Create_Directory_Structure.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Create Directory Structure.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMamIJ+sLkDRiIyiajrVPJX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ammarisme/covid-19/blob/master/Create_Directory_Structure.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Y3v6FDkeYIA",
        "colab_type": "text"
      },
      "source": [
        "Create the input directory structure and copy the minimum and mandatory files to start the rest of the processes in the pipeline."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deeX8ZxCeXDT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from os import path\n",
        "from google.colab import drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osuQTFMiemIy",
        "colab_type": "code",
        "outputId": "676e4e71-0213-4641-a022-626d9a3c9e6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "#configurations\n",
        "#drive.flush_and_unmount()\n",
        "drive.mount('/drive', force_remount=True)\n",
        "ROOT = \"/drive/My Drive/covid\"\n",
        "INPUT_DIR = \"/input_mix\""
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdzzxuC7e0Lo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DIR = ROOT+INPUT_DIR\n",
        "if os.path.exists(DIR) == False:\n",
        "  print('creating directories...')\n",
        "  os.makedirs(DIR+'/training/cleaned')\n",
        "  os.makedirs(DIR+'/training/raw')\n",
        "  os.makedirs(DIR+'/validation/cleaned')\n",
        "  os.makedirs(DIR+'/validation/raw')\n",
        "  drive.flush_and_unmount()\n",
        "  drive.mount('/drive')\n",
        "  print('done')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVSHAPVFfZpW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from shutil import copyfile\n",
        "cleaned_file = ROOT+\"/input/validation/cleaned/cleaned_worldwide_cases_27_04.csv\"\n",
        "if os.path.exists(cleaned_file):\n",
        "  copyfile(ROOT+\"/input/validation/cleaned/cleaned_worldwide_cases_27_04.csv\", DIR+'/validation/cleaned/cleaned_worldwide_cases_27_04.csv')\n",
        "else:\n",
        "  print(\"file doesn\\'t exist\", cleaned_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kCIdWBikyjW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pathlib import Path\n",
        "\n",
        "class DisplayablePath(object):\n",
        "    display_filename_prefix_middle = '├──'\n",
        "    display_filename_prefix_last = '└──'\n",
        "    display_parent_prefix_middle = '    '\n",
        "    display_parent_prefix_last = '│   '\n",
        "\n",
        "    def __init__(self, path, parent_path, is_last):\n",
        "        self.path = Path(str(path))\n",
        "        self.parent = parent_path\n",
        "        self.is_last = is_last\n",
        "        if self.parent:\n",
        "            self.depth = self.parent.depth + 1\n",
        "        else:\n",
        "            self.depth = 0\n",
        "\n",
        "    @property\n",
        "    def displayname(self):\n",
        "        if self.path.is_dir():\n",
        "            return self.path.name + '/'\n",
        "        return self.path.name\n",
        "\n",
        "    @classmethod\n",
        "    def make_tree(cls, root, parent=None, is_last=False, criteria=None):\n",
        "        root = Path(str(root))\n",
        "        criteria = criteria or cls._default_criteria\n",
        "\n",
        "        displayable_root = cls(root, parent, is_last)\n",
        "        yield displayable_root\n",
        "\n",
        "        children = sorted(list(path\n",
        "                               for path in root.iterdir()\n",
        "                               if criteria(path)),\n",
        "                          key=lambda s: str(s).lower())\n",
        "        count = 1\n",
        "        for path in children:\n",
        "            is_last = count == len(children)\n",
        "            if path.is_dir():\n",
        "                yield from cls.make_tree(path,\n",
        "                                         parent=displayable_root,\n",
        "                                         is_last=is_last,\n",
        "                                         criteria=criteria)\n",
        "            else:\n",
        "                yield cls(path, displayable_root, is_last)\n",
        "            count += 1\n",
        "\n",
        "    @classmethod\n",
        "    def _default_criteria(cls, path):\n",
        "        return True\n",
        "\n",
        "    @property\n",
        "    def displayname(self):\n",
        "        if self.path.is_dir():\n",
        "            return self.path.name + '/'\n",
        "        return self.path.name\n",
        "\n",
        "    def displayable(self):\n",
        "        if self.parent is None:\n",
        "            return self.displayname\n",
        "\n",
        "        _filename_prefix = (self.display_filename_prefix_last\n",
        "                            if self.is_last\n",
        "                            else self.display_filename_prefix_middle)\n",
        "\n",
        "        parts = ['{!s} {!s}'.format(_filename_prefix,\n",
        "                                    self.displayname)]\n",
        "\n",
        "        parent = self.parent\n",
        "        while parent and parent.parent is not None:\n",
        "            parts.append(self.display_parent_prefix_middle\n",
        "                         if parent.is_last\n",
        "                         else self.display_parent_prefix_last)\n",
        "            parent = parent.parent\n",
        "\n",
        "        return ''.join(reversed(parts))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXB8Eto6v3R_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9cfa100a-f726-4021-c8c4-5cdc4f22310d"
      },
      "source": [
        "paths = DisplayablePath.make_tree(Path(ROOT))\n",
        "for path in paths:\n",
        "    print(path.displayable())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "covid/\n",
            "├── _Common.ipynb\n",
            "├── common (1).py\n",
            "├── common.py\n",
            "├── CV19 Cleaning.ipynb\n",
            "├── CV19 Dataset Explore.ipynb\n",
            "├── CV19-result-analysis.ipynb\n",
            "├── CV19_Dataset.ipynb\n",
            "├── CV19_Synthesis.ipynb\n",
            "├── CV19Net.ipynb\n",
            "├── input/\n",
            "│   ├── backup_2gb/\n",
            "│   │   ├── Copy of Copy of pre_filter.pt\n",
            "│   │   ├── Copy of Copy of pre_transform.pt\n",
            "│   │   └── Copy of Copy of processed.dt\n",
            "│   ├── categorical_numerical_mapping.dict\n",
            "│   ├── meta_dictionary.dict\n",
            "│   ├── training/\n",
            "│   │   ├── cleaned/\n",
            "│   │   │   ├── synthetic_run_0_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_10_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_11_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_12_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_13_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_14_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_15_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_16_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_17_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_18_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_19_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_1_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_20_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_21_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_22_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_23_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_24_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_25_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_26_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_27_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_28_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_29_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_2_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_30_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_31_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_32_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_33_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_34_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_35_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_36_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_37_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_38_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_39_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_3_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_4_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_5_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_6_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_7_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   ├── synthetic_run_8_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   │   └── synthetic_run_9_252011_dt_27_04_wmean_2_wstd_2.csv\n",
            "│   │   ├── processed/\n",
            "│   │   │   ├── .ipynb_checkpoints/\n",
            "│   │   │   ├── pre_filter.pt\n",
            "│   │   │   ├── pre_transform.pt\n",
            "│   │   │   └── processed.dt\n",
            "│   │   ├── processed_seq2seq_10_10/\n",
            "│   │   │   ├── pre_filter.pt\n",
            "│   │   │   ├── pre_transform.pt\n",
            "│   │   │   └── processed.dt\n",
            "│   │   ├── processed_seq2seq_20_20/\n",
            "│   │   │   ├── pre_filter.pt\n",
            "│   │   │   ├── pre_transform.pt\n",
            "│   │   │   └── processed.dt\n",
            "│   │   ├── processed_seq2seq_5_5/\n",
            "│   │   │   ├── pre_filter.pt\n",
            "│   │   │   ├── pre_transform.pt\n",
            "│   │   │   └── processed.dt\n",
            "│   │   ├── raw/\n",
            "│   │   │   └── synthetic_v1/\n",
            "│   │   └── unwanted/\n",
            "│   │       ├── .ipynb_checkpoints/\n",
            "│   │       ├── cleaned_worldwide_cases_27_04.csv\n",
            "│   │       └── worldwide_cases_27_04.csv\n",
            "│   └── validation/\n",
            "│       ├── cleaned/\n",
            "│       │   └── cleaned_worldwide_cases_27_04.csv\n",
            "│       ├── processed/\n",
            "│       │   └── .ipynb_checkpoints/\n",
            "│       ├── processed_seq2seq_10_10/\n",
            "│       │   ├── pre_filter.pt\n",
            "│       │   ├── pre_transform.pt\n",
            "│       │   └── processed.dt\n",
            "│       ├── processed_seq2seq_15_10/\n",
            "│       │   ├── pre_filter.pt\n",
            "│       │   ├── pre_transform.pt\n",
            "│       │   └── processed.dt\n",
            "│       ├── processed_seq2seq_20_20/\n",
            "│       │   ├── pre_filter.pt\n",
            "│       │   ├── pre_transform.pt\n",
            "│       │   └── processed.dt\n",
            "│       ├── processed_seq2seq_5_5/\n",
            "│       │   ├── pre_filter.pt\n",
            "│       │   ├── pre_transform.pt\n",
            "│       │   └── processed.dt\n",
            "│       └── raw/\n",
            "│           ├── cleaned_worldwide_cases_27_04.csv\n",
            "│           └── raw_worldwide_cases_27_04.csv\n",
            "├── input_6_1/\n",
            "│   ├── training/\n",
            "│   │   ├── cleaned/\n",
            "│   │   │   ├── synthetic_run_0_552022_dt_27_04_wmean_1_wstd_1.csv\n",
            "│   │   │   └── synthetic_run_1_552022_dt_27_04_wmean_1_wstd_1.csv\n",
            "│   │   ├── processed_seq2seq_5_5/\n",
            "│   │   │   ├── pre_filter.pt\n",
            "│   │   │   ├── pre_transform.pt\n",
            "│   │   │   └── processed.dt\n",
            "│   │   └── raw/\n",
            "│   │       └── cleaned_worldwide_cases_27_04.csv\n",
            "│   └── validation/\n",
            "│       ├── cleaned/\n",
            "│       │   └── cleaned_worldwide_cases_27_04.csv\n",
            "│       ├── processed_seq2seq_5_5/\n",
            "│       │   ├── pre_filter.pt\n",
            "│       │   ├── pre_transform.pt\n",
            "│       │   └── processed.dt\n",
            "│       └── raw/\n",
            "│           ├── cleaned_worldwide_cases_27_04.csv\n",
            "│           └── raw_worldwide_cases_27_04.csv\n",
            "├── input_mix/\n",
            "│   ├── training/\n",
            "│   │   ├── cleaned/\n",
            "│   │   │   ├── synthetic_run_0_552022_dt_27_04_wmean_6_wstd_1.csv\n",
            "│   │   │   ├── synthetic_run_1_552022_dt_27_04_wmean_7_wstd_1.csv\n",
            "│   │   │   ├── synthetic_run_2_552022_dt_27_04_wmean_8_wstd_1.csv\n",
            "│   │   │   ├── synthetic_run_3_552022_dt_27_04_wmean_9_wstd_1.csv\n",
            "│   │   │   ├── synthetic_run_4_552022_dt_27_04_wmean_10_wstd_1.csv\n",
            "│   │   │   ├── synthetic_run_5_552022_dt_27_04_wmean_11_wstd_1.csv\n",
            "│   │   │   ├── synthetic_run_6_552022_dt_27_04_wmean_12_wstd_1.csv\n",
            "│   │   │   ├── synthetic_run_7_552022_dt_27_04_wmean_13_wstd_1.csv\n",
            "│   │   │   ├── synthetic_run_8_552022_dt_27_04_wmean_14_wstd_1.csv\n",
            "│   │   │   └── synthetic_run_9_552022_dt_27_04_wmean_15_wstd_1.csv\n",
            "│   │   └── raw/\n",
            "│   └── validation/\n",
            "│       ├── cleaned/\n",
            "│       │   └── cleaned_worldwide_cases_27_04.csv\n",
            "│       └── raw/\n",
            "├── input_rnn/\n",
            "│   ├── training/\n",
            "│   │   ├── cleaned/\n",
            "│   │   │   └── cleaned_worldwide_cases_27_04.csv\n",
            "│   │   ├── processed/\n",
            "│   │   │   ├── .ipynb_checkpoints/\n",
            "│   │   │   ├── pre_filter.pt\n",
            "│   │   │   ├── pre_transform.pt\n",
            "│   │   │   └── processed.dt\n",
            "│   │   └── raw/\n",
            "│   └── validation/\n",
            "│       ├── cleaned/\n",
            "│       │   └── cleaned_worldwide_cases_27_04.csv\n",
            "│       ├── processed/\n",
            "│       │   ├── .ipynb_checkpoints/\n",
            "│       │   ├── pre_filter.pt\n",
            "│       │   ├── pre_transform.pt\n",
            "│       │   └── processed.dt\n",
            "│       └── raw/\n",
            "├── models/\n",
            "│   └── run1/\n",
            "│       ├── 0/\n",
            "│       │   ├── 10_1001_473797.pt\n",
            "│       │   ├── 20_1001_473797.pt\n",
            "│       │   ├── 30_1001_473797.pt\n",
            "│       │   ├── 40_1001_473797.pt\n",
            "│       │   ├── 50_1001_473797.pt\n",
            "│       │   ├── 60_1001_473797.pt\n",
            "│       │   ├── 70_1001_473797.pt\n",
            "│       │   ├── 80_1001_473797.pt\n",
            "│       │   ├── 90_1001_473797.pt\n",
            "│       │   └── backup/\n",
            "│       │       ├── 10_1001_473797.pt\n",
            "│       │       ├── 20_1001_473797.pt\n",
            "│       │       ├── 30_1001_473797.pt\n",
            "│       │       ├── 40_1001_473797.pt\n",
            "│       │       └── 50_1001_473797.pt\n",
            "│       ├── 1/\n",
            "│       │   ├── 100_1001_473797.pt\n",
            "│       │   ├── 10_1001_473797.pt\n",
            "│       │   ├── 20_1001_473797.pt\n",
            "│       │   ├── 30_1001_473797.pt\n",
            "│       │   ├── 40_1001_473797.pt\n",
            "│       │   ├── 50_1001_473797.pt\n",
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            "├── models__rnn/\n",
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            "│   ├── Logs.txt\n",
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            "│   ├── run10/\n",
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            "│   │   │   ├── 80_1001_994392.pt\n",
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            "│   │   │   └── losses_1001_994392.npy\n",
            "│   │   ├── 7/\n",
            "│   │   │   ├── 100_1001_994392.pt\n",
            "│   │   │   ├── 10_1001_994392.pt\n",
            "│   │   │   ├── 20_1001_994392.pt\n",
            "│   │   │   ├── 30_1001_994392.pt\n",
            "│   │   │   ├── 40_1001_994392.pt\n",
            "│   │   │   ├── 50_1001_994392.pt\n",
            "│   │   │   ├── 60_1001_994392.pt\n",
            "│   │   │   ├── 70_1001_994392.pt\n",
            "│   │   │   ├── 80_1001_994392.pt\n",
            "│   │   │   ├── 90_1001_994392.pt\n",
            "│   │   │   └── losses_1001_994392.npy\n",
            "│   │   ├── 8/\n",
            "│   │   ├── rnn_model_configurations_1001_276924.csv\n",
            "│   │   └── rnn_model_configurations_1001_994392.csv\n",
            "│   ├── run4/\n",
            "│   │   ├── 0/\n",
            "│   │   │   ├── 100_1001_293868.pt\n",
            "│   │   │   ├── 10_1001_293868.pt\n",
            "│   │   │   ├── 20_1001_293868.pt\n",
            "│   │   │   ├── 30_1001_293868.pt\n",
            "│   │   │   ├── 40_1001_293868.pt\n",
            "│   │   │   ├── 50_1001_293868.pt\n",
            "│   │   │   ├── 60_1001_293868.pt\n",
            "│   │   │   ├── 70_1001_293868.pt\n",
            "│   │   │   ├── 80_1001_293868.pt\n",
            "│   │   │   ├── 90_1001_293868.pt\n",
            "│   │   │   └── losses_1001_293868.npy\n",
            "│   │   ├── models_1001_293868.pt.npy\n",
            "│   │   └── rnn_model_configurations_1001_293868.csv\n",
            "│   ├── run5/\n",
            "│   │   ├── 0/\n",
            "│   │   │   ├── 0models_1001_68488.pt.npy\n",
            "│   │   │   ├── 100_1001_68488.pt\n",
            "│   │   │   ├── 10_1001_68488.pt\n",
            "│   │   │   ├── 110_1001_68488.pt\n",
            "│   │   │   ├── 120_1001_68488.pt\n",
            "│   │   │   ├── 130_1001_68488.pt\n",
            "│   │   │   ├── 140_1001_68488.pt\n",
            "│   │   │   ├── 150_1001_68488.pt\n",
            "│   │   │   ├── 160_1001_68488.pt\n",
            "│   │   │   ├── 170_1001_68488.pt\n",
            "│   │   │   ├── 180_1001_68488.pt\n",
            "│   │   │   ├── 190_1001_68488.pt\n",
            "│   │   │   ├── 200_1001_68488.pt\n",
            "│   │   │   ├── 20_1001_68488.pt\n",
            "│   │   │   ├── 210_1001_68488.pt\n",
            "│   │   │   ├── 220_1001_68488.pt\n",
            "│   │   │   ├── 230_1001_68488.pt\n",
            "│   │   │   ├── 240_1001_68488.pt\n",
            "│   │   │   ├── 250_1001_68488.pt\n",
            "│   │   │   ├── 260_1001_68488.pt\n",
            "│   │   │   ├── 270_1001_68488.pt\n",
            "│   │   │   ├── 280_1001_68488.pt\n",
            "│   │   │   ├── 290_1001_68488.pt\n",
            "│   │   │   ├── 300_1001_68488.pt\n",
            "│   │   │   ├── 30_1001_68488.pt\n",
            "│   │   │   ├── 40_1001_68488.pt\n",
            "│   │   │   ├── 50_1001_68488.pt\n",
            "│   │   │   ├── 60_1001_68488.pt\n",
            "│   │   │   ├── 70_1001_68488.pt\n",
            "│   │   │   ├── 80_1001_68488.pt\n",
            "│   │   │   ├── 90_1001_68488.pt\n",
            "│   │   │   └── losses_1001_68488.npy\n",
            "│   │   ├── 1/\n",
            "│   │   │   ├── 10_1001_293868.pt\n",
            "│   │   │   ├── 10_1001_68488.pt\n",
            "│   │   │   ├── 20_1001_68488.pt\n",
            "│   │   │   └── 30_1001_68488.pt\n",
            "│   │   ├── rnn_model_configurations_1001_513321.csv\n",
            "│   │   ├── rnn_model_configurations_1001_68488.csv\n",
            "│   │   └── rnn_model_configurations_1001_71469.csv\n",
            "│   ├── run6/\n",
            "│   │   └── 0/\n",
            "│   ├── run7/\n",
            "│   │   └── rnn_model_configurations_1001_941026.csv\n",
            "│   ├── run8/\n",
            "│   └── run9/\n",
            "│       ├── 0/\n",
            "│       │   ├── 0models_1001_876638.pt.npy\n",
            "│       │   ├── 10_1001_876638.pt\n",
            "│       │   ├── 20_1001_876638.pt\n",
            "│       │   ├── 30_1001_876638.pt\n",
            "│       │   ├── 40_1001_876638.pt\n",
            "│       │   ├── 50_1001_876638.pt\n",
            "│       │   └── losses_1001_876638.npy\n",
            "│       ├── 1/\n",
            "│       │   ├── 10_1001_876638.pt\n",
            "│       │   ├── 1models_1001_876638.pt.npy\n",
            "│       │   ├── 20_1001_876638.pt\n",
            "│       │   ├── 30_1001_876638.pt\n",
            "│       │   ├── 40_1001_876638.pt\n",
            "│       │   ├── 50_1001_876638.pt\n",
            "│       │   └── losses_1001_876638.npy\n",
            "│       ├── 2/\n",
            "│       │   ├── 10_1001_876638.pt\n",
            "│       │   ├── 20_1001_876638.pt\n",
            "│       │   ├── 2models_1001_876638.pt.npy\n",
            "│       │   ├── 30_1001_876638.pt\n",
            "│       │   ├── 40_1001_876638.pt\n",
            "│       │   ├── 50_1001_876638.pt\n",
            "│       │   └── losses_1001_876638.npy\n",
            "│       ├── models_1001_876638.pt.npy\n",
            "│       └── rnn_model_configurations_1001_876638.csv\n",
            "├── models_rnn_old/\n",
            "│   ├── 0/\n",
            "│   │   ├── 100_1001_688.pt\n",
            "│   │   ├── 10_1001_247371.pt\n",
            "│   │   ├── 10_1001_688.pt\n",
            "│   │   ├── 20_1001_688.pt\n",
            "│   │   ├── 30_1001_688.pt\n",
            "│   │   ├── 40_1001_688.pt\n",
            "│   │   ├── 50_1001_688.pt\n",
            "│   │   ├── 60_1001_688.pt\n",
            "│   │   ├── 70_1001_688.pt\n",
            "│   │   ├── 80_1001_688.pt\n",
            "│   │   ├── 90_1001_688.pt\n",
            "│   │   └── losses_1001_688.npy\n",
            "│   ├── 1/\n",
            "│   │   ├── 100_1001_688.pt\n",
            "│   │   ├── 10_1001_688.pt\n",
            "│   │   ├── 20_1001_688.pt\n",
            "│   │   ├── 30_1001_688.pt\n",
            "│   │   ├── 40_1001_688.pt\n",
            "│   │   ├── 50_1001_688.pt\n",
            "│   │   ├── 60_1001_688.pt\n",
            "│   │   ├── 70_1001_688.pt\n",
            "│   │   ├── 80_1001_688.pt\n",
            "│   │   ├── 90_1001_688.pt\n",
            "│   │   └── losses_1001_688.npy\n",
            "│   ├── 2/\n",
            "│   │   ├── 100_1001_688.pt\n",
            "│   │   ├── 10_1001_688.pt\n",
            "│   │   ├── 20_1001_688.pt\n",
            "│   │   ├── 30_1001_688.pt\n",
            "│   │   ├── 40_1001_688.pt\n",
            "│   │   ├── 50_1001_688.pt\n",
            "│   │   ├── 60_1001_688.pt\n",
            "│   │   ├── 70_1001_688.pt\n",
            "│   │   ├── 80_1001_688.pt\n",
            "│   │   ├── 90_1001_688.pt\n",
            "│   │   └── losses_1001_688.npy\n",
            "│   ├── 3/\n",
            "│   │   ├── 10_1001_688.pt\n",
            "│   │   ├── 20_1001_688.pt\n",
            "│   │   └── 30_1001_688.pt\n",
            "│   ├── rnn_model_configurations_1001_11316.csv\n",
            "│   ├── rnn_model_configurations_1001_174154.csv\n",
            "│   ├── rnn_model_configurations_1001_234025.csv\n",
            "│   ├── rnn_model_configurations_1001_247371.csv\n",
            "│   ├── rnn_model_configurations_1001_260649.csv\n",
            "│   ├── rnn_model_configurations_1001_266912.csv\n",
            "│   ├── rnn_model_configurations_1001_286379.csv\n",
            "│   ├── rnn_model_configurations_1001_298751.csv\n",
            "│   ├── rnn_model_configurations_1001_343728.csv\n",
            "│   ├── rnn_model_configurations_1001_44965.csv\n",
            "│   ├── rnn_model_configurations_1001_46076.csv\n",
            "│   ├── rnn_model_configurations_1001_464027.csv\n",
            "│   ├── rnn_model_configurations_1001_478593.csv\n",
            "│   ├── rnn_model_configurations_1001_517715.csv\n",
            "│   ├── rnn_model_configurations_1001_559349.csv\n",
            "│   ├── rnn_model_configurations_1001_560827.csv\n",
            "│   ├── rnn_model_configurations_1001_608722.csv\n",
            "│   ├── rnn_model_configurations_1001_619170.csv\n",
            "│   ├── rnn_model_configurations_1001_621371.csv\n",
            "│   ├── rnn_model_configurations_1001_662669.csv\n",
            "│   ├── rnn_model_configurations_1001_667175.csv\n",
            "│   ├── rnn_model_configurations_1001_686499.csv\n",
            "│   ├── rnn_model_configurations_1001_687134.csv\n",
            "│   ├── rnn_model_configurations_1001_688.csv\n",
            "│   ├── rnn_model_configurations_1001_697141.csv\n",
            "│   ├── rnn_model_configurations_1001_698675.csv\n",
            "│   ├── rnn_model_configurations_1001_723831.csv\n",
            "│   ├── rnn_model_configurations_1001_738495.csv\n",
            "│   ├── rnn_model_configurations_1001_822451.csv\n",
            "│   ├── rnn_model_configurations_1001_853281.csv\n",
            "│   ├── rnn_model_configurations_1001_861473.csv\n",
            "│   ├── rnn_model_configurations_1001_927045.csv\n",
            "│   ├── rnn_model_configurations_1001_942309.csv\n",
            "│   └── rnn_model_configurations_1001_969902.csv\n",
            "├── RNN-CV19Net.ipynb\n",
            "├── rnn_model_configurations_1001_165567.csv\n",
            "├── rnn_model_configurations_1001_353166.csv\n",
            "├── rnn_model_configurations_1001_443838.csv\n",
            "├── rnn_model_configurations_1001_57460.csv\n",
            "└── rnn_model_configurations_1001_981028.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bX8mSVouyPrC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "ca58a7b2-36bb-4e93-89be-60fd4f3ddc10"
      },
      "source": [
        "import dill       \n",
        "with open('files.pkl', 'wb') as f:\n",
        "  dill.dump(paths, f)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-67d085745f4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdill\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'files.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m   \u001b[0mdill\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/dill/_dill.py\u001b[0m in \u001b[0;36mdump\u001b[0;34m(obj, file, protocol, byref, fmode, recurse, **kwds)\u001b[0m\n\u001b[1;32m    257\u001b[0m     \u001b[0m_kwds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0m_kwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbyref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbyref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecurse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 259\u001b[0;31m     \u001b[0mPickler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0m_kwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    260\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/dill/_dill.py\u001b[0m in \u001b[0;36mdump\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    443\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mPicklingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m             \u001b[0mStockPickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    446\u001b[0m         \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# clear record of 'recursion-sensitive' pickled objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m         \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/pickle.py\u001b[0m in \u001b[0;36mdump\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    407\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproto\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_framing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    410\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSTOP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend_framing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/pickle.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, obj, save_persistent_id)\u001b[0m\n\u001b[1;32m    494\u001b[0m             \u001b[0mreduce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__reduce_ex__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m                 \u001b[0mrv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproto\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    497\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m                 \u001b[0mreduce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__reduce__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: can't pickle generator objects"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTCh138fxp-8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}