{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# PCSkinAndBlood"
   ]
  },
  {
   "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": "2024-03-05T21:14:39.052447Z",
     "iopub.status.busy": "2024-03-05T21:14:39.052041Z",
     "iopub.status.idle": "2024-03-05T21:14:40.431161Z",
     "shell.execute_reply": "2024-03-05T21:14:40.430851Z"
    }
   },
   "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": "2024-03-05T21:14:40.433096Z",
     "iopub.status.busy": "2024-03-05T21:14:40.432919Z",
     "iopub.status.idle": "2024-03-05T21:14:40.442206Z",
     "shell.execute_reply": "2024-03-05T21:14:40.441957Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class PCSkinAndBlood(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.PCSkinAndBlood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:14:40.443656Z",
     "iopub.status.busy": "2024-03-05T21:14:40.443570Z",
     "iopub.status.idle": "2024-03-05T21:14:40.445262Z",
     "shell.execute_reply": "2024-03-05T21:14:40.445042Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.PCSkinAndBlood()"
   ]
  },
  {
   "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": "2024-03-05T21:14:40.446749Z",
     "iopub.status.busy": "2024-03-05T21:14:40.446664Z",
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     "shell.execute_reply": "2024-03-05T21:14:40.448445Z"
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   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"pcskinandblood\"\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 skin-and-blood age clock, trained against the original Horvath2 score using skin, blood, and fibroblast datasets.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"skin\", \"whole blood\", \"cultured fibroblasts\"]  # Paper: The training datasets represent skin, whole blood, fibroblasts.\n",
    "model.metadata[\"predicts\"] = [\"chronological age\"]  # Paper: The Horvath2 PC model predicts the original skin-and-blood clock value using substitute datasets.\n",
    "model.metadata[\"training_target\"] = [\"skin-and-blood clock output\"]  # Paper: The Horvath2 PC model predicts the original skin-and-blood clock value using 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": "9d89c9e7-8e4c-4593-91b0-63a9aa57108c",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2024-03-05T21:22:30.771042Z",
     "shell.execute_reply": "2024-03-05T21:22:30.770075Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|-----------> Downloading data to ./CalcAllPCClocks.RData\n"
     ]
    },
    {
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     "name": "stdout",
     "output_type": "stream",
     "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": "2024-03-05T21:22:30.777526Z",
     "iopub.status.busy": "2024-03-05T21:22:30.777110Z",
     "iopub.status.idle": "2024-03-05T21:22:30.785776Z",
     "shell.execute_reply": "2024-03-05T21:22:30.784980Z"
    }
   },
   "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(CalcPCHorvath2, \"CalcPCHorvath2.json\", digits = 10)\n",
    "write_json(CpGs, \"PCHorvath2CpGs.json\")\n",
    "write_json(imputeMissingCpGs, \"PCHorvath2ReferenceCpGBetas.json\", digits = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b71a6f03-61ba-462f-a2a6-a5df95e105ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-05T21:22:30.789328Z",
     "iopub.status.busy": "2024-03-05T21:22:30.789064Z",
     "iopub.status.idle": "2024-03-05T21:22:54.286065Z",
     "shell.execute_reply": "2024-03-05T21:22:54.285759Z"
    }
   },
   "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": "2024-03-05T21:22:54.287854Z",
     "iopub.status.busy": "2024-03-05T21:22:54.287751Z",
     "iopub.status.idle": "2024-03-05T21:22:54.292737Z",
     "shell.execute_reply": "2024-03-05T21:22:54.292482Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('PCHorvath2CpGs.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": "2024-03-05T21:22:54.294442Z",
     "iopub.status.busy": "2024-03-05T21:22:54.294356Z",
     "iopub.status.idle": "2024-03-05T21:22:56.060620Z",
     "shell.execute_reply": "2024-03-05T21:22:56.060331Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('CalcPCHorvath2.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": "2024-03-05T21:22:56.062463Z",
     "iopub.status.busy": "2024-03-05T21:22:56.062366Z",
     "iopub.status.idle": "2024-03-05T21:22:56.065322Z",
     "shell.execute_reply": "2024-03-05T21:22:56.065071Z"
    }
   },
   "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": "2024-03-05T21:22:56.066914Z",
     "iopub.status.busy": "2024-03-05T21:22:56.066822Z",
     "iopub.status.idle": "2024-03-05T21:22:56.074254Z",
     "shell.execute_reply": "2024-03-05T21:22:56.074009Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('PCHorvath2ReferenceCpGBetas.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": "2024-03-05T21:22:56.075754Z",
     "iopub.status.busy": "2024-03-05T21:22:56.075668Z",
     "iopub.status.idle": "2024-03-05T21:22:56.077261Z",
     "shell.execute_reply": "2024-03-05T21:22:56.077034Z"
    }
   },
   "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": "2024-03-05T21:22:56.078614Z",
     "iopub.status.busy": "2024-03-05T21:22:56.078542Z",
     "iopub.status.idle": "2024-03-05T21:22:56.080107Z",
     "shell.execute_reply": "2024-03-05T21:22:56.079899Z"
    }
   },
   "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": "2024-03-05T21:22:56.081514Z",
     "iopub.status.busy": "2024-03-05T21:22:56.081437Z",
     "iopub.status.idle": "2024-03-05T21:22:56.084454Z",
     "shell.execute_reply": "2024-03-05T21:22:56.084242Z"
    }
   },
   "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': 'pcskinandblood',\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=139, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.center: [0.6989603042602539, 0.15956708788871765, 0.6852545738220215, 0.8436605334281921, 0.06963495165109634, 0.022423643618822098, 0.3099723756313324, 0.5627376437187195, 0.8343909382820129, 0.08058885484933853, 0.039684172719717026, 0.1338687390089035, 0.07226327806711197, 0.17344607412815094, 0.4125054180622101, 0.6524730920791626, 0.6432204246520996, 0.7295728921890259, 0.08485441654920578, 0.8147767782211304, 0.7249654531478882, 0.8113170266151428, 0.8138924241065979, 0.02371947653591633, 0.6228847503662109, 0.5994234085083008, 0.27681541442871094, 0.6650522947311401, 0.3386619985103607, 0.47046491503715515]... [Tensor of shape torch.Size([78464])]\n",
      "base_model.rotation: [-0.004212469328194857, -0.0018427305622026324, -0.00342817441560328, -0.0019621967803686857, -0.0038084788247942924, -0.001714007230475545, 0.0009071855456568301, 0.0009312129113823175, 0.0005078349495306611, 0.0013207424199208617, 0.004016305319964886, -0.0008206570637412369, 0.002955526579171419, 0.0009135246509686112, -0.003197634592652321, 0.00868172850459814, -0.011338912881910801, 0.006802823860198259, -0.006287768483161926, -0.00478862039744854, 0.001487976755015552, -0.008474800735712051, 0.005175075959414244, -0.002744645345956087, -0.006363819353282452, 0.0027778574731200933, -0.007200028281658888, 0.003951014950871468, 0.0025292737409472466, -0.0006783857243135571]... [Tensor of shape torch.Size([78464, 139])]\n",
      "base_model.linear.weight: [-0.008271735161542892, 0.026615887880325317, 0.06532493233680725, 0.03855971246957779, 0.10452642291784286, -0.24639244377613068, 0.0814928412437439, -0.06538811326026917, 0.029210954904556274, -0.001272108987905085, 0.016053402796387672, 0.08029855042695999, -0.012919272296130657, -0.06098422035574913, 0.028758035972714424, -0.12004351615905762, -0.020917272195219994, -0.04205343499779701, 0.029078252613544464, -0.01387680321931839, 0.06542300432920456, -0.03968583419919014, 0.002720639808103442, -0.0522029809653759, 0.002719870302826166, 0.013684911653399467, 0.014434539712965488, -0.0017883781110867858, -0.07386524230241776, -0.02207360416650772]... [Tensor of shape torch.Size([1, 139])]\n",
      "base_model.linear.bias: tensor([0.4743])\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": "2024-03-05T21:22:56.086104Z",
     "iopub.status.busy": "2024-03-05T21:22:56.085997Z",
     "iopub.status.idle": "2024-03-05T21:22:56.125324Z",
     "shell.execute_reply": "2024-03-05T21:22:56.124964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[8.7096],\n",
       "        [4.7385],\n",
       "        [4.5074],\n",
       "        [1.4422],\n",
       "        [2.7803],\n",
       "        [0.5132],\n",
       "        [0.3571],\n",
       "        [1.6538],\n",
       "        [9.6653],\n",
       "        [3.8396]], 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": "2024-03-05T21:22:56.127334Z",
     "iopub.status.busy": "2024-03-05T21:22:56.127230Z",
     "iopub.status.idle": "2024-03-05T21:22:56.262064Z",
     "shell.execute_reply": "2024-03-05T21:22:56.261645Z"
    }
   },
   "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": "2024-03-05T21:22:56.264087Z",
     "iopub.status.busy": "2024-03-05T21:22:56.263973Z",
     "iopub.status.idle": "2024-03-05T21:22:56.352344Z",
     "shell.execute_reply": "2024-03-05T21:22:56.352037Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: PCHorvath2ReferenceCpGBetas.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: CalcAllPCClocks.RData\n",
      "Deleted file: PCHorvath2CpGs.json\n",
      "Deleted file: download.r\n",
      "Deleted file: CalcPCHorvath2.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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