156', 'chr3:97767095', 'chr3:105052161', 'chr3:107758395', 'chr4:46581731', 'chr4:56740768', 'chr4:78212397', 'chr4:88641066', 'chr4:119415073', 'chr4:137522100', 'chr4:138453876', 'chr5:30647717', 'chr5:32133020', 'chr5:113101756', 'chr5:115632768']... [Total elements: 329]\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=17992, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [-0.0009982034098356962, -0.00023933289048727602, -0.0028150584548711777, -0.007451509591192007, -0.0022230781614780426, -0.001344481366686523, -0.00554255023598671, -0.0003380068519618362, -0.007523837964981794, -0.0013427615631371737, -0.0005768182454630733, -0.01288866437971592, -0.009710701182484627, -8.49335701786913e-05, -0.008504485711455345, -0.0033585140481591225, -0.001603029086254537, -0.00699494918808341, -0.007361156865954399, -0.00831840094178915, -0.005099604371935129, -0.012041870504617691, -0.0066992854699492455, -0.0028678528033196926, -0.006041061598807573, -8.668371447129175e-05, -0.010333182290196419, -0.0035897456109523773, -0.012446772307157516, -0.0065109808929264545]... [Tensor of shape torch.Size([1, 329])]\n",
      "base_model.linear.bias: tensor([0.])\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": 14,
   "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.546266Z",
     "iopub.status.busy": "2024-03-05T21:23:48.546167Z",
     "iopub.status.idle": "2024-03-05T21:23:48.570521Z",
     "shell.execute_reply": "2024-03-05T21:23:48.570010Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.1474],\n",
       "        [-0.2128],\n",
       "        [-0.1608],\n",
       "        [ 2.1481],\n",
       "        [-0.2222],\n",
       "        [ 0.8489],\n",
       "        [ 3.7938],\n",
       "        [ 0.6316],\n",
       "        [-0.1613],\n",
       "        [ 2.0362]], dtype=torch.float64, grad_fn=<MulBackward0>)"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.572585Z",
     "iopub.status.busy": "2024-03-05T21:23:48.572465Z",
     "iopub.status.idle": "2024-03-05T21:23:48.585931Z",
     "shell.execute_reply": "2024-03-05T21:23:48.585597Z"
    }
   },
   "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": 16,
   "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:23:48.588072Z",
     "iopub.status.busy": "2024-03-05T21:23:48.587964Z",
     "iopub.status.idle": "2024-03-05T21:23:48.599406Z",
     "shell.execute_reply": "2024-03-05T21:23:48.598988Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: coefficients.xlsx\n",
      "Deleted folder: MouseEpigeneticClock\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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