최종정리본_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "최종정리본_final",
      "provenance": [],
      "collapsed_sections": [
        "I0lsyXQQL4Lt",
        "IECCEdMvL8mq"
      ],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 패키지 설치"
      ],
      "metadata": {
        "id": "I0lsyXQQL4Lt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fastpivot\n",
        "!pip install pyarrow\n",
        "\n",
        "!nvidia-smi\n",
        "!head /proc/cpuinfo\n",
        "!head -n 3 /proc/meminfo"
      ],
      "metadata": {
        "id": "3ekB7md6zCmx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17082f34-8571-4600-f280-36a6ab45b944"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting fastpivot\n",
            "  Downloading fastpivot-0.1.12-cp37-cp37m-manylinux_2_24_x86_64.whl (548 kB)\n",
            "\u001b[K     |████████████████████████████████| 548 kB 35.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.7/dist-packages (from fastpivot) (1.21.6)\n",
            "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from fastpivot) (1.3.5)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from fastpivot) (1.7.3)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.0->fastpivot) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.0->fastpivot) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0.0->fastpivot) (1.15.0)\n",
            "Installing collected packages: fastpivot\n",
            "Successfully installed fastpivot-0.1.12\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pyarrow in /usr/local/lib/python3.7/dist-packages (6.0.1)\n",
            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from pyarrow) (1.21.6)\n",
            "Fri Aug 12 08:47:51 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n",
            "processor\t: 0\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 79\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0x1\n",
            "cpu MHz\t\t: 2199.998\n",
            "cache size\t: 56320 KB\n",
            "physical id\t: 0\n",
            "MemTotal:       26692024 kB\n",
            "MemFree:        23782140 kB\n",
            "MemAvailable:   25605208 kB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import warnings; warnings.filterwarnings(\"ignore\")\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pyarrow as pa\n",
        "from fastpivot import pivot_table\n",
        "from tqdm import tqdm\n",
        "\n",
        "import sys\n",
        "import random\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set_palette('deep')\n",
        "sns.set_color_codes()\n",
        "sns.set_style('white')\n",
        "\n",
        "from joblib import Parallel, delayed\n",
        "import multiprocessing\n",
        "num_cores = multiprocessing.cpu_count()\n",
        "\n",
        "print(\"Usable Core : \", num_cores)\n",
        "print(\"Pandas Version : \", pd.__version__) # 1.3.5\n",
        "print(\"Pyarrow Version : \", pa.__version__) # 6.0.1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8WmujNCPpUt",
        "outputId": "fff51f83-1e85-4e6e-d669-64ce130877e9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Usable Core :  4\n",
            "Pandas Version :  1.3.5\n",
            "Pyarrow Version :  6.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf\n",
        "!pip install fuzzy-c-means\n",
        "\n",
        "from fcmeans import FCM\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzCji5lI4dXr",
        "outputId": "40b92dfa-2f9a-4657-d094-f4ce0d8524db"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
            "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 2 dirs\n",
            "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
            "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
            "/root/.local/share/fonts: skipping, no such directory\n",
            "/root/.fonts: skipping, no such directory\n",
            "/var/cache/fontconfig: cleaning cache directory\n",
            "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
            "/root/.fontconfig: not cleaning non-existent cache directory\n",
            "fc-cache: succeeded\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting fuzzy-c-means\n",
            "  Downloading fuzzy_c_means-1.6.3-py3-none-any.whl (9.1 kB)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from fuzzy-c-means) (1.21.6)\n",
            "Requirement already satisfied: tabulate<0.9.0,>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from fuzzy-c-means) (0.8.10)\n",
            "Requirement already satisfied: pydantic<2.0.0,>=1.8.2 in /usr/local/lib/python3.7/dist-packages (from fuzzy-c-means) (1.9.1)\n",
            "Collecting typer<0.4.0,>=0.3.2\n",
            "  Downloading typer-0.3.2-py3-none-any.whl (21 kB)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from pydantic<2.0.0,>=1.8.2->fuzzy-c-means) (4.1.1)\n",
            "Requirement already satisfied: click<7.2.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.4.0,>=0.3.2->fuzzy-c-means) (7.1.2)\n",
            "Installing collected packages: typer, fuzzy-c-means\n",
            "  Attempting uninstall: typer\n",
            "    Found existing installation: typer 0.4.2\n",
            "    Uninstalling typer-0.4.2:\n",
            "      Successfully uninstalled typer-0.4.2\n",
            "Successfully installed fuzzy-c-means-1.6.3 typer-0.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install modin[all]"
      ],
      "metadata": {
        "id": "1Bc-5u4k81KC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "582c94cc-ba19-4d91-c0b8-5fdea19e9690"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting modin[all]\n",
            "  Downloading modin-0.12.1-py3-none-any.whl (761 kB)\n",
            "\u001b[K     |████████████████████████████████| 761 kB 33.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from modin[all]) (21.3)\n",
            "Collecting fsspec\n",
            "  Downloading fsspec-2022.7.1-py3-none-any.whl (141 kB)\n",
            "\u001b[K     |████████████████████████████████| 141 kB 49.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.7/dist-packages (from modin[all]) (1.21.6)\n",
            "Requirement already satisfied: pandas==1.3.5 in /usr/local/lib/python3.7/dist-packages (from modin[all]) (1.3.5)\n",
            "Collecting distributed>=2.22.0\n",
            "  Downloading distributed-2022.2.0-py3-none-any.whl (837 kB)\n",
            "\u001b[K     |████████████████████████████████| 837 kB 59.0 MB/s \n",
            "\u001b[?25hCollecting dask>=2.22.0\n",
            "  Downloading dask-2022.2.0-py3-none-any.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 68.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from modin[all]) (1.3.0)\n",
            "Collecting ray[default]>=1.4.0\n",
            "  Downloading ray-1.13.0-cp37-cp37m-manylinux2014_x86_64.whl (54.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 54.5 MB 1.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyarrow>=1.0 in /usr/local/lib/python3.7/dist-packages (from modin[all]) (6.0.1)\n",
            "Collecting rpyc==4.1.5\n",
            "  Downloading rpyc-4.1.5-py3-none-any.whl (68 kB)\n",
            "\u001b[K     |████████████████████████████████| 68 kB 8.5 MB/s \n",
            "\u001b[?25hCollecting boto3\n",
            "  Downloading boto3-1.24.50-py3-none-any.whl (132 kB)\n",
            "\u001b[K     |████████████████████████████████| 132 kB 80.9 MB/s \n",
            "\u001b[?25hCollecting modin-spreadsheet>=0.1.0\n",
            "  Downloading modin_spreadsheet-0.1.2-py2.py3-none-any.whl (1.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8 MB 60.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.3.5->modin[all]) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.3.5->modin[all]) (2.8.2)\n",
            "Collecting plumbum\n",
            "  Downloading plumbum-1.7.2-py2.py3-none-any.whl (117 kB)\n",
            "\u001b[K     |████████████████████████████████| 117 kB 76.4 MB/s \n",
            "\u001b[?25hCollecting pyyaml>=5.3.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 58.0 MB/s \n",
            "\u001b[?25hCollecting partd>=0.3.10\n",
            "  Downloading partd-1.3.0-py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.7/dist-packages (from dask>=2.22.0->modin[all]) (0.12.0)\n",
            "Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.22.0->modin[all]) (7.1.2)\n",
            "Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.22.0->modin[all]) (1.7.0)\n",
            "Requirement already satisfied: zict>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.22.0->modin[all]) (2.2.0)\n",
            "Requirement already satisfied: psutil>=5.0 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.22.0->modin[all]) (5.4.8)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from distributed>=2.22.0->modin[all]) (57.4.0)\n",
            "Requirement already satisfied: tornado>=5 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.22.0->modin[all]) (5.1.1)\n",
            "Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.22.0->modin[all]) (2.4.0)\n",
            "Collecting cloudpickle\n",
            "  Downloading cloudpickle-2.1.0-py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.22.0->modin[all]) (1.0.4)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.22.0->modin[all]) (2.11.3)\n",
            "Collecting notebook>=6.0.3\n",
            "  Downloading notebook-6.4.12-py3-none-any.whl (9.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.9 MB 48.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: ipywidgets>=7.0.0 in /usr/local/lib/python3.7/dist-packages (from modin-spreadsheet>=0.1.0->modin[all]) (7.7.1)\n",
            "Requirement already satisfied: jupyter>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from modin-spreadsheet>=0.1.0->modin[all]) (1.0.0)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (5.1.1)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (0.2.0)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (3.6.1)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (5.5.0)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (1.1.1)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (4.10.1)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (5.3.5)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (0.7.5)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (0.8.1)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (1.0.18)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (4.4.2)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (2.6.1)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (4.8.0)\n",
            "Requirement already satisfied: qtconsole in /usr/local/lib/python3.7/dist-packages (from jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (5.3.1)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (5.6.1)\n",
            "Requirement already satisfied: jupyter-console in /usr/local/lib/python3.7/dist-packages (from jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (5.2.0)\n",
            "Collecting tornado>=5\n",
            "  Downloading tornado-6.2-cp37-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (423 kB)\n",
            "\u001b[K     |████████████████████████████████| 423 kB 56.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: jupyter-core>=4.6.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (4.11.1)\n",
            "Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.7/dist-packages (from notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (1.8.0)\n",
            "Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.7/dist-packages (from notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (21.3.0)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.7/dist-packages (from notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (0.13.3)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.7/dist-packages (from notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (0.14.1)\n",
            "Requirement already satisfied: nbformat in /usr/local/lib/python3.7/dist-packages (from notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (5.4.0)\n",
            "Requirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.7/dist-packages (from notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (23.2.0)\n",
            "Requirement already satisfied: nest-asyncio>=1.5 in /usr/local/lib/python3.7/dist-packages (from notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (1.5.5)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (5.0.1)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (1.5.0)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (0.4)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (0.6.0)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (0.8.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (0.7.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->distributed>=2.22.0->modin[all]) (2.0.1)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.7/dist-packages (from nbformat->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (2.16.1)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.7/dist-packages (from nbformat->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (4.3.3)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (0.18.1)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (22.1.0)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (5.9.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (4.12.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (4.1.1)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (3.8.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->modin[all]) (3.0.9)\n",
            "Collecting locket\n",
            "  Downloading locket-1.0.0-py2.py3-none-any.whl (4.4 kB)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (1.15.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipywidgets>=7.0.0->modin-spreadsheet>=0.1.0->modin[all]) (0.2.5)\n",
            "Collecting virtualenv\n",
            "  Downloading virtualenv-20.16.3-py2.py3-none-any.whl (8.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 8.8 MB 29.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf<4.0.0,>=3.15.3 in /usr/local/lib/python3.7/dist-packages (from ray[default]>=1.4.0->modin[all]) (3.17.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from ray[default]>=1.4.0->modin[all]) (3.7.1)\n",
            "Requirement already satisfied: frozenlist in /usr/local/lib/python3.7/dist-packages (from ray[default]>=1.4.0->modin[all]) (1.3.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from ray[default]>=1.4.0->modin[all]) (2.23.0)\n",
            "Requirement already satisfied: aiosignal in /usr/local/lib/python3.7/dist-packages (from ray[default]>=1.4.0->modin[all]) (1.2.0)\n",
            "Collecting grpcio<=1.43.0,>=1.28.1\n",
            "  Downloading grpcio-1.43.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.1 MB 94.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: smart-open in /usr/local/lib/python3.7/dist-packages (from ray[default]>=1.4.0->modin[all]) (5.2.1)\n",
            "Requirement already satisfied: aiohttp>=3.7 in /usr/local/lib/python3.7/dist-packages (from ray[default]>=1.4.0->modin[all]) (3.8.1)\n",
            "Collecting aiohttp-cors\n",
            "  Downloading aiohttp_cors-0.7.0-py3-none-any.whl (27 kB)\n",
            "Collecting gpustat>=1.0.0b1\n",
            "  Downloading gpustat-1.0.0rc1.tar.gz (89 kB)\n",
            "\u001b[K     |████████████████████████████████| 89 kB 5.6 MB/s \n",
            "\u001b[?25hCollecting py-spy>=0.2.0\n",
            "  Downloading py_spy-0.3.12-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 60.6 MB/s \n",
            "\u001b[?25hCollecting opencensus\n",
            "  Downloading opencensus-0.11.0-py2.py3-none-any.whl (128 kB)\n",
            "\u001b[K     |████████████████████████████████| 128 kB 81.5 MB/s \n",
            "\u001b[?25hCollecting colorful\n",
            "  Downloading colorful-0.5.4-py2.py3-none-any.whl (201 kB)\n",
            "\u001b[K     |████████████████████████████████| 201 kB 93.1 MB/s \n",
            "\u001b[?25hCollecting prometheus-client\n",
            "  Downloading prometheus_client-0.13.1-py3-none-any.whl (57 kB)\n",
            "\u001b[K     |████████████████████████████████| 57 kB 5.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.7->ray[default]>=1.4.0->modin[all]) (4.0.2)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.7->ray[default]>=1.4.0->modin[all]) (0.13.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.7->ray[default]>=1.4.0->modin[all]) (6.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.7->ray[default]>=1.4.0->modin[all]) (1.8.1)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.7->ray[default]>=1.4.0->modin[all]) (2.1.0)\n",
            "Collecting nvidia-ml-py<=11.495.46,>=11.450.129\n",
            "  Downloading nvidia_ml_py-11.495.46-py3-none-any.whl (25 kB)\n",
            "Collecting psutil>=5.0\n",
            "  Downloading psutil-5.9.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (281 kB)\n",
            "\u001b[K     |████████████████████████████████| 281 kB 74.0 MB/s \n",
            "\u001b[?25hCollecting blessed>=1.17.1\n",
            "  Downloading blessed-1.19.1-py2.py3-none-any.whl (58 kB)\n",
            "\u001b[K     |████████████████████████████████| 58 kB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: ptyprocess in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.3->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (0.7.0)\n",
            "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.7/dist-packages (from yarl<2.0,>=1.0->aiohttp>=3.7->ray[default]>=1.4.0->modin[all]) (2.10)\n",
            "Requirement already satisfied: heapdict in /usr/local/lib/python3.7/dist-packages (from zict>=0.1.3->distributed>=2.22.0->modin[all]) (1.0.1)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.7/dist-packages (from argon2-cffi->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (21.2.0)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (1.15.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=6.0.3->modin-spreadsheet>=0.1.0->modin[all]) (2.21)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (0.5.1)\n",
            "Collecting jmespath<2.0.0,>=0.7.1\n",
            "  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
            "Collecting botocore<1.28.0,>=1.27.50\n",
            "  Downloading botocore-1.27.50-py3-none-any.whl (9.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.0 MB 56.1 MB/s \n",
            "\u001b[?25hCollecting s3transfer<0.7.0,>=0.6.0\n",
            "  Downloading s3transfer-0.6.0-py3-none-any.whl (79 kB)\n",
            "\u001b[K     |████████████████████████████████| 79 kB 9.9 MB/s \n",
            "\u001b[?25hCollecting urllib3<1.27,>=1.25.4\n",
            "  Downloading urllib3-1.26.11-py2.py3-none-any.whl (139 kB)\n",
            "\u001b[K     |████████████████████████████████| 139 kB 76.2 MB/s \n",
            "\u001b[?25hCollecting opencensus-context>=0.1.3\n",
            "  Downloading opencensus_context-0.1.3-py2.py3-none-any.whl (5.1 kB)\n",
            "Requirement already satisfied: google-api-core<3.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from opencensus->ray[default]>=1.4.0->modin[all]) (1.31.6)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]>=1.4.0->modin[all]) (1.56.4)\n",
            "Requirement already satisfied: google-auth<2.0dev,>=1.25.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]>=1.4.0->modin[all]) (1.35.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.25.0->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]>=1.4.0->modin[all]) (4.9)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.25.0->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]>=1.4.0->modin[all]) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.25.0->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]>=1.4.0->modin[all]) (4.2.4)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2.0dev,>=1.25.0->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]>=1.4.0->modin[all]) (0.4.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->ray[default]>=1.4.0->modin[all]) (3.0.4)\n",
            "Collecting urllib3<1.27,>=1.25.4\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 93.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->ray[default]>=1.4.0->modin[all]) (2022.6.15)\n",
            "Requirement already satisfied: qtpy>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from qtconsole->jupyter>=1.0.0->modin-spreadsheet>=0.1.0->modin[all]) (2.1.0)\n",
            "Collecting platformdirs<3,>=2.4\n",
            "  Downloading platformdirs-2.5.2-py3-none-any.whl (14 kB)\n",
            "Collecting distlib<1,>=0.3.5\n",
            "  Downloading distlib-0.3.5-py2.py3-none-any.whl (466 kB)\n",
            "\u001b[K     |████████████████████████████████| 466 kB 95.8 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: gpustat\n",
            "  Building wheel for gpustat (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gpustat: filename=gpustat-1.0.0rc1-py3-none-any.whl size=18872 sha256=6f8196e19e8dcfb1358ed0505499c764de176841dfa9b0f796da777a5da3e9ac\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/85/03/7f87ed3a11307c5ad083829b59731788971a8411c265984409\n",
            "Successfully built gpustat\n",
            "Installing collected packages: tornado, prometheus-client, urllib3, notebook, platformdirs, locket, jmespath, distlib, virtualenv, pyyaml, psutil, partd, opencensus-context, nvidia-ml-py, grpcio, fsspec, cloudpickle, botocore, blessed, s3transfer, ray, py-spy, plumbum, opencensus, gpustat, dask, colorful, aiohttp-cors, rpyc, modin-spreadsheet, modin, distributed, boto3\n",
            "  Attempting uninstall: tornado\n",
            "    Found existing installation: tornado 5.1.1\n",
            "    Uninstalling tornado-5.1.1:\n",
            "      Successfully uninstalled tornado-5.1.1\n",
            "  Attempting uninstall: prometheus-client\n",
            "    Found existing installation: prometheus-client 0.14.1\n",
            "    Uninstalling prometheus-client-0.14.1:\n",
            "      Successfully uninstalled prometheus-client-0.14.1\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: notebook\n",
            "    Found existing installation: notebook 5.3.1\n",
            "    Uninstalling notebook-5.3.1:\n",
            "      Successfully uninstalled notebook-5.3.1\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: psutil\n",
            "    Found existing installation: psutil 5.4.8\n",
            "    Uninstalling psutil-5.4.8:\n",
            "      Successfully uninstalled psutil-5.4.8\n",
            "  Attempting uninstall: grpcio\n",
            "    Found existing installation: grpcio 1.47.0\n",
            "    Uninstalling grpcio-1.47.0:\n",
            "      Successfully uninstalled grpcio-1.47.0\n",
            "  Attempting uninstall: cloudpickle\n",
            "    Found existing installation: cloudpickle 1.3.0\n",
            "    Uninstalling cloudpickle-1.3.0:\n",
            "      Successfully uninstalled cloudpickle-1.3.0\n",
            "  Attempting uninstall: dask\n",
            "    Found existing installation: dask 2.12.0\n",
            "    Uninstalling dask-2.12.0:\n",
            "      Successfully uninstalled dask-2.12.0\n",
            "  Attempting uninstall: distributed\n",
            "    Found existing installation: distributed 1.25.3\n",
            "    Uninstalling distributed-1.25.3:\n",
            "      Successfully uninstalled distributed-1.25.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gym 0.17.3 requires cloudpickle<1.7.0,>=1.2.0, but you have cloudpickle 2.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires notebook~=5.3.0, but you have notebook 6.4.12 which is incompatible.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0, but you have tornado 6.2 which is incompatible.\u001b[0m\n",
            "Successfully installed aiohttp-cors-0.7.0 blessed-1.19.1 boto3-1.24.50 botocore-1.27.50 cloudpickle-2.1.0 colorful-0.5.4 dask-2022.2.0 distlib-0.3.5 distributed-2022.2.0 fsspec-2022.7.1 gpustat-1.0.0rc1 grpcio-1.43.0 jmespath-1.0.1 locket-1.0.0 modin-0.12.1 modin-spreadsheet-0.1.2 notebook-6.4.12 nvidia-ml-py-11.495.46 opencensus-0.11.0 opencensus-context-0.1.3 partd-1.3.0 platformdirs-2.5.2 plumbum-1.7.2 prometheus-client-0.13.1 psutil-5.9.1 py-spy-0.3.12 pyyaml-6.0 ray-1.13.0 rpyc-4.1.5 s3transfer-0.6.0 tornado-6.2 urllib3-1.25.11 virtualenv-20.16.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "cloudpickle",
                  "psutil",
                  "tornado"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "FNAkB-axyuqw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd4be0e7-538d-4b09-9a39-0e7801bd7daa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import modin.pandas as mpd\n",
        "import pandas as pd\n",
        " \n",
        "import time\n",
        "import ray\n",
        "ray.init() # 병렬처리를 위한 클러스터 구축"
      ],
      "metadata": {
        "id": "ajdcJACt9CQe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efce0f96-72e0-477e-c91e-04b444297210"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2022-08-12 08:49:21,916\tINFO services.py:1476 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265\u001b[39m\u001b[22m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RayContext(dashboard_url='127.0.0.1:8265', python_version='3.7.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '172.28.0.2', 'raylet_ip_address': '172.28.0.2', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-08-12_08-49-19_592854_80/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-12_08-49-19_592854_80/sockets/raylet', 'webui_url': '127.0.0.1:8265', 'session_dir': '/tmp/ray/session_2022-08-12_08-49-19_592854_80', 'metrics_export_port': 62695, 'gcs_address': '172.28.0.2:53943', 'address': '172.28.0.2:53943', 'node_id': 'a9c4a250ed33ce6240c372b452e73ab43805f5afb58c86ad8d494cc2'})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 불러오기"
      ],
      "metadata": {
        "id": "IECCEdMvL8mq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 구글 드라이브와 연동하여 데이터 위치 경로 설정\n",
        "path = \"/content/drive/MyDrive/AIFactory/Data/LPOINT_BIG_COMP/\""
      ],
      "metadata": {
        "id": "0GaUjrJvzCsQ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DEMO = mpd.read_csv(path+\"/LPOINT_BIG_COMP_01_DEMO.csv\", encoding=\"utf-8\")\n",
        "PDDE = mpd.read_csv(path+\"/LPOINT_BIG_COMP_02_PDDE.csv\", encoding=\"utf-8\")\n",
        "COP_U = mpd.read_csv(path+\"/LPOINT_BIG_COMP_03_COP_U.csv\", encoding=\"utf-8\")\n",
        "PD_CLAC = mpd.read_csv(path+\"/LPOINT_BIG_COMP_04_PD_CLAC.csv\", encoding=\"utf-8\")\n",
        "BR = mpd.read_csv(path+\"/LPOINT_BIG_COMP_05_BR.csv\", encoding=\"utf-8\")\n",
        "LPAY = mpd.read_csv(path+\"/LPOINT_BIG_COMP_06_LPAY.csv\", encoding=\"utf-8\")"
      ],
      "metadata": {
        "id": "aIqbJPwJzCvz"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "PDDE['chnl_dv'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5RTq8vow6VT",
        "outputId": "f5ea4307-e098-4fb2-acee-9169e6d9eb45"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    3953242\n",
              "2     428501\n",
              "Name: chnl_dv, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BR.columns = ['br_c', 'cop_c', 'br_zon_hlv', 'br_zon_mcls']\n",
        "\n",
        "# 데이터 테이블끼리 조인.\n",
        "pdde_cust = mpd.merge(PDDE, DEMO, on=['cust'])\n",
        "pdde_cust = mpd.merge(pdde_cust, PD_CLAC, on=['pd_c'], how='left')\n",
        "pdde_cust = mpd.merge(pdde_cust, BR, on=['br_c', 'cop_c'], how='left')\n",
        "\n",
        "pdde_col = [\n",
        "            \"cust\", \"ma_fem_dv\", \"ages\", \"zon_hlv\", # 고객 정보 (누가)\n",
        "            \"de_dt\", \"de_hr\", # 시간 정보 (언제)\n",
        "            \"chnl_dv\", \"br_zon_hlv\", \"br_zon_mcls\", \"cop_c\", \"br_c\", # 점포 정보 (어디서)\n",
        "            \"clac_hlv_nm\", \"clac_mcls_nm\", \"pd_nm\", \"pd_c\", # 상품 정보 (무엇을)\n",
        "            \"buy_ct\", \"buy_am\",  # 구매 정보 (어떻게)\n",
        "            \"rct_no\"\n",
        "            ]\n",
        "            \n",
        "pdde_cust = pdde_cust.loc[:, pdde_col]\n",
        "\n",
        "\n",
        "copu_cust = mpd.merge(COP_U, DEMO, on=['cust'])\n",
        "copu_cust = mpd.merge(copu_cust, BR, on=['br_c', 'cop_c'], how='left')\n",
        "\n",
        "copu_col = [\n",
        "            \"cust\", \"ma_fem_dv\", \"ages\", \"zon_hlv\", # 고객 정보 (누가)\n",
        "            \"de_dt\", \"de_hr\", # 시간 정보 (언제)\n",
        "            \"chnl_dv\", \"br_zon_hlv\", \"br_zon_mcls\", \"cop_c\", \"br_c\", # 점포 정보 (어디서)\n",
        "            \"vst_dt\",  # 상품 정보 (무엇을) : 방문일자는 상품 정보에 해당. copu의 경우 점포 정보 또한 상품 정보에 해당\n",
        "            \"buy_am\",  # 구매 정보 (어떻게)\n",
        "            ] # 영수증 번호는 제외\n",
        "\n",
        "copu_cust = copu_cust.loc[:, copu_col]\n",
        "\n",
        "\n",
        "lpay_cust = mpd.merge(LPAY, DEMO, on=['cust'])\n",
        "\n",
        "lpay_col = [\n",
        "            \"cust\", \"ma_fem_dv\", \"ages\", \"zon_hlv\", # 고객 정보 (누가)\n",
        "            \"de_dt\", \"de_hr\", # 시간 정보 (언제)\n",
        "            \"chnl_dv\", \"cop_c\", # LPAY 제휴 이용 정보 (어디서)\n",
        "            # 상품 정보 (무엇을) : 존재하지 않음\n",
        "            \"buy_am\",  # 구매 정보 (어떻게)\n",
        "            ] # 영수증 번호는 제외\n",
        "\n",
        "lpay_cust = lpay_cust.loc[:, lpay_col]\n",
        "\n",
        "\n",
        "pdde_cust.to_csv(path+\"/LPOINT_BIG_COMP_PDDE_CUST.csv\", encoding=\"utf-8\") # 장바구니\n",
        "copu_cust.to_csv(path+\"/LPOINT_BIG_COMP_COPU_CUST.csv\", encoding=\"utf-8\")\n",
        "lpay_cust.to_csv(path+\"/LPOINT_BIG_COMP_LPAY_CUST.csv\", encoding=\"utf-8\")"
      ],
      "metadata": {
        "id": "12vVYR_w-lz8"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA"
      ],
      "metadata": {
        "id": "mg1z-cklOC6Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# pdde 사용자 파이차트\n",
        "cust_counts = pdde_cust.drop_duplicates(\"cust\").groupby(\"ages\").cust.count()\n",
        "cust_counts.index = [f\"{i}0s\" for i in range(2,8)]\n",
        "plt.rc('font', size=14)\n",
        "cust_counts.plot(kind='pie', y='ages',\n",
        "                 autopct='%.1f%%', explode=[.03, .01, .01, .03, .2, .2],\n",
        "                 legend='ages', \n",
        "                 figsize=(10,10))\n",
        "plt.title(\"PDDE Count\")"
      ],
      "metadata": {
        "id": "oGsoCBsUOEeP",
        "outputId": "b33e0a14-de86-4774-d70b-4a0eb16f09d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'PDDE Count')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lpay 사용자 파이차트\n",
        "cust_counts = lpay_cust.drop_duplicates(\"cust\").groupby(\"ages\").cust.count()\n",
        "cust_counts.index = [f\"{i}0s\" for i in range(2,8)]\n",
        "plt.rc('font', size=14)\n",
        "cust_counts.plot(kind='pie', y='ages',\n",
        "                 autopct='%.1f%%', explode=[.03, .01, .01, .03, .2, .2],\n",
        "                 legend='ages', \n",
        "                 figsize=(10,10))\n",
        "plt.title(\"Lpay Count\")"
      ],
      "metadata": {
        "id": "kvJodarHOFz4",
        "outputId": "61554e92-a09d-4cca-ea59-3217d50db378",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Lpay Count')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pdde 사용량 파이차트\n",
        "cust_counts = pdde_cust.drop_duplicates(\"rct_no\").groupby(\"ages\").cust.count()\n",
        "cust_counts.index = [f\"{i}0s\" for i in range(2,8)]\n",
        "plt.rc('font', size=14)\n",
        "cust_counts.plot(kind='pie', y='ages',\n",
        "                 autopct='%.1f%%', explode=[.03, .01, .01, .03, .2, .2],\n",
        "                 legend='ages', \n",
        "                 figsize=(12,12))\n",
        "plt.title(\"PDDE Count\")"
      ],
      "metadata": {
        "id": "9LnPCMyHOFw4",
        "outputId": "291c4943-9b14-4602-b104-a624ae54b2c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'PDDE Count')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LPAY 사용량 파이차트\n",
        "cust_counts = lpay_cust.groupby(\"ages\").cust.count()\n",
        "cust_counts.index = [f\"{i}0s\" for i in range(2,8)]\n",
        "plt.rc('font', size=14)\n",
        "cust_counts.plot(kind='pie', y='ages',\n",
        "                 autopct='%.1f%%', explode=[.03, .01, .01, .03, .2, .2],\n",
        "                 legend='ages', \n",
        "                 figsize=(10,10))\n",
        "plt.title(\"Lpay Count\")"
      ],
      "metadata": {
        "id": "8dW2XHWQOFuB",
        "outputId": "ec77fe62-5a8c-4868-e534-b3b30c21c837",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Lpay Count')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pdde 남녀 사용자 파이차트\n",
        "cust_counts = pdde_cust.drop_duplicates(\"cust\").groupby(\"ma_fem_dv\").cust.count()\n",
        "cust_counts.index = [\"M\", \"F\"]\n",
        "plt.rc('font', size=14)\n",
        "cust_counts.plot(kind='pie', y='ages',\n",
        "                 autopct='%.1f%%',\n",
        "                 legend='ages', \n",
        "                 figsize=(10,10))\n",
        "plt.title(\"PDDE Count\")"
      ],
      "metadata": {
        "id": "q7vU2hmiOFrp",
        "outputId": "320acbff-536d-4edb-f021-89a4374f0028",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'PDDE Count')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lpay 남녀 사용자 파이차트\n",
        "cust_counts = lpay_cust.drop_duplicates(\"cust\").groupby(\"ma_fem_dv\").cust.count()\n",
        "cust_counts.index = [\"M\", \"F\"]\n",
        "plt.rc('font', size=14)\n",
        "cust_counts.plot(kind='pie', y='ages',\n",
        "                 autopct='%.1f%%',\n",
        "                 legend='ages', \n",
        "                 figsize=(10,10))\n",
        "plt.title(\"Lpay Count\")"
      ],
      "metadata": {
        "id": "jZhqlsgTOFpH",
        "outputId": "08e72f8b-8453-4175-ff88-1bff581cbced",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Lpay Count')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pdde 남녀 사용량 파이차트\n",
        "cust_counts = pdde_cust.drop_duplicates(\"rct_no\").groupby(\"ma_fem_dv\").cust.count()\n",
        "cust_counts.index = [\"M\", \"F\"]\n",
        "plt.rc('font', size=14)\n",
        "cust_counts.plot(kind='pie', y='ages',\n",
        "                 autopct='%.1f%%',\n",
        "                 legend='ages', \n",
        "                 figsize=(10,10))\n",
        "plt.title(\"PDDE Count\")"
      ],
      "metadata": {
        "id": "xN5-CF1VOFmg",
        "outputId": "9c66df63-4871-4477-9c11-e7fd1877e219",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'PDDE Count')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lpay 남녀 사용량 파이차트\n",
        "cust_counts = lpay_cust.groupby(\"ma_fem_dv\").cust.count()\n",
        "cust_counts.index = [\"M\", \"F\"]\n",
        "plt.rc('font', size=14)\n",
        "cust_counts.plot(kind='pie', y='ages',\n",
        "                 autopct='%.1f%%',\n",
        "                 legend='ages', \n",
        "                 figsize=(10,10))\n",
        "plt.title(\"Lpay Count\")"
      ],
      "metadata": {
        "id": "FVXXexyQOFjp",
        "outputId": "f73af98f-c205-41e8-e3f6-76d3716f69af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Lpay Count')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 지역별 Lpay 사용 비율\n",
        "tem = pd.DataFrame({'values' : pdde_cust.groupby(['br_zon_hlv']).buy_am.sum()})\n",
        "tem2 = pd.DataFrame({'values' : lpay_cust.groupby(['zon_hlv']).buy_am.sum()})\n",
        "tem['values'] = tem2['values'] / tem['values']\n",
        "tem['br_zon_hlv'] = np.array([i for i in range(17)])\n",
        "\n",
        "plt.figure(figsize=[12,8])\n",
        "sns.barplot(x='br_zon_hlv', y='values', data=tem)\n",
        "plt.title('purchasing devide Lpay purchasing By Region', fontsize=25, color='#189AB4')\n",
        "plt.legend(fontsize=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "cRdNbrxB-wVd",
        "outputId": "72ca915d-03f2-446e-b72d-46135419bbaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAH6CAYAAADMVGAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZhjRaH38e/QPSOrbAMYGJZRsRQQERBcEUQQCIJevALqFRWCC7heN9BXFFxQr1e5FxUNIKCyiSJoEFAWwQXZ3FgsLyLLYGSAEdmdrd8/6oTOZJKe7plKn8zM9/M8/XQnZ6ukk3N+p05VnSkjIyNIkiRJWnarlF0ASZIkaUVhuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpk+GyC6AVX6XeeAvwLeDOZq26RbmlGZ9KvdEao3K3Zq16ZZllya1Sb+wKXAHQrFWnlFuaZFnLNIivSSueSr1xB7A58NZmrXpauaVZskq98UngGODnzVp113JLo0Hh/rL/DNeStAzaAtfpzVr1LeWWRloxVOqN04BDekx+FLgTuAr4arNWvWmyytUyRvmeAO4Dfgt8F/hes1b1hiIrGZuFSN3F4uexsguykniM0fdcUh73k75Td5VdkGWwELi37ed+YHVgK+AdwO8q9cah5RVvsfJNATYF9gPOAX5cqTeeUl7xunJ/22fWXEtdNGvVZ5ddhpVJs1a9FvA9lzJq1qonAieWXY5ldHdnc8JKvTEV2B34KvB04GuVeuOyZq16x+QXb9HyVeqNKUAAjgf2B/YBPg78vxLK1pX72/6z5lqSJC03mrXqvGatejGjzTKmAXuXWKQnNWvVkWat+ifg9cCfiqffVmKRVAJrrgdEpd64Eng58Cngs8AHgDcAzwDmAtcD/92sVX/SY/kldsBr30azVv1kr+WBW4CPAvsCM4DVOjs9VOqNPUk7jBcBGwKPA7OAnwNnNmvVX4/xWncAPgK8DFgPuAf4IXBcs1b9R5f5p5J2nPsC2wObAOsDD5LatZ0GnN2rXVul3pgB/CewJ7AF6XP/ANAktdk7s1mrXtfr/Wh/Pyv1xhbAX4uHM4vXfTTpEmAF+Cepo8gnix1sr/dgc+ATwKuADUht9H4KfAaY376NpamNqdQbzybVlOwOrAP8DWgAnx7n8msBR5BqXgKwBjAb+CVwQuf/t1JvvB/472KeTZq16vwe651Cem2bA59o1qrHFc/vyhI62Ez2a+qnLp+jqcDHgFeSPg/3AhcBxzZr1b/1WMcLgdcCLwY2AzYitff8E3A+qS3qIx3L7AX8BFgAbNZr3cW8VwMvZQJtyTv/j5V6Y0fSvuQlwLqkfcT5wGeateqDXZb/JEvogDfWZ6Vz+Uq9cQDwdmA7YDrp/fxk2/zrkz4TVWBLUnODvwN/Lsp5ZrNW/WePckwD3gu8CXgm6Xt7A/CFIvh1W2YmKXTtRqpx3QQYITXbuJS0j+/ZhKNSbxwIvIW0H1yP1Pb4PuBW4BLglGat+kSv96NjXaeRwunpzVr1LZV643XAkcC2wKrFe/At4H+bterCHuWZUpTncGAbUhOJPwGnAPVi+Se30et1LYPftf29Zlu5hkhtsjcBPtKsVb/QawVFk5KTgUeAjZu16sM5CtasVedW6o3LSTXEG1fqjXV7HN82IH2OqqTPxFNI+7YrSJ+Hm8co+1IdR8a5v30a6bi5D+m4CXAHaZ/7pWatem+XZbYgw/FxRWDN9eCZBvwM+BzwHFKwXod00L2o2Fn20zOBPwDvJ7UbWyQkVeqN1Sv1xrmkHfmBpIP6PNJn6bmknfPXe628Um+8Afg18O/AaqSgO7PY3tWVemPNLou9BLgAqAE7kA6AT5B2JnsCZwLnVOqNxT7PlXrjecXreR+pjd5TSAekp5EOUO8jHVyXxtbFut9DOsGg+H0g8Jti24up1BsvAv5IOjnZhBR01iYdpG4AXrCU5Wmtfy/SQecNpMA1j7RjO5J0MjJzCctvRzrB+hzwQuCpwL9IJ1oHAr+s1BtHdSx2ZvE6NiTt6HvZhRSsR4BvD/hrmiw7k/7vh5A+BwtI3723A3+s1Bvb91ju18CHSQF4Oqkd5drF+o4nfQY37FjmEtLBb4gxatOKE5mXFg+/OfGXBJV6Y3/SicsBpO/sFFJlwQdJ7WS3WJr1TmD7XwLOI+07h0nBr336nsD/kSo0dgLWoghYwB7A10ghuJs1SSfmXyDtpxeSPlO7kfbTvd7bb5H+N68ifQ8eJ+0Hn0MKWH+o1Bsv7bZgpd44FTgb2Iv0PXuCdFL2TODVpOYfT+ux3TFV6o0Tge+RKjymFGV6HvCVoszdlhkCzgJOJX2n1iB9dncEvkHqzNdv7fvYJ9sPN2vVBaTADHBYcRLQS634fVauYN2mfbtDnRMr9cYrSScxHyOdAK5GOubOJH0/b6zUG2/utuJ+Hkcq9cbLSSdsHyQdN0eKn62ADwG39vqctlmq4+OKwnA9eN5F2tG/A1irWauuSwqw5xXTj6nUG/v1cftfJtUI7w6s0axVn0qq5Wv5FikYLwQ+D2zarFWf2qxV1yGF3TeSDvrdbEDaEZ9OqjVbh3RAO5IUlrYmhYVOj5F21nsAazdr1bWLcq1POiA9VJTpyC7LfolUY3YjqZZ9arNWXY9UM/Ms0s6jZ83AEnybdHB+QbNWXYN0wN2DVCP+VOB/Oxeo1BvrAN8nve7bgVeQ3ue1SP/3vxavdakUtfTnkE4i/gDsXKx7DVLt/wJSDXOv5SukADYD+AHpQLla8X5vBBxXrOOzlXrjNa3lilqMS4uH/zFGEVvTrh5vjXxZr2kSfYP0f29/Xa8i1WauB5xf1Lp3+hHpQFVp1qprFJ/r1YF/IwWNrYCT2hcoru60Pl+HjhE6WoHjpmat+qulfF2nA78CtmrWqmsXr+tA4B+kYHluEdD6YQfS1b/PAxsV780aFEGxUm88n3TCvi7p+78PsHqzVp1OCjg7kvYdvcLWsaTP02sY/f4+G7iGFKhOqNQba3dZ7nekk/lnkT6D00mf652Bi0nh6JxKvbFa+0JFkHkrab/7EWD9Zq26VrHfmU76vJxOqoyZqP1I/+8PAOsWx5zpjIbTN1fqjVd0We5DpP8npO/fBsX7vC6ptvKgYt3ZVeqNqcXJ0WnFU7eQrvS0q5O+11sCu/ZYz3NJ7z0sw363x7qnMXpy9lCzVr2/y7YvJFWe1Unf19WateqapO/H10iVbacUV4Dal+3bcaRSb2xKupK8Dul9fWmzVl2zKNcupH3LusAFlXpjkzFWNeHj44rEZiGDZ23g0GatemrriWatendxOfAK0of7s6QvZT8sBF7ZrFVntW3/zwCVemN30iVNgCObteoiNdTFzuPM4qeb1UmXB2ttyzwGfLVSbzydtHM/mHSZq3291wLXdq6sWavOAf6nUm/8jVTr8h7gfzpme3Fbea9pW3Yu6Yv/pR5lHY97gT2aterjxTrnAz+r1BtvJ/1/XlapN2a0v5ekE4AKqdbpVc1a9ba2Ml1X1GTcugxlOpq043qgKNvsYt0LgYsr9cbepBONXj5Nql04s1mrvrF9QrGuT1TqjX+QDqafJO2EW84ghd39K/XGU5u16kPty1fqjVWB17XNuzy8pskwn0Vf1whwaVtt/Wakk+0vti/UrFUXCy7FZ/H8Sr1xLfAX4DWVemOzjqYGp5LC4RakKz+XtK+jGNmgVVu2VLXWhXuBfTq+H+dW6o05pEvXLyCdCHxvGbbRy5qkS+ofbT3RrFX/RWoqAGk/sSppH/CS9qYfRa3nDcVPL6sDL26/tN2sVWNR8XFXsf196ai9bdaq7+tcUfG+XFupN/YlfY63JdX2f6dtttZ+7GedTRyateoDpBPbS1k669IxbnexzlpxErIDab98eWt6pd5YA2hd6TmlWav+Z9uyDwGfKz5HxyxlmdptWqk3/t72eIhUsTKF1AziG8DRnU3RmrXqPZV640ekE6DDKZpBdDi8+H1js1Yd6/89bsUJ67NIVyhanQZP6zLrV0gncp9r1qpHt08ovq9HVOqN+aTj2sdJr6Oln8eRo0nB+h/A7s1a9cn3vlmrXl2s+2bSif9RdK/UgqU7Pq4wrLkePHfT5TJcESRabUu3Ls56++HbY3zYW5c6b+oM1hPQq33sBcXvZ1bqjdUnuM5G8fsZRTuxdq12nZUJrnM8vtTacXT4CaM1SJ3/p38vfp/TvkNsKU5Qluq9LXbqrZqkk1phrWP9NzF6FaRz+VVJzS4g1fj10grGz6vUGxu1PX8B6SrCqoy+znb7kU4en+hVhi5lKvs1TYZer+tWRl/XQRNZYbNWvQf4PSmAvLhj2n2kWi8YDRftXkuquXycCTTd6eKL3b4fzVr1Z6QabZjg65qA1pW1xVTqjS0ZbfJydLNHm+olOK9bm9HivW1dudt2IissQn2rrXbnJffWfmyDPtT2302q9e6mVYnT+Vr2JJ3wQmrf282XyDOU6SqkK0ytn+mMNrdYi7RP2aDHsq196Wsr9cb09gnF1YE3FQ+XpdZ600q98ffWD+l78ydGw/Av6RgppGgS9QrSifV/jbHu1n7plR3/974cR4r9basC7aT2YN227lmMXhEb6/u7NMfHFYY114PnymbvAeevJn0Zh0mXLf/Yh+3/coxprYP0j5dy3XO67QgK7R2r1qVjp1xcFn8HqTboOaQz66ld1jOD1CGp5cekS56nV+qNl5AOFtcVNebL6jfdnmzWqvMr9cZ9pHZw67W9hmmkpi+QOn72ciVLN2zTzLbtXT7GfJeTaqI67UAKxpBqTsezzc1JNRQ0a9XHK/XGeaSTsP8gdWpq12oScsEEAk2pr2mSLOl1vQHYtlJvTG3WqvNaE4o+BgcVP9uRAsaqXdYxo8tzJ5Her1dX6o2Nmot2TmpdWTq32aXT4QQs6XW9mLQf64fbup2wFFr7sQWkA/3S6PrdL7T2Zet1m1ipN14GHEpqpzyD1FylU+f/7DLSSenzSX1TTgEub9aqf11syYm7boxjTq/X0uoHcFevMjRr1Ycr9cYNpHbcy2KxO/sWNefbkprIHATsVak39mku3in5p6QrOM8gXY1pbz72OtJx5BF6X20dj1b47+azpI7bCzqef0nbsreMsV9qBeo1SLX1s/t8HGnf3/5sjPl+SmrCuX6l3pjZ4zMwoePjisaa68FzT68JzdQL/IHiYWdHpVx6HZBgtLPMnWPMM5axOou0X9JbJDRX6o1nkdp+fYHULGYDUhvt+xgduL+l80D1YdLlwDVJzU6uBB6q1BvXV+qNTy2hzdiSjOf1tL+W9RjdWfYcpYExPgNL0P6ZGGsdva5MbNz290ZL+GnpvMrQqmnZpZJ6sgNP9ojfq2Oe8RiE19RvY72u1rRhFj1RW5108PsuqTPbpqT9+RxGvxOtIL5YeGvWqleRvlNTSW15W+t9JqPtRJe1Dep4XleZ+7H7m7Xqo0u5/ol+9wGo1BufJ3WEPITUl2VV0uX31v+sVZ5F/mfNWvUvwGGkIPgiUnvo2yv1xuxKvXFOpd7Yfwmd9nK/llZN8Vj7MVj6fdmYmrXqo0WQPgC4jhSSz6h0dGovThpaTZtqi67lyas2ZzY7RtWZoDubteqUZhp1Y5h0cn4UqcP0h+l+Fa+1X+qsle/8aa9tb+2X+nkcWZr9ba/v8FJ9R1YU1lyrU+cZdruybuH6LVJNzh2kTjSXF+2tgSd7rbe+rIscYIqat1cUHYJeTaox2JFUo7kD8KFKvXFos1Y9q98vosMg3g63/bLjas22Ib0m4CrSydfmpEuurUvGB5H2N+0dHydDjtc0iD5GCsGtoa5+QLqZxZOfq8roUHq9QtdJpLbHh1Xqjc8Xyx5WzH9Tl1rA5cnA7ccq9cYejHbY/hrpsv2t7bWalXrjOFL72sX+Z81a9buVeuMnpLC2G6kGflPSZfzXk2q09+3s69Bnpe7HmrXqgkq98S1S+/1nkjrzXdMxW6uPwbMr9cYuzVr1qkqG0XB6lYfU5v74Sr3RJLW1PrVSb/y+aObV0tov3dusVZdqhJfCIB5HhDXXg6hnTWrRQWT94mFnzUxrB93tsnBLt97rE9FqbrH5mHNlVPRcbl3GPbhZq57XHqwLS9w5NWvVXzRr1Y80a9WXkmo59ic1q1mNtPObjHa2cxj9P208xnxLW5ve/pkYax29prU3p1mq/3ER0FodsdpHDWn9fVZnx6MlKP01TYLxvK75pM9PS6ut47HNWvUrzVr1ri6X9pf0vTiD1PzqGaQT0KmkYbwgz8gJ43ldnfux1mdjMvZj04vmBZOl9T+7pFmrHtGsVW/q0lxgzP9Zs1ad06xVv9GsVQ9q1qqbkQLl8aSQ9TJSh9zJcF/xe6z9GCz9vmwi2q+kLjYkZ9H+uNXHoNbx+4ZcHRm7bPd0UmXDaqTOi+2W5TPYz+NI+/exW3OybtPGukq00jJcD56Xj3F572WMXm24vmNaa3D6TbstWLRZfs4ylq3VCenVy7ieiWh/Pb/tMc8rJ7LCZq36RLNWvZA0UgGkA/mSxuxcZs00Qklr2L9dx5h1rGlj+SujAazX+LyQOtJ0cx2jHU2W5X/cavYRKvXGCyr1RmB0zNWJNAmBwXlN/TTW62pN+0N7e2tGvxddvxNFh6lnjrXRot1764rN4aT3ZyNSbfh3ei03AeN5XRPajxV2HmPaeLT2Y0NM7l39lvQ/m0Lvz3FXzVr1L81a9ShG2wzvsfTFm5DW6DybV3qMV15J9yzYYRLK0h70ejXzaXXue13R6T3HaDjj0RotZc+O4QxbfZsm/Bns83GkfX+7+xjztY65D2Rq97/CMVwPns0YvaXrk4q2ZK3hem5p1qqdnRl/X/w+oMd6P0gaT3VZtDqobV2pN965jOsar/aOb4sNOl+cNHy824KVemO4sw1eh/aezF3vQNYHrdEfDqzUG8/onFhJd4x7x9KsuKi5PLd4+I7O3vHF+rdidDi8zuUfZfQg/ZFKvbHZWNur1BtdO6M009CNrc4sb2a01vqmZq3a6wSpq0F5TX3W63UFRl/XOR2TW9+LXjdiOH6c2271+n8No00WlrUjY8sHi9FaFlGpN3ZjtENX5+tq7cc2rtQbi4XoSropTmfb2QkpOlVfVTz8bKXeeOpY82e0pP/ZO0h36FtMcdVyLK192WTtxy4ljQwEo8elTu+nz/0XihOS9o7MXWuhm7XqL4CbSBUp55DaMi9rR8Ylaqa7+7ZO5o5re/7/SP1/AD5T6T4m+pO67Jf6chwp9ret7+Tbu4y+RaXe2Jh0gysYPTlXB9tcD55/Al8vLtF+u1mrPlE0jfgvRmt7uoXJs0hnmq+q1BufIg2D81Bx0P4AqVf1g6QmEUulWateUak3ziZd3jyxCCpfbQ3dV2zrNcCLmrXqoUu7nQ63ktqwbUZqvnFI6zJeJd2h6kTS6CLdzAAuK9rkNYA/tpokVOqNbYETivkeZexe1zmdSLqJxEakMZoPA65q1qojlXSjgG+ybN/Lz5Fu5DMd+Gml3qg1a9Xri4PQHqSbFTxGujlBN0eThtnaGPh1pd74KPDDZnHnsqJj4i6k9tSr0/tujN8m1TAexGht0tIO6zYor2lJntItJHd4pEu776mk13V4M41RO4X0XT6ZdEJ8Nx03gyEN2fYm4OOVeuM24MKiF/5MUjv315NqgXt9NwAo3scbSDWMuW+mUQEalXrjXc00BvQwaf/QWv+NpLbi7X7FaJv90yv1xptIgWkK6X/0NfJUCr2XNGTelqS7c34IuKxZq84r+nBsTxrR47xi6MAcLiadLO1dqTf+H2kc7kcr6YYg7yK1C36A0aZ/7U4sAtg5pBswzYYna4ffxGhN7LiGw1lWRbk/T/qs1Sr1xoPA8c1adU5R4fEuUhOVJX4Gl1bxWT+a0Rrac4ohKHv5BunGJbsUj5e1I+N4fZY0atWLK/XGXs1atTXc4rtJn8FnAdcU+6VLWvuHorP9K0hNtW5n0ZPKfh5HPks6YVmPNC714c3iRlKVNOJWnZQj5jD+k/iVjjXXg+drpEul3ySNajGHFC5bY09+ulmrnt9ludMYHST/E8CDxbKzgY+SwvXvuyw3UYeSDoirFOu9u1Jv/LPYud5H+uJluxTYTON7H0Fqi7k1cH2l3ni0Um88SjoQB0bHQe7m6aQagxuBJyr1xgOVeuNfpPdiV1KTgbd0acfdF81a9R+kA+wjpMv2VwKPVOqNh0lNGGay6NjDE+qA10w3HziY1FN9O+C6Sr3xECngXkIKch8YY/km6ZLfn0lh9AzSZ+mBSr3xCOnzdB4pJI21/zib9N5OJwWlhSxlU4MBek1LchDpOzDWT7fapLeT2j1fW3wOHiENdbU56YT437p0Uvs4qXPoWqT2pI8X38HbSe/Vx0h3sxyP9vFwc3ZkPITUlO1PRdkeId0wZj3SPu11ne3vi+/720kjnQTSd+IR0v/6ClJgOGJZC9asVX9H6nfxT2Ab0pB8j1bqjftJtcDXFuVYc1m31eYM0nCqkIL0w8U++gFSSL2Y3mMTTyV1ZDwPuLdSbzxcSTc+erhYZhrwC3qPOd0PX2C0BvVDwH3F6/kHKXR9l3QXUZjgfqzDIuNIFz8PkT7rhxXzXMmSr2icwaLNRvrdJASAZq3aIN0MCtL/vfX8TaQRlP5OutnMD0nHgvsr9cZjpBE5zqBLU6F+HkeKyrLXkL4bW5NOPh8p9pW/IDUvfRB4zRJOZlZqhuvBM5dUa3U06TajTyF9yC8Dqs1ateu4lUXHmCqpjdefivWMkC7f7dGsVccaqH7cmrXqY81a9QDSeNPnk4YCWpUUfv9AGn2g240plmWbPybVNjRIX+ph4H7SKCI7NGvVy3oseg/pxiVfJvUgb5IOlvNJw5B9FdimWauO64YmuRSXKLcllf9vpNfzIKlX+/akcVlbJnx5vtiZb08KuLNJB957SbUdzye1qxtr+VuL8r2d9Pm5n3TDiCnAbaSAdDijJ3zd1vEAi96O+LJmrbqkYbvGKlPpr6mPfkMaweYM0nd9mPTZrQPPbdaqne2SadaqdxbLnMLocFxPkGrIXtWsVT83ge2fx+ioA9luAd2sVS8gdUb+flG2KaT/05eA7Xq11WzWqpeQQvmPSUFtiFR7fzzpxH2xG1ssZfkuJdVcf4bUDvpx0hB495BO2t7O2GN1T3R780hXUD5FOtGbR3pPrgXeSdpX9Rrl5DjSnfrOJ+3f55P2ZbNJJ2JvA3ZtLv3QghNWnBi9nhRwryW9f8OkyqHDmrXqmxm9UroszYy6DVc3jRQ+LyBVrryidSVqjPI+xOhIRX3ryNjDZ4vfL6iku3i2yvRLUs31B0lNlVpXlxeQrtp+h3TVrtudPft2HGnWqj8nhegvFeVYhfRZvZV0Ff05zVr16t5r0JSREUdyGQSVeuNK4OXAp5q16ifLLY3KVKk3aqRaldubtepi7em0/Cs6gbXC5cxmrXpHiWU5gBSwHwc2Xpb21pV6Y1eKK2jNNO6vVlJF86a7SM3z3tysVZflbp85yvMU0onT+sDbm7XqpNRcl8XjSLmsuZYGSNEBrFVLcfFY80qZvLv4fVamjowSpI7MM0i17LnarS+Lg0nB+iH63JGxbB5HymeHRmmSVeqNg0jtPM8G/tysVecWnb1eTLr0vRXpMvoJvdciLbtKvXE46YrZQha9NbS0RJV64yxSs58ri/GkqaR7BryV1PwF4Iyi30NpihE1WqN1nDRJHRn7yuPIYDNcS5PvaaQOZx8DRoqOSWsyOtrFXOCtxZB2UlaVeuOFpAPy2oy2if1as1a9ufdSUld7U9wcp+iEN49Fb/JzNWlIvlJU6o1fkDr3PY10pX4WafShFYHHkQFmuJYm34+BDUijlWxOGlFjHqn3+xXAV9whqo9WJX3uFpA+c6cz2uFKmoj3kAL284ENSeHuPtLoGGeThpOd13vxvptBGiHoAVKHwQ+vQE2fPI4MMDs0SpIkSZmsUDXXO++888gmm2xSdjEkSZK0Arv55pvvjzFu0G3aChWuN9lkE37wg84bfkmSJEn5hBDu7DXNofgkSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUyQrVoVGSJEkaj3nz5jFr1iyeeOKJrtNXXXVVZsyYwdSpUye0XsO1JEmSVjqzZs1irbXWYosttmDKlCmLTBsZGeGBBx5g1qxZzJw5c0LrtVmIJEmSVjpPPPEE66+//mLBGmDKlCmsv/76PWu1x2K4liRJ0kqpW7Aez7SxGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZJWSiMjI0s1bSyGa0mSJK10hoaGmDdvXs/p8+bNY3h44qNWG64lSZK00llnnXW49957Wbhw4WLTFi5cyL333svaa6894fV6ExlJkiStdKZPn86sWbOIMXadvsYaazB9+vQJr9dwLUmSpJXOKquswmabbZZ/vdnXKEmSJK2kDNeSJElSJoZrSZIkKRPDdUlG5vce+mVF3rYkSdKKzA6NJZkyPJXm1z5WyrYr7/pMKduVJEla0VlzLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpIkScrEcC1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKZPhfq04hHAqsC8wO8a4TZfpHwLe2FaO5wAbxBjnhBDuAB4GFgDzY4w79quckiRJUi59C9fAacCJwBndJsYYvwh8ESCE8Grg/THGOW2z7BZjvL+P5ZMkSZKy6luzkBjjVcCcJc6YHAyc1a+ySJIkSZOh9DbXIYTVgb2A77c9PQJcGkK4IYRweDklkyRJkiam9HANvBr4ZUeTkJfGGLcH9gaOCCHsUk7RJEmSpPEbhHB9EB1NQmKM9xS/ZwPnAzuVUC5JkiRpQkoN1yGEtYGXAxe0PbdGCGGt1t/AnsBN5ZRQkiRJGr9+DsV3FrArMD2EMAs4BpgKEGM8qZjttcClMcZH2xbdCDg/hNAq35kxxov7VU5JkiQpl76F6xjjweOY5zTSkH3tz90OPK8/pZIkSZL6ZxDaXEuSJEkrBMO1JEmSlInhWpIkScrEcC1JknfUCIkAAB+lSURBVCRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpIkScrEcC1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEyG+7XiEMKpwL7A7BjjNl2m7wpcAPy1eOoHMcZji2l7AScAQ8DJMcbj+1VOSZIkKZe+hWvgNOBE4Iwx5rk6xrhv+xMhhCHgq8AewCzguhDChTHGW/pVUEmSJCmHvjULiTFeBcxZikV3Am6LMd4eY5wLnA3sn7VwkiRJUh/0s+Z6PF4UQvg98DfggzHGm4FNgLvb5pkF7FxG4SRJkqSJKLND443A5jHG5wH/C/ywxLJIkiRJy6y0cB1jfCjG+Ejx90XA1BDCdOAeYNO2WWcUz0mSJEkDrbRmISGEpwH3xhhHQgg7kYL+A8CDwJYhhJmkUH0Q8IayyilJkiSNVz+H4jsL2BWYHkKYBRwDTAWIMZ4EvA54ZwhhPvA4cFCMcQSYH0I4EriENBTfqUVbbEmSJGmg9S1cxxgPXsL0E0lD9XWbdhFwUT/KJUmSJPWLd2iUJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpIkScrEcC1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpIkScpkuF8rDiGcCuwLzI4xbtNl+huBjwBTgIeBd8YYf19Mu6N4bgEwP8a4Y7/KKUmSJOXSz5rr04C9xpj+V+DlMcbnAscB3+yYvluMcTuDtSRJkpYXfau5jjFeFULYYozpv2p7eA0wo19lkSRJkibDoLS5PhT4SdvjEeDSEMINIYTDSyqTJEmSNCGlh+sQwm6kcP2RtqdfGmPcHtgbOCKEsEsphZMkSZImoNRwHULYFjgZ2D/G+EDr+RjjPcXv2cD5wE7llFCSJEkav9LCdQhhM+AHwH/EGP/c9vwaIYS1Wn8DewI3lVNKSZIkafz6ORTfWcCuwPQQwizgGGAqQIzxJOATwPrA10IIMDrk3kbA+cVzw8CZMcaL+1VOSZIkKZd+jhZy8BKmHwYc1uX524Hn9atckiRJUr+U3qFRkiRJWlEYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpIkScrEcC1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlMmEwnUIYZUQwlP7VRhJkiRpebbEcB1CODOE8NQQwhrATcAtIYQP9b9okiRJ0vJlPDXXW8UYHwJeA/wEmAn8R19LJUmSJC2Hhscxz9QQwlRSuD4xxjgvhDAynpWHEE4F9gVmxxi36TJ9CnACsA/wGPCWGOONxbRDgI8Xs346xnj6eLYpSZIklWU8NdffAO4A1gCuCiFsDjw0zvWfBuw1xvS9gS2Ln8OBrwOEENYDjgF2BnYCjgkhrDvObUqSJEmlWGLNdYzxf4D/aXvqzhDCbuNZeYzxqhDCFmPMsj9wRoxxBLgmhLBOCKEC7Ar8NMY4ByCE8FNSSD9rPNuVJEmSyrDEcB1C2Aj4LLBxjHHvEMJWwIuAUzJsfxPg7rbHs4rnej0vSZIkDazxNAs5DbgE2Lh4/Gfgff0qkCRJkrS8Gk+4nh5jPBdYCBBjnA8syLT9e4BN2x7PKJ7r9bwkSZI0sMYTrh8NIawPjACEEF4I/DPT9i8E3hxCmNJab4yxSaop3zOEsG7RkXHP4jlJkiRpYI1nKL4PkELwM0IIvwQ2AF43npWHEM4idU6cHkKYRRoBZCpAjPEk4CLSMHy3kYbie2sxbU4I4TjgumJVx7Y6N0qSJEmDajyjhdwYQng5EIAp6ak4bzwrjzEevITpI8ARPaadCpw6nu1IkiRJg2A8o4W8ueOp7UMIxBjP6FOZJEmSpOXSeJqFvKDt71WB3YEbAcO1JEmS1GY8zULe3f44hLAOcHbfSiRJkiQtp8YzWkinR4GZuQsiSZIkLe/G0+b6RxTD8JHC+FbAuf0slCRJkrQ8Gk+b6/9q+3s+cGeMcVafyiNJkiQtt8bT5vrnk1EQSZIkaXnXM1yHEB5mtDlIuynASIzxqX0rlSRJkrQc6hmuY4xrTWZBJEmSpOXdeNpcAxBC2JA0zjUAMca7+lIiSZIkaTk1ntFC9gO+BGwMzAY2B24Ftu5v0SRJkqTly3jGuT4OeCHw5xjjTNIdGq/pa6kkSZKk5dB4wvW8GOMDwCohhFVijFcAO/a5XJIkSdJyZzxtrh8MIawJXA18N4Qwm3SXRkmSJEltxlNzfQWwNvBe4GLgL8Cr+1koSZIkaXk0nnA9DFwKXAmsBZxTNBORJEmS1GaJ4TrG+KkY49bAEUAF+HkI4Wd9L5kkSZK0nBlPzXXLbODvwAPAhv0pjiRJkrT8Gs841+8CXg9sAHwPqMUYb+l3wSRJkqTlzXhGC9kUeF+M8Xf9LowkSZK0PFtiuI4xHjUZBZEkSZKWdxNpcy1JkiRpDIZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpIkScrEcC1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKZPhfq48hLAXcAIwBJwcYzy+Y/qXgd2Kh6sDG8YY1ymmLQD+WEy7K8a4Xz/LKkmSJC2rvoXrEMIQ8FVgD2AWcF0I4cIY4y2teWKM72+b/93A89tW8XiMcbt+lU+SJEnKrZ/NQnYCbosx3h5jnAucDew/xvwHA2f1sTySJElSX/WzWcgmwN1tj2cBO3ebMYSwOTATuLzt6VVDCNcD84HjY4w/7FdBJUmSpBwGpUPjQcB5McYFbc9tHmPcEXgD8JUQwjPKKZokSZI0Pv0M1/cAm7Y9nlE8181BdDQJiTHeU/y+HbiSRdtjS5IkSQOnn+H6OmDLEMLMEMI0UoC+sHOmEMKzgXWBX7c9t24I4SnF39OBlwC3dC4rSZIkDZK+hesY43zgSOAS4Fbg3BjjzSGEY0MI7cPqHQScHWMcaXvuOcD1IYTfA1eQ2lwbriVJkjTQ+jrOdYzxIuCijuc+0fH4k12W+xXw3H6WTZIkScptUDo0SpIkScs9w7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpIkScrEcC1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSBsLcBXNXym1LklYsw2UXQJIApg1NY+8LDi5l2z/Z/6xStitJWvFYcy1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpIkScrEcC1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKZPhfq48hLAXcAIwBJwcYzy+Y/pbgC8C9xRPnRhjPLmYdgjw8eL5T8cYT+9nWSVJkqRl1bdwHUIYAr4K7AHMAq4LIVwYY7ylY9ZzYoxHdiy7HnAMsCMwAtxQLPuPfpVXkiRJWlb9bBayE3BbjPH2GONc4Gxg/3Eu+yrgpzHGOUWg/imwV5/KKUmSJGXRz2YhmwB3tz2eBezcZb4DQgi7AH8G3h9jvLvHspv0q6CSJElSDmV3aPwRsEWMcVtS7bTtqiVJkrTc6me4vgfYtO3xDEY7LgIQY3wgxviv4uHJwA7jXVaSJEkaNP0M19cBW4YQZoYQpgEHARe2zxBCqLQ93A+4tfj7EmDPEMK6IYR1gT2L5yRJkqSB1bc21zHG+SGEI0mheAg4NcZ4cwjhWOD6GOOFwHtCCPsB84E5wFuKZeeEEI4jBXSAY2OMc/pVVkmSJCmHvo5zHWO8CLio47lPtP19FHBUj2VPBU7tZ/kkSZKknMru0NhXI/MXrJTbliRJUjn6WnNdtinDQ9z39e+Usu0N3vmmUrYrSZKk8qzQNdeSJEnSZDJcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpKkATR3wcKVctvS8m647AJIkqTFTRtahQO//+dStn3OAc8qZbvSisCaa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLklZKcxcsWKm3L6k/HIpPkrRSmjY0xGu/f0Vp2z//gN1K27ak/rHmWpIkScrEcC1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJk2Th/JGVctsrk+GyCyBJkrSyWGV4Cv934r2lbHvLIzcqZbsrm76G6xDCXsAJwBBwcozx+I7pHwAOA+YD9wFvizHeWUxbAPyxmPWuGON+/SyrJEmStKz6Fq5DCEPAV4E9gFnAdSGEC2OMt7TN9ltgxxjjYyGEdwJfAA4spj0eY9yuX+WTJEmScutnzfVOwG0xxtsBQghnA/sDT4brGOMVbfNfA7ypj+WR+m7+grkMD01babcvSdLKrp/hehPg7rbHs4Cdx5j/UOAnbY9XDSFcT2oycnyM8Yf5iyjlNTw0jVNP37O07b/tkEtL27YkSRqQ0UJCCG8CdgS+2Pb05jHGHYE3AF8JITyjlMJJkiSpdCPzFywX2+5nzfU9wKZtj2cUzy0ihPBK4GPAy2OM/2o9H2O8p/h9ewjhSuD5wF/6WF5JkiQNqCnDQ8z+38tK2faG79593PP2M1xfB2wZQphJCtUHkWqhnxRCeD7wDWCvGOPstufXBR6LMf4rhDAdeAmps6MkSZI0sPrWLCTGOB84ErgEuBU4N8Z4cwjh2BBCa1i9LwJrAt8LIfwuhHBh8fxzgOtDCL8HriC1ub4FSSrB3AXzV+rtS5LGr6/jXMcYLwIu6njuE21/v7LHcr8CntvPsknSeE0bGmaf8z9f2vYveu1HStu2JGliBqJDoyRJkrQiMFxLK4n5C+au1NuXJGky9LVZiKTBMTw0jU+f86rStv/xAy8pbdsqz9wFC5g2NLTSbl/SysdwLUnqm2lDQ7z6vO+Xtv0fve6A0rat8ixYMMLQ0JSVbtsaDIZrSZK0QhkamsLl372vlG2/4o0blLJdDQ7bXEuSJEmZGK61mIXzy+14Vvb2JUmSlpbNQrSYVYan8duTXl3a9p//jh+NOX3B/LkMDU+bpNIMzrYlSdLgM1xruTM0PI2LTtmnlG3vc+hFS55JkiSttGwWIkmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkaULmLxhZqbcvjcXRQiRJ0oQMD03hmz+YXdr2D/+3DUvbtrQk1lxLkiRJmRiuJWk5N3fB/JVy25LyGpm/cKXefi42C5Gk5dy0oWH2/f4ppWz7xwccWsp2JeU3ZXgV/v7fN5e2/ad9YOvStp2TNdeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZWK4liRJkjIxXEuSJEmZGK4lSZKkTAzXkiRJUiaGa0mSJCkTw7UkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15IkSVImhmtJkiQpE8O1JEmSlInhWpIkScrEcC1JkiRlYriWJEmSMjFcS5IkSZkYriVJkqRMDNeSJElSJoZrSZIkKRPDtSRJkpSJ4VqSJEnKxHAtSZIkZTLcz5WHEPYCTgCGgJNjjMd3TH8KcAawA/AAcGCM8Y5i2lHAocAC4D0xxkv6WVZJkiRpWfWt5jqEMAR8Fdgb2Ao4OISwVcdshwL/iDE+E/gy8Pli2a2Ag4Ctgb2ArxXrkyRJkgZWP5uF7ATcFmO8PcY4Fzgb2L9jnv2B04u/zwN2DyFMKZ4/O8b4rxjjX4HbivVJkiRJA6uf4XoT4O62x7OK57rOE2OcD/wTWH+cy0qSJEkDZcrIyEhfVhxCeB2wV4zxsOLxfwA7xxiPbJvnpmKeWcXjvwA7A58Erokxfqd4/hTgJzHG85awzfuAO/vwciRJkqSWzWOMG3Sb0M8OjfcAm7Y9nlE8122eWSGEYWBtUsfG8Sy7mF4vUpIkSZoM/WwWch2wZQhhZghhGqmD4oUd81wIHFL8/Trg8hjjSPH8QSGEp4QQZgJbAtf2saySJEnSMutbuC7aUB8JXALcCpwbY7w5hHBsCGG/YrZTgPVDCLcBHwA+Wix7M3AucAtwMXBEjHFBv8oqSZIk5dC3NteSJEnSysY7NEqSJEmZGK4lSZKkTPp6+/Pl1ZJu216mEMKpwL7A7BjjNmWXp10IYVPS7ew3AkaAb8YYTyi3VEkIYVXgKuAppM/9eTHGY8ot1aKKu5BeD9wTY9y37PK0CyHcATwMLADmxxh3LLVAbUII6wAnA9uQPndvizH+utxSJSGEAJzT9tTTgU/EGL9SUpEWEUJ4P3AY6X37I/DWGOMT5ZYqCSG8F6gBU4B62e9Zt31vCGE90v93C+AO4PUxxn8MSNn+nTSs7XOAnWKM1092uZZQvi8CrwbmAn8hffYeHJCyHUe6md1CYDbwlhjj3ya7bL3K1zbtP4H/AjaIMd4/CGULIXyS9L29r5jt6BjjRYNQtuL5dwNHkI5ljRjjh/uxfWuuO4zztu1lOo10S/hBNB/4zxjjVsALgSMG6L37F/CKGOPzgO2AvUIILyy5TJ3eS+r8O6h2izFuN0jBunACcHGM8dnA8xig9zAm28UYtwN2AB4Dzi+5WACEEDYB3gPsWBx8hkijOpUuhLAN6QC9E+l/um8I4ZnllqrrvvejwGUxxi2By4rHZTiNxct2E/BvpEqFsp3G4uX7KbBNjHFb4M/AUZNdqMJpLF62L8YYty2+tz8GPjHppRp1Gl2O+UVl1p7AXZNdoDan0T2PfLm13ysjWBdOo6NsIYTdSCdNz4sxbk06MekLw/XixnPb9tLEGK8C5pRdjm5ijM0Y443F3w+TQs5A3FkzxjgSY3ykeDi1+BmY3rwhhBlAlVQDq3EKIawN7EIaeYgY49wyar/GaXfgLzHGQbrR1TCwWnGfgdWBUmrnungO8JsY42PFyFM/JwXF0vTY9+4PnF78fTrwmkktVKFb2WKMt8YYYxnl6dSjfJcW/1uAa0j3s5h0Pcr2UNvDNSjxWDHGMf/LwIcZzLKVrkfZ3gkcH2P8VzHP7H5t33C9OG+9nkEIYQvg+cBvSi7Kk0IIQyGE35Eu8/00xjgwZQO+QtpRLiy7ID2MAJeGEG4IIRxedmHazCRdfvxWCOG3IYSTQwhrlF2oHg4Cziq7EC0xxntINTd3AU3gnzHGS8st1ZNuAl4WQlg/hLA6sA+L3lhsUGwUY2wWf/+d1CROE/c24CdlF6JdCOEzIYS7gTdSbs31YkII+5OaD/6+7LL0cGQI4Q8hhFNDCOuWXZg2zyLtV34TQvh5COEF/dqQ4VrZhRDWBL4PvK+jBqBUMcYFxWW+GcBOxaXn0oUQWu3Cbii7LGN4aYxxe1JzqSNCCLuUXaDCMLA98PUY4/OBRynv0nxPxY209gO+V3ZZWoqD3v6kE5SNgTVCCG8qt1RJjPFW4PPApaR7HfyO1EZyYBU3QBuYq2HLixDCx0hNCr9bdlnaxRg/FmPclFSuI8suT0txsnk0Axb423wdeAap+WUT+FK5xVnEMLAeqdnqh4BzQwhT+rEhw/XilurW60pCCFNJwfq7McYflF2ebopmA1cwOG3XXwLsV3QaPBt4RQjhO6WWqENRy9m6jHY+qfnUIJgFzGq7CnEeKWwPmr2BG2OM95ZdkDavBP4aY7wvxjgP+AHw4pLL9KQY4ykxxh1ijLsA/yC1yx0094YQKgDF775dZl4RhRDeQup09sbi5GQQfRc4oOxCtHkG6YT498UxYwZwYwjhaWUWqiXGeG9RkbUQqDM4xwpIx4sfFM1EryVdKZ7ejw0Zrhc3ntu2q4viDPAU4NYY43+XXZ52IYQNilElCCGsBuwB/KncUiUxxqNijDNijFuQPm+XxxgHogYRIISwRghhrdbfpE40N5VbqiTG+Hfg7mJUDkjtmm8psUi9HMwANQkp3AW8MISwevHd3Z0B6gwaQtiw+L0Zqb31meWWqKsLgUOKvw8BLiixLMuVYlSuDwP7xRgfK7s87UIIW7Y93J8BOVYAxBj/GGPcMMa4RXHMmAVsX+wLS9c62Sy8lgE5VhR+COwGEEJ4FjAN6MsoK96hsYsQwj6kNrBDwKkxxs+UXKQnhRDOAnYlnW3dCxwTYzyl1EIVQggvBa4mDenVajtcyjA8nUII25I6HA2RTirPjTEeW26pFhdC2BX44CANxRdCeDqjI1wMA2cO2HdiO1JH0GnA7aQhvSZ9OLReihOSu4Cnxxj/WXZ52oUQPgUcSLos/1vgsFZnn7KFEK4G1gfmAR+IMV5WcnkW2/eSDtbnApsBd5KG4pv0Dl49yjYH+F9gA+BB4HcxxldNdtnGKN9RpKFRHyhmuybG+I4BKds+QCAdx+4E3tG6ejcI5Ws/5he11zuWNBRft/duV1KTkBHS8JRvb+uXUHbZvg2cWpRvLulYe3k/tm+4liRJkjKxWYgkSZKUieFakiRJysRwLUmSJGViuJYkSZIyMVxLkiRJmRiuJUmSpEwM15I0oEIIW4QQBukmDIQQdg0h/LjHtCtDCDtOdpkkaZAYriVpORZCGCq7DJKkUcNlF0CSNKbhEMJ3ge3/f3v38xpXFcZh/Mmiu26KBZssVOzirWJKLIKN6dZdaenG1mpFBUFaS91U1+6kuxaqCylW1JLShPg/2LgICEGo8etC8AdugghSF1mYuLiDXCaaRHMDk/H5wMA598577mE2886Zc+8L3ANeoinxfht4FrgCTLcDqmoMaFdGHQceBUZoKpTtB5Zpqln+UFU3gd+Ap4ADwFtJZjaY096qmgGeAL4EXkzyV0WyqnodOJjkcq//Mk0VuTf+ywcgSbuJK9eSNNgKeC/JYzQJ8Pne8V+SHEky3R+Q5OckE0kmgA+A2STf05TD/ijJYeBT4ForbBQ4BhwH3t1kTk8CbwKP0yTtU33nZ4FTrf5p+n4ASNKwMrmWpMH2Y5L5XvsTmgQYmpXrDVXVFPAa8Grv0CRwq9f+uDUWwGdJVpN8DTy4ydALSX5KsgosAo+0TyZZBr6rqqNV9QBwCJhfP4wkDR+3hUjSYFv7h/7vGwVV1ShwAziR5P4WrrPSao/8i/f+wd9/l0wDzwHfAHPtbSOSNMxcuZakwfZQVU322meBu5sFVNUe4A7wdpJvW6e+AM702i8An3c50T5zwEngedwSIul/xORakgZbgAtVtQTsA97fQswzNDcnvlNVi73XGHAReKWqvgLOAZd2bNLJr8AS8HCShZ26jiQNmpG1Nf+pkyRJkrrgyrUkSZLUEW9olKRdrKqus/5ReFeTfLjNccdpnijStpLk6e2MK0nDzm0hkiRJUkfcFiJJkiR1xORakiRJ6ojJtSRJktQRk2tJkiSpIybXkiRJUkf+BJbc1NETWq7ZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 연령대별 구매 총 금액\n",
        "plt.figure(figsize=[12,8])\n",
        "plt.text(s=\"purchasing power\",x=0,y=1.3, va='bottom',ha='center',color='#189AB4',fontsize=25)\n",
        "plt.pie(pdde_cust['buy_am'].groupby(pdde_cust['ages']).sum(), autopct='%1.1f%%', pctdistance=1.1)\n",
        "plt.legend(['20s', '30s', '40s', '50s', '60s', '70s'], loc = \"upper right\",title=\"Ages\", prop={'size': 15})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "1Wh5cIb7bi22",
        "outputId": "66a24bfa-20b7-4dc1-be31-db30db6776df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성별에 따른 구매 총 금액\n",
        "plt.figure(figsize=[12,8])\n",
        "plt.text(s=\"purchasing power\",x=0,y=1.3, va='bottom',ha='center',color='#189AB4',fontsize=25)\n",
        "plt.pie(pdde_cust['buy_am'].groupby(pdde_cust['ma_fem_dv']).sum(), autopct='%1.1f%%', pctdistance=1.1)\n",
        "plt.legend(['Man', 'Woman'], loc = \"upper right\",title=\"Ages\", prop={'size': 15})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "XZ7uwKqKonXa",
        "outputId": "0ebd6645-70b2-46d8-e506-1ec4d00fd532"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성별/연령대 별 구매 총 금액\n",
        "tem = pd.DataFrame({'values' : pdde_cust.groupby(['ma_fem_dv', 'ages']).buy_am.sum()})\n",
        "tem['ma_fem_dv'] = np.array(['M'] * 6 + ['W'] * 6)\n",
        "tem['ages'] = np.array(['20s', '30s', '40s', '50s', '60s', '70s'] * 2)\n",
        "\n",
        "plt.figure(figsize=[12,8])\n",
        "sns.barplot(x='ages', y='values', hue='ma_fem_dv', data=tem)\n",
        "plt.title('purchasing power', fontsize=25, color='#189AB4')\n",
        "plt.legend(fontsize=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "Kb3KqVnljjCg",
        "outputId": "2a852fa7-74f4-4ae8-8e2c-a94df42def2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성별/연령대 별 상품 당 금액 소비량\n",
        "tem = pd.DataFrame({'values' : pdde_cust.groupby(['ma_fem_dv', 'ages']).buy_am.mean()})\n",
        "tem['ma_fem_dv'] = np.array(['M'] * 6 + ['W'] * 6)\n",
        "tem['ages'] = np.array(['20s', '30s', '40s', '50s', '60s', '70s'] * 2)\n",
        "\n",
        "plt.figure(figsize=[12,8])\n",
        "sns.barplot(x='ages', y='values', hue='ma_fem_dv', data=tem) \n",
        "plt.title('purchasing power of a product unit', fontsize=25, color='#189AB4')\n",
        "plt.legend(fontsize=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "Vdfa53OBn7j5",
        "outputId": "54e15056-b692-4b12-d0ae-d549760ad798"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 연령대별 상품 구매 개수\n",
        "plt.figure(figsize=[12,8])\n",
        "plt.text(s=\"PDDE Count\",x=0,y=1.3, va='bottom',ha='center',color='#189AB4',fontsize=25)\n",
        "plt.pie(pdde_cust['ages'].value_counts(), autopct='%1.1f%%', pctdistance=1.1)\n",
        "plt.legend(['40s', '30s', '50s', '60s', '20s', '70s'], loc = \"upper right\",title=\"Ages\", prop={'size': 15})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "ZqCm_ZBdbuuV",
        "outputId": "646d8db6-f213-417e-8344-dba4b1070e07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 연령대별 Lpay 상품 구매 개수\n",
        "plt.figure(figsize=[12,8])\n",
        "plt.text(s=\"Lpay Count\",x=0,y=1.3, va='bottom',ha='center',color='#189AB4',fontsize=25)\n",
        "plt.pie(lpay_cust['ages'].value_counts(), autopct='%1.1f%%', pctdistance=1.1)\n",
        "plt.legend(['40s', '30s', '50s', '20s', '60s', '70s'], loc = \"upper right\",title=\"Ages\", prop={'size': 15})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "HFTZJZhRcX06",
        "outputId": "dea66559-5bbe-415a-bf0c-47a446e02117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성별에 따른 상품 구매 개수\n",
        "plt.figure(figsize=[12,8])\n",
        "plt.text(s=\"PDDE Count\",x=0,y=1.3, va='bottom',ha='center',color='#189AB4',fontsize=25)\n",
        "plt.pie(pdde_cust['ma_fem_dv'].value_counts(),autopct='%1.1f%%', pctdistance=1.1, colors = ['orange', 'blue'])\n",
        "plt.legend(['Woman', 'Man'], loc = \"upper right\",title=\"Sex\", prop={'size': 15})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "8i2EAAe1Z-NH",
        "outputId": "974b8ec7-b02c-4f9c-fe5c-4a13fd4cd6e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lpay 상품 구매 개수\n",
        "plt.figure(figsize=[12,8])\n",
        "plt.text(s=\"Lpay Count\",x=0,y=1.3, va='bottom',ha='center',color='#189AB4',fontsize=25)\n",
        "plt.pie(lpay_cust['ma_fem_dv'].value_counts(),autopct='%1.1f%%', pctdistance=1.1, colors = ['orange', 'blue'])\n",
        "plt.legend(['Woman', 'Man'], loc = \"upper right\",title=\"Sex\", prop={'size': 15})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "EixrpqEfbF06",
        "outputId": "bc744327-4350-458c-d23b-5eb8ec4e9515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성별/연령대별 상품 구매 개수\n",
        "tem = pd.DataFrame({'values' : pdde_cust.groupby(['ma_fem_dv', 'ages']).buy_am.count()})\n",
        "tem['ma_fem_dv'] = np.array(['M'] * 6 + ['W'] * 6)\n",
        "tem['ages'] = np.array(['20s', '30s', '40s', '50s', '60s', '70s'] * 2)\n",
        "\n",
        "plt.figure(figsize=[12,8])\n",
        "sns.barplot(x='ages', y='values', hue='ma_fem_dv', data=tem) \n",
        "plt.title('PDDE Count', fontsize=25, color='#189AB4')\n",
        "plt.legend(fontsize=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "2lFHwAK1p90R",
        "outputId": "cb717537-b7be-4ffd-853a-4627e4079eae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성별/연령대별 Lpay 상품 구매 개수\n",
        "\n",
        "tem = pd.DataFrame({'values' : lpay_cust.groupby(['ma_fem_dv', 'ages']).buy_am.count()})\n",
        "tem['ma_fem_dv'] = np.array(['M'] * 6 + ['W'] * 6)\n",
        "tem['ages'] = np.array(['20s', '30s', '40s', '50s', '60s', '70s'] * 2)\n",
        "\n",
        "plt.figure(figsize=[12,8])\n",
        "sns.barplot(x='ages', y='values', hue='ma_fem_dv', data=tem) \n",
        "plt.title('Lpay Count', fontsize=25, color='#189AB4')\n",
        "plt.legend(fontsize=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "2EWEsuy9qN10",
        "outputId": "f5c976d9-1884-46be-807e-8aff1b7e5809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 전처리"
      ],
      "metadata": {
        "id": "c2r05MinOGYx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 날짜 자료형 변환 함수\n",
        "def make_datetime(df, drop=True):\n",
        "    df[\"datetime\"] = df[\"de_dt\"].apply(str).str[:4] \\\n",
        "                                    + \"-\" + df[\"de_dt\"].apply(str).str[4:6] \\\n",
        "                                    + \"-\" + df[\"de_dt\"].apply(str).str[6:8] \\\n",
        "                                    + \" \" + df[\"de_hr\"].apply(lambda x: str(x).zfill(2))\n",
        "    df[\"datetime\"] = pd.to_datetime(df[\"datetime\"])\n",
        "    if drop : df = df.drop([\"de_dt\", \"de_hr\"], axis=1)\n",
        "    return df\n",
        "\n",
        "\n",
        "# 맵핑 함수\n",
        "def class_mapping(df, col, verbose=True):\n",
        "    class_map = {lbl:idx for idx, lbl in enumerate(sorted(df[col].unique()))}\n",
        "    if verbose : print(class_map)\n",
        "    return df[col].map(class_map)"
      ],
      "metadata": {
        "id": "lx6eoHd6PY5_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 램을 효율적으로 쓰기 위해 데이터 전처리 작업을 분할해서 진행\n",
        "# 영수증번호, 고객코드, 시간 데이터 / 영수증번호 중복 제거\n",
        "format_df = pd.read_parquet(path+\"/LPOINT_BIG_COMP_PDDE_CUST.parquet\", \n",
        "                            columns=[\"cust\", \"rct_no\", \"de_dt\", \"de_hr\"], engine=\"pyarrow\").drop_duplicates(\"rct_no\", keep=\"first\")\n",
        "format_df = make_datetime(format_df, drop=True)\n",
        "format_df = format_df.set_index(\"rct_no\")\n",
        "#print(format_df.head(5))\n",
        "\n",
        "\n",
        "# 영수증번호, 성별, 나이, 채널, 거주지, 제휴사 / 영수증번호 중복제거\n",
        "pdde_cust_df = pd.read_parquet(path+\"/LPOINT_BIG_COMP_PDDE_CUST.parquet\", \n",
        "                               columns=[\"rct_no\", \"ma_fem_dv\", \"ages\", \"chnl_dv\", \"zon_hlv\", \"cop_c\"], \n",
        "                               engine=\"pyarrow\").drop_duplicates(\"rct_no\", keep=\"first\").set_index(\"rct_no\")\n",
        "pdde_cust_df[\"ma_fem_dv\"] = class_mapping(pdde_cust_df, \"ma_fem_dv\")\n",
        "pdde_cust_df[\"chnl_dv\"] = class_mapping(pdde_cust_df, \"chnl_dv\")\n",
        "pdde_cust_df[\"ages\"] = class_mapping(pdde_cust_df, \"ages\")\n",
        "#print(pdde_cust_df.head(5))\n",
        "\n",
        "\n",
        "# 두 테이블을 조인\n",
        "format_df = format_df.join(pdde_cust_df)\n",
        "format_df[\"month\"] = format_df.datetime.dt.month\n",
        "format_df[\"dayofweek\"] = format_df.datetime.dt.dayofweek\n",
        "# 나이, 거주지, 제휴사, 달, 요일 원핫인코딩 진행\n",
        "format_df = pd.get_dummies(format_df,\n",
        "                           columns=[\"ages\", \"zon_hlv\", \"cop_c\", \"month\", \"dayofweek\"])\n",
        "\n",
        "#format_df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RppQPsuSQP5w",
        "outputId": "b05ef26f-6588-46ae-e836-7eb66aa89db9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'남성': 0, '여성': 1}\n",
            "{1: 0, 2: 1}\n",
            "{'20대': 0, '30대': 1, '40대': 2, '50대': 3, '60대': 4, '70대': 5}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 영수증 번호, 제품, 구매량 테이블\n",
        "clac_mcls_df = pd.read_parquet(path+\"/LPOINT_BIG_COMP_PDDE_CUST.parquet\", columns=[\"rct_no\", \"clac_mcls_nm\", \"buy_ct\"],engine=\"pyarrow\")\n",
        "# 피벗테이블로 만듬\n",
        "clac_mcls_df = pivot_table(clac_mcls_df, index=\"rct_no\", columns=\"clac_mcls_nm\", values=\"buy_ct\", aggfunc='sum', fill_value=0).astype(\"uint16\")\n",
        "clac_mcls_df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "YbdkaKL0QTWq",
        "outputId": "7b2219ba-f613-4a93-acf6-a0cb53db5173"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clac_mcls_nm  DVD/홈시어터  TV  가공유  가방세트  간식/안주형과자  간편조리  갑각류  거실가구  건강가전  \\\n",
              "rct_no                                                                   \n",
              "A01000001113         0   0    0     0         0     0    0     0     0   \n",
              "A01000002265         0   0    0     0         0     0    0     0     0   \n",
              "A01000003148         0   0    0     0         0     0    0     0     0   \n",
              "A01000004946         0   0    0     0         0     0    0     0     0   \n",
              "A01000005297         0   0    0     0         0     0    0     0     0   \n",
              "\n",
              "clac_mcls_nm  건강관리용품  ...  핸드/풋케어  햄/소시지  향수  향신료  헤어액세서리  헤어케어  호주산소고기  홈웨어  \\\n",
              "rct_no                ...                                                      \n",
              "A01000001113       0  ...       0      0   0    0       0     0       0    0   \n",
              "A01000002265       0  ...       0      0   0    0       0     0       0    0   \n",
              "A01000003148       0  ...       0      0   0    0       0     0       0    0   \n",
              "A01000004946       0  ...       0      0   0    0       0     0       0    0   \n",
              "A01000005297       0  ...       0      0   0    0       0     0       0    0   \n",
              "\n",
              "clac_mcls_nm  홍삼/인삼가공식품  화장지/티슈  \n",
              "rct_no                           \n",
              "A01000001113          0       0  \n",
              "A01000002265          0       0  \n",
              "A01000003148          0       0  \n",
              "A01000004946          0       0  \n",
              "A01000005297          0       0  \n",
              "\n",
              "[5 rows x 349 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd1b248d-b5e4-40be-bd95-622d04063067\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>clac_mcls_nm</th>\n",
              "      <th>DVD/홈시어터</th>\n",
              "      <th>TV</th>\n",
              "      <th>가공유</th>\n",
              "      <th>가방세트</th>\n",
              "      <th>간식/안주형과자</th>\n",
              "      <th>간편조리</th>\n",
              "      <th>갑각류</th>\n",
              "      <th>거실가구</th>\n",
              "      <th>건강가전</th>\n",
              "      <th>건강관리용품</th>\n",
              "      <th>...</th>\n",
              "      <th>핸드/풋케어</th>\n",
              "      <th>햄/소시지</th>\n",
              "      <th>향수</th>\n",
              "      <th>향신료</th>\n",
              "      <th>헤어액세서리</th>\n",
              "      <th>헤어케어</th>\n",
              "      <th>호주산소고기</th>\n",
              "      <th>홈웨어</th>\n",
              "      <th>홍삼/인삼가공식품</th>\n",
              "      <th>화장지/티슈</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rct_no</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A01000001113</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A01000002265</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A01000003148</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A01000004946</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A01000005297</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 349 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd1b248d-b5e4-40be-bd95-622d04063067')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd1b248d-b5e4-40be-bd95-622d04063067 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bd1b248d-b5e4-40be-bd95-622d04063067');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 저장\n",
        "processed_df = format_df.reset_index().set_index(\"rct_no\").join(clac_mcls_df)\n",
        "processed_df.to_parquet(path + \"LPOINT_BIG_COMP_CLAC_MCLS_SUM.parquet\", index=True, engine=\"pyarrow\", compression=None)"
      ],
      "metadata": {
        "id": "XlRcMsSTQbwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 로드\n",
        "processed_df = pd.read_parquet(path + \"LPOINT_BIG_COMP_CLAC_MCLS_SUM.parquet\", engine=\"pyarrow\")\n",
        "processed_df = processed_df.reset_index().set_index(\"cust\")\n",
        "processed_df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "cgSBQOGpQp0j",
        "outputId": "4defb2c0-dadf-44a1-a0ea-ac2073650a38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  rct_no            datetime  ma_fem_dv  chnl_dv  ages_0  \\\n",
              "cust                                                                       \n",
              "M430112881  A01000001113 2021-01-01 10:00:00          1        0       0   \n",
              "M430112881  A01000003148 2021-01-01 10:00:00          1        0       0   \n",
              "M430112881  A01000004946 2021-01-01 10:00:00          1        0       0   \n",
              "\n",
              "            ages_1  ages_2  ages_3  ages_4  ages_5  ...  핸드/풋케어  햄/소시지  향수  \\\n",
              "cust                                                ...                      \n",
              "M430112881       0       0       1       0       0  ...       0      0   0   \n",
              "M430112881       0       0       1       0       0  ...       0      0   0   \n",
              "M430112881       0       0       1       0       0  ...       0      0   0   \n",
              "\n",
              "            향신료  헤어액세서리  헤어케어  호주산소고기  홈웨어  홍삼/인삼가공식품  화장지/티슈  \n",
              "cust                                                           \n",
              "M430112881    0       0     0       0    0          0       0  \n",
              "M430112881    0       0     0       0    0          0       0  \n",
              "M430112881    0       0     0       0    0          0       0  \n",
              "\n",
              "[3 rows x 401 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-59cda542-cb2f-4964-952b-ccb9bb79da61\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rct_no</th>\n",
              "      <th>datetime</th>\n",
              "      <th>ma_fem_dv</th>\n",
              "      <th>chnl_dv</th>\n",
              "      <th>ages_0</th>\n",
              "      <th>ages_1</th>\n",
              "      <th>ages_2</th>\n",
              "      <th>ages_3</th>\n",
              "      <th>ages_4</th>\n",
              "      <th>ages_5</th>\n",
              "      <th>...</th>\n",
              "      <th>핸드/풋케어</th>\n",
              "      <th>햄/소시지</th>\n",
              "      <th>향수</th>\n",
              "      <th>향신료</th>\n",
              "      <th>헤어액세서리</th>\n",
              "      <th>헤어케어</th>\n",
              "      <th>호주산소고기</th>\n",
              "      <th>홈웨어</th>\n",
              "      <th>홍삼/인삼가공식품</th>\n",
              "      <th>화장지/티슈</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cust</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>M430112881</th>\n",
              "      <td>A01000001113</td>\n",
              "      <td>2021-01-01 10:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M430112881</th>\n",
              "      <td>A01000003148</td>\n",
              "      <td>2021-01-01 10:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M430112881</th>\n",
              "      <td>A01000004946</td>\n",
              "      <td>2021-01-01 10:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 401 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59cda542-cb2f-4964-952b-ccb9bb79da61')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-59cda542-cb2f-4964-952b-ccb9bb79da61 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-59cda542-cb2f-4964-952b-ccb9bb79da61');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "LaelutSLRXNg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 사용자 인덱스 모음\n",
        "custs = processed_df.index.unique()\n",
        "\n",
        "features = [col for col in format_df.columns if col not in [\"rct_no\", \"cust\", \"datetime\"]] + [\"date_interval\", \"date_from\"] + list(clac_mcls_df.columns)\n",
        "print(*custs[:400])\n",
        "print(*features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfBPRxHcRd5r",
        "outputId": "130ed309-dee4-4f2d-91dd-4c57b60f1a05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "M430112881 M646853852 M669384418 M886923095 M707991383 M117855953 M641274652 M625839441 M284446967 M401266932 M860973355 M686795542 M878181490 M704866277 M942878878 M174599398 M651841347 M577884977 M511106441 M531271959 M422047369 M234266219 M049345995 M497989990 M009032626 M931922533 M165312283 M996518031 M705629628 M184969427 M741760270 M243577483 M558692172 M377967939 M838446773 M506355556 M879094374 M732600894 M856124318 M263574190 M157204685 M650733020 M017256273 M641186030 M986125865 M232573809 M268811651 M162681947 M163175113 M440960058 M502480776 M191537184 M030477840 M690898061 M506965359 M725878700 M840551273 M746428121 M334943764 M561787985 M982559329 M807374636 M676262214 M988646555 M530294706 M900002349 M694209751 M123475113 M318802112 M038801652 M517637921 M539562667 M615380326 M609491987 M259613010 M338310215 M156930891 M419249862 M474747127 M624588650 M083434555 M049216782 M897579293 M316171414 M583031784 M370732403 M336360825 M835554931 M456838034 M586590453 M618317251 M956523130 M125895076 M053988830 M135902971 M331473499 M590533132 M977958641 M080584695 M048866855 M544570827 M752234615 M503639362 M397267143 M361261196 M781690028 M400682924 M819510622 M690515737 M366378636 M781279343 M238272817 M283308210 M289433991 M889933238 M891510784 M557567409 M467972035 M307151561 M567053439 M542006779 M538975514 M751728576 M471430471 M740718660 M970944841 M063704872 M736347559 M081078897 M478445642 M277171255 M555439355 M041303212 M867122864 M291380287 M355489415 M374870930 M966247621 M685408649 M849831487 M134888191 M436594332 M245811064 M207638686 M411045461 M062661758 M640653493 M072419092 M142311429 M056621722 M081569058 M501683336 M701304871 M398872489 M858126924 M013285074 M896896560 M533330698 M661395471 M594992791 M263406446 M182645944 M311201219 M593107613 M350838042 M298972946 M179653812 M263888884 M448609005 M282287857 M185122523 M185111231 M123783273 M953537328 M330277227 M991142381 M576689847 M738189511 M143027432 M153728920 M645976443 M762374565 M662330091 M577654091 M164780413 M544201308 M085265637 M732454229 M027441945 M519427263 M070544564 M226332649 M633282276 M205363338 M962965060 M984043972 M205485737 M117809504 M598228720 M183680726 M811221360 M830841749 M736029357 M201766112 M695297284 M863620095 M046113622 M038025013 M131789394 M526195887 M018184823 M317849762 M282651149 M686992522 M769600155 M729284172 M325661634 M702291596 M648254795 M253642334 M372939322 M088827294 M455824780 M642054815 M740316079 M281834288 M323702255 M589321117 M154631657 M910235569 M426611726 M321170502 M671806083 M596502154 M326503256 M696696054 M992067686 M523038535 M193455495 M985499664 M819862062 M024101254 M349334435 M083270072 M163037071 M744087833 M963110512 M927459944 M050037422 M540285266 M473840199 M098691956 M638623920 M716420925 M886716967 M743162991 M113763811 M565268465 M206689995 M881295041 M796142629 M463646981 M241023449 M915705833 M624880671 M851088731 M300709577 M138404016 M528097150 M148171146 M783908672 M688055546 M227598269 M994408992 M230976524 M855865931 M973279178 M955472914 M159375452 M588861009 M014339861 M425442290 M293971269 M763821861 M639610559 M790389301 M156626330 M691001733 M137293709 M768886877 M906632032 M962168384 M256210228 M848484390 M910085650 M479079032 M382044219 M408121877 M686863232 M671280935 M358798958 M012936022 M939459373 M228934898 M453663197 M586645156 M773977093 M329379634 M385378751 M943231214 M868511118 M652409781 M144266465 M946555429 M155118619 M172530920 M724735320 M118705305 M523807626 M545634703 M473125141 M567331707 M839993508 M153301641 M837797129 M668130552 M591942182 M830396092 M075995202 M681527141 M142065131 M794513392 M740635403 M876101498 M180581027 M938305951 M676674347 M941739760 M639909786 M805001563 M139230976 M199918823 M837947257 M439565447 M380362011 M675461091 M682413497 M410974362 M116478829 M320474582 M222290281 M402794395 M472417736 M749486808 M975646754 M511952367 M471728550 M111934771 M020136767 M656737591 M023577934 M009731870 M680825569 M852720148 M490359476 M512121304 M493122026 M789404870 M065151598 M348056935 M051581833 M496844384 M941198920 M979862567 M678205637 M546798182 M567637265 M323773246 M464594888 M232452691 M660057347 M635243433 M716073127 M649268943 M270037733 M209483390 M183296106 M236433557 M109733557 M254848060 M064408352 M228124869 M370260069 M812923328 M317778161 M310879031 M420179972 M239456864 M260108058 M205853827\n",
            "ma_fem_dv chnl_dv ages_0 ages_1 ages_2 ages_3 ages_4 ages_5 zon_hlv_Z01 zon_hlv_Z02 zon_hlv_Z03 zon_hlv_Z04 zon_hlv_Z05 zon_hlv_Z06 zon_hlv_Z07 zon_hlv_Z08 zon_hlv_Z09 zon_hlv_Z10 zon_hlv_Z11 zon_hlv_Z12 zon_hlv_Z13 zon_hlv_Z14 zon_hlv_Z15 zon_hlv_Z16 zon_hlv_Z17 cop_c_A01 cop_c_A02 cop_c_A03 cop_c_A04 cop_c_A05 cop_c_A06 month_1 month_2 month_3 month_4 month_5 month_6 month_7 month_8 month_9 month_10 month_11 month_12 dayofweek_0 dayofweek_1 dayofweek_2 dayofweek_3 dayofweek_4 dayofweek_5 dayofweek_6 date_interval date_from DVD/홈시어터 TV 가공유 가방세트 간식/안주형과자 간편조리 갑각류 거실가구 건강가전 건강관리용품 건강보조식품 건강진액 건강측정용품 건과일 건면 건어물 건해산물선물세트 격투기 견과류 계란류 고양이용품 골프 공구 공기청정/가습/제습 과일선물세트 과자세트 과채음료 교육보조재 교육완구 교환/정비용품 구강케어 국산과일 국산돼지고기 국산소고기 그릇/식기 기능성음료 기타 기타가구 기타고기류 기타구기/필드스포츠 기타국소고기 기타시즌스포츠 기타아웃도어/레저 기타애완동물 기타지갑 기프트 껌 꿀 나물류 낚시 난방가전 남성가방 남성골프의류 남성등산/아웃도어의류 남성속옷 남성스포츠화 남성양말류 남성의류상의 남성의류세트 남성의류아우터 남성의류전신 남성의류하의 남성일반스포츠의류 남성지갑 남성케어 남성특수목적의류 남성특수소재의류 남성패션액세서리 남성화 남아완구 남아의류상의 남아의류세트 남아의류아우터 남아의류전신 남아의류하의 남아특수목적의류 남아특수소재의류 냉동간편식 냉동과일 냉동포장생선류 냉방가전 냉장/냉동고 냉장간편식 냉장음료 냉장조리 노트 농구 농산물병통조림 닭고기류 동물병원 두부/콩나물류 두유 등산 디저트 떡 레토르트 렌탈서비스 렌탈수수료 맥주 메이크업 면도용품 모바일기기 모바일상품권 모바일액세서리 모자 미국산소고기 미술용품 미용소품 밀폐/보관용기 바디케어 반건생선 방향/제습/탈취제 배구 배드민턴 버섯류 베이커리 벨트 벽면/바닥용품 병통조림선물세트 보석 보수용품 보험 봉지라면 분말류 분말조리식 비스킷류 비식품테넌트 빵 뿌리채소 사무기기 사무용/학생용가구 사무용지/서식지 사탕/캔디 살충/방충제 생선회 생수 생활가전 생활서비스 생활요금상품판매/충전 서적 선식류 선케어 성인침구 세차용품 세탁/건조용품 세탁기 세탁세제 소모품 소스류 소주 소형디지털가전 속옷/양말/홈웨어세트 수납가구 수산물통조림 수산선물세트 수영/물놀이 수예소품 수예시즌소품 수예용품 수유/이유용품 수입과일 수입돼지고기 스낵류 스케이트/썰매 스키/보드 스킨케어 스포츠댄스 스포츠잡화 승용완구 시계 시공/DIY가구 시리얼 시트/쿠션/매트 식당 식용유류 실외놀이/스포츠완구 쌀 아이스크림 악기 안경/선글라스 안마/찜질용품 안전보호용품 안전용품 애견용품 액세서리편의용품 야구 야외용가구 양곡선물세트 양념육 양주 양채류 어류 어묵/맛살 여성가방 여성골프의류 여성등산/아웃도어의류 여성속옷 여성스포츠화 여성양말류 여성위생용품 여성의류상의 여성의류세트 여성의류아우터 여성의류전신 여성의류하의 여성일반스포츠의류 여성지갑 여성특수목적의류 여성특수소재의류 여성화 여아완구 여아의류상의 여아의류세트 여아의류아우터 여아의류전신 여아의류특수목적의류 여아의류하의 여아특수소재의류 여행/레저 여행용가방류 연체류 열매채소 영양제 오디오 오토바이 온장조리 와인 요구르트 욕실용품 용기라면 용기보증금 우산/양산류 우유 원두커피 원예 원예용품 웨이트 유가공품 유아간식 유아동가구 유아동속옷 유아동스포츠화 유아동식기 유아동양말류 유아동일반스포츠의류 유아동침구 유아동화 유아발육용품 유아스킨/바디케어 유아식 유아안전용품 유아위생용품 유아의류상의 유아의류세트 유아의류아우터 유아의류전신 유아의류하의 유아특수목적의류 육가공품 음료세트 음반/영상물 이미용가전 이용권/입장권 인라인/스케이트보드/킥보드 인스턴트커피 일반담배 일반문구/사무용품 일반의약외품 일반의약품 임대매출 임산부케어용품 잎채소 자동차음향/가전기기 자전거 잡곡 잡화균일가 장갑 장류 장식용품 잼 전기용품 전자/비디오게임 전자담배 전통과자류 전통주 전화기 정리용품 조개류 조리기구 조리도구 조명용품 조미료류 조미료세트 주류세트 주방가구 주방가전 주방세제 주방일회용품 주방정리용품/소모품 즉석반찬 즉석음료류 지류상품권 차류 차음료 채소선물세트 청소기 청소세제 청소용품 초콜릿 축구 축산물통조림 축산선물세트 침실가구 카메라/캠코더 카페트/러그/매트류 캐쥬얼가방 캠핑 커튼/블라인드류 커피/차세트 커피음료 컴퓨터/노트북 컴퓨터주변기기 탁구 탄산음료 테니스 특수담배 특수위생용품 특판 파이류 파티/팬시용품 패션액세서리 포장반찬 푸드코트 프리믹스 피트니스 필기도구 한방약재 해조류 해초류 핸드/풋케어 햄/소시지 향수 향신료 헤어액세서리 헤어케어 호주산소고기 홈웨어 홍삼/인삼가공식품 화장지/티슈\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 사람 별 영수증 묶음을 만드는 함수.\n",
        "# 사람을 입력하면 영수증 묶음 값이 출력되는 함수.\n",
        "# window = 영수증 묶음 개수, slide = 묶음별로 겹치는 개수\n",
        "def make_rct_tensor_by_cust(cust_idx, features=features, window=10, slide=5, dtype=torch.int16, drop=True):\n",
        "    global processed_df\n",
        "\n",
        "    cust_df = processed_df.loc[cust_idx]\n",
        "    rct_tensor_by_cust = []\n",
        "    rct_nos =[]\n",
        "    rct_length = len(cust_df)\n",
        "\n",
        "    if rct_length < window or not isinstance(cust_df, pd.core.frame.DataFrame): # 길이가 1인 시리즈는 None 반환\n",
        "        return None\n",
        "\n",
        "    # cust index는 버려진다. rct_no도 index가 되면서 버려질 것.\n",
        "    # 구입일 간격에 대한 feature들을 생성하기 위해 sorting한다.\n",
        "    cust_df = cust_df.set_index(\"datetime\").sort_values(\"datetime\") \\\n",
        "                 .reset_index().set_index(\"rct_no\")\n",
        "    cust_df[\"date_interval\"] = (cust_df.datetime - cust_df.datetime.shift(-1)).dt.days.fillna(-1) # 몇일만에 새로운 구입을 하였는지\n",
        "\n",
        "    cust_rcts = list(range(rct_length))\n",
        "    for i in cust_rcts[::slide]:\n",
        "        if rct_length-window < i: # 고객의 남은 영수증이 window개 이하인 경우 break\n",
        "            break\n",
        "        else:\n",
        "            temp = cust_df.iloc[i : i+window]\n",
        "            temp[\"date_from\"] = (temp.datetime.iloc[0] - temp.datetime).dt.days # 최근 구입일로부터 몇일만에 구입한 영수증인지.  ex) [2]: 01/22, [1]: 02/11, [0]: 02/13 \n",
        "                                                                                                                                # => [2]: 22,    [1]: 20,    [0]: 0\n",
        "            if not drop:\n",
        "                rct_no = list(temp.index)\n",
        "                rct_nos.append(rct_no)\n",
        "\n",
        "            temp = temp[features] # 피쳐 집합으로 원하는 피쳐를 골라 저장\n",
        "            temp = torch.tensor(temp.values, dtype=dtype) # window x features\n",
        "\n",
        "            rct_tensor_by_cust.append(temp)\n",
        "\n",
        "        \n",
        "    rct_tensor_by_cust = torch.stack(rct_tensor_by_cust, dim=0) # cust's rcts x window x features\n",
        "    if not drop:\n",
        "        return rct_tensor_by_cust, rct_nos\n",
        "    else:\n",
        "        return rct_tensor_by_cust"
      ],
      "metadata": {
        "id": "u3pWjtT4RgUt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(custs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSVDST9HR0Y4",
        "outputId": "3ffc2819-4962-4ebd-ba3e-5f6804de5cc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26917"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window = 10 # 한 텐서 안에 들어갈 \"최근 영수증 개수\"\n",
        "slide = 5 # 한 고객의 영수증 기록에서 window로 스캔 시의 간격\n",
        "chunk_num = 16 \n",
        "sample_size = 26912 # 26912\n",
        "rct_tensors = []\n",
        "sampled_custs = np.random.choice(list(custs), size=sample_size)\n",
        "\n",
        "# 램 초과 방지 위해 분할작업.\n",
        "for j in tqdm(range(sample_size//chunk_num), position=0, desc=\"making_data...\", leave=None):\n",
        "    if j == sample_size//chunk_num-1:\n",
        "        rct_chunk_tensors = Parallel(n_jobs=num_cores)(delayed(make_rct_tensor_by_cust)(cust_idx, features=features, window=window, slide=10, dtype=torch.int16) for cust_idx in sampled_custs[j*chunk_num:])\n",
        "    else:\n",
        "        rct_chunk_tensors = Parallel(n_jobs=num_cores)(delayed(make_rct_tensor_by_cust)(cust_idx, features=features, window=window, slide=10, dtype=torch.int16) for cust_idx in sampled_custs[j*chunk_num : (j+1)*chunk_num])\n",
        "    rct_chunk_tensors = [t for t in rct_chunk_tensors if t is not None] # None을 없앤다.\n",
        "    rct_chunk_tensors = torch.cat(rct_chunk_tensors, dim=0)\n",
        "\n",
        "    print(f\"{j}번째 chunk의 Tensor Shape : \", rct_chunk_tensors.shape)\n",
        "    rct_tensors.append(rct_chunk_tensors) # 전체 tensor에 추가\n",
        "    del rct_chunk_tensors\n",
        "\n",
        "rct_tensors = torch.cat(rct_tensors, dim = 0)\n",
        "rct_tensors.shape\n",
        "# 최종 데이터 전처리 후 데이터 중간저장\n",
        "torch.save(rct_tensors, path + \"LPOINT_BIG_COMP_RCT_TENSORS26912.pt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjBGGzWwR3jH",
        "outputId": "f0758a1a-492d-44bc-e78a-af520a6c78f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "making_data...:   0%|          | 1/1682 [00:09<4:38:52,  9.95s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0번째 chunk의 Tensor Shape :  torch.Size([55, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   0%|          | 2/1682 [00:18<4:07:57,  8.86s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1번째 chunk의 Tensor Shape :  torch.Size([85, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   0%|          | 3/1682 [00:27<4:11:40,  8.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2번째 chunk의 Tensor Shape :  torch.Size([36, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   0%|          | 4/1682 [00:37<4:26:53,  9.54s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3번째 chunk의 Tensor Shape :  torch.Size([65, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   0%|          | 5/1682 [00:48<4:40:20, 10.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4번째 chunk의 Tensor Shape :  torch.Size([78, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   0%|          | 6/1682 [00:57<4:34:15,  9.82s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5번째 chunk의 Tensor Shape :  torch.Size([61, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   0%|          | 7/1682 [01:07<4:30:03,  9.67s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6번째 chunk의 Tensor Shape :  torch.Size([55, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   0%|          | 8/1682 [01:15<4:15:32,  9.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7번째 chunk의 Tensor Shape :  torch.Size([78, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 9/1682 [01:23<4:06:56,  8.86s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8번째 chunk의 Tensor Shape :  torch.Size([74, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 10/1682 [01:32<4:05:31,  8.81s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9번째 chunk의 Tensor Shape :  torch.Size([68, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 11/1682 [01:40<3:59:20,  8.59s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10번째 chunk의 Tensor Shape :  torch.Size([90, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 12/1682 [01:48<3:55:28,  8.46s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11번째 chunk의 Tensor Shape :  torch.Size([76, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 13/1682 [01:56<3:53:54,  8.41s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12번째 chunk의 Tensor Shape :  torch.Size([65, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 14/1682 [02:04<3:51:17,  8.32s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13번째 chunk의 Tensor Shape :  torch.Size([104, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 15/1682 [02:13<3:50:04,  8.28s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14번째 chunk의 Tensor Shape :  torch.Size([89, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 16/1682 [02:21<3:47:21,  8.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15번째 chunk의 Tensor Shape :  torch.Size([92, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 17/1682 [02:29<3:53:09,  8.40s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16번째 chunk의 Tensor Shape :  torch.Size([43, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 18/1682 [02:38<3:50:58,  8.33s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17번째 chunk의 Tensor Shape :  torch.Size([96, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 19/1682 [02:46<3:49:23,  8.28s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18번째 chunk의 Tensor Shape :  torch.Size([98, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 20/1682 [02:54<3:47:42,  8.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19번째 chunk의 Tensor Shape :  torch.Size([55, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|          | 21/1682 [03:02<3:46:37,  8.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20번째 chunk의 Tensor Shape :  torch.Size([43, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|▏         | 22/1682 [03:10<3:44:58,  8.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21번째 chunk의 Tensor Shape :  torch.Size([51, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|▏         | 23/1682 [03:18<3:45:21,  8.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22번째 chunk의 Tensor Shape :  torch.Size([69, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|▏         | 24/1682 [03:26<3:45:07,  8.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23번째 chunk의 Tensor Shape :  torch.Size([53, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   1%|▏         | 25/1682 [03:35<3:53:06,  8.44s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24번째 chunk의 Tensor Shape :  torch.Size([68, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 26/1682 [03:45<4:03:40,  8.83s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "25번째 chunk의 Tensor Shape :  torch.Size([95, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 27/1682 [03:53<3:58:33,  8.65s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26번째 chunk의 Tensor Shape :  torch.Size([79, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 28/1682 [04:02<3:56:17,  8.57s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "27번째 chunk의 Tensor Shape :  torch.Size([66, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 29/1682 [04:10<3:51:55,  8.42s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "28번째 chunk의 Tensor Shape :  torch.Size([52, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 30/1682 [04:18<3:50:05,  8.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29번째 chunk의 Tensor Shape :  torch.Size([115, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 31/1682 [04:27<3:54:15,  8.51s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30번째 chunk의 Tensor Shape :  torch.Size([56, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 32/1682 [04:36<3:57:37,  8.64s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31번째 chunk의 Tensor Shape :  torch.Size([72, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 33/1682 [04:44<3:53:07,  8.48s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32번째 chunk의 Tensor Shape :  torch.Size([50, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 34/1682 [04:52<3:50:54,  8.41s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33번째 chunk의 Tensor Shape :  torch.Size([113, 10, 401])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rmaking_data...:   2%|▏         | 35/1682 [05:00<3:47:15,  8.28s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34번째 chunk의 Tensor Shape :  torch.Size([71, 10, 401])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 오토인토더 모델링"
      ],
      "metadata": {
        "id": "jE1rdx-2USjL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 저장한 전처리 데이터 불러오기\n",
        "# 데이터 파일이 존재한다면 불러와서 모델링 부터 진행 가능\n",
        "rct_tensors = torch.load(path + \"LPOINT_BIG_COMP_RCT_TENSORS26912.pt\")\n",
        "rct_tensors.shape"
      ],
      "metadata": {
        "id": "c5LAiAr0S08r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3beefaa-5ce3-4489-f7f8-dbaae6ec6788"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([112953, 10, 401])"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 50 이상 열에 대해 로그 스케일과 min-max 스케일 적용\n",
        "tem = rct_tensors[:,:, 50:]\n",
        "tem = torch.log1p(tem)\n",
        "\n",
        "for i in range(351):\n",
        "    maxs = tem[:,:,i].max()\n",
        "    if maxs == 0:\n",
        "        continue\n",
        "    tem[:,:,i] = tem[:,:,i] / maxs\n",
        "\n",
        "rct_tensors = torch.cat((rct_tensors[:,:,:50], tem), dim = 2)\n",
        "\n",
        "\n",
        "# -1 값인경우 로그 스케일 진행시 -Inf로 값이 바뀜. 후처리해줌. \n",
        "idx_1, idx_2, idx_3 = np.where(rct_tensors.detach().numpy() == -np.Inf)\n",
        "print(np.unique(idx_2))\n",
        "print(np.unique(idx_3))\n",
        "rct_tensors[idx_1,9,50] = -1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFjoXtmOk-j0",
        "outputId": "b0303f0b-310c-49ac-a07e-ca6001d5b736"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9]\n",
            "[50]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, rct_tensors = None, cust_idx = None, train_mode=True, transforms = None):\n",
        "        self.transforms = transforms\n",
        "        self.rct_tensors = rct_tensors\n",
        "        self.train_mode = train_mode\n",
        "        if not self.train_mode:\n",
        "            if (make_rct_tensor_by_cust(cust_idx, drop=False) is None):\n",
        "                self.tensors = []\n",
        "            else:\n",
        "                # 테스트 모드에서 데이터를 새로 불러옴.\n",
        "                self.tensors, self.rct_nos = make_rct_tensor_by_cust(cust_idx, drop=False)\n",
        "\n",
        "    def __len__(self):\n",
        "        if self.train_mode :\n",
        "            return self.rct_tensors.shape[0]\n",
        "        else: \n",
        "            return len(self.tensors)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # 오토인코더이기 때문에 트레인 테스트 여부에 따라 아웃풋 값이 달라짐 \n",
        "        if self.train_mode: \n",
        "            input = self.rct_tensors[idx]\n",
        "            if self.transforms is not None:\n",
        "                input = self.transforms(input)[0]\n",
        "            return input, input[:,52:]\n",
        "        else:\n",
        "            return self.tensors[idx], self.rct_nos[idx][0]"
      ],
      "metadata": {
        "id": "dHPhrJmmTblW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM 역방향 구현 위한 클래스\n",
        "class TimeDistributed(nn.Module):\n",
        "    def __init__(self, module, batch_first=False):\n",
        "        super(TimeDistributed, self).__init__()\n",
        "        self.module = module\n",
        "        self.batch_first = batch_first\n",
        "\n",
        "    def forward(self, x):\n",
        "        if len(x.size()) <= 2:\n",
        "            return self.module(x)\n",
        "        # Squash samples and timesteps into a single axis\n",
        "        x_reshape = x.contiguous().view(-1, x.size(-1))  # (samples * timesteps, input_size)\n",
        "        y = self.module(x_reshape)\n",
        "        # We have to reshape Y\n",
        "        if self.batch_first:\n",
        "            y = y.contiguous().view(x.size(0), -1, y.size(-1))  # (samples, timesteps, output_size)\n",
        "        else:\n",
        "            y = y.view(-1, x.size(1), y.size(-1))  # (timesteps, samples, output_size)\n",
        "        return y"
      ],
      "metadata": {
        "id": "M9hCvhPSck6Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Encoder(nn.Module):\n",
        "    def __init__(self, seq_len, n_features, embedding_dim=64):\n",
        "        super(Encoder, self).__init__()\n",
        "        self.seq_len, self.n_features = seq_len, n_features\n",
        "        self.embedding_dim, self.hidden_dim = (\n",
        "            embedding_dim, 2 * embedding_dim\n",
        "        )\n",
        "        self.rnn1 = nn.LSTM(\n",
        "          input_size=n_features,\n",
        "          hidden_size=self.hidden_dim,\n",
        "          num_layers=1,\n",
        "          batch_first=True\n",
        "        )\n",
        "        self.rnn2 = nn.LSTM(\n",
        "          input_size=self.hidden_dim,\n",
        "          hidden_size=embedding_dim,\n",
        "          num_layers=1,\n",
        "          batch_first=True\n",
        "        )\n",
        "    def forward(self, x):\n",
        "        x, (_, _) = self.rnn1(x)\n",
        "        x, (hidden_n, _) = self.rnn2(x)\n",
        "        return  x[:,-1,:]"
      ],
      "metadata": {
        "id": "2_gwjY7BUZRy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Decoder(nn.Module):\n",
        "    def __init__(self, seq_len, input_dim=64, n_features=1):\n",
        "        super(Decoder, self).__init__()\n",
        "        self.seq_len, self.input_dim = seq_len, input_dim\n",
        "        self.hidden_dim, self.n_features = 2 * input_dim, n_features\n",
        "        self.rnn1 = nn.LSTM(\n",
        "          input_size=input_dim,\n",
        "          hidden_size=input_dim,\n",
        "          num_layers=1,\n",
        "          batch_first=True\n",
        "        )\n",
        "        self.rnn2 = nn.LSTM(\n",
        "          input_size=input_dim,\n",
        "          hidden_size=self.hidden_dim,\n",
        "          num_layers=1,\n",
        "          batch_first=True\n",
        "        )\n",
        "        self.output_layer = torch.nn.Linear(self.hidden_dim, n_features)\n",
        "        self.timedist = TimeDistributed(self.output_layer)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x=x.reshape(-1,1,self.input_dim).repeat(1,self.seq_len,1)       \n",
        "        x, (hidden_n, cell_n) = self.rnn1(x)\n",
        "        x, (hidden_n, cell_n) = self.rnn2(x)\n",
        "        return self.timedist(x)"
      ],
      "metadata": {
        "id": "wGbpr98fUmm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class RecurrentAutoencoder(nn.Module):\n",
        "    def __init__(self, seq_len, n_features, embedding_dim=64):\n",
        "        super(RecurrentAutoencoder, self).__init__()\n",
        "        self.encoder = Encoder(seq_len, n_features, embedding_dim)#.to(device)\n",
        "        self.decoder = Decoder(seq_len, embedding_dim, n_features-52)#.to(device)\n",
        "\n",
        "    # 트레인의 경우 인코더와 디코더를 모두 통과\n",
        "    # 테스트의 경우 인코더를 통과한 자료 요약된 히든 레이어 필요.\n",
        "    def forward(self, x, train_mode = True):\n",
        "        if train_mode:\n",
        "            x = self.encoder(x)\n",
        "            x = self.decoder(x)\n",
        "        else:\n",
        "            x = self.encoder(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "-UDEGC6PYFA1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "\n",
        "model = RecurrentAutoencoder(window, 401, 5)\n",
        "model = model.to(device)\n",
        "\n",
        "batch_size = 512"
      ],
      "metadata": {
        "id": "c3ZwJa4mYOzJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "=train_id, valid_id = train_test_split(range(rct_tensors.shape[0]), test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "e7a3ZXKdYbUX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = CustomDataset(rct_tensors[train_id], train_mode = True)\n",
        "train_loader = DataLoader(train_dataset, batch_size = batch_size, shuffle=True, num_workers=0)\n",
        "\n",
        "valid_dataset = CustomDataset(rct_tensors[valid_id], train_mode = True)\n",
        "valid_loader = DataLoader(valid_dataset, batch_size = batch_size, shuffle=False, num_workers=0)"
      ],
      "metadata": {
        "id": "D8CKe91zYpex"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)\n",
        "# Loss Function\n",
        "criterion = nn.L1Loss(reduction='sum').to(device)"
      ],
      "metadata": {
        "id": "raAaGlN9Yu4w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(model, optimizer, train_loader, vali_loader, scheduler, device):\n",
        "\n",
        "    best_mse = 9999\n",
        "    \n",
        "    for epoch in range(60):\n",
        "        model.train()\n",
        "        train_loss = []\n",
        "        for idx, batch in tqdm(enumerate(train_loader)):\n",
        "            x, y = batch[0].float().to(device), batch[1].float().to(device)\n",
        "            optimizer.zero_grad()\n",
        "            # Data -> Model -> Output\n",
        "            pred = model(x)\n",
        "            \n",
        "            # Calc loss\n",
        "            loss = criterion(pred, y)\n",
        "\n",
        "            # backpropagation\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            train_loss.append(loss.item()/batch_size)\n",
        "                        \n",
        "        # Evaluation Validation set\n",
        "        vali_mse = validation(model, valid_loader, criterion, device)\n",
        "        if vali_mse < best_mse:\n",
        "            torch.save(model, 'model.pt')\n",
        "        \n",
        "        print(f'Epoch [{epoch}] Train MSE : [{np.mean(train_loss):.5f}] Validation MSE : [{vali_mse:.5f}]\\n')"
      ],
      "metadata": {
        "id": "KgVUDHuGY23P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validation(model, vali_loader, criterion, device):\n",
        "    model.eval() # Evaluation\n",
        "    vali_loss = []\n",
        "    with torch.no_grad():\n",
        "        for idx, batch in enumerate(valid_loader):\n",
        "            x, y = batch[0].float().to(device), batch[1].float().to(device)\n",
        "\n",
        "            pred = model(x)\n",
        "            loss = criterion(pred, y)\n",
        "            \n",
        "            vali_loss.append(loss.item()/batch_size)\n",
        "\n",
        "    vali_mae_loss = np.mean(vali_loss)\n",
        "    return vali_mae_loss"
      ],
      "metadata": {
        "id": "6NWtLER7rDuA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train(model, optimizer, train_loader,valid_loader, None, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WZ0Zid4YY7pA",
        "outputId": "cd815c93-bcb3-49c1-945d-7649e3d70aed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [0] Train MSE : [584.36792] Validation MSE : [539.40944]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 45.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1] Train MSE : [516.66198] Validation MSE : [476.92420]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [2] Train MSE : [454.25582] Validation MSE : [416.00328]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [3] Train MSE : [392.97063] Validation MSE : [356.17094]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:04, 41.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [4] Train MSE : [333.44067] Validation MSE : [300.12227]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.36it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [5] Train MSE : [280.36516] Validation MSE : [251.22145]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.78it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [6] Train MSE : [232.34950] Validation MSE : [205.95388]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.85it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [7] Train MSE : [188.80078] Validation MSE : [165.69649]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [8] Train MSE : [149.79876] Validation MSE : [129.26637]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.63it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [9] Train MSE : [115.87124] Validation MSE : [99.24168]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [10] Train MSE : [88.68966] Validation MSE : [76.44065]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:04, 39.84it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [11] Train MSE : [69.22651] Validation MSE : [60.36798]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [12] Train MSE : [55.03805] Validation MSE : [48.17663]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.28it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [13] Train MSE : [43.55738] Validation MSE : [37.61073]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [14] Train MSE : [33.94254] Validation MSE : [29.58559]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.36it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [15] Train MSE : [27.18963] Validation MSE : [23.94519]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.04it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [16] Train MSE : [21.98854] Validation MSE : [19.43201]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.04it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [17] Train MSE : [18.01760] Validation MSE : [15.97886]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 49.49it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [18] Train MSE : [14.83190] Validation MSE : [13.27380]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.06it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [19] Train MSE : [12.39979] Validation MSE : [11.09362]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [20] Train MSE : [10.30017] Validation MSE : [9.19692]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [21] Train MSE : [8.67047] Validation MSE : [7.92659]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 49.27it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [22] Train MSE : [7.70861] Validation MSE : [7.30816]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [23] Train MSE : [7.29591] Validation MSE : [7.04648]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.97it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [24] Train MSE : [7.13096] Validation MSE : [6.95273]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [25] Train MSE : [7.04775] Validation MSE : [6.87945]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [26] Train MSE : [6.98362] Validation MSE : [6.82505]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [27] Train MSE : [6.93149] Validation MSE : [6.77446]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.52it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [28] Train MSE : [6.88706] Validation MSE : [6.73480]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [29] Train MSE : [6.84920] Validation MSE : [6.70247]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [30] Train MSE : [6.81637] Validation MSE : [6.67068]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.34it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [31] Train MSE : [6.78697] Validation MSE : [6.64804]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.37it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [32] Train MSE : [6.76142] Validation MSE : [6.62253]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [33] Train MSE : [6.73851] Validation MSE : [6.59865]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [34] Train MSE : [6.71733] Validation MSE : [6.58478]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.57it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [35] Train MSE : [6.69898] Validation MSE : [6.56460]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.32it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [36] Train MSE : [6.68208] Validation MSE : [6.54617]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 45.94it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [37] Train MSE : [6.66654] Validation MSE : [6.53658]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.97it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [38] Train MSE : [6.65311] Validation MSE : [6.52131]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [39] Train MSE : [6.64214] Validation MSE : [6.51306]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.69it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [40] Train MSE : [6.63100] Validation MSE : [6.50589]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.15it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [41] Train MSE : [6.62096] Validation MSE : [6.48899]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 45.97it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [42] Train MSE : [6.61355] Validation MSE : [6.48415]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [43] Train MSE : [6.60615] Validation MSE : [6.47714]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [44] Train MSE : [6.59977] Validation MSE : [6.47356]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.00it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [45] Train MSE : [6.59533] Validation MSE : [6.46852]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.55it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [46] Train MSE : [6.59073] Validation MSE : [6.46267]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.59it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [47] Train MSE : [6.58703] Validation MSE : [6.45988]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.32it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [48] Train MSE : [6.58470] Validation MSE : [6.46206]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [49] Train MSE : [6.58459] Validation MSE : [6.45635]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 47.78it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [50] Train MSE : [6.58116] Validation MSE : [6.45788]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 48.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [51] Train MSE : [6.57998] Validation MSE : [6.46073]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 45.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [52] Train MSE : [6.57902] Validation MSE : [6.45381]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "177it [00:03, 46.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [53] Train MSE : [6.57848] Validation MSE : [6.45404]\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "53it [00:01, 47.22it/s]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-227-5765d1c66594>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalid_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\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<ipython-input-225-4d9ff7c51978>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, optimizer, train_loader, vali_loader, scheduler, device)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;31m# backpropagation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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[0m\u001b[1;32m     19\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    394\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 396\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\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    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\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/local/lib/python3.7/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    173\u001b[0m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[1;32m    174\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m def grad(\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = torch.load(\"model.pt\")"
      ],
      "metadata": {
        "id": "gSdpNZSYUYtM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 군집화"
      ],
      "metadata": {
        "id": "0vcs5IAa4RCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame()\n",
        "\n",
        "# 사용자마다 데이터 셋 제작후 오토인코더 모델에 넣어 5개 히든 값 추출해 df로 저장\n",
        "for i in tqdm(range(26917)):    \n",
        "    cust_idx = custs[i] \n",
        "    test_dataset = CustomDataset(rct_tensors = None, cust_idx = cust_idx, train_mode = False, transforms = None) # 어떤 고객은 데이터셋이 없을 수 있음.\n",
        "    if test_dataset.__len__() == 0:\n",
        "        continue\n",
        "    test_loader = DataLoader(test_dataset, batch_size=1000, shuffle=False, num_workers=0)\n",
        "\n",
        "    model.eval() # Evaluation\n",
        "    results = pd.DataFrame()\n",
        "    with torch.no_grad():\n",
        "        for idx, batch in enumerate(test_loader):\n",
        "            x, rct_no = batch[0].float().to(device), batch[1]\n",
        "\n",
        "            pred = model(x, train_mode = False).to(\"cpu\").detach().numpy()\n",
        "            result = pd.DataFrame(pred)\n",
        "            result = pd.concat((pdde_cust_df.loc[list(rct_no)].reset_index(), result), axis=1)\n",
        "            \n",
        "            results = pd.concat((results, result), axis=0)\n",
        "            # break\n",
        "\n",
        "    df = pd.concat((df, result), axis=0)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "I5WWHZufXp2-",
        "outputId": "3b9d0fc0-c57e-4f1f-fad0-d74a691988f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26917/26917 [52:53<00:00,  8.48it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          rct_no  ma_fem_dv  ages  chnl_dv zon_hlv cop_c         0         1  \\\n",
              "0   A01000001113          1     3        0     Z17   A01 -0.875327  0.749697   \n",
              "1   A01000007648          1     3        0     Z17   A01 -0.258074  0.035256   \n",
              "2   A03104921319          1     3        0     Z17   A03 -0.341924  0.050866   \n",
              "3   A03104969721          1     3        0     Z17   A03 -0.419069  0.124873   \n",
              "4   A03089375637          1     3        0     Z17   A03 -0.219258 -0.009472   \n",
              "..           ...        ...   ...      ...     ...   ...       ...       ...   \n",
              "4   E06043646151          1     2        1     Z17   A06 -0.243524  0.001610   \n",
              "5   E06045000396          1     2        1     Z17   A06 -0.185293 -0.065875   \n",
              "6   E06047254820          1     2        1     Z17   A06 -0.185614 -0.056340   \n",
              "0   E06043099892          0     2        1     Z10   A06 -0.164147 -0.081122   \n",
              "1   E06046190490          0     2        1     Z10   A06 -0.164193 -0.078292   \n",
              "\n",
              "           2         3         4  \n",
              "0  -0.799011  0.764606 -0.725992  \n",
              "1   0.128591  0.085433 -0.148208  \n",
              "2   0.099332  0.166484  0.067953  \n",
              "3  -0.153551  0.184719 -0.220108  \n",
              "4   0.168269  0.056567 -0.084262  \n",
              "..       ...       ...       ...  \n",
              "4   0.149039  0.090350 -0.087709  \n",
              "5   0.184559  0.043331 -0.107194  \n",
              "6   0.191060  0.044220 -0.111981  \n",
              "0   0.193445  0.034761 -0.113489  \n",
              "1   0.191027  0.038166 -0.112214  \n",
              "\n",
              "[219753 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-93a1a400-1f12-4fb3-8dab-7f4c507b4768\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rct_no</th>\n",
              "      <th>ma_fem_dv</th>\n",
              "      <th>ages</th>\n",
              "      <th>chnl_dv</th>\n",
              "      <th>zon_hlv</th>\n",
              "      <th>cop_c</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A01000001113</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Z17</td>\n",
              "      <td>A01</td>\n",
              "      <td>-0.875327</td>\n",
              "      <td>0.749697</td>\n",
              "      <td>-0.799011</td>\n",
              "      <td>0.764606</td>\n",
              "      <td>-0.725992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A01000007648</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Z17</td>\n",
              "      <td>A01</td>\n",
              "      <td>-0.258074</td>\n",
              "      <td>0.035256</td>\n",
              "      <td>0.128591</td>\n",
              "      <td>0.085433</td>\n",
              "      <td>-0.148208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A03104921319</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Z17</td>\n",
              "      <td>A03</td>\n",
              "      <td>-0.341924</td>\n",
              "      <td>0.050866</td>\n",
              "      <td>0.099332</td>\n",
              "      <td>0.166484</td>\n",
              "      <td>0.067953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A03104969721</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Z17</td>\n",
              "      <td>A03</td>\n",
              "      <td>-0.419069</td>\n",
              "      <td>0.124873</td>\n",
              "      <td>-0.153551</td>\n",
              "      <td>0.184719</td>\n",
              "      <td>-0.220108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A03089375637</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Z17</td>\n",
              "      <td>A03</td>\n",
              "      <td>-0.219258</td>\n",
              "      <td>-0.009472</td>\n",
              "      <td>0.168269</td>\n",
              "      <td>0.056567</td>\n",
              "      <td>-0.084262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>E06043646151</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Z17</td>\n",
              "      <td>A06</td>\n",
              "      <td>-0.243524</td>\n",
              "      <td>0.001610</td>\n",
              "      <td>0.149039</td>\n",
              "      <td>0.090350</td>\n",
              "      <td>-0.087709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>E06045000396</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Z17</td>\n",
              "      <td>A06</td>\n",
              "      <td>-0.185293</td>\n",
              "      <td>-0.065875</td>\n",
              "      <td>0.184559</td>\n",
              "      <td>0.043331</td>\n",
              "      <td>-0.107194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>E06047254820</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Z17</td>\n",
              "      <td>A06</td>\n",
              "      <td>-0.185614</td>\n",
              "      <td>-0.056340</td>\n",
              "      <td>0.191060</td>\n",
              "      <td>0.044220</td>\n",
              "      <td>-0.111981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>E06043099892</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Z10</td>\n",
              "      <td>A06</td>\n",
              "      <td>-0.164147</td>\n",
              "      <td>-0.081122</td>\n",
              "      <td>0.193445</td>\n",
              "      <td>0.034761</td>\n",
              "      <td>-0.113489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>E06046190490</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Z10</td>\n",
              "      <td>A06</td>\n",
              "      <td>-0.164193</td>\n",
              "      <td>-0.078292</td>\n",
              "      <td>0.191027</td>\n",
              "      <td>0.038166</td>\n",
              "      <td>-0.112214</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>219753 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93a1a400-1f12-4fb3-8dab-7f4c507b4768')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-93a1a400-1f12-4fb3-8dab-7f4c507b4768 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-93a1a400-1f12-4fb3-8dab-7f4c507b4768');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5개의 히든 레이어로 퍼지 군집 진행.\n",
        "summary = df.iloc[:,6:]\n",
        "fcm = FCM(n_clusters=20, random_state= 0)\n",
        "fcm.fit(summary.to_numpy())\n",
        "\n",
        "fcm_labels = fcm.predict(summary.to_numpy())\n",
        "new_df = pd.DataFrame(fcm.u) # fcm.u : 20개의 확률벡터로 저장됨\n",
        "new_df['rct_no'] = df['rct_no'].reset_index(drop = True)\n",
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "PgMucX0qxQru",
        "outputId": "7a64917d-e6d0-4391-a130-29c490ed8f83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              0         1         2         3         4         5         6   \\\n",
              "0       0.061704  0.018176  0.179670  0.171544  0.006814  0.192833  0.013966   \n",
              "1       0.000027  0.000048  0.000013  0.000020  0.000154  0.000015  0.000060   \n",
              "2       0.000844  0.389793  0.000114  0.000303  0.000472  0.000156  0.006416   \n",
              "3       0.000762  0.001940  0.000288  0.000487  0.035211  0.000340  0.003043   \n",
              "4       0.000007  0.000016  0.000003  0.000005  0.000144  0.000003  0.000023   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "219748  0.000018  0.000032  0.000009  0.000013  0.000102  0.000010  0.000040   \n",
              "219749  0.000080  0.000304  0.000006  0.000021  0.000013  0.000009  0.000075   \n",
              "219750  0.000114  0.000431  0.000009  0.000029  0.000019  0.000013  0.000107   \n",
              "219751  0.000053  0.000198  0.000004  0.000014  0.000009  0.000006  0.000049   \n",
              "219752  0.000026  0.000100  0.000002  0.000007  0.000004  0.000003  0.000025   \n",
              "\n",
              "              7         8         9         10        11        12        13  \\\n",
              "0       0.063156  0.003107  0.159941  0.004790  0.010691  0.004790  0.025136   \n",
              "1       0.000012  0.995351  0.000023  0.000433  0.000079  0.000433  0.000039   \n",
              "2       0.000094  0.000095  0.000444  0.000211  0.001881  0.000211  0.006927   \n",
              "3       0.000250  0.003074  0.000596  0.040295  0.005372  0.040098  0.001351   \n",
              "4       0.000003  0.000066  0.000006  0.328252  0.000036  0.670190  0.000012   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "219748  0.000008  0.996977  0.000015  0.000287  0.000052  0.000287  0.000026   \n",
              "219749  0.000005  0.000003  0.000032  0.000007  0.000036  0.000007  0.998968   \n",
              "219750  0.000007  0.000005  0.000046  0.000010  0.000051  0.000010  0.998540   \n",
              "219751  0.000003  0.000002  0.000021  0.000004  0.000024  0.000004  0.999327   \n",
              "219752  0.000002  0.000001  0.000011  0.000002  0.000012  0.000002  0.999662   \n",
              "\n",
              "              14            15        16        17        18        19  \n",
              "0       0.004090  2.759869e-03  0.044186  0.018242  0.008685  0.005718  \n",
              "1       0.000976  1.894549e-03  0.000031  0.000048  0.000103  0.000242  \n",
              "2       0.000155  7.591839e-05  0.001211  0.589574  0.000720  0.000304  \n",
              "3       0.011390  1.944036e-03  0.000971  0.001943  0.010143  0.840504  \n",
              "4       0.000562  3.407693e-05  0.000009  0.000016  0.000054  0.000560  \n",
              "...          ...           ...       ...       ...       ...       ...  \n",
              "219748  0.000648  1.197294e-03  0.000021  0.000032  0.000068  0.000160  \n",
              "219749  0.000005  2.747963e-06  0.000092  0.000304  0.000019  0.000009  \n",
              "219750  0.000007  3.887079e-06  0.000130  0.000430  0.000026  0.000013  \n",
              "219751  0.000003  1.810845e-06  0.000059  0.000197  0.000012  0.000006  \n",
              "219752  0.000002  9.090124e-07  0.000030  0.000099  0.000006  0.000003  \n",
              "\n",
              "[219753 rows x 20 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-54a1418b-8898-48eb-8268-361af9a3e070\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.061704</td>\n",
              "      <td>0.018176</td>\n",
              "      <td>0.179670</td>\n",
              "      <td>0.171544</td>\n",
              "      <td>0.006814</td>\n",
              "      <td>0.192833</td>\n",
              "      <td>0.013966</td>\n",
              "      <td>0.063156</td>\n",
              "      <td>0.003107</td>\n",
              "      <td>0.159941</td>\n",
              "      <td>0.004790</td>\n",
              "      <td>0.010691</td>\n",
              "      <td>0.004790</td>\n",
              "      <td>0.025136</td>\n",
              "      <td>0.004090</td>\n",
              "      <td>2.759869e-03</td>\n",
              "      <td>0.044186</td>\n",
              "      <td>0.018242</td>\n",
              "      <td>0.008685</td>\n",
              "      <td>0.005718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.000154</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.995351</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000433</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>0.000433</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>0.000976</td>\n",
              "      <td>1.894549e-03</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>0.000103</td>\n",
              "      <td>0.000242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000844</td>\n",
              "      <td>0.389793</td>\n",
              "      <td>0.000114</td>\n",
              "      <td>0.000303</td>\n",
              "      <td>0.000472</td>\n",
              "      <td>0.000156</td>\n",
              "      <td>0.006416</td>\n",
              "      <td>0.000094</td>\n",
              "      <td>0.000095</td>\n",
              "      <td>0.000444</td>\n",
              "      <td>0.000211</td>\n",
              "      <td>0.001881</td>\n",
              "      <td>0.000211</td>\n",
              "      <td>0.006927</td>\n",
              "      <td>0.000155</td>\n",
              "      <td>7.591839e-05</td>\n",
              "      <td>0.001211</td>\n",
              "      <td>0.589574</td>\n",
              "      <td>0.000720</td>\n",
              "      <td>0.000304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000762</td>\n",
              "      <td>0.001940</td>\n",
              "      <td>0.000288</td>\n",
              "      <td>0.000487</td>\n",
              "      <td>0.035211</td>\n",
              "      <td>0.000340</td>\n",
              "      <td>0.003043</td>\n",
              "      <td>0.000250</td>\n",
              "      <td>0.003074</td>\n",
              "      <td>0.000596</td>\n",
              "      <td>0.040295</td>\n",
              "      <td>0.005372</td>\n",
              "      <td>0.040098</td>\n",
              "      <td>0.001351</td>\n",
              "      <td>0.011390</td>\n",
              "      <td>1.944036e-03</td>\n",
              "      <td>0.000971</td>\n",
              "      <td>0.001943</td>\n",
              "      <td>0.010143</td>\n",
              "      <td>0.840504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000144</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.328252</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.670190</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000562</td>\n",
              "      <td>3.407693e-05</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.000560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219748</th>\n",
              "      <td>0.000018</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.000040</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>0.996977</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000287</td>\n",
              "      <td>0.000052</td>\n",
              "      <td>0.000287</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000648</td>\n",
              "      <td>1.197294e-03</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000068</td>\n",
              "      <td>0.000160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219749</th>\n",
              "      <td>0.000080</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000075</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.998968</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>2.747963e-06</td>\n",
              "      <td>0.000092</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.000009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219750</th>\n",
              "      <td>0.000114</td>\n",
              "      <td>0.000431</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.998540</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>3.887079e-06</td>\n",
              "      <td>0.000130</td>\n",
              "      <td>0.000430</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219751</th>\n",
              "      <td>0.000053</td>\n",
              "      <td>0.000198</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.999327</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>1.810845e-06</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.000197</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219752</th>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.999662</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>9.090124e-07</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>219753 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54a1418b-8898-48eb-8268-361af9a3e070')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-54a1418b-8898-48eb-8268-361af9a3e070 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-54a1418b-8898-48eb-8268-361af9a3e070');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['pred'] = fcm_labels\n",
        "df['pred'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f63M5L8dxVKi",
        "outputId": "f9775a63-e8b4-4f0d-f414-b20d37c6c4e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3     24116\n",
              "11    23524\n",
              "4     22577\n",
              "1     18177\n",
              "5     13731\n",
              "9     12675\n",
              "10    12657\n",
              "17    12155\n",
              "13    11415\n",
              "15    11197\n",
              "19     9581\n",
              "8      8310\n",
              "6      6796\n",
              "18     6639\n",
              "2      5237\n",
              "14     4635\n",
              "16     4362\n",
              "0      4223\n",
              "7      3873\n",
              "12     3873\n",
              "Name: pred, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "EHx8NehTAIDm",
        "outputId": "92c74ec3-d482-4080-a7e4-b1290dd00639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               0         1         2         3         4         5         6  \\\n",
              "0       0.061704  0.018176  0.179670  0.171544  0.006814  0.192833  0.013966   \n",
              "1       0.000027  0.000048  0.000013  0.000020  0.000154  0.000015  0.000060   \n",
              "2       0.000844  0.389793  0.000114  0.000303  0.000472  0.000156  0.006416   \n",
              "3       0.000762  0.001940  0.000288  0.000487  0.035211  0.000340  0.003043   \n",
              "4       0.000007  0.000016  0.000003  0.000005  0.000144  0.000003  0.000023   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "219748  0.000018  0.000032  0.000009  0.000013  0.000102  0.000010  0.000040   \n",
              "219749  0.000080  0.000304  0.000006  0.000021  0.000013  0.000009  0.000075   \n",
              "219750  0.000114  0.000431  0.000009  0.000029  0.000019  0.000013  0.000107   \n",
              "219751  0.000053  0.000198  0.000004  0.000014  0.000009  0.000006  0.000049   \n",
              "219752  0.000026  0.000100  0.000002  0.000007  0.000004  0.000003  0.000025   \n",
              "\n",
              "               7         8         9  ...        11        12        13  \\\n",
              "0       0.063156  0.003107  0.159941  ...  0.010691  0.004790  0.025136   \n",
              "1       0.000012  0.995351  0.000023  ...  0.000079  0.000433  0.000039   \n",
              "2       0.000094  0.000095  0.000444  ...  0.001881  0.000211  0.006927   \n",
              "3       0.000250  0.003074  0.000596  ...  0.005372  0.040098  0.001351   \n",
              "4       0.000003  0.000066  0.000006  ...  0.000036  0.670190  0.000012   \n",
              "...          ...       ...       ...  ...       ...       ...       ...   \n",
              "219748  0.000008  0.996977  0.000015  ...  0.000052  0.000287  0.000026   \n",
              "219749  0.000005  0.000003  0.000032  ...  0.000036  0.000007  0.998968   \n",
              "219750  0.000007  0.000005  0.000046  ...  0.000051  0.000010  0.998540   \n",
              "219751  0.000003  0.000002  0.000021  ...  0.000024  0.000004  0.999327   \n",
              "219752  0.000002  0.000001  0.000011  ...  0.000012  0.000002  0.999662   \n",
              "\n",
              "              14            15        16        17        18        19  \\\n",
              "0       0.004090  2.759869e-03  0.044186  0.018242  0.008685  0.005718   \n",
              "1       0.000976  1.894549e-03  0.000031  0.000048  0.000103  0.000242   \n",
              "2       0.000155  7.591839e-05  0.001211  0.589574  0.000720  0.000304   \n",
              "3       0.011390  1.944036e-03  0.000971  0.001943  0.010143  0.840504   \n",
              "4       0.000562  3.407693e-05  0.000009  0.000016  0.000054  0.000560   \n",
              "...          ...           ...       ...       ...       ...       ...   \n",
              "219748  0.000648  1.197294e-03  0.000021  0.000032  0.000068  0.000160   \n",
              "219749  0.000005  2.747963e-06  0.000092  0.000304  0.000019  0.000009   \n",
              "219750  0.000007  3.887079e-06  0.000130  0.000430  0.000026  0.000013   \n",
              "219751  0.000003  1.810845e-06  0.000059  0.000197  0.000012  0.000006   \n",
              "219752  0.000002  9.090124e-07  0.000030  0.000099  0.000006  0.000003   \n",
              "\n",
              "              rct_no  \n",
              "0       A01000001113  \n",
              "1       A01000007648  \n",
              "2       A03104921319  \n",
              "3       A03104969721  \n",
              "4       A03089375637  \n",
              "...              ...  \n",
              "219748  E06043646151  \n",
              "219749  E06045000396  \n",
              "219750  E06047254820  \n",
              "219751  E06043099892  \n",
              "219752  E06046190490  \n",
              "\n",
              "[219753 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe9ac3d0-5af3-4bd6-8ede-024ffcaf9223\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>rct_no</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.061704</td>\n",
              "      <td>0.018176</td>\n",
              "      <td>0.179670</td>\n",
              "      <td>0.171544</td>\n",
              "      <td>0.006814</td>\n",
              "      <td>0.192833</td>\n",
              "      <td>0.013966</td>\n",
              "      <td>0.063156</td>\n",
              "      <td>0.003107</td>\n",
              "      <td>0.159941</td>\n",
              "      <td>...</td>\n",
              "      <td>0.010691</td>\n",
              "      <td>0.004790</td>\n",
              "      <td>0.025136</td>\n",
              "      <td>0.004090</td>\n",
              "      <td>2.759869e-03</td>\n",
              "      <td>0.044186</td>\n",
              "      <td>0.018242</td>\n",
              "      <td>0.008685</td>\n",
              "      <td>0.005718</td>\n",
              "      <td>A01000001113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.000154</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.995351</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>0.000433</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>0.000976</td>\n",
              "      <td>1.894549e-03</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>0.000103</td>\n",
              "      <td>0.000242</td>\n",
              "      <td>A01000007648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000844</td>\n",
              "      <td>0.389793</td>\n",
              "      <td>0.000114</td>\n",
              "      <td>0.000303</td>\n",
              "      <td>0.000472</td>\n",
              "      <td>0.000156</td>\n",
              "      <td>0.006416</td>\n",
              "      <td>0.000094</td>\n",
              "      <td>0.000095</td>\n",
              "      <td>0.000444</td>\n",
              "      <td>...</td>\n",
              "      <td>0.001881</td>\n",
              "      <td>0.000211</td>\n",
              "      <td>0.006927</td>\n",
              "      <td>0.000155</td>\n",
              "      <td>7.591839e-05</td>\n",
              "      <td>0.001211</td>\n",
              "      <td>0.589574</td>\n",
              "      <td>0.000720</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>A03104921319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000762</td>\n",
              "      <td>0.001940</td>\n",
              "      <td>0.000288</td>\n",
              "      <td>0.000487</td>\n",
              "      <td>0.035211</td>\n",
              "      <td>0.000340</td>\n",
              "      <td>0.003043</td>\n",
              "      <td>0.000250</td>\n",
              "      <td>0.003074</td>\n",
              "      <td>0.000596</td>\n",
              "      <td>...</td>\n",
              "      <td>0.005372</td>\n",
              "      <td>0.040098</td>\n",
              "      <td>0.001351</td>\n",
              "      <td>0.011390</td>\n",
              "      <td>1.944036e-03</td>\n",
              "      <td>0.000971</td>\n",
              "      <td>0.001943</td>\n",
              "      <td>0.010143</td>\n",
              "      <td>0.840504</td>\n",
              "      <td>A03104969721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000144</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.670190</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000562</td>\n",
              "      <td>3.407693e-05</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.000560</td>\n",
              "      <td>A03089375637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219748</th>\n",
              "      <td>0.000018</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.000040</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>0.996977</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000052</td>\n",
              "      <td>0.000287</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000648</td>\n",
              "      <td>1.197294e-03</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000068</td>\n",
              "      <td>0.000160</td>\n",
              "      <td>E06043646151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219749</th>\n",
              "      <td>0.000080</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000075</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.998968</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>2.747963e-06</td>\n",
              "      <td>0.000092</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>E06045000396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219750</th>\n",
              "      <td>0.000114</td>\n",
              "      <td>0.000431</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.000019</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.998540</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>3.887079e-06</td>\n",
              "      <td>0.000130</td>\n",
              "      <td>0.000430</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>E06047254820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219751</th>\n",
              "      <td>0.000053</td>\n",
              "      <td>0.000198</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.999327</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>1.810845e-06</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.000197</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>E06043099892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219752</th>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.999662</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>9.090124e-07</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>E06046190490</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>219753 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe9ac3d0-5af3-4bd6-8ede-024ffcaf9223')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fe9ac3d0-5af3-4bd6-8ede-024ffcaf9223 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fe9ac3d0-5af3-4bd6-8ede-024ffcaf9223');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 추천시스템 적용"
      ],
      "metadata": {
        "id": "29s5xSqcbQgb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "DEMO = pd.read_csv(path + 'LPOINT_BIG_COMP_01_DEMO.csv')\n",
        "PDDE = pd.read_csv(path + 'LPOINT_BIG_COMP_02_PDDE.csv')\n",
        "COP_U = pd.read_csv(path + 'LPOINT_BIG_COMP_03_COP_U.csv')\n",
        "PD_CLAC = pd.read_csv(path + 'LPOINT_BIG_COMP_04_PD_CLAC.csv')\n",
        "BR = pd.read_csv(path + 'LPOINT_BIG_COMP_05_BR.csv')\n",
        "LPAY = pd.read_csv(path + 'LPOINT_BIG_COMP_06_LPAY.csv')\n",
        "\n",
        "# case2, case3 방식 추천 시스템 구현 함수\n",
        "# case2 : 상위 5개 영수증 묶음 내 상품 추천 \n",
        "# case3 : 상위 5개 영수증 묶음의 소유 고객들의 다른 구매목록 중 상품 추천\n",
        "def recommend(rct_num, similar = True):\n",
        "    receipt = new_df[new_df['rct_no'] == rct_num] # receipt : 영수증\n",
        "    tem = new_df.copy()\n",
        "    tem['cosine'] = cosine_similarity(receipt.iloc[:,:20].to_numpy().reshape(1,-1), new_df.iloc[:,:20].to_numpy()).T\n",
        "    \n",
        "    tem = tem[['rct_no', 'cosine']]\n",
        "    tem = tem.sort_values(by = ['cosine'], ascending = False)\n",
        "    #tem = tem.reset_index()\n",
        "    tem = tem['rct_no'][1:6].values\n",
        "    people = []\n",
        "\n",
        "    # 코사인 유사도가 높은 상위 5명(tem[0]은 본인이 나옴)\n",
        "    for i in tem:\n",
        "        # 영수증에 맞는 사람 찾기\n",
        "        people.append(PDDE[PDDE['rct_no'] == i]['cust'].values[0])\n",
        "    \n",
        "    product = [] # case2\n",
        "    product2 = [] # case3\n",
        "    for i in range(5):\n",
        "        reco = PDDE[PDDE['cust'] == people[i]].sort_values(by = ['de_dt'], ascending = False)\n",
        "        reco.reset_index(drop=True, inplace=True)\n",
        "        idx = reco.index[reco['rct_no'] == tem[i]][0]\n",
        "        rand = random.randint(0, 9)\n",
        "        num = tem[i]\n",
        "        \n",
        "        # 영수증 그룹 내 랜덤상품 추출\n",
        "        for k in range(10):\n",
        "            #idx += 1\n",
        "            while (True):\n",
        "                if reco.iloc[idx,1] == num:\n",
        "                    idx = idx - 1\n",
        "                else:\n",
        "                    break\n",
        "            num = reco.iloc[idx,1]\n",
        "            if k == rand:\n",
        "                product.append(reco.iloc[idx,5])\n",
        "\n",
        "        # 영수증의 사람 내 랜덤상품 추출(같은 영수증 그룹x)\n",
        "        # eidx : 끝 인덱스, idx는 영수증 그룹 첫 인덱스\n",
        "        eidx = reco.index[reco['rct_no'] == tem[i]][0] \n",
        "        # 전체 테이블 길이\n",
        "        lens = len(reco)\n",
        "        tnum = lens - eidx + idx - 1\n",
        "\n",
        "        if tnum <= 1: # 별로 가능성 없지만 딱 10개 산 고객인 경우\n",
        "            product2.append(reco.iloc[0,5]) # 가장 최근에 구매한 상품 추천\n",
        "        else:\n",
        "            rand = random.randint(0, tnum-1)\n",
        "            if rand >= idx: # 만약 같은 영수증 그룹 이상 값이 나온다면\n",
        "                rand += (eidx - idx + 1)\n",
        "            product2.append(reco.iloc[rand,5])    \n",
        "    \n",
        "\n",
        "    # 본인 선택 상품과 일치 여부(성능평가)\n",
        "    myname = PDDE[PDDE['rct_no'] == rct_num]['cust'][:1].values[0]\n",
        "    myproduct = set(PDDE[PDDE['cust'] == myname]['pd_c'])\n",
        "    mycnt = len(myproduct)\n",
        "\n",
        "    # 상품 이름 출력 위해\n",
        "    names = []\n",
        "    overlap = 0\n",
        "    for i in product:\n",
        "        if i in myproduct: # 이미 산 제품이라면\n",
        "            overlap += 1\n",
        "        else:\n",
        "            names.append(PD_CLAC[PD_CLAC['pd_c'] == i]['pd_nm'].values[0])\n",
        "    #print(*names) \n",
        "    #print(mycnt, '개 제품 구매했는데,', overlap, '개 겹치게 추천')\n",
        "\n",
        "\n",
        "    names2 = []\n",
        "    overlap2 = 0\n",
        "    for i in product2:\n",
        "        if i in myproduct: # 이미 산 제품이라면\n",
        "            overlap2 += 1\n",
        "        else:\n",
        "            names2.append(PD_CLAC[PD_CLAC['pd_c'] == i]['pd_nm'].values[0])\n",
        "    #print(*names2) \n",
        "    #print(mycnt, '개 제품 구매했는데,', overlap2, '개 겹치게 추천')\n",
        "\n",
        "    # 성능지표 값 출력, similar = T : case2, similar = F : case3\n",
        "    if similar:\n",
        "        return overlap / mycnt\n",
        "    \n",
        "    else:\n",
        "        return overlap2 / mycnt\n",
        "\n",
        "recommend('A03089375637')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZqjWkwarzgyV",
        "outputId": "8553789e-34bb-42ae-cc33-f4c228c2f616"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.023809523809523808"
            ]
          },
          "metadata": {},
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# case 1 : 무작위 5개 상품 추천 함수\n",
        "def randomrecommend(rct_num):\n",
        "    myname = PDDE[PDDE['rct_no'] == rct_num]['cust'][:1].values[0]\n",
        "    myproduct = set(PDDE[PDDE['cust'] == myname]['pd_c'])\n",
        "    mycnt = len(myproduct)\n",
        "\n",
        "    overlap = 0\n",
        "    for i in PD_CLAC['pd_c'].sample(n = 5).values:\n",
        "        if i in myproduct: # 이미 산 제품이라면\n",
        "            overlap += 1\n",
        "\n",
        "    return overlap / mycnt\n",
        "\n",
        "randomrecommend('A03089375637')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIF-VleO4whv",
        "outputId": "bcc75bbc-a89b-4483-bb84-feb4384dd33c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score1 = 0\n",
        "score2 = 0\n",
        "score3 = 0\n",
        "# 300개 무작위 샘플링으로 case별 상품 추천 성능평가 시뮬레이션 \n",
        "for i in tqdm(new_df['rct_no'].sample(n = 300).values):\n",
        "    score1 += recommend(i)\n",
        "    score2 += recommend(i, False)\n",
        "    score3 += randomrecommend(i)\n",
        "\n",
        "print(score1,score2,score3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94eVM8vN8Wyf",
        "outputId": "08d82684-58c7-4e02-e842-5d892fa929bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 300/300 [34:21<00:00,  6.87s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.4894647988910155 4.778259644577935 0.6820823564789319\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(score1,score2,score3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1jO6TDa8sop",
        "outputId": "6c3f860e-e55a-4ec4-eadd-8050a807cee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.4894647988910155 4.778259644577935 0.6820823564789319\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "vWHY36feeyFR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}