{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# PCHorvath2013"
   ]
  },
  {
   "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": "2024-03-05T21:01:00.984260Z",
     "iopub.status.busy": "2024-03-05T21:01:00.983856Z",
     "iopub.status.idle": "2024-03-05T21:01:02.504320Z",
     "shell.execute_reply": "2024-03-05T21:01:02.504017Z"
    }
   },
   "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": "2024-03-05T21:01:02.506190Z",
     "iopub.status.busy": "2024-03-05T21:01:02.506029Z",
     "iopub.status.idle": "2024-03-05T21:01:02.515402Z",
     "shell.execute_reply": "2024-03-05T21:01:02.515149Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class PCHorvath2013(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[\n",
      "            mask_non_negative\n",
      "        ] + 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.PCHorvath2013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:01:02.516852Z",
     "iopub.status.busy": "2024-03-05T21:01:02.516771Z",
     "iopub.status.idle": "2024-03-05T21:01:02.518516Z",
     "shell.execute_reply": "2024-03-05T21:01:02.518259Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.PCHorvath2013()"
   ]
  },
  {
   "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": "2024-03-05T21:01:02.520071Z",
     "iopub.status.busy": "2024-03-05T21:01:02.519980Z",
     "iopub.status.idle": "2024-03-05T21:01:02.522034Z",
     "shell.execute_reply": "2024-03-05T21:01:02.521798Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"pchorvath2013\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: The study constructs clocks from DNA methylation measurements.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: The analyzed samples and clock are human.\n",
    "model.metadata[\"year\"] = 2022\n",
    "model.metadata[\"approved_by_author\"] = \"⌛\"\n",
    "model.metadata[\"citation\"] = \"Higgins-Chen, Albert T., et al. \\\"A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking.\\\" Nature Aging 2 (2022): 644–661.\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1038/s43587-022-00248-2\"\n",
    "model.metadata[\"notes\"] = \"Principal-component proxy of the 2013 Horvath pan-tissue clock, trained against the original clock score using substituted multi-tissue datasets.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"multi-tissue\"]  # Paper: The training datasets represent multi-tissue.\n",
    "model.metadata[\"predicts\"] = [\"chronological age\"]  # Paper: The Horvath1 PC model predicts the original CpG clock value using multi-tissue substitute datasets.\n",
    "model.metadata[\"training_target\"] = [\"Horvath clock output\"]  # Paper: The Horvath1 PC model predicts the original CpG clock value using multi-tissue substitute datasets.\n",
    "model.metadata[\"unit\"] = [\"years\"]  # Paper: The reported output scale is years.\n",
    "model.metadata[\"model_type\"] = \"PCA + elastic net regression\"  # Paper: Principal components were supplied to elastic-net regression.\n",
    "model.metadata[\"platform\"] = [\"Illumina 450K\", \"Illumina EPIC\"]  # Paper: The listed arrays are those used for the model's training datasets.\n",
    "model.metadata[\"population\"] = \"all ages\"  # Paper: Training cohorts and sample types are specified in the study dataset table.\n",
    "model.metadata[\"journal\"] = \"Nature Aging\"\n",
    "model.metadata[\"last_author\"] = \"Morgan E. Levine\"\n",
    "model.metadata[\"n_features\"] = 78464\n",
    "model.metadata[\"citations\"] = 497\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": "code",
   "execution_count": 5,
   "id": "c8f14b7d-2d57-4516-97ed-2c3a47c0732e",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2024-03-05T21:01:02.523352Z",
     "iopub.status.idle": "2024-03-05T21:06:57.815477Z",
     "shell.execute_reply": "2024-03-05T21:06:57.814153Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|-----------> Downloading data to ./CalcAllPCClocks.RData\n"
     ]
    },
    {
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     "text": [
      "\r",
      "|-----------> in progress: 86.0258%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 87.0261%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 88.0264%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 89.0267%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 90.0270%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 91.0273%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 92.0276%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 93.0279%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 94.0282%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 95.0285%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 96.0288%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 97.0291%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 98.0294%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 99.0297%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "|-----------> in progress: 100.0000%\n"
     ]
    }
   ],
   "source": [
    "#download PCClock Rdata file from https://yale.app.box.com/s/kq0b0a7lxckxjvaz7x5n4keaug7tewry\n",
    "logger = pya.logger.Logger()\n",
    "url = \"https://huggingface.co/lucascamillomd/pyaging-data/resolve/main/supporting_files/CalcAllPCClocks.RData\"\n",
    "dir = \".\"\n",
    "pya.utils.download(url, dir, logger, indent_level=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971ff75c-c5bf-4e8e-89d7-01861e9bc107",
   "metadata": {},
   "source": [
    "#### Download from R package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2cfbee5-bcaf-46f0-a8d8-3b6150e09bf0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:06:57.822263Z",
     "iopub.status.busy": "2024-03-05T21:06:57.821863Z",
     "iopub.status.idle": "2024-03-05T21:06:57.830369Z",
     "shell.execute_reply": "2024-03-05T21:06:57.829140Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing download.r\n"
     ]
    }
   ],
   "source": [
    "%%writefile download.r\n",
    "\n",
    "library(dplyr)\n",
    "library(tibble)\n",
    "library(tidyr)\n",
    "library(jsonlite)\n",
    "\n",
    "load(file = \"CalcAllPCClocks.RData\")\n",
    "\n",
    "print(ls(all.names = TRUE))\n",
    "\n",
    "write_json(CalcPCHorvath1, \"CalcPCHorvath1.json\", digits = 10)\n",
    "write_json(CpGs, \"PCHorvath1CpGs.json\")\n",
    "write_json(imputeMissingCpGs, \"PCHorvath1ReferenceCpGBetas.json\", digits = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b71a6f03-61ba-462f-a2a6-a5df95e105ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:06:57.834701Z",
     "iopub.status.busy": "2024-03-05T21:06:57.834437Z",
     "iopub.status.idle": "2024-03-05T21:07:18.968803Z",
     "shell.execute_reply": "2024-03-05T21:07:18.968475Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system(\"Rscript download.r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a14c7fc1-abe5-42a3-8bc9-0987521ddf33",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e737582-3a28-4f55-8da9-3e34125362cc",
   "metadata": {},
   "source": [
    "#### From JSON file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97e5b47b-0599-4ec3-aab4-dcfe9d3e4515",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:07:18.970732Z",
     "iopub.status.busy": "2024-03-05T21:07:18.970631Z",
     "iopub.status.idle": "2024-03-05T21:07:18.975473Z",
     "shell.execute_reply": "2024-03-05T21:07:18.975187Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('PCHorvath1CpGs.json', 'r') as f:\n",
    "    model.features = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de92ee28-39b1-4356-a734-6b28a20e7bfe",
   "metadata": {},
   "source": [
    "#### From JSON file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:07:18.977073Z",
     "iopub.status.busy": "2024-03-05T21:07:18.976993Z",
     "iopub.status.idle": "2024-03-05T21:07:20.454325Z",
     "shell.execute_reply": "2024-03-05T21:07:20.454033Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('CalcPCHorvath1.json', 'r') as f:\n",
    "    weights_dict = json.load(f)\n",
    "\n",
    "weights = torch.tensor(weights_dict['model']).unsqueeze(0).float()\n",
    "intercept = torch.tensor(weights_dict['intercept']).float()\n",
    "center = torch.tensor(weights_dict['center']).float()\n",
    "rotation = torch.tensor(weights_dict['rotation']).float()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5fb949f-500a-49d0-a792-2a774b0de18f",
   "metadata": {},
   "source": [
    "#### PC linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6e1baed-fb83-4b97-a2e7-c2f17254ca47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:07:20.456104Z",
     "iopub.status.busy": "2024-03-05T21:07:20.456011Z",
     "iopub.status.idle": "2024-03-05T21:07:20.458661Z",
     "shell.execute_reply": "2024-03-05T21:07:20.458429Z"
    }
   },
   "outputs": [],
   "source": [
    "base_model = pya.models.PCLinearModel(input_dim=len(model.features), pc_dim=rotation.shape[1])\n",
    "\n",
    "base_model.center.data = center.float()\n",
    "base_model.rotation.data = rotation.float()\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": "93271aee-d045-45ba-b030-7ec8e57add42",
   "metadata": {},
   "source": [
    "#### From JSON file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2089b66f-9cc4-4528-9bdc-5e45efc6d06b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:07:20.460308Z",
     "iopub.status.busy": "2024-03-05T21:07:20.460214Z",
     "iopub.status.idle": "2024-03-05T21:07:20.467639Z",
     "shell.execute_reply": "2024-03-05T21:07:20.467424Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('PCHorvath1ReferenceCpGBetas.json', 'r') as f:\n",
    "    reference_feature_values = json.load(f)\n",
    "model.reference_values = reference_feature_values"
   ]
  },
  {
   "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": "2024-03-05T21:07:20.469160Z",
     "iopub.status.busy": "2024-03-05T21:07:20.469087Z",
     "iopub.status.idle": "2024-03-05T21:07:20.470669Z",
     "shell.execute_reply": "2024-03-05T21:07:20.470435Z"
    }
   },
   "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": "2024-03-05T21:07:20.472006Z",
     "iopub.status.busy": "2024-03-05T21:07:20.471932Z",
     "iopub.status.idle": "2024-03-05T21:07:20.473523Z",
     "shell.execute_reply": "2024-03-05T21:07:20.473253Z"
    }
   },
   "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": 14,
   "id": "2168355c-47d9-475d-b816-49f65e74887c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:07:20.474959Z",
     "iopub.status.busy": "2024-03-05T21:07:20.474880Z",
     "iopub.status.idle": "2024-03-05T21:07:20.477725Z",
     "shell.execute_reply": "2024-03-05T21:07:20.477458Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '⌛',\n",
      " 'citation': 'Higgins-Chen, Albert T., et al. \"A computational solution for '\n",
      "             'bolstering reliability of epigenetic clocks: Implications for '\n",
      "             'clinical trials and longitudinal tracking.\" Nature aging 2.7 '\n",
      "             '(2022): 644-661.',\n",
      " 'clock_name': 'pchorvath2013',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1038/s43587-022-00248-2',\n",
      " 'notes': None,\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2022}\n",
      "reference_values: [0.82635363384, 0.18898814441, 0.72938889209, 0.8680421375, 0.090353927561, 0.0066895021761, 0.48924643338, 0.87262052546, 0.87955373232, 0.04847264273, 0.0093070979947, 0.16393676218, 0.058440936082, 0.18857484916, 0.58239394253, 0.86564960457, 0.58457176982, 0.82903550669, 0.065646928047, 0.8500055061, 0.79155429878, 0.83499889314, 0.7754384128, 0.0039641831799, 0.50570339787, 0.60547040884, 0.29093154314, 0.88154845595, 0.46844171936, 0.79205361021]... [Total elements: 78464]\n",
      "preprocess_name: None\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: 'anti_log_linear'\n",
      "postprocess_dependencies: None\n",
      "features: ['cg00000292', 'cg00000714', 'cg00001099', 'cg00001446', 'cg00001747', 'cg00002116', 'cg00002224', 'cg00002426', 'cg00002646', 'cg00002660', 'cg00002719', 'cg00002810', 'cg00003091', 'cg00003287', 'cg00003345', 'cg00003529', 'cg00003578', 'cg00003625', 'cg00003994', 'cg00004429', 'cg00004608', 'cg00004806', 'cg00005072', 'cg00005306', 'cg00005619', 'cg00005849', 'cg00006081', 'cg00006459', 'cg00007076', 'cg00007221']... [Total elements: 78464]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: PCLinearModel(\n",
      "  (linear): Linear(in_features=120, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.center: [0.7290785312652588, 0.16658970713615417, 0.7259728312492371, 0.8467999696731567, 0.07713332772254944, 0.020072568207979202, 0.35924986004829407, 0.6348602175712585, 0.8571182489395142, 0.07178357243537903, 0.046470556408166885, 0.13662011921405792, 0.07353769242763519, 0.21539008617401123, 0.5204617977142334, 0.8018048405647278, 0.5394126176834106, 0.629653811454773, 0.08743196725845337, 0.8038381934165955, 0.638761579990387, 0.823479950428009, 0.7942575216293335, 0.02160545252263546, 0.6240652799606323, 0.6269707083702087, 0.2557828724384308, 0.810107409954071, 0.42385315895080566, 0.6233302354812622]... [Tensor of shape torch.Size([78464])]\n",
      "base_model.rotation: [-0.0048322658985853195, 0.0016888284590095282, 0.0043016355484724045, 0.0006329840398393571, -0.0018616552697494626, 0.00015872654330451041, 0.0047627720050513744, -0.0007954642060212791, 0.001977873034775257, -0.0038264873437583447, 0.002861293265596032, -0.0020775371231138706, 0.0016555585898458958, 0.006291448138654232, 0.002375122159719467, 0.013304566964507103, -0.00033986676135100424, -0.0005006726132705808, 0.002878241939470172, -0.004465107340365648, -0.0033801733516156673, 0.00140372384339571, 0.0010448938701301813, -0.006161581724882126, -0.0026484185364097357, -0.0017679710872471333, -0.0001990400196518749, 0.0012213967274874449, 0.0035699992440640926, -0.0028738761320710182]... [Tensor of shape torch.Size([78464, 120])]\n",
      "base_model.linear.weight: [0.0033615094143897295, 0.005445790942758322, -0.0690847635269165, 0.04485338553786278, -0.03673980012536049, 0.02644198387861252, -0.21757060289382935, 0.12505319714546204, -0.007363998331129551, -0.0007567511056549847, 0.0277174673974514, 0.01490762084722519, 0.05097680911421776, 0.02121218666434288, 0.030744116753339767, -0.000863946508616209, 0.0261214692145586, 0.008246997371315956, -0.01691156066954136, 0.12098846584558487, -0.16675393283367157, -0.032332953065633774, 0.04423817619681358, -0.10392844676971436, 0.024131860584020615, 0.04625745117664337, -0.019460106268525124, 0.02763879857957363, 0.027718640863895416, -0.08220915496349335]... [Tensor of shape torch.Size([1, 120])]\n",
      "base_model.linear.bias: tensor([1.1583])\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": "2024-03-05T21:07:20.479197Z",
     "iopub.status.busy": "2024-03-05T21:07:20.479121Z",
     "iopub.status.idle": "2024-03-05T21:07:20.515391Z",
     "shell.execute_reply": "2024-03-05T21:07:20.515059Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[59.4351],\n",
       "        [58.9732],\n",
       "        [36.1313],\n",
       "        [11.8124],\n",
       "        [37.2571],\n",
       "        [42.6807],\n",
       "        [35.9014],\n",
       "        [45.7016],\n",
       "        [61.6516],\n",
       "        [59.3143]], dtype=torch.float64, grad_fn=<IndexPutBackward0>)"
      ]
     },
     "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": "2024-03-05T21:07:20.517358Z",
     "iopub.status.busy": "2024-03-05T21:07:20.517244Z",
     "iopub.status.idle": "2024-03-05T21:07:20.668047Z",
     "shell.execute_reply": "2024-03-05T21:07:20.667536Z"
    }
   },
   "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": "2024-03-05T21:07:20.670161Z",
     "iopub.status.busy": "2024-03-05T21:07:20.670044Z",
     "iopub.status.idle": "2024-03-05T21:07:20.775559Z",
     "shell.execute_reply": "2024-03-05T21:07:20.775255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: PCHorvath1CpGs.json\n",
      "Deleted file: CalcPCHorvath1.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: CalcAllPCClocks.RData\n",
      "Deleted file: download.r\n",
      "Deleted file: PCHorvath1ReferenceCpGBetas.json\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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