{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# Horvath2013"
   ]
  },
  {
   "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": "2025-04-07T17:51:53.979350Z",
     "iopub.status.busy": "2025-04-07T17:51:53.979155Z",
     "iopub.status.idle": "2025-04-07T17:51:55.337257Z",
     "shell.execute_reply": "2025-04-07T17:51:55.336910Z"
    }
   },
   "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": "2025-04-07T17:51:55.338955Z",
     "iopub.status.busy": "2025-04-07T17:51:55.338738Z",
     "iopub.status.idle": "2025-04-07T17:51:55.346182Z",
     "shell.execute_reply": "2025-04-07T17:51:55.345901Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class Horvath2013(pyagingModel):\n",
      "    def __init__(self):\n",
      "        super().__init__()\n",
      "\n",
      "    def preprocess(self, x):\n",
      "        return x\n",
      "\n",
      "    def postprocess(self, x):\n",
      "        \"\"\"\n",
      "        Applies an anti-logarithmic linear transformation to a PyTorch tensor.\n",
      "        \"\"\"\n",
      "        adult_age = 20\n",
      "\n",
      "        # Create a mask for negative and non-negative values\n",
      "        mask_negative = x < 0\n",
      "        mask_non_negative = ~mask_negative\n",
      "\n",
      "        # Initialize the result tensor\n",
      "        age_tensor = torch.empty_like(x)\n",
      "\n",
      "        # Exponential transformation for negative values\n",
      "        age_tensor[mask_negative] = (1 + adult_age) * torch.exp(x[mask_negative]) - 1\n",
      "\n",
      "        # Linear transformation for non-negative values\n",
      "        age_tensor[mask_non_negative] = (1 + adult_age) * x[mask_non_negative] + adult_age\n",
      "\n",
      "        return age_tensor\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_entire_class(cls):\n",
    "    source = inspect.getsource(cls)\n",
    "    print(source)\n",
    "\n",
    "print_entire_class(pya.models.Horvath2013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:55.347464Z",
     "iopub.status.busy": "2025-04-07T17:51:55.347373Z",
     "iopub.status.idle": "2025-04-07T17:51:55.349004Z",
     "shell.execute_reply": "2025-04-07T17:51:55.348766Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.Horvath2013()"
   ]
  },
  {
   "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": "2025-04-07T17:51:55.350282Z",
     "iopub.status.busy": "2025-04-07T17:51:55.350197Z",
     "iopub.status.idle": "2025-04-07T17:51:55.352160Z",
     "shell.execute_reply": "2025-04-07T17:51:55.351928Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"horvath2013\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: The predictor uses CpG DNA-methylation levels.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: The predictor was trained on human tissues and cell types.\n",
    "model.metadata[\"year\"] = 2013\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Horvath, S. DNA methylation age of human tissues and cell types. Genome Biology 14, R115 (2013).\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1186/gb-2013-14-10-r115\"\n",
    "model.metadata[\"notes\"] = \"Pan-tissue DNAm-age predictor fitted by elastic net to a transformed chronological-age outcome and returned to the year scale; it uses 353 CpGs shared between the 27K and 450K arrays.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"multi-tissue\"]  # Paper: Training datasets represented a wide spectrum among 51 healthy tissues and cell types.\n",
    "model.metadata[\"predicts\"] = [\"chronological age\"]  # Paper: The model returns DNA-methylation age calibrated to chronological age.\n",
    "model.metadata[\"training_target\"] = [\"chronological age\"]  # Paper: A transformed version of chronological age was the elastic-net outcome.\n",
    "model.metadata[\"unit\"] = [\"years\"]  # Paper: DNAm age and prediction error are reported in years after inverse transformation.\n",
    "model.metadata[\"model_type\"] = \"elastic net regression\"  # Paper: The paper identifies the penalized regression model as elastic net.\n",
    "model.metadata[\"platform\"] = [\"Illumina 27K\", \"Illumina 450K\"]  # Paper: Training used CpGs shared by the Illumina 27K and 450K platforms.\n",
    "model.metadata[\"population\"] = \"all ages\"  # Paper: The training and validation collection spans many tissues and ages, including newborn and centenarian-range samples.\n",
    "model.metadata[\"journal\"] = \"Genome Biology\"\n",
    "model.metadata[\"last_author\"] = \"Steve Horvath\"\n",
    "model.metadata[\"n_features\"] = 353\n",
    "model.metadata[\"citations\"] = 7318\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": "9a5b163b-1b25-4c2f-ad0c-86f0f2ed39d0",
   "metadata": {},
   "source": [
    "#### Download directly with curl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28fdcad4-1f62-4da7-b556-1ecc8cf3d0e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:55.353438Z",
     "iopub.status.busy": "2025-04-07T17:51:55.353359Z",
     "iopub.status.idle": "2025-04-07T17:51:55.634472Z",
     "shell.execute_reply": "2025-04-07T17:51:55.633635Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_url = \"https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2013-14-10-r115/MediaObjects/13059_2013_3156_MOESM3_ESM.csv\"\n",
    "supplementary_file_name = \"coefficients.csv\"\n",
    "os.system(f\"curl -o {supplementary_file_name} {supplementary_url}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86accc98-8ad9-4ca8-9a53-dbec3c32e816",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:55.637819Z",
     "iopub.status.busy": "2025-04-07T17:51:55.637526Z",
     "iopub.status.idle": "2025-04-07T17:51:56.021415Z",
     "shell.execute_reply": "2025-04-07T17:51:56.020455Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_url = \"https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2013-14-10-r115/MediaObjects/13059_2013_3156_MOESM22_ESM.csv\"\n",
    "supplementary_file_name = \"reference_feature_values.csv\"\n",
    "os.system(f\"curl -o {supplementary_file_name} {supplementary_url}\")"
   ]
  },
  {
   "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": 7,
   "id": "8a3d5de6-6303-487a-8b4d-e6345792f7be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:56.026340Z",
     "iopub.status.busy": "2025-04-07T17:51:56.025990Z",
     "iopub.status.idle": "2025-04-07T17:51:56.038097Z",
     "shell.execute_reply": "2025-04-07T17:51:56.037463Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('coefficients.csv', skiprows=2)\n",
    "df['feature'] = df['CpGmarker']\n",
    "df['coefficient'] = df['CoefficientTraining']\n",
    "model.features = df['feature'][1:].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": "2025-04-07T17:51:56.040927Z",
     "iopub.status.busy": "2025-04-07T17:51:56.040703Z",
     "iopub.status.idle": "2025-04-07T17:51:56.044360Z",
     "shell.execute_reply": "2025-04-07T17:51:56.043798Z"
    }
   },
   "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": 9,
   "id": "d7f43b99-26f2-4622-9a76-316712058877",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:56.046690Z",
     "iopub.status.busy": "2025-04-07T17:51:56.046525Z",
     "iopub.status.idle": "2025-04-07T17:51:56.050147Z",
     "shell.execute_reply": "2025-04-07T17:51:56.049637Z"
    }
   },
   "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": "markdown",
   "id": "f7fdae64-096a-4640-ade7-6a17b78a01d5",
   "metadata": {},
   "source": [
    "#### From CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "86de757f-fb38-4bcb-b91e-fc3372d22aad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:56.052658Z",
     "iopub.status.busy": "2025-04-07T17:51:56.052471Z",
     "iopub.status.idle": "2025-04-07T17:51:56.075773Z",
     "shell.execute_reply": "2025-04-07T17:51:56.075318Z"
    }
   },
   "outputs": [],
   "source": [
    "reference_feature_values_df = pd.read_csv('reference_feature_values.csv', index_col=0)\n",
    "reference_feature_values_df = reference_feature_values_df.loc[model.features]\n",
    "model.reference_values = reference_feature_values_df['goldstandard2'].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": "2025-04-07T17:51:56.077812Z",
     "iopub.status.busy": "2025-04-07T17:51:56.077659Z",
     "iopub.status.idle": "2025-04-07T17:51:56.079552Z",
     "shell.execute_reply": "2025-04-07T17:51:56.079224Z"
    }
   },
   "outputs": [],
   "source": [
    "model.preprocess_name = None\n",
    "model.preprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:56.081113Z",
     "iopub.status.busy": "2025-04-07T17:51:56.080983Z",
     "iopub.status.idle": "2025-04-07T17:51:56.082811Z",
     "shell.execute_reply": "2025-04-07T17:51:56.082450Z"
    }
   },
   "outputs": [],
   "source": [
    "model.postprocess_name = 'anti_log_linear'\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": "2025-04-07T17:51:56.084524Z",
     "iopub.status.busy": "2025-04-07T17:51:56.084394Z",
     "iopub.status.idle": "2025-04-07T17:51:56.088597Z",
     "shell.execute_reply": "2025-04-07T17:51:56.088298Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '⌛',\n",
      " 'citation': 'Horvath, Steve. \"DNA methylation age of human tissues and cell '\n",
      "             'types.\" Genome biology 14.10 (2013): 1-20.',\n",
      " 'clock_name': 'horvath2013',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1186/gb-2013-14-10-r115',\n",
      " 'notes': None,\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2013}\n",
      "reference_values: [0.790221397, 0.89001929, 0.059106387, 0.23651937, 0.073668777, 0.563295909, 0.864999404, 0.027047887, 0.660721193, 0.033420176, 0.047913033, 0.517283973, 0.050756537, 0.072267723, 0.014877693, 0.876157036, 0.187082703, 0.170303406, 0.019217238, 0.560726569, 0.845964086, 0.447995921, 0.055406903, 0.059821557, 0.533869814, 0.065190933, 0.896227421, 0.090932158, 0.032431793, 0.480007151]... [Total elements: 353]\n",
      "preprocess_name: None\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: 'anti_log_linear'\n",
      "postprocess_dependencies: None\n",
      "features: ['cg00075967', 'cg00374717', 'cg00864867', 'cg00945507', 'cg01027739', 'cg01353448', 'cg01584473', 'cg01644850', 'cg01656216', 'cg01873645', 'cg01968178', 'cg02085507', 'cg02154074', 'cg02217159', 'cg02331561', 'cg02332492', 'cg02364642', 'cg02388150', 'cg02479575', 'cg02489552', 'cg02580606', 'cg02654291', 'cg02827112', 'cg02972551', 'cg03103192', 'cg03167275', 'cg03270204', 'cg03565323', 'cg03588357', 'cg03760483']... [Total elements: 353]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=353, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [0.12933661043643951, 0.005017857067286968, 1.599764108657837, 0.056852418929338455, 0.10286285728216171, 0.23856045305728912, 0.08862839639186859, 0.1599487066268921, 0.04280552640557289, -0.6040563583374023, 1.1692458391189575, 0.006127551198005676, 0.04475700482726097, -0.08651260286569595, 0.12692885100841522, 0.06277134269475937, -0.024270255118608475, 0.42597126960754395, 1.8754462003707886, 0.0737413838505745, 0.34927448630332947, 0.14058348536491394, 0.05064608156681061, 0.4739460051059723, 0.02353634312748909, -0.5510412454605103, 0.013544725254178047, -0.2076532244682312, -0.8588430285453796, 0.014946399256587029]... [Tensor of shape torch.Size([1, 353])]\n",
      "base_model.linear.bias: tensor([0.6955])\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": "2025-04-07T17:51:56.090196Z",
     "iopub.status.busy": "2025-04-07T17:51:56.090093Z",
     "iopub.status.idle": "2025-04-07T17:51:56.094564Z",
     "shell.execute_reply": "2025-04-07T17:51:56.094250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ -0.9718],\n",
       "        [  1.8615],\n",
       "        [ -0.9980],\n",
       "        [ 67.1194],\n",
       "        [253.6647],\n",
       "        [ 61.3025],\n",
       "        [ -0.9928],\n",
       "        [ -0.6078],\n",
       "        [ -0.9995],\n",
       "        [ -0.7832]], dtype=torch.float64, grad_fn=<IndexPutBackward0>)"
      ]
     },
     "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": "2025-04-07T17:51:56.096031Z",
     "iopub.status.busy": "2025-04-07T17:51:56.095930Z",
     "iopub.status.idle": "2025-04-07T17:51:56.100301Z",
     "shell.execute_reply": "2025-04-07T17:51:56.099998Z"
    }
   },
   "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": "2025-04-07T17:51:56.101705Z",
     "iopub.status.busy": "2025-04-07T17:51:56.101603Z",
     "iopub.status.idle": "2025-04-07T17:51:56.105358Z",
     "shell.execute_reply": "2025-04-07T17:51:56.105059Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: coefficients.csv\n",
      "Deleted file: reference_feature_values.csv\n",
      "Deleted file: coefficients.xlsx\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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