HAR.ipynb

{
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  "metadata": {
    "colab": {
      "name": "HAR.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RE-yura/HAR/blob/main/HAR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Q6G6UYKGaTs"
      },
      "source": [
        "# HAR ( Human Activity Recognition : 人間行動認識 )\n",
        "\n",
        "　人間行動認識とは，カメラやセンサなどの様々な機器から得られた情報をもとに，人間の行動を学習･推定する認識技術分野のことです．今回は，スマホのIMU情報(姿勢･重力･角速度･ユーザの加速度)をもとに，ユーザの行動を下記の行動1～6の中から予測します．\n",
        "\n",
        "1.\t階段を降りている\n",
        "1.\t階段を登っている\n",
        "1.\t歩いている\n",
        "1.\tジョギングしている\n",
        "1.\t座っている\n",
        "1.\t立っている  \n",
        "\n",
        "**【注意】** \n",
        "random seedの設定ミス等で実行結果とテキストで書き込んでいる値が異なる部分がありますが，ご了承下さい．(やる気と時間ができたら直します...)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LuM6g-vqqG6L"
      },
      "source": [
        "## 1 | データセットの準備\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "今回はKaggle ( https://www.kaggle.com/malekzadeh/motionsense-dataset/ ) でダウンロード可能なスマホIMUのデータセットを使用します．\n",
        "時間に余裕のある方はIMU情報のログを吐く簡単なNativeアプリを作ってみると良いと思います．\n",
        "\n",
        "**【注意】**\n",
        "以下のコードは上記URLからダウンロードした**motionsense-dataset.zipをアップロードしてから実行する**必要があります．  \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_Vn_LQouiCe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a404d70f-5241-4f86-f0d9-0d9f59c72b23"
      },
      "source": [
        "# zipを解凍\n",
        "!unzip motionsense-dataset.zip"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  motionsense-dataset.zip\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9YGfe9cYAfHE"
      },
      "source": [
        "　解凍したデータセットには，24人の被験者の情報および，被験者が上記の6つの行動をとっている時のIMU情報(姿勢･重力･角速度･ユーザの加速度)が含まれています．\n",
        "\n",
        "　ただし，各被験者が行動1～3は3回ずつ，行動4～6は2回ずつ測定を行っているため，合計24人×(3回×3 + 2回×3) = 360個のCSVファイルが含まれます．また，IMUのサンプリング周波数は50Hzです．  \n",
        "　360個のCSVファイルからトレーニングデータとテストデータを作成します．今回は，行動1～6それぞれ1回の試行をテストデータとし，残りをトレーニングデータとします．つまり，24人×6回 = 144個のCSVをテストデータに，24人×9回 = 216個のCSVをトレーニングデータとして扱います．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMbp43acdvE9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4414d166-38c7-42f6-ec67-a95b7cac20ee"
      },
      "source": [
        "# 360個のCSVファイルからトレーニングデータとテストデータを作成\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# ==== data_subjects_info.csvに記載されている24人の被験者の情報を取り出す ===========================\n",
        "def get_ds_infos():\n",
        "  dss = np.genfromtxt(\"data_subjects_info.csv\",delimiter=',')\n",
        "  dss = dss[1:]   # 1行目を除く\n",
        "  return dss      # 0:被験者ID, 1:体重, 2:身長, 3:年齢, 4:性別\n",
        "\n",
        "# ==== 各被験者ごとに分かれているcsvファイルを1つにまとめ，時系列データを作成 ========================\n",
        "def create_time_series_data(num_features, num_act_labels, num_gen_labels, act_labels, trial_id):\n",
        "  ds_list = get_ds_infos()                                              # 被験者情報を取得\n",
        "  dataset_columns = num_features + num_gen_labels + num_act_labels      # データセットの列数 = センサデータ数 + 性別ラベル数 + 行動ラベル数\n",
        "  \n",
        "  # 訓練データとテストデータを初期化(これを最後に返す)\n",
        "  train_data = np.zeros((0, dataset_columns))\n",
        "  test_data = np.zeros((0, dataset_columns))\n",
        "  \n",
        "  for i, sub_id in enumerate(ds_list[:, 0]):      # 被験者IDでループ\n",
        "    for j, act in enumerate(act_labels):          # 行動ラベルでループ\n",
        "      for trial in trial_id[act]:                 # 試行回数でループ\n",
        "        fname = 'A_DeviceMotion_data/'+'A_DeviceMotion_data/'+act+'_'+str(trial)+'/sub_'+str(int(sub_id))+'.csv'\n",
        "        raw_data = pd.read_csv(fname)                                     # csvから生データを取得\n",
        "        raw_data = raw_data.drop(['Unnamed: 0'], axis=1)                  # ゴミデータ(1列目)を除く\n",
        "        unlabel_data = raw_data.values  \n",
        "        label_data = np.zeros((len(unlabel_data), dataset_columns))\n",
        "        label_data[:,:-(num_act_labels + num_gen_labels)] = unlabel_data\n",
        "        label_data[:,-1] = act_labels[act]\n",
        "        label_data[:,-2] = int(ds_list[i,4])\n",
        "\n",
        "        if trial < 11:          # 10回目以下の試行はトレーニングデータ\n",
        "          train_data = np.append(train_data, label_data, axis = 0)\n",
        "        else:                   # 11回目以上の試行はテストデータ\n",
        "          test_data = np.append(test_data, label_data, axis = 0)\n",
        "\n",
        "  return train_data , test_data\n",
        "\n",
        "# ==== 訓練データとテストデータの作成 ===================================================================\n",
        "num_features = 12       #    IMU情報   : 姿勢(ロール, ピッチ, ヨー), 重力(x, y, z), 角速度(x, y, z), ユーザ加速度(x,y,z)\n",
        "num_act_labels = 1      # 行動ラベル数 : dws, ups, wlk, jog, sit, std\n",
        "num_gen_labels = 1      #     性別     : 0/1(female/male)\n",
        "\n",
        "act_labels = {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}\n",
        "trial_id = {\"dws\":[1,2,11], \"ups\":[3,4,12], \"wlk\":[7,8,15], \"jog\":[9,16], \"sit\":[5,13], \"std\":[6,14]}    \n",
        "\n",
        "# データ作成\n",
        "train_ts, test_ts = create_time_series_data(num_features, num_act_labels, num_gen_labels, act_labels, trial_id)\n",
        "\n",
        "print(\"トレーニングデータ形状:\", train_ts.shape)\n",
        "print(\"テストデータ形状:\", test_ts.shape)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "トレーニングデータ形状: (1081446, 14)\n",
            "テストデータ形状: (331419, 14)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVwfzcOq2bAT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc2aad2a-1f30-4f7c-81d9-4694a0bf9554"
      },
      "source": [
        "# CSVの保存\n",
        "header = \"attitude.roll,attitude.pitch,attitude.yaw,gravity.x,gravity.y,gravity.z,rotationRate.x,rotationRate.y,rotationRate.z,userAcceleration.x,userAcceleration.y,userAcceleration.z,Gender,CorrectAnserLabel\"\n",
        "fmt = \"%.6f, %.6f, %.6f, %.6f, %.6f, %.6f, %.6f, %.6f, %.6f, %.6f, %.6f, %.6f, %d, %d\"\n",
        "np.savetxt('train_raw.csv', train_ts, fmt=fmt, delimiter=',', header=header, comments=\"\")\n",
        "np.savetxt('test_raw.csv', test_ts, fmt=fmt, delimiter=',', header=header, comments=\"\")\n",
        "print(\"CSV saved successfully\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CSV saved successfully\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRnbgCh4D-nN"
      },
      "source": [
        "　ひとまずCSVの保存まで完了しましたが，少しデータ量が多いので削減します．\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15Le2OgRtQ11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2919447d-27b1-4610-93f1-0f8361aaca98"
      },
      "source": [
        "# データセットが150MB程度あるので，5行に1行だけ使うことでデータを削減\n",
        "import pandas as pd\n",
        "\n",
        "# CSVファイルの読み込み\n",
        "def load_data():\n",
        "  return pd.read_csv(\"train_raw.csv\"), pd.read_csv(\"test_raw.csv\")\n",
        "\n",
        "train_data_raw, test_data_raw = load_data()\n",
        "\n",
        "# 5行に1行取り出し\n",
        "train_data_reduction = train_data_raw.query('index % 5 == 0')\n",
        "test_data_reduction = test_data_raw.query('index % 5 == 0')\n",
        "\n",
        "# CSVの保存\n",
        "train_data_reduction.to_csv(\"train.csv\")\n",
        "test_data_reduction.to_csv(\"test.csv\")\n",
        "print(\"CSV saved successfully\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CSV saved successfully\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HwXbmk3EQJ9V"
      },
      "source": [
        "## 2 | データの読み込みと加工\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awJArm9no2F3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcb69dcf-9281-46fa-81b1-c5473f271d44"
      },
      "source": [
        "# まずは，CSVを読み込んで中身を確認\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# CSVファイルの読み込み\n",
        "def load_data():\n",
        "  return pd.read_csv(\"train.csv\"), pd.read_csv(\"test.csv\")\n",
        "\n",
        "train_data, test_data = load_data()\n",
        "\n",
        "# ゴミデータ(1列目)を削除\n",
        "train_data = train_data.drop(['Unnamed: 0'], axis=1)\n",
        "test_data = test_data.drop(['Unnamed: 0'], axis=1)\n",
        "\n",
        "print(\"========= トレーニングデータ =============================================\")\n",
        "print(train_data,\"\\n\")\n",
        "print(\"============= テストデータ ===============================================\")\n",
        "print(test_data)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "========= トレーニングデータ =============================================\n",
            "        attitude.roll  attitude.pitch  ...  Gender  CorrectAnserLabel\n",
            "0            1.528132       -0.733896  ...       1                  1\n",
            "1            1.476302       -0.700807  ...       1                  1\n",
            "2            1.434186       -0.693527  ...       1                  1\n",
            "3            1.137867       -0.639672  ...       1                  1\n",
            "4            0.916433       -0.655642  ...       1                  1\n",
            "...               ...             ...  ...     ...                ...\n",
            "216285      -0.245861       -1.449192  ...       0                  6\n",
            "216286      -0.144484       -1.447449  ...       0                  6\n",
            "216287      -0.054519       -1.450084  ...       0                  6\n",
            "216288       0.014894       -1.445758  ...       0                  6\n",
            "216289       0.068046       -1.445550  ...       0                  6\n",
            "\n",
            "[216290 rows x 14 columns] \n",
            "\n",
            "============= テストデータ ===============================================\n",
            "       attitude.roll  attitude.pitch  ...  Gender  CorrectAnserLabel\n",
            "0           0.086504       -1.172129  ...       1                  1\n",
            "1          -0.076909       -1.124464  ...       1                  1\n",
            "2          -0.288029       -1.256848  ...       1                  1\n",
            "3          -0.170030       -1.238264  ...       1                  1\n",
            "4          -0.325727       -1.215860  ...       1                  1\n",
            "...              ...             ...  ...     ...                ...\n",
            "66279      -2.228347       -1.490832  ...       0                  6\n",
            "66280      -2.353866       -1.480216  ...       0                  6\n",
            "66281      -2.426975       -1.476196  ...       0                  6\n",
            "66282      -2.476378       -1.477116  ...       0                  6\n",
            "66283      -2.514569       -1.474699  ...       0                  6\n",
            "\n",
            "[66284 rows x 14 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tHFo5qq5FKNb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d444cc53-39b3-40e7-f524-9dcb7d2a6e82"
      },
      "source": [
        "# データの欠損がないか確認\n",
        "pd.set_option('display.max_columns', 14)\n",
        "print(train_data.describe())\n",
        "pd.reset_option('display.max_columns')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       attitude.roll  attitude.pitch   attitude.yaw      gravity.x  \\\n",
            "count  216290.000000   216290.000000  216290.000000  216290.000000   \n",
            "mean       -0.180388       -1.027508      -0.126891       0.037576   \n",
            "std         1.588032        0.450585       1.541152       0.332303   \n",
            "min        -3.141551       -1.567685      -3.141588      -0.999612   \n",
            "25%        -1.537733       -1.364888      -1.242785      -0.169055   \n",
            "50%         0.101366       -1.193118      -0.138958       0.018564   \n",
            "75%         0.901633       -0.781504       0.847209       0.250680   \n",
            "max         3.141565        1.567175       3.141574       0.999901   \n",
            "\n",
            "           gravity.y      gravity.z  rotationRate.x  rotationRate.y  \\\n",
            "count  216290.000000  216290.000000   216290.000000   216290.000000   \n",
            "mean        0.784542      -0.124646        0.004909        0.014150   \n",
            "std         0.297075       0.410916        1.320371        1.245072   \n",
            "min        -0.999993      -0.999999      -16.020784      -17.232466   \n",
            "25%         0.704348      -0.370828       -0.370029       -0.213028   \n",
            "50%         0.929523      -0.093192       -0.000572        0.000320   \n",
            "75%         0.978876       0.091003        0.172148        0.277600   \n",
            "max         0.999995       0.977251        9.944287       13.542882   \n",
            "\n",
            "       rotationRate.z  userAcceleration.x  userAcceleration.y  \\\n",
            "count   216290.000000       216290.000000       216290.000000   \n",
            "mean         0.013293           -0.005624            0.047037   \n",
            "std          0.823923            0.329618            0.535456   \n",
            "min        -10.829004           -5.805335           -3.325982   \n",
            "25%         -0.130471           -0.059578           -0.079322   \n",
            "50%          0.000156            0.000958            0.004075   \n",
            "75%          0.147765            0.051084            0.076055   \n",
            "max          9.621073            5.287045            7.067639   \n",
            "\n",
            "       userAcceleration.z         Gender  CorrectAnserLabel  \n",
            "count       216290.000000  216290.000000      216290.000000  \n",
            "mean             0.038143       0.571298           3.832304  \n",
            "std              0.380139       0.494892           1.632987  \n",
            "min             -7.743481       0.000000           1.000000  \n",
            "25%             -0.030807       0.000000           3.000000  \n",
            "50%              0.004977       1.000000           4.000000  \n",
            "75%              0.115237       1.000000           5.000000  \n",
            "max              5.240689       1.000000           6.000000  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r46xXqVxXSlw"
      },
      "source": [
        "# 各種定数の定義\n",
        "num_features = 12       #    IMU情報   : 姿勢(ロール, ピッチ, ヨー), 重力(x, y, z), 角速度(x, y, z), ユーザ加速度(x,y,z)\n",
        "num_act_labels = 1      # 行動ラベル数 : dws, ups, wlk, jog, sit, std\n",
        "num_gen_labels = 1      #     性別     : 0/1(female/male)\n",
        "\n",
        "act_labels = {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRDhjWzszBCe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "3b6a86fe-9699-4de5-d25a-ef3286de4ae1"
      },
      "source": [
        "# データの中身を描画\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (12.0, 8.0)\n",
        "\n",
        "def get_column(act, type):\n",
        "  data = train_data[train_data[\"CorrectAnserLabel\"]==act_labels[act]][type].values\n",
        "  length = len(train_data[train_data[\"CorrectAnserLabel\"]==act_labels[act]][type].values)\n",
        "  return length, data\n",
        "\n",
        "act = \"ups\"    # {\"dws\", \"ups\", \"wlk\", \"jog\", \"sit\", \"std\"} から選択\n",
        "num_rows = 3\n",
        "num_columns = 4\n",
        "types = [\"attitude.roll\",\"gravity.x\",\"rotationRate.x\",\"userAcceleration.x\",\"attitude.pitch\",\"gravity.y\",\"rotationRate.y\",\"userAcceleration.y\",\"attitude.yaw\",\"gravity.z\",\"rotationRate.z\",\"userAcceleration.z\"]\n",
        "\n",
        "for i, type in enumerate(types):\n",
        "  data = get_column(\"ups\", type)\n",
        "  plt.subplot(num_rows, num_columns, i+1)\n",
        "  plt.plot(range(data[0]), data[1])\n",
        "  \n",
        "  if i+1==1:\n",
        "    plt.title(\"Attitude\")\n",
        "    plt.ylabel(\"x\")\n",
        "  elif i+1==2:\n",
        "    plt.title(\"Gravity\")\n",
        "  elif i+1==3:\n",
        "    plt.title(\"Rotation Rate\")\n",
        "  elif i+1==4:\n",
        "    plt.title(\"User Acceleration\")\n",
        "  elif i+1==5:\n",
        "    plt.ylabel(\"y\")\n",
        "  elif i+1==9:\n",
        "    plt.ylabel(\"z\")\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caf5tHr_FRES"
      },
      "source": [
        "　上のコードセルで描画したグラフは，被験者が階段を登っている時のスマホIMU情報です．センサの生信号を目で見ても，ノイズが大きく，被験者がの行動を予測することは難しいことが分かります．\n",
        "このセンサの値から行動ラベルを予測します．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "od_aZ35By--_"
      },
      "source": [
        "# NumPy配列に変換\n",
        "train_data = train_data.values\n",
        "test_data = test_data.values"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1rWIOwYOShD"
      },
      "source": [
        "　最初に，CSVから取得したトレーニングデータとテストデータを，学習に用いる形状に加工します．IMU情報は時系列データであり，ある時刻でのデータとラベルだけでは意味を持ちません．そこで，一定時間分のデータを1つのセクションとして扱い，セクションの集合を学習に用いるトレーニングデータとします．  \n",
        "　セクションに分割する際に必要となるパラメータはセクションのサイズと，スライドサイズです．セクションサイズは1セクションに何サンプル含めるかを決めるパラメータで，サンプリング周波数が50Hzであることから，セクションサイズが50の場合は1セクションに1分間のデータが含まれることになります．(ただし，今回はデータを1/5に削減しているので，1セクション12秒間に相当) また，スライドサイズは1つ後のセクションをどれだけずらして開始するかを決めるパラメータで，小さすぎると計算コストが高くなります．今回はセクションサイズを50，スライドサイズを10として，以下のようにセクション分割しました．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLIhyMjR9Zgm"
      },
      "source": [
        "# 時系列データをセクションに分割する関数\n",
        "def time_series_to_section(dataset, num_act_labels, num_gen_labels, size_sections, size_slide, standardize = False, **options):\n",
        "  data = dataset[: , 0:-num_act_labels]           # IMUデータ+性別ラベルを取得\n",
        "  act_labels = dataset[: , -(num_act_labels)]     # 行動ラベルを取得\n",
        " \n",
        "  # === 標準化 =============================\n",
        "  if standardize:\n",
        "    if options:             # 引数で平均･分散を指定した場合\n",
        "      mean = options.get(\"mean\")\n",
        "      std = options.get(\"std\")\n",
        "      print(\"トレーニングデータの平均と分散を用いてテストデータを標準化しました．\")\n",
        "    else:                   # 指定のない場合は標準化\n",
        "      mean = data.mean(axis=0)\n",
        "      std = data.std(axis=0)\n",
        "      print(\"平均:\",str(mean.mean()),\", 分散:\",str(std.mean()), \"---> 標準化しました．\")           \n",
        "    data = (data - mean) / std\n",
        "  else:\n",
        "    print(\"標準化していません．\")\n",
        "\n",
        "  # あとで3次元配列に格納するために，一旦転置しておく\n",
        "  data = data.T\n",
        "          \n",
        "  size_features = data.shape[0]\n",
        "  size_data = data.shape[1]\n",
        "  num_sections = 1 + ( (size_data - size_sections) // size_slide ) \n",
        "          \n",
        "  # 1セクションのデータ形状を定義\n",
        "  data_sections = np.zeros((num_sections, size_features, size_sections))\n",
        "  act_secs_labels = np.zeros(num_sections)\n",
        "   \n",
        "  section_first_index = 0\n",
        "  for count_section in range(num_sections):   # 各セクションの第1要素のindex\n",
        "    section_first_index += size_slide     # セクションカウント\n",
        "    if(section_first_index+size_sections > size_data):    # セクション数を超えたら終了     \n",
        "      break\n",
        "    if act_labels[section_first_index] != act_labels[section_first_index+size_sections-1]:    # セクション内で行動ラベルが全て一緒でない時は無視\n",
        "      continue\n",
        "    # data_sections = np.append(data_sections, data[0:size_features, section_first_index:section_first_index+size_sections].reshape(1,size_features,size_sections), axis=0)   # セクションデータのcount_section要素目に代入\n",
        "    # act_secs_labels = np.append(act_secs_labels, act_labels[section_first_index].astype(int))\n",
        "    data_sections[count_section] = data[0:size_features, section_first_index:section_first_index+size_sections]   # セクションデータのcount_section要素目に代入\n",
        "    act_secs_labels[count_section] = act_labels[section_first_index].astype(int)\n",
        "  \n",
        "  return data_sections, act_secs_labels, mean, std"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6qwsWWc_vu0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73d6ee0f-ebce-4662-d25a-4864abc922b1"
      },
      "source": [
        "size_sections = 50   # セクションサイズ\n",
        "size_slide = 10      # スライドサイズ\n",
        "\n",
        "# 時系列データをセクション分割\n",
        "print(\"分割後のセクション形状: (\",num_features+num_gen_labels,\"x\",size_sections,\")\")\n",
        "_X_train, _y_train, train_mean, train_std = time_series_to_section( train_data.copy(),\n",
        "                                                                  num_act_labels,\n",
        "                                                                  num_gen_labels,\n",
        "                                                                  size_sections,\n",
        "                                                                  size_slide,\n",
        "                                                                  standardize = True )\n",
        "\n",
        "_X_test, _y_test, test_mean, test_std = time_series_to_section( test_data.copy(),\n",
        "                                                              num_act_labels,\n",
        "                                                              num_gen_labels,\n",
        "                                                              size_sections,\n",
        "                                                              size_slide,\n",
        "                                                              standardize = True,\n",
        "                                                              mean = train_mean, \n",
        "                                                              std = train_std  )\n",
        "\n",
        "y_train = np.delete(_y_train, np.where(_y_train==0))\n",
        "X_train = np.delete(_X_train, np.where(_y_train==0), axis=0)\n",
        "y_test = np.delete(_y_test, np.where(_y_test==0))\n",
        "X_test = np.delete(_X_test, np.where(_y_test==0), axis=0)\n",
        "\n",
        "print(\"トレーニングデータ形状:\", X_train.shape)\n",
        "print(\"テストデータ形状:\", X_test.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "分割後のセクション形状: ( 13 x 50 )\n",
            "平均: 0.0035300797597241063 , 分散: 0.749962379300086 ---> 標準化しました．\n",
            "トレーニングデータの平均と分散を用いてテストデータを標準化しました．\n",
            "トレーニングデータ形状: (20918, 13, 50)\n",
            "テストデータ形状: (5922, 13, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqhrZ-5-ZdtS"
      },
      "source": [
        "　セクションに分割できたので，トレーニングデータの一部をバリデーションデータに分割し，DatasetとDataLoaderを準備します．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHxpu2stn6Od",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81077f69-f2ee-485c-c96d-eb96a1a102d2"
      },
      "source": [
        "# X_trainをトレーニングデータ･バリデーションデータに分割\n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# trainを17000件のtrainingデータと3918件のvalidationデータに分割する\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, train_size=17000, random_state=0)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_valid.shape)\n",
        "print(y_valid.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(17000, 13, 50)\n",
            "(17000,)\n",
            "(3918, 13, 50)\n",
            "(3918,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4xIrYGUHMC6"
      },
      "source": [
        "# 通常のNNは2次元形式で与えられても処理できないため，28x28の要素がある1次元配列に変換してからPyTorch形式に変換する\n",
        "X_train_flatten = X_train.reshape(X_train.shape[0], X_train.shape[1]*X_train.shape[2]) # 17000件の2次元データ（12×50）を17000件の1次元データ(600)に変換\n",
        "X_valid_flatten  = X_valid.reshape(X_valid.shape[0], X_valid.shape[1]*X_valid.shape[2]) \n",
        "X_test_flatten  = X_test.reshape(X_test.shape[0], X_test.shape[1]*X_test.shape[2]) \n",
        "\n",
        "X_train_torch = torch.tensor(X_train_flatten, dtype=torch.float) # dtype=torch.floatを忘れずに\n",
        "X_valid_torch = torch.tensor(X_valid_flatten, dtype=torch.float) \n",
        "X_test_torch  = torch.tensor(X_test_flatten,  dtype=torch.float) \n",
        "\n",
        "# yについてもPyTorch形式に変換する\n",
        "y_train_torch = torch.tensor(y_train, dtype=torch.long) # dtype=torch.longを忘れずに\n",
        "y_valid_torch = torch.tensor(y_valid, dtype=torch.long) \n",
        "y_test_torch  = torch.tensor(y_test,  dtype=torch.long)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6aI8FcD8o6rU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27156d2c-5072-429a-d6dc-cfc5830243a3"
      },
      "source": [
        "# datasetとdataloaderを用意\n",
        "\n",
        "# XとYの組み合わせを保持してくれる便利なモノ\n",
        "train_dataset = torch.utils.data.TensorDataset(X_train_torch, y_train_torch)\n",
        "valid_dataset = torch.utils.data.TensorDataset(X_valid_torch, y_valid_torch)\n",
        "test_dataset  = torch.utils.data.TensorDataset(X_test_torch,  y_test_torch)\n",
        "\n",
        "batch_size = 256 # 今回は256件のデータごとに学習を行う\n",
        "\n",
        "# XとYの組み合わせを(batch_size)個ずつ出力する便利なモノ\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size)\n",
        "valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=batch_size)\n",
        "test_loader  = torch.utils.data.DataLoader(test_dataset,  batch_size=batch_size)\n",
        "\n",
        "for X, y in train_loader:\n",
        "  print(X)\n",
        "  print(y)\n",
        "  break"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-0.3279, -0.2462, -0.2577,  ...,  0.8663,  0.8663,  0.8663],\n",
            "        [ 1.1728,  1.2717,  1.3469,  ..., -1.1544, -1.1544, -1.1544],\n",
            "        [ 0.6108,  0.3398,  0.2401,  ...,  0.8663,  0.8663,  0.8663],\n",
            "        ...,\n",
            "        [ 0.0903,  0.2408,  0.0995,  ..., -1.1544, -1.1544, -1.1544],\n",
            "        [-1.1895, -1.1894, -1.1895,  ...,  0.8663,  0.8663,  0.8663],\n",
            "        [ 1.0007,  1.1063,  1.1099,  ..., -1.1544, -1.1544, -1.1544]])\n",
            "tensor([2, 4, 3, 6, 5, 6, 3, 5, 2, 3, 6, 5, 2, 5, 6, 5, 3, 5, 6, 1, 3, 4, 3, 3,\n",
            "        2, 3, 4, 3, 6, 5, 5, 2, 2, 4, 3, 6, 3, 4, 6, 5, 2, 2, 5, 5, 3, 3, 6, 3,\n",
            "        4, 2, 4, 6, 6, 3, 1, 3, 3, 4, 6, 5, 3, 3, 2, 3, 1, 5, 1, 5, 5, 6, 6, 5,\n",
            "        5, 6, 5, 1, 3, 6, 6, 4, 2, 3, 6, 3, 2, 5, 3, 2, 2, 2, 1, 4, 5, 5, 3, 4,\n",
            "        1, 5, 5, 3, 3, 3, 2, 2, 6, 6, 5, 5, 3, 3, 3, 1, 6, 2, 3, 1, 5, 3, 6, 2,\n",
            "        2, 5, 3, 3, 5, 4, 2, 3, 5, 1, 5, 2, 6, 2, 3, 5, 3, 2, 3, 6, 3, 5, 5, 3,\n",
            "        2, 2, 6, 4, 2, 3, 1, 5, 2, 6, 3, 2, 3, 2, 4, 6, 1, 5, 6, 3, 5, 1, 3, 4,\n",
            "        2, 5, 2, 3, 5, 2, 3, 5, 1, 1, 4, 4, 5, 4, 3, 5, 3, 2, 5, 6, 6, 3, 3, 2,\n",
            "        5, 5, 1, 6, 2, 6, 1, 6, 5, 6, 5, 1, 2, 3, 6, 5, 4, 6, 3, 3, 2, 6, 5, 1,\n",
            "        2, 2, 6, 2, 6, 3, 5, 2, 6, 3, 1, 6, 5, 4, 3, 6, 3, 6, 6, 3, 2, 3, 3, 6,\n",
            "        1, 5, 2, 1, 5, 5, 4, 5, 2, 5, 6, 6, 1, 4, 5, 3])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZVGlgjtckZx"
      },
      "source": [
        "## 3 | AIエンジニア的?な予測\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ma367Txpp65J"
      },
      "source": [
        "### 3.1 | 多層NNによる予測\n",
        "\n",
        "\n",
        " まず，多層ニューラルネットワークを用いて，IMU情報と性別から行動ラベルを予測します．\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-B9SHNUSHaFI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6589bce-17ac-463e-aaca-ed8a58e2696f"
      },
      "source": [
        "# リアルタイムで学習の状況を見る1つの方法として，livelossplotを用いる\n",
        "!pip install livelossplot"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting livelossplot\n",
            "  Downloading https://files.pythonhosted.org/packages/0f/08/1884157a3de72d41fa97cacacafaa49abf00eba53cb7e08615b2b65b4a9d/livelossplot-0.5.3-py3-none-any.whl\n",
            "Requirement already satisfied: bokeh; python_version >= \"3.6\" in /usr/local/lib/python3.6/dist-packages (from livelossplot) (2.1.1)\n",
            "Requirement already satisfied: matplotlib; python_version >= \"3.6\" in /usr/local/lib/python3.6/dist-packages (from livelossplot) (3.2.2)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from livelossplot) (5.5.0)\n",
            "Requirement already satisfied: pillow>=4.0 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (7.0.0)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (3.13)\n",
            "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (20.4)\n",
            "Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (5.1.1)\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib; python_version >= \"3.6\"->livelossplot) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib; python_version >= \"3.6\"->livelossplot) (0.10.0)\n",
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            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from packaging>=16.8->bokeh; python_version >= \"3.6\"->livelossplot) (1.15.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.7->bokeh; python_version >= \"3.6\"->livelossplot) (1.1.1)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->livelossplot) (0.2.5)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->livelossplot) (0.2.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->livelossplot) (0.6.0)\n",
            "Installing collected packages: livelossplot\n",
            "Successfully installed livelossplot-0.5.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4zKvKW9o6um"
      },
      "source": [
        "# モデル model 、誤差関数 loss_fn、および最適化手法 opt を引数とすることで、汎用的に学習や予測を行えるコードを作成\n",
        "import torch\n",
        "from livelossplot import PlotLosses\n",
        "\n",
        "device_GPU = torch.device(\"cuda:0\") \n",
        "\n",
        "def calculate_model(model, loss_fn, opt, dataloader, device, mode=\"evaluate\"):\n",
        "  if mode == 'evaluate':\n",
        "    model.eval() # 学習を行わない時は evaluate 状態にする\n",
        "\n",
        "  sum_loss = 0\n",
        "  correct = 0\n",
        "  count = len(dataloader.dataset)\n",
        "  \n",
        "  for X, y in dataloader:\n",
        "    y-=1\n",
        "    X = X.to(device) # GPUへデータを転送\n",
        "    y = y.to(device) # GPUへデータを転送\n",
        "    y_pred = model(X) # Xからyを予測（softmaxを行う前の値が出力される）\n",
        "    \n",
        "    _, predicted = torch.max(y_pred.data, 1) # 6クラスのうち、予測確率最大のクラス番号を取得\n",
        "    correct += (predicted == y).sum().item() # 予測に成功した件数をカウント（accuracy計算用）\n",
        "    \n",
        "    loss = loss_fn(y_pred, y)        # ミニバッチ内の訓練誤差の 平均 を計算\n",
        "    sum_loss += loss.item()*len(y) # エポック全体の訓練誤差の 合計 を計算しておく\n",
        "    \n",
        "    if mode == 'update':\n",
        "      # 重みの更新\n",
        "      opt.zero_grad()\n",
        "      loss.backward()\n",
        "      opt.step()\n",
        "    \n",
        "  # エポック内の訓練誤差の平均値と予測精度を計算\n",
        "  mean_train_loss = sum_loss / count\n",
        "  train_accuracy = correct / count\n",
        "  \n",
        "  if mode == 'evaluate':\n",
        "    model.train() # evaluate状態からtrain状態に戻しておく\n",
        "\n",
        "  return mean_train_loss, train_accuracy    \n",
        "\n",
        "def train(model, loss_fn, opt, train_loader, valid_loader, device, epoch=50):\n",
        "  liveloss = PlotLosses() # 描画の初期化\n",
        "  for i in range(epoch):\n",
        "    train_loss, train_accuracy = calculate_model(model, loss_fn, opt, train_loader, device, mode='update')\n",
        "    valid_loss, valid_accuracy = calculate_model(model, loss_fn,   _, valid_loader, device, mode='evaluate')\n",
        "  \n",
        "    # Visualize the loss and accuracy values.\n",
        "    liveloss.update({\n",
        "      'log loss': train_loss,\n",
        "      'val_log loss': valid_loss,\n",
        "      'accuracy': train_accuracy,\n",
        "      'val_accuracy': valid_accuracy,\n",
        "    })\n",
        "    liveloss.draw()  \n",
        "  print('Accuracy: {:.4f} (valid), {:.4f} (train)'.format(valid_accuracy, train_accuracy))\n",
        "  return model # 学習したモデルを返す"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipSTReCKpQT8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "outputId": "d3cecd88-a0f5-484f-f1c9-511172a3e2d8"
      },
      "source": [
        "# モデルの作成と学習の実行\n",
        "torch.manual_seed(0)\n",
        "\n",
        "model = torch.nn.Sequential(\n",
        "  torch.nn.Linear(13*50, 512), \n",
        "  torch.nn.ReLU(),\n",
        "  torch.nn.Dropout(p=0.5),\n",
        "  torch.nn.Linear(512, 256), \n",
        "  torch.nn.ReLU(),\n",
        "  torch.nn.Dropout(p=0.5),\n",
        "  torch.nn.Linear(256, 6),    # 行動ラベル数は6 : dws, ups, wlk, jog, sit, std \n",
        ")\n",
        "model.to(device_GPU) # deviceへモデルを転送\n",
        "\n",
        "# 誤差関数と最適化手法を準備\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.2)\n",
        "\n",
        "# 学習の実行\n",
        "trained_model = train(model, loss_fn, optimizer, train_loader, valid_loader, device_GPU)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\ttraining         \t (min:    0.665, max:    0.996, cur:    0.996)\n",
            "\tvalidation       \t (min:    0.879, max:    0.974, cur:    0.973)\n",
            "log loss\n",
            "\ttraining         \t (min:    0.011, max:    0.947, cur:    0.011)\n",
            "\tvalidation       \t (min:    0.111, max:    0.440, cur:    0.144)\n",
            "Accuracy: 0.9735 (valid), 0.9962 (train)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g14l-ZTKuS5L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7854620b-5d8b-495e-eabb-2743a064b8e7"
      },
      "source": [
        "# 予測の実行\n",
        "test_loss, test_accuracy = calculate_model(model, loss_fn,   _, test_loader, device_GPU, mode='evaluate')\n",
        "# print(test_loss)\n",
        "print(\"テストデータの予測正解率 : \", test_accuracy)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "テストデータの予測正解率 :  0.9638635596082404\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LK8UIvQ17Upb"
      },
      "source": [
        "　以上より， 多層NNでは**96.39 %**で予測できることが分かりました．"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8RBeq4rioPm"
      },
      "source": [
        "### 3.2 | CVによるハイパーパラメータ探索\n",
        "\n",
        "　3.1節の多層NNについてハイパーパラメータ探索を行います．今回はStochastic Gradient Decentの学習率`lr`の探索を行います．  \n",
        "【注意】ハイパーパラメータ探索には4分程度かかります．\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcCC6PjgNgYM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc044301-fdca-4f1b-c155-8daa46d966b0"
      },
      "source": [
        "! [ ! -z \"$COLAB_GPU\" ] && pip install torch skorch"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.7.0+cu101)\n",
            "Collecting skorch\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/c7/2f6434f9360c91a4bf14ae85f634758e5dacd3539cca4266a60be9f881ae/skorch-0.9.0-py3-none-any.whl (125kB)\n",
            "\u001b[K     |████████████████████████████████| 133kB 4.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch) (1.18.5)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch) (0.16.0)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch) (0.8)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch) (3.7.4.3)\n",
            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from skorch) (0.22.2.post1)\n",
            "Requirement already satisfied: tqdm>=4.14.0 in /usr/local/lib/python3.6/dist-packages (from skorch) (4.41.1)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from skorch) (1.4.1)\n",
            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.6/dist-packages (from skorch) (0.8.7)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.19.1->skorch) (0.17.0)\n",
            "Installing collected packages: skorch\n",
            "Successfully installed skorch-0.9.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGu4Hj4D7mCB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f9cef49-8b7a-4c07-a70a-6e6504e3fb81"
      },
      "source": [
        "# クロスバリデーションからlrの最適な値を求める\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from skorch import NeuralNetClassifier\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class Model(torch.nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Model, self).__init__()\n",
        "    self.l1 = torch.nn.Linear(13*50, 512)\n",
        "    self.relu1 = torch.nn.ReLU()\n",
        "    self.drop1 = torch.nn.Dropout(0.5)\n",
        "    self.l2 = torch.nn.Linear(512, 256)\n",
        "    self.relu2 = torch.nn.ReLU()\n",
        "    self.drop2 = torch.nn.Dropout(0.5)\n",
        "    self.l3 = torch.nn.Linear(256, 6)\n",
        "\n",
        "  def forward(self, x):\n",
        "    y = self.l1(x)\n",
        "    y = self.relu1(y)\n",
        "    y = self.drop1(y)\n",
        "    y = self.l2(y)\n",
        "    y = self.relu2(y)\n",
        "    y = self.drop2(y)\n",
        "    y = self.l3(y)\n",
        "    return y\n",
        "    \n",
        "model2 = Model()\n",
        "\n",
        "net = NeuralNetClassifier(\n",
        "    module=model2,\n",
        "    max_epochs=20,\n",
        "    optimizer=torch.optim.SGD,\n",
        "    criterion= torch.nn.CrossEntropyLoss,\n",
        "    lr=0.1,\n",
        ")\n",
        "np.random.seed(0)\n",
        "\n",
        "# 辞書型が1つ入った配列\n",
        "param_grid = [  \n",
        "  { \n",
        "    \"lr\": [0.1, 0.15, 0.2, 0.25]\n",
        "  } \n",
        "] \n",
        "grid_search_dt = GridSearchCV(net, param_grid, cv=4, verbose=2)   # 4-fold CVをしながら最適なモデルを決定する\n",
        "grid_search_dt.fit(X_train_torch, y_train_torch-1)\n",
        "print(\"\\n\", \"最適ハイパーパラメータ : \", grid_search_dt.best_params_)\n",
        "# print(grid_search_dt.best_score_)\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 4 folds for each of 4 candidates, totalling 16 fits\n",
            "[CV] lr=0.1 ..........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m1.1054\u001b[0m       \u001b[32m0.7686\u001b[0m        \u001b[35m0.6841\u001b[0m  0.6620\n",
            "      2        \u001b[36m0.5844\u001b[0m       \u001b[32m0.9055\u001b[0m        \u001b[35m0.3323\u001b[0m  0.5769\n",
            "      3        \u001b[36m0.3644\u001b[0m       \u001b[32m0.9325\u001b[0m        \u001b[35m0.2276\u001b[0m  0.5580\n",
            "      4        \u001b[36m0.2692\u001b[0m       \u001b[32m0.9447\u001b[0m        \u001b[35m0.1865\u001b[0m  0.5671\n",
            "      5        \u001b[36m0.2212\u001b[0m       \u001b[32m0.9510\u001b[0m        \u001b[35m0.1697\u001b[0m  0.5622\n",
            "      6        \u001b[36m0.1981\u001b[0m       \u001b[32m0.9553\u001b[0m        \u001b[35m0.1549\u001b[0m  0.5570\n",
            "      7        \u001b[36m0.1658\u001b[0m       0.9549        0.1557  0.5545\n",
            "      8        \u001b[36m0.1490\u001b[0m       \u001b[32m0.9561\u001b[0m        \u001b[35m0.1472\u001b[0m  0.5552\n",
            "      9        \u001b[36m0.1285\u001b[0m       \u001b[32m0.9565\u001b[0m        \u001b[35m0.1396\u001b[0m  0.5726\n",
            "     10        \u001b[36m0.1220\u001b[0m       \u001b[32m0.9576\u001b[0m        0.1405  0.5553\n",
            "     11        \u001b[36m0.1087\u001b[0m       \u001b[32m0.9600\u001b[0m        \u001b[35m0.1328\u001b[0m  0.5719\n",
            "     12        \u001b[36m0.0970\u001b[0m       0.9580        \u001b[35m0.1291\u001b[0m  0.5678\n",
            "     13        \u001b[36m0.0828\u001b[0m       \u001b[32m0.9608\u001b[0m        0.1340  0.5597\n",
            "     14        \u001b[36m0.0804\u001b[0m       \u001b[32m0.9635\u001b[0m        \u001b[35m0.1259\u001b[0m  0.5598\n",
            "     15        \u001b[36m0.0739\u001b[0m       \u001b[32m0.9643\u001b[0m        \u001b[35m0.1236\u001b[0m  0.5562\n",
            "     16        \u001b[36m0.0698\u001b[0m       0.9624        0.1240  0.5529\n",
            "     17        \u001b[36m0.0605\u001b[0m       0.9635        0.1296  0.5674\n",
            "     18        \u001b[36m0.0582\u001b[0m       \u001b[32m0.9647\u001b[0m        0.1311  0.5560\n",
            "     19        \u001b[36m0.0542\u001b[0m       0.9643        0.1306  0.5500\n",
            "     20        \u001b[36m0.0473\u001b[0m       0.9631        0.1353  0.5509\n",
            "[CV] ........................................... lr=0.1, total=  11.6s\n",
            "[CV] lr=0.1 ..........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   11.6s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m1.0999\u001b[0m       \u001b[32m0.7784\u001b[0m        \u001b[35m0.6867\u001b[0m  0.5562\n",
            "      2        \u001b[36m0.5778\u001b[0m       \u001b[32m0.9012\u001b[0m        \u001b[35m0.3463\u001b[0m  0.5623\n",
            "      3        \u001b[36m0.3716\u001b[0m       \u001b[32m0.9275\u001b[0m        \u001b[35m0.2326\u001b[0m  0.5459\n",
            "      4        \u001b[36m0.2728\u001b[0m       \u001b[32m0.9325\u001b[0m        \u001b[35m0.2032\u001b[0m  0.5551\n",
            "      5        \u001b[36m0.2264\u001b[0m       \u001b[32m0.9412\u001b[0m        \u001b[35m0.1753\u001b[0m  0.5628\n",
            "      6        \u001b[36m0.2074\u001b[0m       \u001b[32m0.9416\u001b[0m        \u001b[35m0.1639\u001b[0m  0.5558\n",
            "      7        \u001b[36m0.1676\u001b[0m       \u001b[32m0.9482\u001b[0m        \u001b[35m0.1511\u001b[0m  0.5497\n",
            "      8        \u001b[36m0.1448\u001b[0m       \u001b[32m0.9506\u001b[0m        \u001b[35m0.1481\u001b[0m  0.5530\n",
            "      9        \u001b[36m0.1384\u001b[0m       \u001b[32m0.9518\u001b[0m        \u001b[35m0.1418\u001b[0m  0.5861\n",
            "     10        \u001b[36m0.1245\u001b[0m       \u001b[32m0.9529\u001b[0m        \u001b[35m0.1418\u001b[0m  0.5514\n",
            "     11        \u001b[36m0.1067\u001b[0m       \u001b[32m0.9561\u001b[0m        \u001b[35m0.1336\u001b[0m  0.5599\n",
            "     12        0.1075       0.9557        0.1385  0.5628\n",
            "     13        \u001b[36m0.0924\u001b[0m       0.9557        0.1420  0.5574\n",
            "     14        \u001b[36m0.0845\u001b[0m       0.9561        0.1405  0.5592\n",
            "     15        \u001b[36m0.0719\u001b[0m       0.9553        0.1401  0.5560\n",
            "     16        \u001b[36m0.0717\u001b[0m       \u001b[32m0.9573\u001b[0m        0.1362  0.5501\n",
            "     17        \u001b[36m0.0589\u001b[0m       \u001b[32m0.9592\u001b[0m        \u001b[35m0.1331\u001b[0m  0.5532\n",
            "     18        \u001b[36m0.0578\u001b[0m       \u001b[32m0.9620\u001b[0m        \u001b[35m0.1326\u001b[0m  0.5706\n",
            "     19        \u001b[36m0.0550\u001b[0m       0.9612        \u001b[35m0.1299\u001b[0m  0.5726\n",
            "     20        \u001b[36m0.0541\u001b[0m       0.9592        0.1349  0.5739\n",
            "[CV] ........................................... lr=0.1, total=  11.5s\n",
            "[CV] lr=0.1 ..........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m1.1015\u001b[0m       \u001b[32m0.7569\u001b[0m        \u001b[35m0.6765\u001b[0m  0.5577\n",
            "      2        \u001b[36m0.5785\u001b[0m       \u001b[32m0.9024\u001b[0m        \u001b[35m0.3309\u001b[0m  0.5490\n",
            "      3        \u001b[36m0.3600\u001b[0m       \u001b[32m0.9278\u001b[0m        \u001b[35m0.2398\u001b[0m  0.5523\n",
            "      4        \u001b[36m0.2685\u001b[0m       \u001b[32m0.9345\u001b[0m        \u001b[35m0.1982\u001b[0m  0.5527\n",
            "      5        \u001b[36m0.2160\u001b[0m       \u001b[32m0.9400\u001b[0m        \u001b[35m0.1750\u001b[0m  0.5697\n",
            "      6        \u001b[36m0.1916\u001b[0m       \u001b[32m0.9467\u001b[0m        \u001b[35m0.1649\u001b[0m  0.5523\n",
            "      7        \u001b[36m0.1685\u001b[0m       \u001b[32m0.9471\u001b[0m        \u001b[35m0.1564\u001b[0m  0.5997\n",
            "      8        \u001b[36m0.1494\u001b[0m       \u001b[32m0.9486\u001b[0m        \u001b[35m0.1530\u001b[0m  0.5592\n",
            "      9        \u001b[36m0.1308\u001b[0m       \u001b[32m0.9549\u001b[0m        \u001b[35m0.1424\u001b[0m  0.5514\n",
            "     10        \u001b[36m0.1182\u001b[0m       \u001b[32m0.9569\u001b[0m        \u001b[35m0.1368\u001b[0m  0.5570\n",
            "     11        \u001b[36m0.1032\u001b[0m       0.9565        0.1401  0.5720\n",
            "     12        \u001b[36m0.0959\u001b[0m       \u001b[32m0.9588\u001b[0m        0.1372  0.5564\n",
            "     13        \u001b[36m0.0840\u001b[0m       \u001b[32m0.9604\u001b[0m        \u001b[35m0.1354\u001b[0m  0.5414\n",
            "     14        \u001b[36m0.0785\u001b[0m       \u001b[32m0.9620\u001b[0m        \u001b[35m0.1298\u001b[0m  0.5559\n",
            "     15        \u001b[36m0.0736\u001b[0m       \u001b[32m0.9639\u001b[0m        0.1352  0.5535\n",
            "     16        \u001b[36m0.0704\u001b[0m       0.9600        0.1416  0.5494\n",
            "     17        \u001b[36m0.0578\u001b[0m       0.9592        0.1420  0.5509\n",
            "     18        \u001b[36m0.0560\u001b[0m       0.9631        0.1349  0.5512\n",
            "     19        \u001b[36m0.0496\u001b[0m       0.9604        0.1409  0.5940\n",
            "     20        0.0522       \u001b[32m0.9655\u001b[0m        0.1345  0.5541\n",
            "[CV] ........................................... lr=0.1, total=  11.5s\n",
            "[CV] lr=0.1 ..........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m1.1069\u001b[0m       \u001b[32m0.6341\u001b[0m        \u001b[35m0.8919\u001b[0m  0.5598\n",
            "      2        \u001b[36m0.5837\u001b[0m       \u001b[32m0.7729\u001b[0m        \u001b[35m0.5067\u001b[0m  0.5727\n",
            "      3        \u001b[36m0.3703\u001b[0m       \u001b[32m0.8443\u001b[0m        \u001b[35m0.3647\u001b[0m  0.5434\n",
            "      4        \u001b[36m0.2815\u001b[0m       \u001b[32m0.9114\u001b[0m        \u001b[35m0.2460\u001b[0m  0.5617\n",
            "      5        \u001b[36m0.2240\u001b[0m       \u001b[32m0.9345\u001b[0m        \u001b[35m0.2041\u001b[0m  0.5787\n",
            "      6        \u001b[36m0.1976\u001b[0m       \u001b[32m0.9388\u001b[0m        \u001b[35m0.1894\u001b[0m  0.5679\n",
            "      7        \u001b[36m0.1598\u001b[0m       \u001b[32m0.9447\u001b[0m        \u001b[35m0.1705\u001b[0m  0.5588\n",
            "      8        \u001b[36m0.1500\u001b[0m       \u001b[32m0.9494\u001b[0m        \u001b[35m0.1571\u001b[0m  0.5662\n",
            "      9        \u001b[36m0.1280\u001b[0m       \u001b[32m0.9506\u001b[0m        \u001b[35m0.1481\u001b[0m  0.5718\n",
            "     10        \u001b[36m0.1180\u001b[0m       \u001b[32m0.9533\u001b[0m        \u001b[35m0.1439\u001b[0m  0.5734\n",
            "     11        \u001b[36m0.1066\u001b[0m       \u001b[32m0.9588\u001b[0m        \u001b[35m0.1276\u001b[0m  0.5707\n",
            "     12        \u001b[36m0.0966\u001b[0m       0.9549        0.1386  0.5699\n",
            "     13        \u001b[36m0.0900\u001b[0m       0.9588        \u001b[35m0.1257\u001b[0m  0.5701\n",
            "     14        \u001b[36m0.0827\u001b[0m       0.9573        0.1278  0.5700\n",
            "     15        \u001b[36m0.0668\u001b[0m       \u001b[32m0.9608\u001b[0m        0.1267  0.5602\n",
            "     16        \u001b[36m0.0635\u001b[0m       \u001b[32m0.9616\u001b[0m        \u001b[35m0.1238\u001b[0m  0.5593\n",
            "     17        0.0646       \u001b[32m0.9620\u001b[0m        0.1269  0.5530\n",
            "     18        \u001b[36m0.0542\u001b[0m       0.9588        0.1287  0.5678\n",
            "     19        \u001b[36m0.0522\u001b[0m       0.9612        0.1310  0.5487\n",
            "     20        \u001b[36m0.0468\u001b[0m       0.9612        0.1363  0.5557\n",
            "[CV] ........................................... lr=0.1, total=  11.6s\n",
            "[CV] lr=0.15 .........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.9930\u001b[0m       \u001b[32m0.8710\u001b[0m        \u001b[35m0.5032\u001b[0m  0.5633\n",
            "      2        \u001b[36m0.4355\u001b[0m       \u001b[32m0.9353\u001b[0m        \u001b[35m0.2305\u001b[0m  0.5519\n",
            "      3        \u001b[36m0.2871\u001b[0m       \u001b[32m0.9475\u001b[0m        \u001b[35m0.1856\u001b[0m  0.5738\n",
            "      4        \u001b[36m0.2205\u001b[0m       \u001b[32m0.9490\u001b[0m        \u001b[35m0.1652\u001b[0m  0.5466\n",
            "      5        \u001b[36m0.1751\u001b[0m       \u001b[32m0.9522\u001b[0m        \u001b[35m0.1520\u001b[0m  0.5499\n",
            "      6        \u001b[36m0.1497\u001b[0m       \u001b[32m0.9561\u001b[0m        \u001b[35m0.1488\u001b[0m  0.5680\n",
            "      7        \u001b[36m0.1273\u001b[0m       0.9541        \u001b[35m0.1423\u001b[0m  0.5655\n",
            "      8        \u001b[36m0.1153\u001b[0m       \u001b[32m0.9596\u001b[0m        \u001b[35m0.1385\u001b[0m  0.5601\n",
            "      9        \u001b[36m0.1104\u001b[0m       \u001b[32m0.9604\u001b[0m        \u001b[35m0.1287\u001b[0m  0.5561\n",
            "     10        \u001b[36m0.0960\u001b[0m       \u001b[32m0.9612\u001b[0m        \u001b[35m0.1239\u001b[0m  0.5550\n",
            "     11        \u001b[36m0.0817\u001b[0m       0.9596        0.1300  0.5518\n",
            "     12        \u001b[36m0.0712\u001b[0m       \u001b[32m0.9624\u001b[0m        0.1305  0.5730\n",
            "     13        0.0753       0.9608        0.1274  0.5475\n",
            "     14        \u001b[36m0.0663\u001b[0m       0.9620        0.1256  0.5610\n",
            "     15        \u001b[36m0.0542\u001b[0m       \u001b[32m0.9627\u001b[0m        0.1267  0.5736\n",
            "     16        \u001b[36m0.0501\u001b[0m       \u001b[32m0.9639\u001b[0m        0.1337  0.5567\n",
            "     17        \u001b[36m0.0442\u001b[0m       0.9639        0.1384  0.5547\n",
            "     18        0.0460       0.9584        0.1431  0.5513\n",
            "     19        0.0463       0.9624        0.1339  0.5506\n",
            "     20        \u001b[36m0.0376\u001b[0m       \u001b[32m0.9671\u001b[0m        0.1323  0.5577\n",
            "[CV] .......................................... lr=0.15, total=  11.5s\n",
            "[CV] lr=0.15 .........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.9752\u001b[0m       \u001b[32m0.8604\u001b[0m        \u001b[35m0.5069\u001b[0m  0.5767\n",
            "      2        \u001b[36m0.4308\u001b[0m       \u001b[32m0.9263\u001b[0m        \u001b[35m0.2433\u001b[0m  0.5652\n",
            "      3        \u001b[36m0.2894\u001b[0m       \u001b[32m0.9369\u001b[0m        \u001b[35m0.1913\u001b[0m  0.5510\n",
            "      4        \u001b[36m0.2226\u001b[0m       \u001b[32m0.9404\u001b[0m        \u001b[35m0.1744\u001b[0m  0.5623\n",
            "      5        \u001b[36m0.1864\u001b[0m       \u001b[32m0.9439\u001b[0m        \u001b[35m0.1657\u001b[0m  0.5535\n",
            "      6        \u001b[36m0.1586\u001b[0m       \u001b[32m0.9478\u001b[0m        \u001b[35m0.1442\u001b[0m  0.5575\n",
            "      7        \u001b[36m0.1353\u001b[0m       \u001b[32m0.9514\u001b[0m        0.1451  0.5502\n",
            "      8        \u001b[36m0.1274\u001b[0m       \u001b[32m0.9525\u001b[0m        0.1447  0.5640\n",
            "      9        \u001b[36m0.1092\u001b[0m       0.9498        0.1488  0.5606\n",
            "     10        \u001b[36m0.0999\u001b[0m       \u001b[32m0.9553\u001b[0m        \u001b[35m0.1401\u001b[0m  0.5506\n",
            "     11        \u001b[36m0.0895\u001b[0m       \u001b[32m0.9557\u001b[0m        \u001b[35m0.1326\u001b[0m  0.5626\n",
            "     12        \u001b[36m0.0742\u001b[0m       \u001b[32m0.9620\u001b[0m        \u001b[35m0.1244\u001b[0m  0.5559\n",
            "     13        \u001b[36m0.0692\u001b[0m       0.9569        0.1375  0.5648\n",
            "     14        \u001b[36m0.0683\u001b[0m       0.9588        0.1370  0.5500\n",
            "     15        \u001b[36m0.0636\u001b[0m       0.9588        0.1508  0.5656\n",
            "     16        \u001b[36m0.0489\u001b[0m       0.9604        0.1431  0.5646\n",
            "     17        0.0534       \u001b[32m0.9627\u001b[0m        0.1339  0.5469\n",
            "     18        0.0491       0.9624        0.1371  0.5568\n",
            "     19        \u001b[36m0.0427\u001b[0m       0.9596        0.1514  0.5730\n",
            "     20        \u001b[36m0.0415\u001b[0m       0.9620        0.1458  0.5684\n",
            "[CV] .......................................... lr=0.15, total=  11.5s\n",
            "[CV] lr=0.15 .........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.9778\u001b[0m       \u001b[32m0.8482\u001b[0m        \u001b[35m0.5438\u001b[0m  0.5646\n",
            "      2        \u001b[36m0.4275\u001b[0m       \u001b[32m0.9275\u001b[0m        \u001b[35m0.2450\u001b[0m  0.5504\n",
            "      3        \u001b[36m0.2826\u001b[0m       \u001b[32m0.9361\u001b[0m        \u001b[35m0.1975\u001b[0m  0.5593\n",
            "      4        \u001b[36m0.2206\u001b[0m       \u001b[32m0.9435\u001b[0m        \u001b[35m0.1751\u001b[0m  0.5551\n",
            "      5        \u001b[36m0.1794\u001b[0m       \u001b[32m0.9471\u001b[0m        \u001b[35m0.1578\u001b[0m  0.5668\n",
            "      6        \u001b[36m0.1545\u001b[0m       \u001b[32m0.9494\u001b[0m        \u001b[35m0.1513\u001b[0m  0.5460\n",
            "      7        \u001b[36m0.1266\u001b[0m       \u001b[32m0.9573\u001b[0m        \u001b[35m0.1396\u001b[0m  0.5652\n",
            "      8        \u001b[36m0.1117\u001b[0m       0.9561        \u001b[35m0.1390\u001b[0m  0.5466\n",
            "      9        \u001b[36m0.1000\u001b[0m       0.9561        0.1486  0.5798\n",
            "     10        \u001b[36m0.0937\u001b[0m       \u001b[32m0.9580\u001b[0m        \u001b[35m0.1321\u001b[0m  0.5502\n",
            "     11        \u001b[36m0.0855\u001b[0m       \u001b[32m0.9588\u001b[0m        \u001b[35m0.1306\u001b[0m  0.5612\n",
            "     12        \u001b[36m0.0741\u001b[0m       \u001b[32m0.9631\u001b[0m        \u001b[35m0.1302\u001b[0m  0.5531\n",
            "     13        \u001b[36m0.0680\u001b[0m       0.9600        0.1460  0.5582\n",
            "     14        \u001b[36m0.0605\u001b[0m       0.9616        0.1409  0.5609\n",
            "     15        0.0605       0.9616        0.1459  0.5475\n",
            "     16        \u001b[36m0.0487\u001b[0m       0.9627        0.1335  0.5638\n",
            "     17        \u001b[36m0.0434\u001b[0m       0.9592        0.1435  0.5748\n",
            "     18        0.0454       0.9608        0.1604  0.5918\n",
            "     19        \u001b[36m0.0386\u001b[0m       0.9631        0.1529  0.5909\n",
            "     20        0.0392       \u001b[32m0.9655\u001b[0m        0.1405  0.5594\n",
            "[CV] .......................................... lr=0.15, total=  11.6s\n",
            "[CV] lr=0.15 .........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.9840\u001b[0m       \u001b[32m0.6514\u001b[0m        \u001b[35m0.9408\u001b[0m  0.5624\n",
            "      2        \u001b[36m0.4432\u001b[0m       \u001b[32m0.8118\u001b[0m        \u001b[35m0.4267\u001b[0m  0.5814\n",
            "      3        \u001b[36m0.2909\u001b[0m       \u001b[32m0.8506\u001b[0m        \u001b[35m0.3395\u001b[0m  0.5959\n",
            "      4        \u001b[36m0.2286\u001b[0m       \u001b[32m0.9039\u001b[0m        \u001b[35m0.2589\u001b[0m  0.5699\n",
            "      5        \u001b[36m0.1890\u001b[0m       \u001b[32m0.9122\u001b[0m        \u001b[35m0.2366\u001b[0m  0.5619\n",
            "      6        \u001b[36m0.1505\u001b[0m       \u001b[32m0.9404\u001b[0m        \u001b[35m0.1735\u001b[0m  0.5927\n",
            "      7        \u001b[36m0.1361\u001b[0m       \u001b[32m0.9506\u001b[0m        \u001b[35m0.1615\u001b[0m  0.5634\n",
            "      8        \u001b[36m0.1198\u001b[0m       0.9502        \u001b[35m0.1513\u001b[0m  0.5616\n",
            "      9        \u001b[36m0.1079\u001b[0m       \u001b[32m0.9525\u001b[0m        \u001b[35m0.1434\u001b[0m  0.5566\n",
            "     10        \u001b[36m0.0875\u001b[0m       \u001b[32m0.9584\u001b[0m        \u001b[35m0.1346\u001b[0m  0.5801\n",
            "     11        \u001b[36m0.0847\u001b[0m       0.9549        0.1388  0.5692\n",
            "     12        \u001b[36m0.0819\u001b[0m       0.9557        \u001b[35m0.1328\u001b[0m  0.5565\n",
            "     13        \u001b[36m0.0679\u001b[0m       \u001b[32m0.9596\u001b[0m        0.1329  0.5773\n",
            "     14        \u001b[36m0.0600\u001b[0m       \u001b[32m0.9639\u001b[0m        \u001b[35m0.1306\u001b[0m  0.5693\n",
            "     15        \u001b[36m0.0534\u001b[0m       \u001b[32m0.9655\u001b[0m        \u001b[35m0.1250\u001b[0m  0.5970\n",
            "     16        \u001b[36m0.0519\u001b[0m       0.9616        0.1478  0.5667\n",
            "     17        \u001b[36m0.0474\u001b[0m       0.9643        0.1311  0.5697\n",
            "     18        \u001b[36m0.0459\u001b[0m       0.9569        0.1517  0.5893\n",
            "     19        \u001b[36m0.0450\u001b[0m       0.9639        0.1368  0.5760\n",
            "     20        \u001b[36m0.0383\u001b[0m       0.9631        0.1542  0.5777\n",
            "[CV] .......................................... lr=0.15, total=  11.8s\n",
            "[CV] lr=0.2 ..........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.8979\u001b[0m       \u001b[32m0.8961\u001b[0m        \u001b[35m0.3873\u001b[0m  0.5733\n",
            "      2        \u001b[36m0.3915\u001b[0m       \u001b[32m0.9365\u001b[0m        \u001b[35m0.2272\u001b[0m  0.5790\n",
            "      3        \u001b[36m0.2553\u001b[0m       \u001b[32m0.9494\u001b[0m        \u001b[35m0.1710\u001b[0m  0.5602\n",
            "      4        \u001b[36m0.2038\u001b[0m       \u001b[32m0.9522\u001b[0m        \u001b[35m0.1551\u001b[0m  0.5577\n",
            "      5        \u001b[36m0.1676\u001b[0m       \u001b[32m0.9549\u001b[0m        \u001b[35m0.1439\u001b[0m  0.5586\n",
            "      6        \u001b[36m0.1410\u001b[0m       0.9541        0.1467  0.5663\n",
            "      7        \u001b[36m0.1265\u001b[0m       \u001b[32m0.9624\u001b[0m        \u001b[35m0.1424\u001b[0m  0.5545\n",
            "      8        \u001b[36m0.1097\u001b[0m       0.9608        \u001b[35m0.1343\u001b[0m  0.5625\n",
            "      9        \u001b[36m0.0941\u001b[0m       0.9616        \u001b[35m0.1299\u001b[0m  0.5615\n",
            "     10        \u001b[36m0.0838\u001b[0m       0.9565        0.1474  0.5621\n",
            "     11        \u001b[36m0.0761\u001b[0m       0.9608        0.1398  0.5446\n",
            "     12        \u001b[36m0.0636\u001b[0m       0.9612        0.1409  0.5541\n",
            "     13        0.0676       0.9608        \u001b[35m0.1279\u001b[0m  0.5555\n",
            "     14        \u001b[36m0.0540\u001b[0m       \u001b[32m0.9631\u001b[0m        0.1306  0.5583\n",
            "     15        \u001b[36m0.0533\u001b[0m       0.9592        0.1413  0.5501\n",
            "     16        \u001b[36m0.0467\u001b[0m       0.9616        0.1409  0.5534\n",
            "     17        0.0493       0.9573        0.1738  0.5370\n",
            "     18        \u001b[36m0.0452\u001b[0m       0.9620        0.1416  0.5483\n",
            "     19        \u001b[36m0.0396\u001b[0m       0.9624        0.1413  0.5519\n",
            "     20        \u001b[36m0.0346\u001b[0m       0.9627        0.1447  0.5524\n",
            "[CV] ........................................... lr=0.2, total=  11.5s\n",
            "[CV] lr=0.2 ..........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.8962\u001b[0m       \u001b[32m0.8878\u001b[0m        \u001b[35m0.3816\u001b[0m  0.5588\n",
            "      2        \u001b[36m0.3842\u001b[0m       \u001b[32m0.9286\u001b[0m        \u001b[35m0.2296\u001b[0m  0.5547\n",
            "      3        \u001b[36m0.2665\u001b[0m       \u001b[32m0.9380\u001b[0m        \u001b[35m0.1857\u001b[0m  0.5556\n",
            "      4        \u001b[36m0.1966\u001b[0m       \u001b[32m0.9420\u001b[0m        \u001b[35m0.1694\u001b[0m  0.5406\n",
            "      5        \u001b[36m0.1676\u001b[0m       \u001b[32m0.9451\u001b[0m        \u001b[35m0.1569\u001b[0m  0.5545\n",
            "      6        \u001b[36m0.1447\u001b[0m       \u001b[32m0.9482\u001b[0m        \u001b[35m0.1482\u001b[0m  0.5648\n",
            "      7        \u001b[36m0.1274\u001b[0m       \u001b[32m0.9506\u001b[0m        \u001b[35m0.1441\u001b[0m  0.5651\n",
            "      8        \u001b[36m0.1104\u001b[0m       \u001b[32m0.9541\u001b[0m        \u001b[35m0.1350\u001b[0m  0.5646\n",
            "      9        \u001b[36m0.0974\u001b[0m       0.9525        0.1532  0.5540\n",
            "     10        \u001b[36m0.0932\u001b[0m       0.9518        0.1465  0.5696\n",
            "     11        \u001b[36m0.0847\u001b[0m       \u001b[32m0.9588\u001b[0m        0.1386  0.5882\n",
            "     12        \u001b[36m0.0712\u001b[0m       \u001b[32m0.9596\u001b[0m        \u001b[35m0.1338\u001b[0m  0.5503\n",
            "     13        \u001b[36m0.0706\u001b[0m       \u001b[32m0.9627\u001b[0m        \u001b[35m0.1327\u001b[0m  0.5606\n",
            "     14        \u001b[36m0.0614\u001b[0m       0.9627        0.1330  0.5502\n",
            "     15        \u001b[36m0.0484\u001b[0m       0.9627        0.1440  0.5535\n",
            "     16        0.0552       0.9616        0.1378  0.5623\n",
            "     17        0.0489       0.9604        0.1462  0.5488\n",
            "     18        \u001b[36m0.0417\u001b[0m       0.9627        0.1465  0.5511\n",
            "     19        \u001b[36m0.0408\u001b[0m       0.9624        0.1505  0.5724\n",
            "     20        0.0413       0.9624        0.1567  0.5475\n",
            "[CV] ........................................... lr=0.2, total=  11.5s\n",
            "[CV] lr=0.2 ..........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.8921\u001b[0m       \u001b[32m0.8459\u001b[0m        \u001b[35m0.4558\u001b[0m  0.5598\n",
            "      2        \u001b[36m0.3756\u001b[0m       \u001b[32m0.9255\u001b[0m        \u001b[35m0.2293\u001b[0m  0.5745\n",
            "      3        \u001b[36m0.2500\u001b[0m       \u001b[32m0.9412\u001b[0m        \u001b[35m0.1847\u001b[0m  0.5566\n",
            "      4        \u001b[36m0.1947\u001b[0m       \u001b[32m0.9478\u001b[0m        \u001b[35m0.1615\u001b[0m  0.5723\n",
            "      5        \u001b[36m0.1705\u001b[0m       \u001b[32m0.9482\u001b[0m        \u001b[35m0.1455\u001b[0m  0.5600\n",
            "      6        \u001b[36m0.1351\u001b[0m       \u001b[32m0.9533\u001b[0m        0.1466  0.5671\n",
            "      7        \u001b[36m0.1154\u001b[0m       0.9522        0.1471  0.5571\n",
            "      8        \u001b[36m0.1072\u001b[0m       \u001b[32m0.9592\u001b[0m        \u001b[35m0.1401\u001b[0m  0.5663\n",
            "      9        \u001b[36m0.0968\u001b[0m       0.9573        0.1543  0.5865\n",
            "     10        \u001b[36m0.0793\u001b[0m       \u001b[32m0.9612\u001b[0m        \u001b[35m0.1395\u001b[0m  0.5630\n",
            "     11        \u001b[36m0.0712\u001b[0m       0.9537        0.1643  0.5808\n",
            "     12        \u001b[36m0.0711\u001b[0m       \u001b[32m0.9616\u001b[0m        0.1552  0.5610\n",
            "     13        \u001b[36m0.0601\u001b[0m       0.9608        0.1626  0.5604\n",
            "     14        \u001b[36m0.0515\u001b[0m       0.9604        0.1600  0.5467\n",
            "     15        0.0542       \u001b[32m0.9624\u001b[0m        0.1545  0.5593\n",
            "     16        \u001b[36m0.0487\u001b[0m       \u001b[32m0.9627\u001b[0m        0.1524  0.5651\n",
            "     17        \u001b[36m0.0461\u001b[0m       0.9600        0.1741  0.5505\n",
            "     18        \u001b[36m0.0395\u001b[0m       0.9620        0.1631  0.5510\n",
            "     19        \u001b[36m0.0369\u001b[0m       0.9576        0.1852  0.5576\n",
            "     20        \u001b[36m0.0354\u001b[0m       0.9600        0.1863  0.5567\n",
            "[CV] ........................................... lr=0.2, total=  11.6s\n",
            "[CV] lr=0.2 ..........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.9084\u001b[0m       \u001b[32m0.6643\u001b[0m        \u001b[35m0.8683\u001b[0m  0.5546\n",
            "      2        \u001b[36m0.3875\u001b[0m       \u001b[32m0.7945\u001b[0m        \u001b[35m0.5001\u001b[0m  0.5528\n",
            "      3        \u001b[36m0.2665\u001b[0m       \u001b[32m0.8678\u001b[0m        \u001b[35m0.3181\u001b[0m  0.5528\n",
            "      4        \u001b[36m0.2104\u001b[0m       \u001b[32m0.9110\u001b[0m        \u001b[35m0.2422\u001b[0m  0.5498\n",
            "      5        \u001b[36m0.1696\u001b[0m       \u001b[32m0.9118\u001b[0m        0.2441  0.5757\n",
            "      6        \u001b[36m0.1421\u001b[0m       \u001b[32m0.9392\u001b[0m        \u001b[35m0.2067\u001b[0m  0.5391\n",
            "      7        \u001b[36m0.1271\u001b[0m       \u001b[32m0.9412\u001b[0m        \u001b[35m0.1729\u001b[0m  0.5648\n",
            "      8        \u001b[36m0.1083\u001b[0m       \u001b[32m0.9537\u001b[0m        \u001b[35m0.1490\u001b[0m  0.5453\n",
            "      9        \u001b[36m0.0905\u001b[0m       \u001b[32m0.9565\u001b[0m        0.1499  0.5460\n",
            "     10        \u001b[36m0.0863\u001b[0m       0.9549        \u001b[35m0.1389\u001b[0m  0.5552\n",
            "     11        \u001b[36m0.0766\u001b[0m       \u001b[32m0.9616\u001b[0m        \u001b[35m0.1295\u001b[0m  0.5411\n",
            "     12        \u001b[36m0.0700\u001b[0m       0.9561        0.1365  0.5485\n",
            "     13        \u001b[36m0.0620\u001b[0m       0.9596        0.1295  0.5533\n",
            "     14        \u001b[36m0.0545\u001b[0m       0.9576        0.1586  0.5480\n",
            "     15        \u001b[36m0.0492\u001b[0m       0.9616        0.1533  0.5559\n",
            "     16        \u001b[36m0.0448\u001b[0m       \u001b[32m0.9627\u001b[0m        0.1443  0.5514\n",
            "     17        0.0483       0.9612        0.1610  0.5587\n",
            "     18        0.0560       0.9620        0.1502  0.5648\n",
            "     19        0.0502       \u001b[32m0.9635\u001b[0m        0.1452  0.5537\n",
            "     20        \u001b[36m0.0400\u001b[0m       \u001b[32m0.9671\u001b[0m        0.1337  0.5573\n",
            "[CV] ........................................... lr=0.2, total=  11.4s\n",
            "[CV] lr=0.25 .........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.8643\u001b[0m       \u001b[32m0.8820\u001b[0m        \u001b[35m0.3757\u001b[0m  0.5564\n",
            "      2        \u001b[36m0.3611\u001b[0m       \u001b[32m0.9345\u001b[0m        \u001b[35m0.2212\u001b[0m  0.5439\n",
            "      3        \u001b[36m0.2524\u001b[0m       \u001b[32m0.9502\u001b[0m        \u001b[35m0.1677\u001b[0m  0.5485\n",
            "      4        \u001b[36m0.2009\u001b[0m       \u001b[32m0.9522\u001b[0m        \u001b[35m0.1625\u001b[0m  0.5464\n",
            "      5        \u001b[36m0.1520\u001b[0m       \u001b[32m0.9553\u001b[0m        \u001b[35m0.1459\u001b[0m  0.5442\n",
            "      6        \u001b[36m0.1458\u001b[0m       \u001b[32m0.9561\u001b[0m        0.1467  0.5592\n",
            "      7        \u001b[36m0.1093\u001b[0m       \u001b[32m0.9608\u001b[0m        \u001b[35m0.1388\u001b[0m  0.5796\n",
            "      8        \u001b[36m0.1066\u001b[0m       0.9573        0.1564  0.5693\n",
            "      9        \u001b[36m0.0977\u001b[0m       0.9541        0.1564  0.5426\n",
            "     10        \u001b[36m0.0849\u001b[0m       0.9604        \u001b[35m0.1367\u001b[0m  0.5644\n",
            "     11        \u001b[36m0.0710\u001b[0m       \u001b[32m0.9631\u001b[0m        0.1378  0.5533\n",
            "     12        \u001b[36m0.0700\u001b[0m       0.9631        0.1407  0.5485\n",
            "     13        \u001b[36m0.0571\u001b[0m       \u001b[32m0.9663\u001b[0m        \u001b[35m0.1327\u001b[0m  0.5478\n",
            "     14        \u001b[36m0.0548\u001b[0m       0.9592        0.1505  0.5467\n",
            "     15        \u001b[36m0.0541\u001b[0m       0.9631        0.1370  0.5479\n",
            "     16        \u001b[36m0.0490\u001b[0m       0.9620        0.1521  0.5526\n",
            "     17        \u001b[36m0.0466\u001b[0m       0.9620        0.1480  0.5538\n",
            "     18        \u001b[36m0.0449\u001b[0m       0.9647        0.1536  0.5487\n",
            "     19        \u001b[36m0.0410\u001b[0m       0.9620        0.1649  0.5511\n",
            "     20        \u001b[36m0.0340\u001b[0m       0.9596        0.1760  0.5691\n",
            "[CV] .......................................... lr=0.25, total=  11.4s\n",
            "[CV] lr=0.25 .........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.8551\u001b[0m       \u001b[32m0.8725\u001b[0m        \u001b[35m0.4117\u001b[0m  0.5638\n",
            "      2        \u001b[36m0.3539\u001b[0m       \u001b[32m0.9349\u001b[0m        \u001b[35m0.2148\u001b[0m  0.5545\n",
            "      3        \u001b[36m0.2448\u001b[0m       0.9294        \u001b[35m0.1981\u001b[0m  0.5420\n",
            "      4        \u001b[36m0.2000\u001b[0m       \u001b[32m0.9412\u001b[0m        \u001b[35m0.1729\u001b[0m  0.5720\n",
            "      5        \u001b[36m0.1620\u001b[0m       \u001b[32m0.9451\u001b[0m        \u001b[35m0.1567\u001b[0m  0.5543\n",
            "      6        \u001b[36m0.1436\u001b[0m       \u001b[32m0.9478\u001b[0m        0.1631  0.5533\n",
            "      7        \u001b[36m0.1228\u001b[0m       \u001b[32m0.9502\u001b[0m        0.1669  0.5542\n",
            "      8        \u001b[36m0.1072\u001b[0m       \u001b[32m0.9529\u001b[0m        \u001b[35m0.1550\u001b[0m  0.5592\n",
            "      9        \u001b[36m0.0982\u001b[0m       0.9510        \u001b[35m0.1535\u001b[0m  0.5519\n",
            "     10        \u001b[36m0.0863\u001b[0m       \u001b[32m0.9596\u001b[0m        \u001b[35m0.1340\u001b[0m  0.5521\n",
            "     11        \u001b[36m0.0796\u001b[0m       0.9557        0.1502  0.5583\n",
            "     12        \u001b[36m0.0715\u001b[0m       0.9565        0.1551  0.5506\n",
            "     13        \u001b[36m0.0649\u001b[0m       \u001b[32m0.9604\u001b[0m        0.1454  0.5679\n",
            "     14        \u001b[36m0.0639\u001b[0m       0.9596        0.1522  0.5631\n",
            "     15        \u001b[36m0.0489\u001b[0m       0.9573        0.1557  0.5659\n",
            "     16        0.0497       0.9576        0.1620  0.5680\n",
            "     17        0.0548       0.9604        0.1598  0.5655\n",
            "     18        \u001b[36m0.0432\u001b[0m       0.9596        0.1663  0.5629\n",
            "     19        0.0455       \u001b[32m0.9627\u001b[0m        0.1661  0.5640\n",
            "     20        \u001b[36m0.0356\u001b[0m       \u001b[32m0.9671\u001b[0m        0.1513  0.5620\n",
            "[CV] .......................................... lr=0.25, total=  11.5s\n",
            "[CV] lr=0.25 .........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.8485\u001b[0m       \u001b[32m0.9031\u001b[0m        \u001b[35m0.3356\u001b[0m  0.5766\n",
            "      2        \u001b[36m0.3563\u001b[0m       \u001b[32m0.9286\u001b[0m        \u001b[35m0.2097\u001b[0m  0.5618\n",
            "      3        \u001b[36m0.2569\u001b[0m       \u001b[32m0.9404\u001b[0m        \u001b[35m0.1881\u001b[0m  0.5596\n",
            "      4        \u001b[36m0.1969\u001b[0m       \u001b[32m0.9482\u001b[0m        \u001b[35m0.1702\u001b[0m  0.5692\n",
            "      5        \u001b[36m0.1575\u001b[0m       0.9455        \u001b[35m0.1639\u001b[0m  0.5852\n",
            "      6        \u001b[36m0.1312\u001b[0m       \u001b[32m0.9584\u001b[0m        \u001b[35m0.1349\u001b[0m  0.5697\n",
            "      7        \u001b[36m0.1179\u001b[0m       0.9553        0.1435  0.5600\n",
            "      8        \u001b[36m0.1071\u001b[0m       0.9537        0.1480  0.5580\n",
            "      9        \u001b[36m0.0928\u001b[0m       0.9537        0.1558  0.5681\n",
            "     10        0.0966       0.9553        0.1433  0.5557\n",
            "     11        \u001b[36m0.0719\u001b[0m       0.9569        0.1562  0.5690\n",
            "     12        \u001b[36m0.0654\u001b[0m       \u001b[32m0.9600\u001b[0m        0.1608  0.5814\n",
            "     13        \u001b[36m0.0614\u001b[0m       \u001b[32m0.9608\u001b[0m        0.1567  0.5599\n",
            "     14        \u001b[36m0.0571\u001b[0m       0.9600        0.1647  0.5793\n",
            "     15        \u001b[36m0.0468\u001b[0m       \u001b[32m0.9620\u001b[0m        0.1595  0.5760\n",
            "     16        \u001b[36m0.0441\u001b[0m       0.9600        0.1597  0.6018\n",
            "     17        0.0463       0.9580        0.1715  0.5644\n",
            "     18        \u001b[36m0.0413\u001b[0m       0.9596        0.1551  0.5801\n",
            "     19        \u001b[36m0.0390\u001b[0m       0.9620        0.1729  0.5710\n",
            "     20        \u001b[36m0.0367\u001b[0m       \u001b[32m0.9659\u001b[0m        0.1634  0.5650\n",
            "[CV] .......................................... lr=0.25, total=  11.7s\n",
            "[CV] lr=0.25 .........................................................\n",
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m0.8507\u001b[0m       \u001b[32m0.6349\u001b[0m        \u001b[35m0.9570\u001b[0m  0.5742\n",
            "      2        \u001b[36m0.3687\u001b[0m       \u001b[32m0.8333\u001b[0m        \u001b[35m0.3990\u001b[0m  0.5641\n",
            "      3        \u001b[36m0.2543\u001b[0m       \u001b[32m0.8792\u001b[0m        \u001b[35m0.3130\u001b[0m  0.5642\n",
            "      4        \u001b[36m0.1986\u001b[0m       \u001b[32m0.9282\u001b[0m        \u001b[35m0.2211\u001b[0m  0.5609\n",
            "      5        \u001b[36m0.1648\u001b[0m       0.9271        \u001b[35m0.2103\u001b[0m  0.5782\n",
            "      6        \u001b[36m0.1398\u001b[0m       \u001b[32m0.9443\u001b[0m        \u001b[35m0.1867\u001b[0m  0.5933\n",
            "      7        \u001b[36m0.1169\u001b[0m       \u001b[32m0.9565\u001b[0m        \u001b[35m0.1528\u001b[0m  0.5677\n",
            "      8        \u001b[36m0.1005\u001b[0m       \u001b[32m0.9569\u001b[0m        \u001b[35m0.1428\u001b[0m  0.5757\n",
            "      9        \u001b[36m0.0980\u001b[0m       0.9525        0.1545  0.5751\n",
            "     10        \u001b[36m0.0827\u001b[0m       0.9545        0.1466  0.5776\n",
            "     11        \u001b[36m0.0811\u001b[0m       \u001b[32m0.9600\u001b[0m        0.1507  0.5844\n",
            "     12        0.0844       \u001b[32m0.9608\u001b[0m        \u001b[35m0.1403\u001b[0m  0.5728\n",
            "     13        \u001b[36m0.0558\u001b[0m       0.9592        0.1532  0.5634\n",
            "     14        0.0569       0.9569        0.1578  0.5694\n",
            "     15        \u001b[36m0.0514\u001b[0m       \u001b[32m0.9616\u001b[0m        0.1481  0.5789\n",
            "     16        \u001b[36m0.0506\u001b[0m       0.9569        0.1679  0.5780\n",
            "     17        \u001b[36m0.0450\u001b[0m       0.9596        0.1580  0.5942\n",
            "     18        \u001b[36m0.0442\u001b[0m       0.9580        0.1761  0.5796\n",
            "     19        \u001b[36m0.0436\u001b[0m       \u001b[32m0.9643\u001b[0m        0.1456  0.5681\n",
            "     20        \u001b[36m0.0343\u001b[0m       0.9604        0.1683  0.5709\n",
            "[CV] .......................................... lr=0.25, total=  11.8s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:  3.1min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "  epoch    train_loss    valid_acc    valid_loss     dur\n",
            "-------  ------------  -----------  ------------  ------\n",
            "      1        \u001b[36m1.0095\u001b[0m       \u001b[32m0.8379\u001b[0m        \u001b[35m0.5280\u001b[0m  0.8014\n",
            "      2        \u001b[36m0.4543\u001b[0m       \u001b[32m0.9241\u001b[0m        \u001b[35m0.2604\u001b[0m  0.7633\n",
            "      3        \u001b[36m0.2932\u001b[0m       \u001b[32m0.9415\u001b[0m        \u001b[35m0.2010\u001b[0m  0.7747\n",
            "      4        \u001b[36m0.2307\u001b[0m       \u001b[32m0.9450\u001b[0m        \u001b[35m0.1847\u001b[0m  0.7694\n",
            "      5        \u001b[36m0.1884\u001b[0m       \u001b[32m0.9476\u001b[0m        \u001b[35m0.1658\u001b[0m  0.7553\n",
            "      6        \u001b[36m0.1660\u001b[0m       \u001b[32m0.9553\u001b[0m        \u001b[35m0.1506\u001b[0m  0.7722\n",
            "      7        \u001b[36m0.1457\u001b[0m       0.9547        \u001b[35m0.1425\u001b[0m  0.7739\n",
            "      8        \u001b[36m0.1222\u001b[0m       \u001b[32m0.9565\u001b[0m        \u001b[35m0.1371\u001b[0m  0.7694\n",
            "      9        \u001b[36m0.1138\u001b[0m       \u001b[32m0.9588\u001b[0m        \u001b[35m0.1316\u001b[0m  0.7667\n",
            "     10        \u001b[36m0.0977\u001b[0m       \u001b[32m0.9618\u001b[0m        \u001b[35m0.1247\u001b[0m  0.7705\n",
            "     11        \u001b[36m0.0915\u001b[0m       0.9612        \u001b[35m0.1223\u001b[0m  0.7838\n",
            "     12        \u001b[36m0.0818\u001b[0m       0.9606        0.1305  0.7596\n",
            "     13        \u001b[36m0.0749\u001b[0m       \u001b[32m0.9641\u001b[0m        0.1254  0.7652\n",
            "     14        \u001b[36m0.0672\u001b[0m       \u001b[32m0.9659\u001b[0m        \u001b[35m0.1218\u001b[0m  0.7821\n",
            "     15        \u001b[36m0.0632\u001b[0m       \u001b[32m0.9674\u001b[0m        \u001b[35m0.1204\u001b[0m  0.7805\n",
            "     16        \u001b[36m0.0578\u001b[0m       0.9656        0.1265  0.7853\n",
            "     17        \u001b[36m0.0527\u001b[0m       0.9624        0.1379  0.7669\n",
            "     18        0.0590       0.9668        0.1291  0.7671\n",
            "     19        \u001b[36m0.0466\u001b[0m       \u001b[32m0.9688\u001b[0m        0.1254  0.7783\n",
            "     20        \u001b[36m0.0456\u001b[0m       0.9688        0.1244  0.7711\n",
            "\n",
            " 最適ハイパーパラメータ :  {'lr': 0.1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2p16eNCke4m"
      },
      "source": [
        "　クロスバリデーションから最適な学習率 `lr` が0.15と求まったので，再度ハイパーパラメータを変更して学習してみます．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agKCos0uYb3S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "outputId": "7a8b92ba-568b-4cab-e3c4-d9a292a2e3c2"
      },
      "source": [
        "torch.manual_seed(0)\n",
        "\n",
        "model = torch.nn.Sequential(\n",
        "  torch.nn.Linear(13*50, 512), \n",
        "  torch.nn.ReLU(),\n",
        "  torch.nn.Dropout(p=0.5),\n",
        "  torch.nn.Linear(512, 256), \n",
        "  torch.nn.ReLU(),\n",
        "  torch.nn.Dropout(p=0.5),\n",
        "  torch.nn.Linear(256, 6), \n",
        ")\n",
        "model.to(device_GPU) # deviceへモデルを転送\n",
        "\n",
        "# 誤差関数と最適化手法を準備\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.15)\n",
        "\n",
        "# 学習の実行\n",
        "trained_model = train(model, loss_fn, optimizer, train_loader, valid_loader, device_GPU)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UtrCntoW29s4vVCNH5LFk6mguX1zBkqmjOa1iFCPCwV5fM5l0mNHvKXuhYICxJfmMLck/ofcqIumlCpaIiP8pYIn0oa09wYs7avj9lir+sKXqSOWpNyPygkwtLWBqaSHnzC5namkBk8cUMK20kKljCgZ87aZ0XOtJRLIvEgoqYImI+JwCluSk1liC1yoP055wBAyCZgQCRsCMYACcg/WVdfx+SxUv7awlFk8SCQU4a0YpHz1rKqeOLz7Smjycug8FvBblBeEQZUVhNYgQkeOE1eRCRMT3FLAkZ7xT38ZvNx/kt5sO8fy26n6tg5g1tojrzprKuXPKOWv6GPLzep/KJyLSm0goQCyhCpaIiJ8pYIlvtSeSbDrQwLObDvHbTQfZsL8BgMljRnDNsimcO6eckvwQiSQknSOZdCScI+m832ePLaJidEGW34WIDJSZ3Qd8EDjknFvQzXYD/h1YBbQA1zvnXs3E2MKhANF2BSwRET9TwJKTXn1LO9urm9h+qIntVc3sSF04d3dNC/GkNwVw8ZTR/N1Fp3LB3LHMGluk6Xsi/nY/8B3ggR62vx+YnbqdBXw/dT/kwqEATdF4Jl5KRESyRAFLhr0N++vZsK+BqqYoVY3RI/fVjd59Y5cvK3lBY1ppIbPGFvG++eOZM76Yc2aXM6YwnMV3ICKZ5Jz7g5lN62WXS4AHnHMOeNHMRpnZBOfcgaEeW0QVLBER31PAkmGrJRbnX3/9Nvf/adeRx4ojIcqLI5QVR5g7oYR3nxJhwsh8ZpYXMXNsEZNHjyAUDGRv0CJyMpgEVHb5fW/qsSEPWOFQUGuwRER8TgFLhqUXttfwd4+/wZ7aFq7/s2l8csV0xpZE1GRCRDLKzG4CbgKYMmXKCR/Pq2Cpi6CIiJ8pYMmw0hyN881fbebBF3cztbSAR25azlkzSrM9LBHxl33A5C6/V6QeO45z7m7gboClS5e6E33hsLoIioj4ngKWDBt/2lbN3z7+BvvqWvnUu6bzpZVzGBFWxUpE0u4p4BYzexivuUV9JtZfgdZgiYjkAgUsybhE0lHdFOWd+jYONni31yrr+Nmr+5hRVshPP302S6eNyfYwReQkZWYPAecBZWa2F7gNyANwzv0AWI3Xon0bXpv2T2RqbOFQgKgqWCIivqaAJUOuNZbg4bV7eHL9fg7Ut1LVGCV5zESbcDDAjedM54sr52idlYicEOfcNX1sd8BnMzSco0RCQWLxJM45XS5CRMSnFLBkyNS3tvPjF3dz75qd1DbHOK1iJO+eXc74kfmMLclnfOo2riRCaVGEYEBfNkTE3yIhr8tpLJEkEtIfk0RE/EgBS9KuuinKfWt28uALu2mMxjl/Tjl/cf4sztS0PxHJcR0BKxpXwBIR8SsFLDlhzjlqm2Nsr2rml2/s5+G1lcQSSVYtnMDN585kwaSR2R6iiMiwEO6oYMW1DktExK8UsGRAqhqjrK+sY3tVEzuqmthe1cz2qibqWtoBCAWMyxdP4tPnzmRmeVGWRysiMrx0rWCJiIg/KWBJv1Q1Rvn+c9v58Uu7j/zltbw4woyyQlYtnMDM8iJmlBeyYOJIyosjWR6tiAAQa4Edv4PNv4Q9L8KMc2HJJ2DCoqF93XgM6ivh8C6YvAwixUP7eicRVbBERPxPAUt6VdcS4z//sIP7n99FLJHkI4sruPLMycwaW8TIEXnZHp4ItNbB4Z0Qa4byuVDYjwtTOwe1O2DvWti/HgpKYdx87zZqCpxId7dYC7y9GipfhjEzOo9bkKE1iC21sOVp2PwL2P6/0N4C+SNh0hJY/xNYdx9MWgpLPwHzL4dwweBep+NzP7wLalP3Hb/X7wWXChCffBqmLE/Tmzv5day7UsASEfEvBSzpVmNbOz96fhf3/GEHTbE4H1o0kc9fMJsZmvYnmZSIQ0sNNB+CpkPeF/euX+Rrd0Jb3dHPKRrfGWrGLUiFpsnwzluw92Uv+FS+DC3V3v6hERBv7Xx+pATGzjvmGPN6r8IkE7DzD/DGo7DpKYg1QTACiWjnPiWTOo85dp4XuMJFkFcA4cLOW14BBHppftDelvo8qjo/l477qs2w63lwCSieAKd/FE79IEx7FwTzvPD1xiNeyHrys/Drv4fTrvbC1pgZXkiNNXuhLNbkhcVYMzQd7PtzLyyH0dNg8nJYNA3GTPd+H7egj3/k3BIOdkwRTGR5JCIiMlQUsOSI9kSSDfsbWLO1invX7ORwSzsr543jCytP4dTxJdkeXu6q3uZ9GU5E4aybYcHl3pfldIs2wf5XofIlqFwL+9ZB0Tg49QPel/QJp51YZacnyaQXDCpf8l6zrhKaq7zA0FIDHHPRtEAIRk72vsAvOANGp77I5xVA1SY4uAEOvgUv/RESseNfr3QWzF7pTV2bvAzKT/UCxaFN3vMObvBub/4U1t3b+bxRUzsDW0fwam+BNx+FNx+DxgNeOJt/KSy6Cqau8N5H12Me3ADbfwfJ9t4/Ewv0vM31UPmIjPSC5IrPwakfgolnQOCY4xSMgeU3w1mfgd1/8oLWKz+Cl/+z9/GA97mPmuJ91gsWdwaojpumAfZLJE9TBEVE/E4BK4cdbo7xyu7DvLLnMK/sPswbe+toa/dO+u8+pZwvrTyFRRWjsjzKfnAu/V/8E3HvmL1VEgYjmRjYMTf8Dzz5l16gKiyH/7kJnr0dzvo0LLkeRgzi38c5aK72qhE1271QU/myFwQ6vryXzYFTLoLDu+GP/xf+8G9eqOkIW1POhmAP//lIJiHe1vPrx9tg/2vea+59Gfaug2iDt62gFMbM9Kopk8+CorHe++64L5kIJRU9v/bsCzp/TrRDzTYv1NTthrHzoeLM7qcQRoo7A1fXz6luDxza2CUkbYQtvzo65ARCXmBb9M/eZ5Y3onNb8XjvNqvLuOIxb3piW71XJWpvOaZy1AzJeM+fXyg/9XmMhaJy776wHPLye37Oscxg2grv1lwNbz3u/Rt0W1Er9D6z3j536bfOCpYCloiIX+lsmWOqGqM8uq6Sn726l+1VzYDX+W/+pJF8dNlUlkwdzZKpoxk/cgBf1rpKJo//q/lQSCa88PGHb0HDPph2Dsw8H2acD6Uz+x+4nPMqDx1Vj44qQ/UW70tuKD/1hbPIW6vSMYXrqN87voymHse8ysVx07iqvIrKoqvgnC964+xJPAbPfBVe+r4XCq64H4onwrZn4YX/gGdv80LPGR/zKhKjp3Y+N9p09Gs3vpOa3tXlFmvq3D9c5K3POedLXsCoWAojRndub66GLb/2GiW8cj+89ANv+7gF0N56/HSyrtPtemMBL/Qs/IgXpirO9IJVusJyMA/GzvVug2Hmfa6jp8Kc93c+3t4KVW97/ztJxr3A2Z91Xx1CYRh76uDGNBQKy7zALhmhJhciIv6ngJUDnHOs232YB1/Yza/eOkB7wrF8xhg+vKSCJVNGs6hiFCPCJ1ipqdkOT94C+17xFrR3hJ3xi/oOXM55fz2PlPT95ToRhw0/88JF9RZvete8S7z1L2//0ttn5GSYcZ43holneIvxO6acHRs8Dm2E1sOdxy+p8KZ/nfI+b23OsRWGjlvD3s5A0REwjpq6Zd4X144qQ0c1JtYMrz8Erz8Mi670Qk3ZrKPfY10lPPYJrwHDWTfDhXd4X8oBTlnp3Q68AS98B9be403vGr/Iex/NVd54jhXK75zKNe0c775jilfprN6raoVlcMZ13i3a5DVO2PwLr7qVXwIlE44PmaGI9xl0JxCC8Qu8UHcyTivLGwETT/duIgPU0eRCa7BERPxLAcuvnKOpLcoTrx/kxy/uZvM7jRTnh7hu+VSuPWsqs8amqVlFMul9yX/mNgiG4fRrvKlfz94O3O5N+Zp+birsLE4tlu/aeWx3qqLSCAVlXhCZfKZ3P/GMzulWibi3JuYP/wa1270mAVfcD3Mv6QxwtTu89S07fuc1Gnjtwe7HHC72Qk/ROC+cdayrGTv36MrNQDgH8agXoFwCRozpeTrVeV+GP90Fa+/1Gg4s+Ai8+0tQPge2Pgs/u9Gb3nbF/TD/su6PMWERXH43vPc2ePluOPA6lM0+etpYx7S64vHe7+moLEaKYN7F3k1EBiys62CJiPieApbfOEfb5t/Q8PP/w8jmXTQl3kdJ6XV88/KFXHz6RArCvfyTO+dNQVt3n9c44LSrvMpIT1Wlw7u8qtWuP8KsC+Hiu7w1MuBVh3Y81xl4Nvzs6OcGI6npV9O9dSDF46Fqi7cmp6MSFQh5rz/xDK9qcngnjFsIVz7oTcs6NjCMmeHdzvyUN4Vw/2te44KC0s6wUVg++LbUvTHz1sD0Zx1M8Th43zdgxedTQeuHXnic9i7YtcYLj1c+cHxlqzsjJ8GFXzvx8YtIRuhCwyIi/qeA5SNu7ytUP/Flyqtf4lCynMqSFXy66Zd8OvpHrP2LYDfR7T+5c951c37/L14XucKxsPUZePG73hS8RVfCwiu8DmId+7/yI/jNPwAGF/+HtxaoaxArHu+1fz7tam//Q6nubiUTvWlpxRN6rqg013jT4ypf8u5ffwjKToGrfwJzVvVvjU4g6K0lqlg6wE8xg4rKYeU/woq/Sk33uxdOvxZW/dvQhEARybqI1mCJiPieApYf1Gyn7hf/wKidvyTgivlB4ac588NfYMnM8d61f377tVTDhP/0pqed/lEvgDjnXRD19//iTTEbNRU+dBecdo23pmjjE951fX57h3ebusJrEb7pF15VasZ5cPF3vNbQvTHzriM0bl7/3k9hKcy5yLvB0HQJHE4Ky+CC273pfn5+nyLSZQ2WApaIiF8pYJ2MkgnvgqGNB2h98V7Crz9Ingtxd+AKSld+gRuXzyUYSH1RH78Arv0p7Pyj13nuqVu8asniP/cqQ++86U3Tu+S7Xne7jusrhcbA0k96t8O7vClsrz8Cv/yi19DgA9/2tmUiEORK6MiV9ymSw9RFUETE/xSwhotEHFqqj+9019Heu+t9S/WRjnUhF+Sh5HuoXvJXfPKi5ZTk93AB2unnwA2/9Zo//PYOePrvvesNXfoDb/pfb9e3GT0N3v03Xse7Qxu9NU3F49P/GYiI+Fxnkwt1ERQR8SsFrGyINnlrnSpf9m77X/PCE+74fUMjOrvCjZoCk5ZwODCKx9+O8WpNHiOmLeUvLnsvM8v70RXQzOuaN2eVF5TGzh/YhUPNvG57IiIyKMGAEQqYKlgiIj6mgJUJbfWw5Tde04bKl7xmDy7118uyU2DWBd46psJU6/COjndFY71rCqWmjiWSjvvW7ORbv3mb/Lwgt18xj0tPn4QNdGpZMA8mnJbmNykiIv0RCQW0BktExMcUsIZSXSW89AN45b+86zyFi7yLq57zBahY5nW4KxjTr0NtO9TE3zz2Oq/tqePCeeP4xqULGFvSj5bgIiIyrIRDAVWwRER8TAFrKOx71WskseEJ7/f5l8FZn/bCVSA4oEPFE0l+uGYn335mCwXhIP9+9elcfNrEgVetRERkWIiEglqDJSLiYwpY6ZJMwpZfe8Fq9/MQKYHlN8NZn+m7jXkP2toTfOzel1i76zAXzR/PP166gPLiSJoHLiIimaQKloiIvylgpUNLLTzyMdi9BkZOhpXf8Nqg55ec0GH/efUm1u46zL99ZBEfWVKhqpWIiA9oDZaIiL8pYJ2o6m3wkyugfh986N/h9OsG1pmvB89sPMh/vbCbT66YzhVLB1cBExGR4UcVLBERf1PAOhG71sAj14EF4OM/hylnpeWw79S38bePvc68CSX83fvnpOWYIiIyPERCAWIJBSwREb8KZHsAJ631D8EDl3rt1G/4bdrCVSLp+OtH1tPWnuQ/PnoGkdDAmmKIiMjwFg4FiLYrYImI+JUC1kAlk/C/X4cnPgNTz4ZP/QbGTE/b4X/w++28sKOGr10yv38XDxYRkZNKOBQkqgqWiIhvaYrgQLS3wRM3w4afwRkfgw/+P++ivWnyyu7DfPuZLXzotIlcsaQibccVEZHhIxIKEG1Xm3YREb9SwOqv9jb48eVeC/YLvgYr/grS2NWvoa2dv3r4NSaMzOcbly1Qx0AREZ8Kaw2WiIivKWD1h3Pw87/ywtXl98CiK9N8eMff/+xNDtS38dPPnC46wMEAACAASURBVE1JfvqqYiIiMrxEtAZLRMTXtAarP/50F7zxMJz35bSHK4CfvrKXX7xxgC9ceAqLp4xO+/FFRGT4UBdBERF/U8Dqy5an4ZnbYN4l8O6/Tfvhn914kNue3MCfzSzlM+fOTPvxRURkeImEglqDJSLiY/0KWGZ2kZm9bWbbzOzWbrZPNbPfmtkbZvacmVV02ZYws/Wp21PpHPyQO7QZHvsUjF8Al34fAunLo62xBF954k1ueGAd08sKufOq0wkGtO5KRMTvtAZLRMTf+lyDZWZB4LvAhcBeYK2ZPeWc29hlt28BDzjn/svM3gP8M/Cx1LZW59zpaR730GuphYeuhrx8uPohCBem7dAb9zfwuYdfY9uhJm48Zzpfet8cXe9KRCRHREIBovEkzjk1NBIR8aH+NLlYBmxzzu0AMLOHgUuArgFrHvCF1M+/A55I5yAzLtEOP70eGvbBx38Boyan5bDJpOO+53fyr79+m1EFeTz4qWWcM7s8LccWEZGTQzgYwDmIJx15QQUsERG/6c+ct0lAZZff96Ye6+p14PLUz5cBxWZWmvo938zWmdmLZnZpdy9gZjel9llXVVU1gOEPkaf/Hnb+Hj54J0w5Ky2HPNTQxsd/9DJf/+Umzp1Tzq8//26FKxGRHBTJ80690bimCYqI+FG62rR/CfiOmV0P/AHYB3Ss4J3qnNtnZjOA/zWzN51z27s+2Tl3N3A3wNKlS12axjQ4634EL98NZ98CZ1yblkO+vLOWz/z4FVpicb5x2QI+umyKpoWIiOSocNALWLF4EiJZHoyIiKRdfwLWPqDrHLmK1GNHOOf2k6pgmVkR8GHnXF1q277U/Q4zew44AzgqYA0bh3fD6i/BrAvgwjvScsjXK+v45P1rGVsS4dFPL2fW2OK0HFdERE5OkTxvzW00rk6CIiJ+1J8pgmuB2WY23czCwNXAUd0AzazMzDqO9WXgvtTjo80s0rEPsIKj124NL68/BMmENzUwcOJNJ95+p5GP/+hlRhfm8ZMbFK5EROSYCpaIiPhOnwHLORcHbgGeBjYBjzrnNpjZHWZ2cWq384C3zWwLMA74RurxucA6M3sdr/nFN4/pPjh8JJOw/r9hxrlpaWqxs7qZa3/4EpFQgJ/csJzxI/PTMEgRETnZaQ2WiIi/9WsNlnNuNbD6mMe+2uXnx4DHunnen4CFJzjGzNj9PNTtgff8wwkfal9dK9f98CWSzvHwDcuZPKYgDQMUERE/UAVLRMTf0nfl3JPd+v+GSAmc+sETOkxVY5TrfvgSDW3tPPDJZZoWKCIiR+lcg6WAJSLiRwpYANFG2PgkzL8MwoOvNtW1xPjYvS/xTn0bP7r+TBZMGpnGQYqIiB90VLDU5EJExJ8UsAA2PAHtLXDGdYM+RFM0zsd/tJYdVc3c8+dLWTptTBoHKCIifhEOaYqgiIifpes6WCe39f8NpbOh4sxBH+K2Jzfw1r56vn/tYt41uyyNgxMRET+JhNTkQkTEz1TBqtkOe16A0z8Kg7z4b31LOz9/Yz/XnjWFlfPHp3mAIiLiJxFVsEREfE0Ba/1PwAJw2tWDPsQT6/cRiye56swTb+8uIiL+FgmpyYWIiJ/ldsBKJryLC898D5RMHNQhnHM8vLaSBZNKmD9RTS1ERKR3WoMlIuJvuR2wdv4eGvbB6dcO+hBv7Wtg04EGrjpzShoHJiIiftW5BktdBEVE/Ci3A9b6n0D+KJizatCHeGTdHiKhABefNrgKmIiI5BZVsERE/C13A1ZrHWz6OSz8COTlD+4QsQRPrt/PqoUTGDkiL80DFBERP1IXQRERf8vdgLXhfyDedkLTA3/11gEa2+JqbiEiIv0WCgYImCpYIiJ+lbsBa/1/Q/lcmHjGoA/xyNpKppUWcNZ0XVRYRET6LxIKag2WiIhP5WbAqtoCe9fCGdcO+tpXO6ubeWlnLVcsnYwN8hgiIpKbwqGAKlgiIj6VmwFr/X+DBWHRVYM+xKPrKgkGjI8sqUjjwEREJBdEQgGtwRIR8ancC1iJOLz+MMxeCUVjB3WIeCLJ46/s5fw55YwrGVyDDBERyV2qYImI+FfuBaxdf4Smd+D0jw76EM+9XcWhxihXLlVzCxERGThVsERE/Cv3Albdbu++YumgD/HIukrKiyOcf+rgKmAiIpLbwqGgApaIiE/lXsBqa/DuI8WDevqhhjb+d/MhPry4grxg7n18IiJy4rwKlroIioj4Ue4lhGgjYBAuGtTTH391H4mk48qlam4hIiKDozVYIiL+lZsBK1IyqPbszjkeXVfJsmljmFE+uIAmIiISCQWIJRSwRET8KAcDVsOgpwe+vLOWndXNXHWmmluIiMjgRUIBou0KWCIifpSbASu/ZFBPfWRdJcWREKsWTkjzoEREJJeEVcESEfGt3AtYbYOrYLUnkjz91jt8YNEERoSDQzAwERHJFZFQUE0uRER8KvcCVscarAFaX1lHcyzBeXPUml1ERE5MOKgmFyIifpWjAWvgFaw1W6sJGJw9o3QIBiUiIrkkkqcLDYuI+FUOBqzBrcFas62ahRWjGFmQNwSDEhGRXKIKloiIf+VgwBp4BauxrZ31lXWcM6tsiAYlIiK5RBUsERH/yq2AlWiH9pYBr8F6aUctiaRjhQKWiIikQTgYJJF0xNVJUETEd3IrYEUbvfsBBqw126rJzwuweOqoIRiUiIjkmkied/pVq3YREf/JsYDV4N0PcIrgmm3VLJteSiSk9uwiInLiwsFUwNI0QRER38mxgJWqYA2gycU79W1sO9Sk9VciIpI2HRUsrcMSEfGf3AxYA6hgPb+tGkDrr0REJG1UwRIR8a/cClhtHVME+1/BWrOtmtLCMKeOH/i1s0RERLoTyfOmnEfjiSyPRERE0i23AtYAm1w451izrZoVs8oIBGwIByYiIrmko4KlKYIiIv6TYwGr3rvv5xTBrYeaqGqM8i5NDxQRkTTSGiwREf/KsYA1sCYXa7am1l/NVsASEZH0iWgNloiIb+VWwGprgEAIQvn92n3NtmqmlxUyadSIIR6YiIjkknBIAUtExK9yK2BFG731V9b3eqr2RJIXd9RoeqCIiKRdx3UVNUVQRMR/cjBg9W/91frKOlpiCbVnFxGRtFMFS0TEv3IsYDX0u4Pgmq3VBAzOnlk6xIMSEZFcEwl1NLlQm3YREb/JsYDV2P8GF9uqWVQxipEj8oZ4UCIikm5mdpGZvW1m28zs1m62TzGz35nZa2b2hpmtyuT4VMESEfGv3ApYbfX9miLY2NbO+so6rb8SETkJmVkQ+C7wfmAecI2ZzTtmt68AjzrnzgCuBr6XyTF2VrAUsERE/Ca3AlZHk4s+vLSjlkTSaf2ViMjJaRmwzTm3wzkXAx4GLjlmHwd0nBBGAvszOD5VsEREfCzHAlZDvypYa7ZVMyIvyOKpozIwKBERSbNJQGWX3/emHuvqduA6M9sLrAb+srsDmdlNZrbOzNZVVVWlbYCdXQS1BktExG9yLGD1bw3Wmm3VLJs+5sgJUEREfOca4H7nXAWwCnjQzI47Jzrn7nbOLXXOLS0vL0/bi+cFvcuFqIIlIuI/uROw4lFIxPqsYL1T38a2Q01afyUicvLaB0zu8ntF6rGuPgU8CuCcewHIBzL2H34zIxIKaA2WiIgP5U7Aamvw7vtYg/X8tmoArb8SETl5rQVmm9l0MwvjNbF46ph99gDvBTCzuXgBK31zAPshrIAlIuJLuROwov0LWGu2VVNWFObU8f27ILGIiAwvzrk4cAvwNLAJr1vgBjO7w8wuTu32ReBGM3sdeAi43jnnMjnOSCiogCUi4kOhbA8gY44ErJ6Dk3OONduq+bOZZQQClqGBiYhIujnnVuM1r+j62Fe7/LwRWJHpcXUVCQW0BktExIdyqILV6N330uRi7+FWqhqjnDVjTIYGJSIiucpbg6UugiIifpM7Aaut7wpWVVMUgImjRmRiRCIiksPCqmCJiPhS7gSsjgpWL2uwappiAJQWhjMxIhERyWHqIigi4k8KWF3UNnsVrNKiSCZGJCIiOUwVLBERf8qhgFXv3fcyRbBaFSwREckQr4ug1mCJiPhNvwKWmV1kZm+b2TYzu7Wb7VPN7Ldm9oaZPWdmFV22fdzMtqZuH0/n4Ack2gihfAj1HJ5qmmIUhoPk5wUzODAREclF4VCAWEIVLBERv+kzYJlZEPgu8H5gHnCNmc07ZrdvAQ845xYBdwD/nHruGOA24CxgGXCbmY1O3/AHoK2h1+oVeFMENT1QREQyIRzUFEERET/qTwVrGbDNObfDORcDHgYuOWafecD/pn7+XZft7wOecc7VOucOA88AF534sAch2tjnRYZrmmOUFml6oIiIDL1InppciIj4UX8C1iSgssvve1OPdfU6cHnq58uAYjMr7edzMyPa2GcFq7oppvVXIiKSEapgiYj4U7qaXHwJONfMXgPOBfYB/V65a2Y3mdk6M1tXVVWVpiEdI9rPKYKFmiIoIiJDTxUsERF/6k/A2gdM7vJ7ReqxI5xz+51zlzvnzgD+T+qxuv48N7Xv3c65pc65peXl5QN8C/0UbYT8kT1uds5R06QpgiIikhnhYFAVLBERH+pPwFoLzDaz6WYWBq4Gnuq6g5mVmVnHsb4M3Jf6+WlgpZmNTjW3WJl6LPP6aHLR0BonnnSM0RRBERHJAK+CpTbtIiJ+02fAcs7FgVvwgtEm4FHn3AYzu8PMLk7tdh7wtpltAcYB30g9txb4R7yQtha4I/VY5kUbem1yUZO6yHCZugiKiEgGhIMB2hOOZNJleygiIpJGof7s5JxbDaw+5rGvdvn5MeCxHp57H50Vrexwrs8mFzXNqYsMa4qgiIhkQCTP+xtnLJEkP6DrL4qI+EW6mlwMb+0t4BKQ30sFq8mrYGmKoIiIZEI46J2C1ehCRMRfciNgRRu9+35UsDRFUEREMiGS51WttA5LRMRfciNgtTV4972twWryAtboAlWwRERk6EVSFSx1EhQR8ZfcCFhHKli9TxEsyQ8RDuXGRyIiItnVsQZLUwRFRPwlN9JEtN6772OKoKYHiohIpoRVwRIR8aUcCVipClavTS50kWEREckcVbBERPwpNwLWkTVYvVWwouogKCIiGRMOek0uVMESEfGX3AhY/ViDVdsco1RTBEVEJEM6K1jqIigi4ic5FrC6r2Alko7a5hhlqmCJiEiGaA2WiIg/5UjAaoC8QggEu91c1xIj6XSRYRERyZyOrrUKWCIi/pI7AauXBhe1qYsMa4qgiIhkSiSkJhciIn6UGwGrraHXBhfVTR0BSxUsERHJDFWwRET8KTcCVrSx94sMN0cBKC1UBUtERDIjEvKmravJhYiIv+RIwOq9gtU5RVAVLBERyYywpgiKiPhSjgSsxl7XYFU3xTCD0QUKWCIikhlagyUi4k+5E7B6u8hwU5TRBWGCAcvgoEREJJepTbuIiD/lRsBqa+j7IsNq0S4iIhkUCBjhYEAVLBERn/F/wEomIdZHk4ummNZfiYhIxoVDAVWwRER8xv8BK9bo3ffWpr05qg6CIiKScZFQQF0ERUR8xv8BK5oKWH1caFgVLBERyTRVsERE/Mf/AautwbvvoYLVnkhS19LOGK3BEhGRDPMqWApYIiJ+4v+AFe19iuDhI9fA0hRBERHJLFWwRET8J4cC1shuN9ekAlaZKlgiIpJhkVBQa7BERHwmBwJWvXffQwWrpskLWJoiKCIimRYOBYglVMESEfGTHAhYvTe5qGmOApoiKCIimRcJBYi2K2CJiPiJ/wNWH00uOipYZeoiKCIiGaYKloiI//g/YEUbAYNwUbeba5qjBANGSX5eZsclIiI5LxxUBUtExG9yI2BFSsCs2821zTHGFIYJBLrfLiIiMlQieUFVsEREfCYHAlZDj9MDAaqbYpSqwYWIiGRBOKg27SIifpMbAauHBhcANU1RSrX+SkREsiCSF1CbdhERn/F/wGrrvYJV2xyjtFAdBEVEJPPCwQBRVbBERHzF/wGrYw1WD2qaYqpgiYhIVngVLAUsERE/yYGA1XMFKxpP0BiNaw2WiIhkRSS1Bss5l+2hiIhImuRAwGrscQ1WbbN3DSxdZFhERLIhkhcEUCdBEREfyY2A1cdFhlXBEhGRbAgHvdOwOgmKiPiHvwNWoh3aW3pcg1XdFAXQGiwREcmKSJ53GtY6LBER//B3wIo2evc9BKwjUwTVRVBERLJAFSwREf/xecBq8O77miKoCpaIiGSBKlgiIv7j84CVqmD10OSiujlKOBigKBLK4KBEREQ84WCqyYUCloiIb/g7YLX1XsGqTV0Dy8wyOCgRERFPJNRRwUpkeSQiIpIu/g5YR9Zg9TBFsFkXGRYRkewJh7QGS0TEb3IkYI3sdnNNU5QxanAhIiJZ0lnBUsASEfELnweseu++lwpWma6BJSIiWaIKloiI//g8YPXe5KKmSVMERUQke8JagyUi4jv+DlhtDRAIQSj/uE0tsTit7QlNERQRkayJhLwugpoiKCLiH/4OWNFG7yLD3XQJ1DWwREQk2yKaIigi4js+D1gNva6/AihTwBIRkSxRkwsREf/xecBKVbC6UdMUBdAUQRERyRo1uRAR8R//B6yeGlykKlil6iIoIiJZojVYIiL+4++A1Vbf8xRBrcESEZEsUwVLRMR//B2w+pgiOCIvSEE4lOFBiYiIeIIBIxQwtWkXEfERnwesnptc1DbrGlgiIpJ94VBAFSwRER/xecDqeQ1WdXOM0iI1uBARkeyKhAJagyUi4iP+DVjtbZCI9bIGK6oGFyIiknWqYImI+It/A1a00bvvYQ1WbXNMAUtERLIuEgpqDZaIiI/0K2CZ2UVm9raZbTOzW7vZPsXMfmdmr5nZG2a2KvX4NDNrNbP1qdsP0v0GehRt8O67CVjOOWqaNEVQRESyLxwKEEuogiUi4hd9ttAzsyDwXeBCYC+w1syecs5t7LLbV4BHnXPfN7N5wGpgWmrbdufc6ekddj8cCVjHTxFsjMaJJZKqYImISNZFQgGi7QpYIiJ+0Z8K1jJgm3Nuh3MuBjwMXHLMPg7oKBWNBPanb4iD1DFFsJsmF7W6BpaIiAwTqmCJiPhLfwLWJKCyy+97U491dTtwnZntxate/WWXbdNTUwd/b2bndPcCZnaTma0zs3VVVVX9H31v2nquYNU0RwE0RVBERLJOFSwREX9JV5OLa4D7nXMVwCrgQTMLAAeAKc65M4AvAD8xs+NKSs65u51zS51zS8vLy9MzoiNNLo4PWNUdFSxNERQRkSwLh4JEVcESEfGN/gSsfcDkLr9XpB7r6lPAowDOuReAfKDMORd1ztWkHn8F2A6ccqKD7pcja7BGHreptllTBEVEZHgIBwNE29VFUETEL/oTsNYCs81supmFgauBp47ZZw/wXgAzm4sXsKrMrDzVJAMzmwHMBnaka/C96qXJRU2TN0VwjCpYIiKSZZE8rcESEfGTPrsIOufiZnYL8DQQBO5zzm0wszuAdc65p4AvAveY2V/jNby43jnnzOzdwB1m1g4kgc8452qH7N10FW2EUD6Ejg9R1U0xiiMhIqFgRoYiIiLSk0hQa7BERPykz4AF4Jxbjde8outjX+3y80ZgRTfPexx4/ATHODhtDd1WryB1kWFNDxQRkWFAFSwREX9JV5OL4Sfa2O1FhsHrIqgOgiIiMhyEgwFicQUsERG/8HHA6rmCVdMU0/orEREZFiJ5QaJxNbkQEfELHwesxp4DVnOMMk0RFBGRYaCjguWcy/ZQREQkDfwdsPKPb9GeTDpvDVahpgiKiPiVmV1kZm+b2TYzu7WHfa40s41mtsHMfpLpMXaIhAIkHcSTClgiIn7QryYXJ6UemlzUt7aTSDpNERQR8anU5UG+C1wI7AXWmtlTqYZMHfvMBr4MrHDOHTazsdkZLYRD3t86Y/EkeUH//t1TRCRX+Pe/5NGGbptc1OgiwyIifrcM2Oac2+GciwEPA5ccs8+NwHedc4cBnHOHMjzGIyKpgBVVowsREV/wZ8Byrsc1WB0XGS5TF0EREb+aBFR2+X1v6rGuTgFOMbPnzexFM7uouwOZ2U1mts7M1lVVVQ3JYMOpazKqk6CIiD/4M2C1t4BLQP7xFazaVAVLUwRFRHJaCJgNnAdcA9xjZqOO3ck5d7dzbqlzbml5efmQDKSzgqVOgiIifuDPgNXW4N13U8FqisYBKIr4d/mZiEiO2wdM7vJ7ReqxrvYCTznn2p1zO4EteIEr47quwRIRkZOfPwNWtNG772YNVizhncA6TmgiIuI7a4HZZjbdzMLA1cBTx+zzBF71CjMrw5syuCOTg+ygNVgiIv7iz5TRW8BKncDC6tQkIuJLzrk4cAvwNLAJeNQ5t8HM7jCzi1O7PQ3UmNlG4HfA3zjnarIx3rACloiIr/hznly03rvvZorgkYClCpaIiG8551YDq4957KtdfnbAF1K3rIqkmlxoDZaIiD/4M2V0VLC6aXKhgCUiIsOJ1mCJiPiLP1NGL00uYokkZhAKWIYHJSIicjytwRIR8Rd/Bqwja7C6nyIYCQUwU8ASEZHsi6iCJSLiKz4NWB0VrOOnCEbjSTW4EBGRYUNNLkRE/MWfSSPaCHmFEAgetymWSBIOHf+4iIhINnQ0uVAFS0TEH/wZsAJBGFnR7aaOKYIiIiLDQWeTC3URFBHxA38mjQvvgFte7nZTLJ5UB0ERERk21ORCRMRfci5pxLQGS0REsiHaBLueh+bqox5Wm3YREX/JuaQRjSdUwRIRkcyr2w33r4Jda456OBQwAqYKloiIX+Rc0vCaXOTc2xYRkWzruHRIx6VEUsyMcChALKGAJSLiBzmXNDRFUEREsqLj0iHHBCzwOglG29XkQkTED0LZHkCmxeJJCgpy7m2LyBBob29n7969tLW1ZXsovpCfn09FRQV5eXnZHsrQ6KGCBaiCJSJpo3NT+g30/JRzSSOqLoIikiZ79+6luLiYadOmYWbZHs5JzTlHTU0Ne/fuZfr06dkeztAIBL1rNEYbjtsUCQWItitgiciJ07kpvQZzfsq5pKE1WCKSLm1tbZSWluoElgZmRmlpqf//4hop7rGCFVUFS0TSQOem9BrM+SnnkkYsniSiNVgikiY6gaVPTnyWPQQsbw2WApaIpEdO/Pc0gwb6eeZc0tCFhkXEL+rq6vje97434OetWrWKurq6Xvf56le/yrPPPjvYoUlPeqlgaQ2WiPiBzk25GLASSSIKWCLiAz2dxOLxeK/PW716NaNGjep1nzvuuIMLLrjghMYn3eixghVQF0ER8QWdm3IxYKmCJSI+ceutt7J9+3ZOP/10zjzzTM455xwuvvhi5s2bB8Cll17KkiVLmD9/PnffffeR502bNo3q6mp27drF3LlzufHGG5k/fz4rV66ktbUVgOuvv57HHnvsyP633XYbixcvZuHChWzevBmAqqoqLrzwQubPn88NN9zA1KlTqa6uzvCncJLpJWCpgiUifqBzUw52EVTAEpGh8LWfb2Dj/uO7w52IeRNLuO1D83vc/s1vfpO33nqL9evX89xzz/GBD3yAt95660iXo/vuu48xY8bQ2trKmWeeyYc//GFKS0uPOsbWrVt56KGHuOeee7jyyit5/PHHue666457rbKyMl599VW+973v8a1vfYsf/vCHfO1rX+M973kPX/7yl/n1r3/Nvffem9b370uRku6nCAbVRVBE0k/npuycm3IqaSSTjnjSEQ4Gsz0UEZG0W7Zs2VEtZO+66y5OO+00li9fTmVlJVu3bj3uOdOnT+f0008HYMmSJezatavbY19++eXH7bNmzRquvvpqAC666CJGjx6dxnfjU5FiiNYf/3CeKlgi4k+5eG7KqQpWx8lLFSwRSbfe/pqXKYWFhUd+fu6553j22Wd54YUXKCgo4Lzzzuu2xWwkEjnyczAYPDINo6f9gsFgn/PopRcdUwSdgy5dqcLBANG41mCJSHrp3JQdOZU0onEFLBHxj+LiYhobj59uBlBfX8/o0aMpKChg8+bNvPjii2l//RUrVvDoo48C8Jvf/IbDhw+n/TV8J1IMLgntLUc/HAoSi6uCJSInP52bcqyC1fHXQQUsEfGD0tJSVqxYwYIFCxgxYgTjxo07su2iiy7iBz/4AXPnzmXOnDksX7487a9/2223cc011/Dggw9y9tlnM378eIqLi9P+Or4SSX0+0UYId/5VNxwKHPkjoIjIyUznJjDnXEZfsC9Lly5169atG5Jj7z3cwrv+5Xf864cXceWZk4fkNUQkd2zatIm5c+dmexhZE41GCQaDhEIhXnjhBW6++WbWr19/Qsfs7jM1s1ecc0tP6MBpkJbz05uPweOfglvWQdnsIw9//Rcb+cnLe9h4x0UnOEoRyXU6N6X/3AQDOz/lVAUrpimCIiJps2fPHq688kqSySThcJh77rkn20Ma/o5UsI7u6hUOBTRFUEQkDYbDuSm3ApaaXIiIpM3s2bN57bXXsj2Mk0vXKYJdHw4FiScdiaQjGLBunigiIv0xHM5NOZU0jlSwgjn1tkVEZLjoIWB1/OFPVSwRkZNfTiUNTREUEZGs6rGC5Z2X1KpdROTkl1NJQwFLRESyKlLi3auCJSLiWzmVNKJagyUiItkULvLuj2ly0VnBUsASETnZ5VTS0BosEcllRUXel/v9+/fzkY98pNt9zjvvPPpqRX7nnXfS0tJ5odxVq1ZRV1eXvoH6WSgMofweK1gKWCKSi/x2fsqppNERsPLzcupti4gcZeLEiTz22GODfv6xJ7DVq1czatSodAwtN0SKoe3YClYQ0BosEcltfjk/5VTS6KxgBbM8EhGRE3frrbfy3e9+98jvt99+O1//+td573vfy+LFi1m4cCFPPvnkcc/btWsXCxYsAKC1tZWrr76auXPnctlll9HazFRh/wAAIABJREFU2npkv5tvvpmlS5cyf/58brvtNgDuuusu9u/fz/nnn8/5558PwLRp06iurgbg29/+NgsWLGDBggXceeedR15v7ty53HjjjcyfP5+VK1ce9To5J1LcY5MLrcESET/I9fOTroMlIpIOv7oV3nkzvcccvxDe/80eN1911VV8/vOf57Of/SwAjz76KE8//TSf+9znKCkpobq6muXLl3PxxRdj1v21lb7//e9TUFDApk2beOP/s3ff4XFVd/7H39+Z0YzaqFjVtlxxNzY2FobQQguYJEBIQgtsApuETbLpZTeVkGTZkPw2bTdsEkJIB0JJwCw1oXdsio1tbGNcwE2WuyRbM5qZ8/vjjmTZVhnbkqZ9Xs8zz525c2d05oJ89ZlzzvcsXsyxxx7b9dx1113HsGHDiMfjnHnmmSxevJjPfvaz/PjHP+axxx6jurp6v/d66aWX+O1vf8sLL7yAc47jjz+ed77znVRWVvLGG29w66238utf/5qLL76Yu+66iyuuuGIATlIW6iNgaYigiAyoNFybQNenvEoaqiIoIrlk9uzZbNmyhY0bN7Jo0SIqKyupr6/n61//OjNnzuSss85iw4YNNDU19foeTz75ZNeFZObMmcycObPrudtvv51jjz2W2bNns3TpUpYtW9Zne55++mkuvPBCSkpKKC0t5f3vfz9PPfUUAOPGjWPWrFkAzJkzh7Vr1x7hp89ioTJVERSRnJbv16f86sFSwBKRwdLPt3mD5aKLLuLOO+9k8+bNXHLJJfz5z3+mubmZl156iYKCAsaOHUt7e/shv++aNWv4r//6LxYsWEBlZSVXXnnlYb1Pp1Ao1HXf7/fn+RDBMtj51n67VORCRAZFmq5NkN/Xp7xKGp2Th1VFUERyxSWXXMJtt93GnXfeyUUXXcSuXbuora2loKCAxx57jHXr1vX5+lNPPZVbbrkFgCVLlrB48WIAdu/eTUlJCeXl5TQ1NfHAAw90vSYcDtPS0nLQe51yyincfffd7Nmzh7a2Nv72t79xyimnDOCnzRGhcA9l2r25werBEpFckc/Xp7zswSrw9zzWU0Qk20yfPp2WlhZGjhzJ8OHDufzyyznvvPOYMWMGjY2NTJkypc/Xf/KTn+Sqq65i6tSpTJ06lTlz5gBwzDHHMHv2bKZMmcKoUaM46aSTul5z9dVXM2/ePEaMGMFjjz3Wtf/YY4/lyiuvZO7cuQB87GMfY/bs2fk9HLAnPczB2teDpSqCIpIb8vn6ZM65QXnjw9XY2Oj6q3F/uL7/wOv89pm1rPyPcwfl/UUkv7z++utMnTo13c3IKT2dUzN7yTnXmKYmdRmw69M/vgPP/g98qxmSk7s37tzLidc/yvXvn8Glc0cf+c8Qkbyla9PgOJTrU16NlYvGEoQ0PFBERNIpFIZEB8QiXbs0B0tEJHfkVdqIxhIqcCEiIukVCnvbbsMEtQ6WiEjuyKu0oYAlIiJpFyrztt0KXXSVaY8rYImIZLu8ShvRuAKWiAysTJvHms3y5lx29WB1C1jJ4euRDhW5EJEjlzf/ng6RQz2fKaUNM5tnZivMbJWZfbWH50eb2WNm9oqZLTazd3d77mvJ160ws3MOqXUDLBpLqES7iAyYwsJCtm3bpgvZAHDOsW3bNgoLC9PdlMHXwxBBMyMY8BFRD5aIHCFdmwbW4Vyf+i3TbmZ+4AbgXcB6YIGZzXfOdV8y+ZvA7c65X5jZNOB+YGzy/qXAdGAE8A8zm+ScS8tXdBoiKCIDqaGhgfXr19Pc3JzupuSEwsJCGhoa0t2MwddDwAIoDvppi8TS0CARySW6Ng28Q70+pbIO1lxglXNuNYCZ3QZcAHQPWA5IDiqnHNiYvH8BcJtzLgKsMbNVyfd7LuUWDiANERSRgVRQUMC4cePS3QzJNr0ErKqSINtao2lokIjkEl2b0i+VtDESeLvb4/XJfd1dC1xhZuvxeq8+cwivxcyuNrOFZrZwMNN2JJboqtQkIiKSFl1FLvYPWNWlIba2Rnp4gYiIZJOBShuXAb9zzjUA7wb+aGYpv7dz7kbnXKNzrrGmpmaAmnQwb4igf9DeX0REpF89FLkAqA6H2KoeLBGRrJfKEMENwKhujxuS+7r7KDAPwDn3nJkVAtUpvnbIqMiFiIikXUEh+IMH9WDVlIbY2qIeLBGRbJdK2lgATDSzcWYWxCtaMf+AY94CzgQws6lAIdCcPO5SMwuZ2ThgIvDiQDX+UEXjGiIoIiIZIBTuYYhgkJZIjHaVahcRyWr99mA552Jm9mngIcAP3OycW2pm3wUWOufmA18Cfm1mX8AreHGl82pDLjWz2/EKYsSAf01XBUGASCyuIhciIpJ+PQasEADb2qKMrChKR6tERGQApDJEEOfc/XjFK7rvu6bb/WXASb289jrguiNo44DREEEREckIPQSsqmTA2toSUcASEclieZU2tA6WiIhkhFBZj0MEAVUSFBHJcnmVNhSwREQkI4TCB1cR7OzBUsASEclqeZU2tNCwiIhkhFAY2vcPWDXhzoClUu0iItksb9JGIuHoiDvNwRIRkfTrYQ5WYYGf0lCAZpVqFxHJanmTNqLxBIB6sEREJP16CFjgzcPSEEERkeyWN2mjM2BpHSwREUm7UBjiEYjtH6aqS0Ns0xBBEZGsljdpIxpTD5aIiGSIUJm3jbTut7tKPVgiIlkvb9JGV8DSHCwREUm3UNjb9lBJUAFLRCS75U3a6AxYoYK8+cgiIpKpunqwDlwLK8SOPR10JIe1i4hI9smbtNFV5MLvT3NLREQk73X1YB0QsJKl2re3aR6WiEi2yp+ApTlYIiKSKXoJWDWlQQCVahcRyWJ5kzYiClgiIpIp+hgiCLBNPVgiIlkrb9KGilyIiEjG6KPIBcBW9WCJiGStvEkbkVgcUA+WiIhkgF4CVlVyiKAqCYqIZK+8SRtdVQQVsEREJN0KisD8Bw0RLA0FCAV8ClgiIlksb9JGVxVBBSwREUk3M68X64CAZWbJtbA0B0tEJFvlTdrQHCwREckoobKDAhZ4pdrVgyUikr3yJm2oTLuIiGSUHnqwwCvVrh4sEZHslTdpQ0MERUQko4TCBxW5AJJDBNWDJSKSrfImbagHS0REMkovPVjVpSG2t0VJJFwaGiUiIkcqb9JGRHOwREQkk/QSsKpKg8QTjh17NExQRCQb5U3aUJELERHJKIW9FLnoXGxY87BERLJS3qSNaDxBgd/w+SzdTREREelziCBosWERkWyVPwErliAU8Ke7GSIiIp5QGXTsgXhsv9014SCggCUikq3yKmCpwIWIiGSMUNjbRvfvxdIQQRGR7JY3iSMaS2j+lYiIZI7OgNW+f6n28qICAj5TD5aISJbKm8QRjasHS0REMkhnwDpgHpaZUVUaZGuLApaISDbKm8QRicUVsEREJHP0ErBAiw2LiGSzvEkcGiIoIiIZJVTmbXsNWJqDJSKSjfImcURU5EJEJG+Y2TwzW2Fmq8zsq30c9wEzc2bWOJTtA7r1YO0+6Cn1YImIZK+8SRyqIigikh/MzA/cAJwLTAMuM7NpPRwXBj4HvDC0LUzqa4hgOMi21ijOuSFulIiIHKm8SRzReIKQApaISD6YC6xyzq12zkWB24ALejjue8APgPahbFyXPgJWTWmIaDzB7vbYQc+JiEhmy5vEoTlYIiJ5YyTwdrfH65P7upjZscAo59x9fb2RmV1tZgvNbGFzc/PAtrKgBLBe52CBFhsWEclGeZM4NERQREQAzMwH/Bj4Un/HOududM41Oucaa2pqBrYhPp/Xi9VDwKoqDQKoVLuISBbKm8ShdbBERPLGBmBUt8cNyX2dwsDRwONmthY4AZifnkIXZf30YKmSoIhItsmbxKEhgiIieWMBMNHMxplZELgUmN/5pHNul3Ou2jk31jk3FngeON85t3DIWxoK91pFEDREUEQkG+VN4tAQQRGR/OCciwGfBh4CXgdud84tNbPvmtn56W3dAXoZIjisJIjPYJsClohI1gmkuwFDRQFLRCR/OOfuB+4/YN81vRx72lC0qUehMLTvPGi332cMKwnSrCGCIiJZJ28SRySeIBTwp7sZIiIi+/TSgwVabFhEJFvlRcByzqkHS0REMo8ClohIzsmLxNERdwBaaFhERDJLL1UEwSvVroAlIpJ98iJxRGJxAFURFBGRzBIKQ7QVEvGDnqouDbG1RXOwRESyTV4kjmgsAaAhgiIikllCYW8bbT3oqerSEHs74rRFYkPcKBERORJ5kTiicQUsERHJQJ0Bq8fFhoMAbFMlQRGRrJIXiaOrB0tDBEVEJJP0FbDC3mLDzZqHJSKSVfIicWiIoIiIZKRQmbftIWDVlHoBS4UuRESyS14kjogCloiIZKLCzoC1+6CnqpJDBBWwRESyS14kDs3BEhGRjNTHEMGqkmQPlioJiohklbxIHJ1DBEOagyUiIpmkM2C1H9yDFQz4KC8qUA+WiEiWyYvEoTlYIiKSkfrowQKvkuC2NgUsEZFskheJQwFLREQyUrDU2/YasLTYsIhItsmLxKE5WCIikpF8fi9k9RawwiENERQRyTJ5kTi0DpaIiGSsULjHKoLglWrXOlgiItklLxJHV5GLAn+aWyIiInKAULjXHqyqkiAt7THaO+JD3CgRETlcKQUsM5tnZivMbJWZfbWH539iZq8mbyvNbGe35+Ldnps/kI1PVSSuHiwREclQfQSs6rBXqn1bm+ZhiYhki0B/B5iZH7gBeBewHlhgZvOdc8s6j3HOfaHb8Z8BZnd7i73OuVkD1+RDF0l+86c5WCIiknH6ClilyYDVGmFkRdFQtkpERA5TKoljLrDKObfaORcFbgMu6OP4y4BbB6JxA6WzyEVIAUtERDJNnwErCKBCFyIiWSSVxDESeLvb4/XJfQcxszHAOODRbrsLzWyhmT1vZu877JYeARW5EBGRjBUq67cHS6XaRUSyR79DBA/RpcCdzrnus3HHOOc2mNl44FEze80592b3F5nZ1cDVAKNHjx7gJnkBK+AzfD4b8PcWERE5In30YNUk52CpkqCISPZIpUtnAzCq2+OG5L6eXMoBwwOdcxuS29XA4+w/P6vzmBudc43OucaampoUmnRoorGE5l+JiEhmCpV5ZdqdO+ipwgI/paGAhgiKiGSRVFLHAmCimY0zsyBeiDqoGqCZTQEqgee67as0s1DyfjVwErDswNcOtmhcAUtERDJUKAw4iLb2+HRVaZCtrRoiKCKSLfodIuici5nZp4GHAD9ws3NuqZl9F1jonOsMW5cCtzm331dwU4FfmVkCL8xd37364FCJxhKafyUiIpkpFPa2kZZ997upLg2xTT1YIiJZI6U5WM65+4H7D9h3zQGPr+3hdc8CM46gfQNCQwRFRCRjdQ9YPaguDbJma9sQNkhERI5EXqSOiIYIiohIpgqVeds+KglqiKCISPbIi9ShIYIiIpKxunqwdvf4dHVpiB17osSSazqKiEhmy4vUEY0ltMiwiIhkpv6GCIZDOAfb29SLJSKSDfIidWgOloiIZKx+AlZNaRDQWlgiItkiL1JHNJ4gFPCnuxkiIiIH6ydgVZV6iw1rHpaISHbIj4ClHiwREclU/VYR9AKWSrWLiGSHvEgdkVhcRS5ERCQz+QsgUNRHkQtviOBWBSwRkayQF6lDPVgiIpLRQuFee7BKQwFCAZ+GCIqIZIm8SB0KWCIiktEKy3oNWGbmrYXVoh4sEZFskBepI6qFhkVEJJOFwtDe8xBB8Eq1q4qgiEh2yIvUEdFCwyIiksn6GCIIMKK8kLe27xnCBomIyOHKi9ShhYZFRCSjhXofIggwe3QF67btUaELEZEskPOpwzmnIYIiIpLZ+unBmjOmEoCX1u0YqhaJiMhhyvnUEUs4nENDBEVEJHOFwr2WaQeYPqKcoN/HywpYIiIZL+dTRzSWAFAPloiIZK7OHizneny6sMDP0SPL1IMlIpIFcj51KGCJiEjGC4XBxaFjb6+HNI4dxuINu4jE4kPYMBEROVQ5nzqicQUsERHJcKGwt+1jHtaxoyuJxhIs2dD7UEIREUm/nE8dXT1YmoMlIiKZKlTmbfsKWGMqADQPS0Qkw+V86ohoiKCIiGS6rh6s3nunasOFjB5WrHlYIiIZLudTR2cPVijgT3NLREREepHCEEGAxjGVvPTWDlwvxTBERCT9cj5gdU4G1kLDIiKSsVLowQI4dkwlzS0R3t7eezEMERFJr5xPHaoiKCIiGS+FOVjQbcHht7YPdotEROQw5XzqUBVBERHJeCkGrEl1YcKhgOZhiYhksJxPHaoiKCIiGS9U6m37GSLo9xmzRlfw0rqdQ9AoERE5HDmfOjREUEREMl4gBP4QtPe/xtWcMZWs2LyblvaOIWiYiIgcqpxPHRoiKCIiWaFyDGxd2e9hc8ZUknDw6tvqxRIRyUQ5nzoiGiIoIiLZoGEurF8A/ZRgnzWqAp+heVgiIhkq51PHvnWwcv6jiohINmtohD3bYPvqPg8LFxYwub5MAUtEJEPlfOrQHCwREckKDcd52/UL+z10zpgKXnlrJ/GEFhwWEck0OZ86NAdLRESyQu1UCJZ6wwT7MWdMJa2RGCub+i7rLiIiQy/nU4fKtIuISFbw+WHksbD+xX4PbRwzDNA8LBGRTJTzqSMaS+AzCChgiYhIpms4DjYvgeievg+rLKImHFLAEhHJQDmfOqLxhIYHiohIdmiYCy4Om17t8zAzY87oSgUsEZEMlPPJIxpLaHigiIhkh4ZGb/t2/8ME54yp5K3te9jS0j7IjRIRkUOR88kjEosTKvCnuxkiIiL9K6mGynGpFboYWwnAy+u04LCISCbJg4ClHiwREckio1JbcHj6iDKCAR8vrds+RA0TEZFU5HzyiMYSWmRYRESyR8Nx0NoEu97u87BQwM/MkeWahyUikmFyPnlEYypyISIiWaRzHlaK62Et2bCb9o74IDdKRERSlfPJQ1UERUQkq9QdDYEiWL+w30PnjKkkGk+wdOOuIWiYiIikIueTh6oIiohIVvEXwIjZKVUSPHaMV+hi4VoNExQRyRQ5nzw0RFBERLJOQyNsXgyxSJ+HVZeGGFtVrHlYIiIZJOeTh4YIiohI1hk1F+JR2LS430OPHVPJy2/twPVTdVBERIZGzicPDREUEZGsMzL1QhenTKxma2uUF9eoXLuISCbI+eShIYIiIpJ1yoZD+ShY3/88rHnTh1MaCnD7wvVD0DAREelPziePiAKWiIhko4bjUqokWBT0c94xI7j/tU20tHcMQcNERKQvOZ88onEtNCwiIlmo4ThvseHdm/o99JLjRrG3I87/Le7/WBERGVw5nzw0B0tERLJSw3HeNoV5WMc0lDOprpS/LHh7kBslIiL9yfnkoTlYIiKSlYbPBH8wpYBlZlzcOIpX397JyqaWIWiciIj0JueThzdE0J/uZoiIiByaQAiGH5PSPCyAC2ePJOAz7lioXiwRkXTK6YAViyeIJ5x6sEREJDs1HAcbX4F4/8UrqkpDnDW1jr++vIFoLDEEjRMRkZ7kdPKIxr0LjAKWiIhkpYbjILYXmpakdPglx41iW1uUR5dvGeSGiYhIb3I6eXR+g6ciFyIikpW6Cl2kNkzwlInV1JWFuF3DBEVE0iank0dXwFIPloiIZKPyBiith7f7X3AYIOD38YFjG3h8xRaadrcPcuNERKQnOZ08IgpYIiKSzcxg1HEpVRLsdHHjKBIO7np5/SA2TEREepPTyaNzDpYWGhYRkazVcBzsWANtW1M6fGx1CXPHDeOOhetxzg1y40RE5EApJQ8zm2dmK8xslZl9tYfnf2JmryZvK81sZ7fnPmJmbyRvHxnIxvdHc7BERCTrHeI8LPB6sdZsbWPB2h2D1CgREelNv8nDzPzADcC5wDTgMjOb1v0Y59wXnHOznHOzgP8B/pp87TDg28DxwFzg22ZWObAfoXeagyUikp9S+GLwi2a2zMwWm9kjZjYmHe1MyfBZ4AvA+tTmYQG8e0Y9paGAil2IiKRBKsljLrDKObfaORcFbgMu6OP4y4Bbk/fPAf7unNvunNsB/B2YdyQNPhQq0y4ikn9S+WIQeAVodM7NBO4Efji0rTwEwWKoOxpWPwEpDvkrDgY475jh3Ld4Ey3t/a+hJSIiAyeV5DES6P4V2PrkvoMkvwEcBzx6KK81s6vNbKGZLWxubk6l3SnREEERkbzU7xeDzrnHnHN7kg+fBxqGuI2HZvYVsGEhLLs75Zdc3DiKvR1x7lu8aRAbJiIiBxro5HEpcKdzLn4oL3LO3eica3TONdbU1AxYYzREUEQkL6X8xWDSR4EHenpisL4APGSN/wz1M+HBr0OkNaWXzBpVwcTaUv6iYYIiIkMqleSxARjV7XFDcl9PLmXf8MBDfe2AU5l2ERHpi5ldATQC/6+n5wfrC8BD5vPDe34ELRvhydRGM5oZFzeO4pW3drJkw65BbqCIiHRKJXksACaa2TgzC+KFqPkHHmRmU4BK4Lluux8CzjazymRxi7OT+4aEyrSLiOSllL7cM7OzgG8A5zvnIkPUtsM3aq43VPC5G6B5RUovuaixgaqSIF//22vEktdEEREZXP0mD+dcDPg0XjB6HbjdObfUzL5rZud3O/RS4DbXbdEN59x24Ht4IW0B8N3kviGxbw6Wf6h+pIiIpF+/Xwya2WzgV3jhaksa2nh4zvoOBEvhvi+lVPCiojjItedPZ/H6Xdz8zJohaKCIiARSOcg5dz9w/wH7rjng8bW9vPZm4ObDbN8RicS8qWChAvVgiYjkC+dczMw6vxj0Azd3fjEILHTOzccbElgK3GFmAG85587v9U0zRUk1nHkN3PdFWHIXzPhgvy9578zh3PPqRn708ErOnlbP2OqSIWioiEj+yunkoSqCIiL5yTl3v3NuknPuKOfcdcl91yTDFc65s5xzdZ1rOGZFuOo050pvbayHvgGRln4PNzOuu/BoggEf/37XYhKJ1Eq9i4jI4cnp5KEqgiIiknN8fnjPj6G1CR6/PqWX1JUV8o13T+WFNdu5dcFbg9xAEZH8ltPJQwFLRERyUsMcOPbD8PwvoGlZSi+55LhRnHhUFd+/fzmbdu0d5AaKiOSvnE4e0XgCMwj4LN1NERERGVhnXQuFZXD/l1MqeGFmXP/+mcQSCb7xtyW4FF4jIiKHLrcDVixB0O8jOYFZREQkdxQP80LWumfgtTtSesnoqmK+fPZkHl2+hfmLNg5q80RE8lVOB6xILKHhgSIikrtmfxhGzvEKXrRtTeklV500jlmjKrh2/lK2tWb+8l8iItkmp9NHNJ7QIsMiIpK7fD4472fQvgv+9glI9L+YsN9n/PCDM2mNxLj23tTmb4mISOpyOn10DhEUERHJWfUzYN5/wqq/w3P/k9JLJtWF+fTpE7l30Ubuf23TIDdQRCS/5HT6iGqIoIiI5IPGj8K0C+CR78LbL6b0kk+edhTHjKrgi7e/yitv7RjkBoqI5I+cTh8KWCIikhfM4Lz/hrKRcOc/w97+A1Mw4OOmDzdSEw7xsd8vZN22tiFoqIhI7svp9BGNK2CJiEieKKqAi34LLZvhnk+nVLq9Jhzi91fNJeEcH7n5RRW9EBEZADmdPjQHS0RE8srIOfCu78Dy/4MXb0zpJeNrSrnpI41s2tXOx/6wkL3R+CA3UkQkt+V0+tAQQRERyTsnfAomzYOHvwkbX0npJXPGDONnl87i1bd38rnbXiGe0CLEIiKHK6fTRyQWJxjwp7sZIiIiQ8cM3vcLKKmBO66C9t0pvWze0cO55r3TeHhZE9/7v2W4FIYYiojIwXI8YGkdLBERyUPFw+ADv4Gdb8G9n0tpPhZ4ixB/7ORx/O7Ztdz01JpBbqSISG7K6fShIhciIpK3xrwDzvgGLP2rF7Ji0ZRe9vV3T+U9M4dz3f2vc++ijYPcSBGR3BNIdwMGUzSWIKQiFyIikq9O+gJE2+CpH8G2VXDxH6Gkqs+X+HzGjy46hi272/nSHYsYNayYWaMqhqjBIiLZL6fTh4pciIhIXvP54Mxr4P03wYaX4NenQdPSfl9WWODnl1fMoTYc4uo/LGTzrvbBb6uISI7I6fShIYIiIiLAzIvgqvu9YYK/ORuW39/vS6pKQ/zmI8fRFonxcZVvFxFJWc4PEdQ6WCIiInhrZF39GNz2Ie925rfg5C96VQd7Mbk+zM8unc3H/7iQL9+5iJ9fNhvr43gRkSO2Zztse9Mb1tyyCVzcK9TjEpCIe1sXB/NBSS2E6/fdSuuhoDDdnyAPApZ6sERERDxlI+CqB+Cef4VHvgtbXof3/AgKy3t9yVnT6vj3eVO4/oHlTK4L89kzJw5hg0UkZ8VjsO5pb/jy1lVeoNq2CvZu7/t15vfCVWfQOlBhBYSHQ9VRUDsVaqZ4t6oJQxa+cjZgJRKOWMIpYImIiHRXUOSVcK+dBo9+D5b+DUa/Ayad4y1QXDXhoF6tfzl1PCubWvjx31cysbaUc2cMT1PjRSSrJRKw/kV47U5Ydje0NXv7w8O9f3umXeBtqyZA9URvvy8AvmSo6v5vUyIBe3d4vVwtm6F18777uzdB8wpY8cC+EGY+GDbeC1uT5sGx/zRoHzNnA1Y0ngBQwBIRETmQGZz6ZZhwJiy7B1Y+DA9/07tVjvP++Jh0Now5GQJBzIz/vHAGa7e28YXbX2XUsGKOHtl7r5eIpEm8AzYt8oJE8bCh+7ntu7ytPwSB0P5ByDmvTUvu8r7Q2fU2BAq9f2dmfBDGnwah8KH/TJ/Pq4paUgX1R/d8TCzi9Ypted0LXM3JbXnDof+8Q5CzASsSSwYszcESERHp2YjZ3u2sa71FiVc+BG88DAtvhhd+4X17/I5PQ+NVFAZL+OU/zeF9P3+Gj/9hIfd8+iRqwwM83KZ1CwRLvJtIrmtthkSHN2/Id4R/r279dXWPAAAgAElEQVR+DV69FV67PdkrZN7v9lGnw/jTYdRcL/gMFOe8oPL6vfD6fNi8eP/n/cFk2Ap6j/ds83qijjrTq2w6+dzDC1WHKhCCuune7cD2D+aPHdR3T6NoMmCF1IMlIiLSv4rRMPfj3i26B1Y/Bs//Ah7+hreO1gmfpHbu1dz44UYu+uVzfPwPL3HLx46nJDQAf0rEO+DZ/4HHr4ey4XDprVA37cjfVyTTxCLesLVX/gRvPuLNIwoUeT1Ow8Z526qjvG35KCiphmBpz8Vo2rbCa3fAq3/2ApavwAsu0y7wem3efAye/qn3+1tQDGNO9MJWKOzNc9qTvHXd3+YFo+oJUDXRG6JXNdF7XFjuhZKNLydD1b3ezwBomAunf9Mbfhxrh3jU+5zdtw2NMPX8oe1V68sgF+sxN8gJ7lA1Nja6hQsXHvH7bNi5l5Ouf5QffGAGlxw3egBaJiIi6WBmLznnGtPdjoG6PmWdt1/0/kBb+SAEwzD3Yzxe+UE+etdbHDe2kt9dNoXCHSu89bW2vA5blnnfoM+8GI77GBRV9v3+G1+F+Z/xvgGfNM97HGmBC38J084fms8o0pd4zBvWFotAbG9y275v6xJQNhIqxkBpbc9/vG9+DV75Myz+ixdowiNg1mVe4Znta7yqedtXw441XiDpzh+C4uRQuOIqKK6GaCus+gckYl5P1azL4egPHBxg2nfD2qe9L0zefAy2vXHw+xYP825Fw7zPs/UN2LF2/wISJbXePKiWTV6RiXGnwNTzYPJ7vC9F8lRv16ec78HSHCwREZEjMGoufOgv3h+IT/0Ynv4ppwV+wXMjZ9Ox4Q0Kf7R137HBsFe1q7QOHv0PeOonMOdKeMenDp7z0LEXHv8+PPtz71v6i//oBardm+AvV8Dt/wSn/huc9rX+h0917PX+8Bs2fsA/vuSpWBRWPw6v3wPL7/OKKaQiUOT1BleO8QJXSbXXY7XpVa93aMp7YPYVXk+Sz3/w6xNx2L3BC1u71nu9Snu2Qds22LPVu79jnRfqTvgUzPqQ9zvXm8IymPJu7wbe71ci5gWqguLee3JiUS/sbX3DC2VbV0FHG0w8xyuIkyk9URkqZwNWJOal7qC/h/95RURE5NDUz4CLfgunfwOe+Qm1mxaxbuRx/HBdGUUNM/jExedRMGzMvj/YNi+BZ/8bXvglvPgrmHERnPhZb+jfmqfg3s96f0TO/ic4+3v7errKhsOV98F9X4Inf+gFu/ff6P2heKCmpfDS72Hxbd4k+6nnwTnfh4pRqX2mLcvhmZ/CmJMGtaKYZIj2Xd5w1GDpwYUYwAvqqx7x5hSteBAiu7wvDSaf6/XYBEu94gyBUHJbuG9e0671sHOdN5dxx1rv/lsveO9RPwPO/aH3O9BfMPH5vYBWMUijr1LtbQoEoWayd5NDlrMBS3OwREREBkH1BLjgBgDGALXPrOHae5ex8uEd/PSSMfg7/2atP9oLRmd8E577X3j597DoVm8408ZXoHIsfHg+jH/nwT+joBAu+DkMPwYe/CrcdKY3L6t6AkTbvEpkL/0O1i/wegWmnu/NX3n2594fyKd+xSvO0TnB/kC71sNj34dFt3iPF93qrcVz7g97f40MnXiHN1Stt7lHh2rTIu//jaV/9XpvwBvmFiz1CqqESr3enK1veL00hRVeWJ92vlfhLpXiEL1VsYu2qWhLHsr5gKUhgiIiIoPnypPG0R5LcP0DywkFfPzwAzPx+br9UVwxGs69Ht75b7DgN96k/BM/A6d9HYLFvb+xGRx/tTf86Y6PwK/PgKnv9SbXR3ZD9SQ45z9h5qXe3BSAYz8MD34NHvkOvHoLvOe/vD+QO+3Z7s0ne/HXgIPjPwknfx6e/194+ifeHLKL/wDhukE4U0nRPV4PSSLu9SaUjfSqNfbUQ5duibg3P6h9lze3qLRuYANoPOYNQ9vyerKM9uter+K2VV51PfN5BRkKyyFU7p2jwnKvLaPf4fU89tZb6Zw3R+nZ/4Y1T3ph6riPewUkIi1e8Im2QTR5P9Lqhf9p58PYU8BfMDCfUeEqLylgiYiIyBH5xDuPor0jzk//8QaFBT6+d8HR2IE9D8XD4J1f8W6HYtwpcPXjcNvl3jo6097nzesafcLBvRsVo+HSP8Mbf4f7vwJ/uACmXwhnfMvr9XrmZ15xgGMu8+Z2df5xfta1UD8T7vlXuPE0uORP0DDncE5F7/buhAU3eZUZ92w9+PlgqVfwIDwciiq84Wf+oNd70lnu2h/yjpl2gXfMQGrbBluWesMum5Yki5Ys94o6dFdc5bWxtA7C9V7YCYW9oXShsNcb1Pm4oMgr6NCyGVqbDt5uX71/QYfKsVAzFSbP84aMtu/2wnTXdhfsfBvWPeP1YIL333zMyTD2JBh7slfy/LXb4bkboHm5V0zirO94/88M9DkT6UXOBqyIFhoWEREZMp87cyJ7O+L86onVFAb8fP3dU/fvyToSFaPhX570/hhPZbjWxHfBp573AtXTP/bCFcDkd3tr8PRUFODo93tlqW/7EPz2XHjvT2D25Ufe9pYmeP4GWHCz11sy8Ww46XNeUNq90Ss60LIxeT95a9l0cJnrWMTr1QF44N9h+vu8HrvR70h9GJ1z3ntvXQnNK2HrCm8to60rvcDTqbjaG/LW+M/e+kHFw/YFo85b62avYmTrlv2rzfXFH/QCULgOqiZ456J2KtRM8eb6pNrbk0h4YXDtM7Duaa/CZedwT38I4hGomwEX/gqmv1/DPmXI5WzAimqhYRERkSFjZnx13hQiHQluenoNz6/ZxrfeM43jx1cN1A84tIVSCwrhtH/3ysW/egtMONPr9epL/Qy4+gm440q451Ne6fiz/8MrftDW7K071Na873601esVKao8+Bbd4w09fPUWLxhNvxBO/oL3MzodatXDRAI2L4KX/wCL7/DmjlVN9ILWMZdBaY13XDzmFVloXpEMUckwtfUNryeoU6gcaibBhHdB7ZTkgqxHe71SqXLOmy8VafXeO9rqDcGLtELHHu9chOu9Hq+iyoGZU+Xzeeexfgac8AnvvDQv93q2mpfDlPd6Q0MHea0jkd7k7DpY9y7ayGdufYW/f+FUJtYNwUrRIiIyKLQOVnZxznHPqxv5wYPL2bSrnXnT6/nquVMYW51Fc1HiMfjHt+G5n3vFEHrrofEF9hVN6Ik/6K1PdNJnB76EfLQNlt7tFQ95+wVvkdkx7/CC37ZV+w+9K633glR1sipc9SRvW1qnECJyBLQOloiIiAw6M+N9s0dyzvR6bnpqNb944k0eWd7ER94xls+cOZHyogEqHjCY/AE45zpv+N3bL0BJTbdb9b6tP+j1bu3dcfAtHvUq0YXrB6eNwRJvCOPsy725Ui//AdY84Q2nnHBWMkhN9oY9au6RyJDK3YClOVgiIiJpUxT085kzJ3LJcaP40cMr+c0za7jr5fV8/qxJXH78aALZMIR/6nu9W1+Cxd6tfOTQtKkntVNg3n+m7+eLyH6y4F+3w6M5WCIiIulXW1bIDz44k/s+cwpTh5fx7flL+cAvnuXN5tZ0N01EZFDkbPrQEEEREZHMMW1EGX/+2PH8/EOzWbd9D+/576f4w3NrybS54CIiRypn04eGCIqIiGQWM+O9M0fw0OdP5fhxVVxzz1I+fPOLbN7Vnu6miYgMmJxNHxENERQREclIdWWF/O6q4/iP9x3NwrU7OOenTzJ/0cZ0N0tEZEDkbPqIxhIE/b6DV5IXERGRtDMzrjhhDPd/7hTGVZfw2Vtf4TO3vkJzSyTdTRMROSI5W0UwEotreKCIiEiGG1ddwp2feAe/ePxNfvbIG9y3eCNzxw1j3vR6zp5ez4iKonQ3UUTkkORswIrGEgpYIiIiWSDg9/GZMydy7ox67n5lIw8t3cy19y7j2nuXcUxDOeccXc+86fWMrylNd1NFRPqV0wErpIAlIiKSNSbUhvnyOZP58jmTebO5lYeWbuahJZv54YMr+OGDK5gxspzPnzWRM6bUagqAiGSs3A1YcfVgiYiIZKujakr51GkT+NRpE9i4cy8PLd3M755dy0d/v5BZoyr48tmTOWlClYKWiGScnE0gnUUuREREJLuNqCjiqpPG8Y8vvpPr3z+DLbvbueI3L3Dpjc/z4prt6W6eiMh+cjaBaA6WiIhIbinw+7h07mge+8ppfOf86aze2sbFv3qOf/rNC7zy1o50N09EBNAQQREREckyoYCfj5w4losbR/Gn59fxiyfe5ML/fZZjRlVw+dzRvPeY4RQHc/ZPHBHJcDmbQCIaIigiIpLTioJ+Pn7qeJ78t9P59nnT2BOJ8W93Leb4/3yEb9+zhBWbW9LdRBHJQzn79U40liBcmLMfT0RERJJKQwGuOmkcV544lgVrd3DLC+u49cW3+f1z62gcU8mHjh/N2dPrKQ3p7wIRGXw5+y+NyrSLiIjkFzNj7rhhzB03jGvOi3LXS+u55cW3+OLtiwgGXuPUiTXMO7qes6bWUlEcTHdzRSRH5W7A0hwsERGRvDWsJMjHTx3Px04Zx4trtvNgck2tf7zehN9nvGN8FeccXc850+qoLStMd3NFJIfkbsDSHCwREZG8Z2YcP76K48dXcc17p/Hahl08uGQzDy7ZzLfuXsI19yxhbFUJE2tLmVhXyqS6MBNrw4yvKaGwwJ/u5otIFsrtgKUeLBEREUkyM2Y2VDCzoYKvnDOZVVtaeXhZE0s27GJlUwuPLN9CPOEA8BmMqSrhtMk1fPnsyZRo/paIpChn/7XQEEERERHpjZkxsS7MxLpw175oLMGarW2sbGrhjS2tLNu4m989u5ZHXt/Cjy4+huPGDktji0UkW6QUsMxsHvAzwA/c5Jy7vodjLgauBRywyDn3oeT+OPBa8rC3nHPnD0C7+xXpiBP0q2tfREREUhMM+JhcH2Zy/b7Q9cLqbXz5zkVc/KvnuPrU8XzxXZMIBfT3hYj0rt+AZWZ+4AbgXcB6YIGZzXfOLet2zETga8BJzrkdZlbb7S32OudmDXC7+6UeLBERETlSx4+v4oHPncp19y3jV0+s5vHlzfz4kmOYPqI83U0TkQyVSgKZC6xyzq12zkWB24ALDjjm48ANzrkdAM65LQPbzEOTSDg64k4BS0RERI5YaSjA998/k5uvbGT7nijvu+EZbnhsFbF4ouuYSCxO0+52lm/ezfOrt/HEymb2RuNpbLWIpEsqQwRHAm93e7weOP6AYyYBmNkzeMMIr3XOPZh8rtDMFgIx4Hrn3N0H/gAzuxq4GmD06NGH9AF6Ek3+g6d1sERERGSgnDGljoc/X8k3717C/3toBX9+fh0AO/Z0sLfj4DBVVhjgA3MauPz40UyoDR/0vIjkpoEqchEAJgKnAQ3Ak2Y2wzm3ExjjnNtgZuOBR83sNefcm91f7Jy7EbgRoLGx0R1pYxSwREREZDBUlgT5+Ydmc87ieu5bvJHSUAGVxQVUFBdQURykoriAyuIg0XiCv768gT89v47fPrOW48cN4/ITxnDO9DrN4RLJcakErA3AqG6PG5L7ulsPvOCc6wDWmNlKvMC1wDm3AcA5t9rMHgdmA28yiKIxL2BpiKCIiIgMNDPj/GNGcP4xI/o87vTJtWxtncYdC9dzy4vr+Oytr1BVEuSDjQ2cPrmW6SPKCBcWDFGrRWSopBKwFgATzWwcXrC6FPjQAcfcDVwG/NbMqvGGDK42s0pgj3Muktx/EvDDAWt9L7oClhYaFhERkTSqLg3xydOO4l9OHc9Tq7ZyywvruOmpNfzqidUAjKsu4eiR5Rw9oiy5Lae8WKFLJJv1G7CcczEz+zTwEN78qpudc0vN7LvAQufc/ORzZ5vZMiAOfMU5t83MTgR+ZWYJvIIa13evPjhY1IMlIiIimcTnM945qYZ3Tqphe1uURet3snTDLl7bsIuX1+3g3kUbu44dNayIGSPLOXpkubcdUU5lSTCNrReRQ5HSHCzn3P3A/Qfsu6bbfQd8MXnrfsyzwIwjb+ah6ZyDpYAlIiIimWZYSZDTJ9dy+uR9q9psb4uydKMXuJYkg9f9r23uen5khRe6po8oY3xNKeNrShhXXUJhgeZziWSagSpykVE0RFBERESyybCSIKdMrOGUiTVd+3buibJkw26WdAteDy7dF7rMvOA1vqaU8dUlHFVTwtjqEsZWlTCiogi/z9LxUUTyXk4GrIiGCIqIiEiWqygOcvLEak6eWN21ry0SY83WNlZvbWN1cyurm9tYvbWVhWu3s6fbulsFfmPUsGLGVnmBa2x1MRNrw0ypD2u4ocggy8mApTlYIiIikotKQgGvGMbI8v32O+do2h1h7bY21m1rY83WPcltG8+9uW2/dbpqwiGm1IeZXBdmcr13m1gbpiio4YYiAyE3A5bWwRIREZE8YmbUlxdSX17ICeOr9nuuM3ytbGphxeYWlm9uYUXTbv74/LquUT/gDTecUFvKhNpSjqop7bpfXlRAS3sHO/d0sHNvBzv3RNm113scTzhOGF/F1OFhzDQkUQRyNWB1zcHSNzEiIiKS37qHr1Mn7ZvjFU841m1rY8XmFt7Y0sqq5O2FNdto70j08Y4HqwmHuqoknjyhWsMQJa/lZMCKxLxucA0RFBEREemZ32fJioSlnNttfyLh2LBzL6uaW3lzSyu793ZQXhyksriAiuICyouCVBQXUFFUQDSe4Ok3tvLEymb+vqyJO19aj89gZkMFJ0+opjjkJxZ3dMQTdMQdsXiCWMJ7XBoKUBMOdd1qw4XUhEOUFQbUGyZZLScDluZgiYiIiBwen88rkDFqWPF+peR7c1HjKC5qHEU84Vi8fidPrGzmiZXN/O/jq0i4fccF/T4CfiPgMwJ+H63tsa5pHd0FAz5GVhQxtqqYcdWljKspYXy1V5a+vqwQn6ojSoZTwBIRERGRI+b3GbNHVzJ7dCWfP2sS7cnCGgGf4ffZQb1Szjl2743R3NrOlpYIzcnblpYIG3buZXVzG8+v3r5fgY7CAh/jqkuZVFfKpDqvUMekujANlUUKXpIxcjNgqciFiIiISFr1twiymVFeXEB5cQETasM9HpNIOJpa2lnT7JWmX7O1jTebW1m4dgf3vLqx67iiAj+T6rzhjkG/D4fXdeYcdOtEo7yogNoehiVWFBUooMmAyc2ApR4sERERkazn8xnDy4sYXl7EiROq93tud3sHbzS18kZTCyuaWljZ1MKLa7YTT45L7OwwM7wwl3COnXs69usR6xTwGXVlhYysLKKhooiRlUWM7LatLy+kqMCvuWGSkpwMWF0LDfsVsERERERyUVlhAXPGVDJnTOUhva4tEttvSGJzizdEcfOudtbv2MsLa7az6dW9+80fA/CZtw5ZaShASfJWGvJTURRkSn2YoxvKmTGynOrS0AB+yn127e3gyZXNPLp8C282t3LiUdWcM72OYxoq1PuWYXIyYEUVsERERESkByWhAONCAcZVl/R6TEc8weZd7WzYuZcNO/aypSVCWyRGayRGWyRGWzRGayROWyTG0h27uO+1TV2vHV5eyIyRXtiaPrKMUMBPNJYgEksQjSeIxrxbRzxBcdBPXVkhtWUh6sKFVBQXdPWSOedYvbWNR1/fwiPLm1iwdgfxhGNYSZDx1SXc9NRqfvnEm9SVhXjXtDrOmV7P8eOqDmsEV0c8wdvb99DSHiNU4KMw4N9vGwr48SvEpSw3A1Y8QYHflOZFRERE5JAV+H1dlRRT0dLewdKNu1myYReL1+9iyYZdPLys6ZB/btDv8+aGlYXY0RZl7bY9AEypD/OJd47njCl1zBpVgd9n7NrTwaMrmnhoSRN3vbSBPz3/FuHCAKdNrmVUZRFlRQWUFxVQVlhAWVGA8qICSkMBtrVFWd3cyurmNt5sbmP11lbe2raH2IFddgcoCfqZUBdmcmeBkXqvyEhNOKShkwfIzYAVS6j3SkRERESGRLiwgBPGV3HC+KqufbvbO1i5uYV4whEM+AgGfIQCPoJ+P8GAjwK/0RaJ09TSzpbdEZp2t9PU0k7z7ghNLe0MKy7lo6eM54wptYysKDroZ5YXF3Dh7AYunN1Ae0ecp97YysNLN/PkG8088Fq038AU9PsYW13MpNow86bXM76mlMriAiKxBJFYnPaOBJGOOJFYgvaOBDv2RFnZ1MKjy7dw+8L1Xe9TUVzA+OoS/D4jlnDEE45YPLlNJIgnHAV+H0VBP4UFfoo6b8nH4K1hG4kliHR4vXydP9cBZYWBrpDobQsoKwxQFAywNxqjJRKjtd3rWey8v7cjTllRAXXhQurKQl09hLXJ3sJwaHDXWsvdgKUCFyIiIiKSJmWFBTSOHdbnMVWlMLoqtV6yvhQW+HnXtDreNa0O8IYX7u2Is2tvB7v3xpLbDna3d1BZEuSo6lJGVhYd9rC/ra0RVja18EZTKyuaWli7tS3ZDkuW5fd5W7/hNyOWSLA3GmdvR5ydezvYvKudvR1x9kTjmHmVv0MBH8GAv+t+WVEBzjlaIzE27tzL7vYYu/d2dNVa6M7vM8KFAUqCAcKFAQoL/GzYsZfHd2+hLXpwUZP3zRrBTy+dfVifPRU5GbDOmFrLmAH4n1VEREREJNuYGcXBAMXBAMPLB/79q0tDVJeGOPGo6v4PHmDtHXFa2mPsjcYpDvkpDQUIBXy99ki1RmJs2d1O0+4IW5K9hQMRavuSkwHr9Mm1Ka08LiIiIiIi2aOwwN/vGmvdlYYClNZ4a6QNFY2jExERERERGSAKWCIiknPMbJ6ZrTCzVWb21R6eD5nZX5LPv2BmY4e+lSIikosUsEREJKeYmR+4ATgXmAZcZmbTDjjso8AO59wE4CfAD4a2lSIikqsUsEREJNfMBVY551Y756LAbcAFBxxzAfD75P07gTNNC7mIiMgAUMASEZFcMxJ4u9vj9cl9PR7jnIsBu4CqA47BzK42s4VmtrC5uXmQmisiIrlEAUtERKQXzrkbnXONzrnGmpqadDdHRESygAKWiIjkmg3AqG6PG5L7ejzGzAJAObBtSFonIiI5TQFLRERyzQJgopmNM7MgcCkw/4Bj5gMfSd7/IPCoc84NYRtFRCRH5eRCwyIikr+cczEz+zTwEOAHbnbOLTWz7wILnXPzgd8AfzSzVcB2vBAmIiJyxBSwREQk5zjn7gfuP2DfNd3utwMXDXW7REQk92mIoIiIiIiIyABRwBIRERERERkgClgiIiIiIiIDRAFLRERERERkgChgiYiIiIiIDBAFLBERERERkQGigCUiIiIiIjJAFLBEREREREQGiAKWiIiIiIjIAFHAEhERERERGSAKWCIiIiIiIgPEnHPpbsN+zKwZWDcAb1UNbB2A98lVOj/90znqm85P33R++pfqORrjnKsZ7Mb0R9enIaPz0zedn/7pHPVN56dvh3J+erw+ZVzAGihmttA515judmQqnZ/+6Rz1Teenbzo//cvXc5SvnztVOj990/npn85R33R++jYQ50dDBEVERERERAaIApaIiIiIiMgAyeWAdWO6G5DhdH76p3PUN52fvun89C9fz1G+fu5U6fz0TeenfzpHfdP56dsRn5+cnYMlIiIiIiIy1HK5B0tERERERGRIKWCJiIiIiIgMkJwMWGY2z8xWmNkqM/tqutuTbmZ2s5ltMbMl3fYNM7O/m9kbyW1lOtuYTmY2ysweM7NlZrbUzD6X3K9zlGRmhWb2opktSp6j7yT3jzOzF5K/a38xs2C625pOZuY3s1fM7P+Sj3V+ksxsrZm9ZmavmtnC5L68+h3Ttelguj71TdenvunalBpdm/o2GNennAtYZuYHbgDOBaYBl5nZtPS2Ku1+B8w7YN9XgUeccxOBR5KP81UM+JJzbhpwAvCvyf9ndI72iQBnOOeOAWYB88zsBOAHwE+ccxOAHcBH09jGTPA54PVuj3V+9ne6c25Wt/VF8uZ3TNemXv0OXZ/6outT33RtSo2uTf0b0OtTzgUsYC6wyjm32jkXBW4DLkhzm9LKOfcksP2A3RcAv0/e/z3wviFtVAZxzm1yzr2cvN+C94/QSHSOujhPa/JhQfLmgDOAO5P78/ocmVkD8B7gpuRjQ+enP/n0O6ZrUw90feqbrk9907Wpf7o2HbYj+h3LxYA1Eni72+P1yX2yvzrn3Kbk/c1AXTobkynMbCwwG3gBnaP9JIcYvApsAf4OvAnsdM7Fkofk++/aT4F/AxLJx1Xo/HTngIfN7CUzuzq5L59+x3RtSl0+/X+RMl2feqZrU790berfgF+fAgPZOslOzjlnZnlfr9/MSoG7gM8753Z7X/J4dI7AORcHZplZBfA3YEqam5QxzOy9wBbn3Etmdlq625OhTnbObTCzWuDvZra8+5P6HZOe6P8Lj65PvdO1qXe6NqVswK9PudiDtQEY1e1xQ3Kf7K/JzIYDJLdb0tyetDKzAryL15+dc39N7tY56oFzbifwGPAOoMLMOr+oyefftZOA881sLd7QrzOAn6Hz08U5tyG53YL3R9Bc8ut3TNem1OXT/xf90vUpNbo29UjXphQMxvUpFwPWAmBiskJKELgUmJ/mNmWi+cBHkvc/AtyTxrakVXI88m+A151zP+72lM5RkpnVJL8dxMyKgHfhzQV4DPhg8rC8PUfOua855xqcc2Px/s151Dl3OTo/AJhZiZmFO+8DZwNLyK/fMV2bUpdP/1/0Sdenvuna1Dddm/o3WNcncy73epXN7N14Y079wM3OuevS3KS0MrNbgdOAaqAJ+DZwN3A7MBpYB1zsnDtwonFeMLOTgaeA19g3RvnreOPcdY4AM5uJN8nTj/fFzO3Oue+a2Xi8b8WGAa8AVzjnIulrafolh2F82Tn3Xp0fT/I8/C35MADc4py7zsyqyKPfMV2bDqbrU990feqbrk2p07WpZ4N1fcrJgCUiIiIiIpIOuThEUEREREREJC0UsERERERERHILl2YAAAJCSURBVAaIApaIiIiIiMgAUcASEREREREZIApYIiIiIiIiA0QBSySLmdlpZvZ/6W6HiIhIJ12bJN8pYImIiIiIiAwQBSyRIWBmV5jZi2b2qpn9ysz8ZtZqZj8xs6Vm9oiZ1SSPnWVmz5vZYjP7m5lVJvdPMLN/mNkiM3vZzI5Kvn2pmd1pZsvN7M9mZmn7oCIikjV0bRIZHApYIoPMzKYClwAnOedmAXHgcqAEWOicmw48AXw7+ZI/AP/unJsJvNZt/5+BG5xzxwAnApuS+2cDnwemAeOBkwb9Q4mISFbTtUlk8ATS3QCRPHAmMAdYkPwCrwjYAiSAvySP+RPwVzMrByqcc08k9/8euMPMwsBI59zfAJxz7QDJ93vRObc++fhVYCzw9OB/LBERyWK6NokMEgUskcFnwO+dc1/bb6fZtw44zh3m+0e63Y+j32sREemfrk0ig0RDBEUG3yPAB82sFsDMhpnZGLzfvw8mj/kQ8LRzbhfw/9u1Q5sIgyAMoN+HISHUg6MHDPJC0LSAuiqgFRxNIFEoDAJQiEXw0wC3/5Fc3pOzyWRXTWZn3tqeL/FNkscxxnuSl7YXS47jtid7fQUAh0RtgpX4TYCVjTGe2t4meWh7lOQryU2SzyRny9lrfnbhk+Qqyd1SpJ6TXC/xTZL7ttslx+UenwHAAVGbYD0d46+TX2AXbT/GGKf/fQ8A+KU2we6sCAIAAExiggUAADCJCRYAAMAkGiwAAIBJNFgAAACTaLAAAAAm0WABAABM8g13y71xefty4wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\ttraining         \t (min:    0.634, max:    0.996, cur:    0.996)\n",
            "\tvalidation       \t (min:    0.832, max:    0.974, cur:    0.973)\n",
            "log loss\n",
            "\ttraining         \t (min:    0.014, max:    1.040, cur:    0.014)\n",
            "\tvalidation       \t (min:    0.113, max:    0.568, cur:    0.140)\n",
            "Accuracy: 0.9732 (valid), 0.9960 (train)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tU07cAobp8Wh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95a03b8f-c4d7-4573-f1b7-c83c5dfb1339"
      },
      "source": [
        "# 予測の実行\n",
        "test_loss, test_accuracy = calculate_model(model, loss_fn,   _, test_loader, device_GPU, mode='evaluate')\n",
        "# print(test_loss)\n",
        "print(\"テストデータの予測正解率 : \", test_accuracy)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "テストデータの予測正解率 :  0.9636946977372509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xgk3J6t275C8"
      },
      "source": [
        "　クロスバリデーションによって決定されたハイパーパラメータはモデルの汎化性能を向上させたため，予測正解率が**96.39 %**→**96.55 %**と少し高くなりました．"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ycXs9mOaHQp"
      },
      "source": [
        "### 3.3 | CNNによる予測\n",
        "\n",
        "　CNNは画像処理に良く使われる手法ですが，今回は時系列データを2次元的に扱うことでCNNを適用してみます．\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4f0sxJ_4ph5r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d94a5124-5a93-41e1-a02b-568702cf834f"
      },
      "source": [
        "# リシェイプ\n",
        "X_train_4D = X_train.reshape(X_train.shape[0], 1, X_train.shape[1], X_train.shape[2])\n",
        "X_valid_4D  = X_valid.reshape(X_valid.shape[0], 1, X_valid.shape[1], X_valid.shape[2]) \n",
        "X_test_4D  = X_test.reshape(X_test.shape[0], 1, X_test.shape[1], X_test.shape[2]) \n",
        "print(X_train_4D.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(17000, 1, 13, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcMKITH5aOXY"
      },
      "source": [
        "# datasetおよびdataloaderの作成\n",
        "batch_size = 256 # 今回は256件のデータごとに学習を行う\n",
        "\n",
        "# training set\n",
        "X_train_torch = torch.tensor(X_train_4D, dtype=torch.float) # dtype=torch.floatを忘れずに\n",
        "y_train_torch = torch.tensor(y_train, dtype=torch.long)      # dtype=torch.longを忘れずに\n",
        "train_dataset = torch.utils.data.TensorDataset(X_train_torch, y_train_torch)\n",
        "train_loader  = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size)\n",
        "\n",
        "# validation set\n",
        "X_valid_torch = torch.tensor(X_valid_4D, dtype=torch.float)\n",
        "y_valid_torch = torch.tensor(y_valid, dtype=torch.long)\n",
        "valid_dataset = torch.utils.data.TensorDataset(X_valid_torch, y_valid_torch)\n",
        "valid_loader  = torch.utils.data.DataLoader(valid_dataset, batch_size=batch_size)\n",
        "\n",
        "# test set\n",
        "X_test_torch = torch.tensor(X_test_4D, dtype=torch.float)\n",
        "y_test_torch = torch.tensor(y_test, dtype=torch.long)\n",
        "test_dataset = torch.utils.data.TensorDataset(X_test_torch, y_test_torch)\n",
        "test_loader  = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tkxqb0shc2ga"
      },
      "source": [
        "# GPUの利用設定および学習･予測のための関数群の定義\n",
        "import torch\n",
        "from livelossplot import PlotLosses\n",
        "\n",
        "device_GPU = torch.device(\"cuda:0\") \n",
        "\n",
        "# モデルの更新と評価(modeによって切り替え)\n",
        "def calculate_model(model, loss_fn, opt, dataloader, device, mode):\n",
        "  if mode == 'evaluate':\n",
        "    model.eval() # 学習を行わない時は evaluate 状態にする （補足資料※1）\n",
        "\n",
        "  sum_loss = 0\n",
        "  correct = 0\n",
        "  count = len(dataloader.dataset)\n",
        "  \n",
        "  for X, y in dataloader:\n",
        "    y-=1\n",
        "    X = X.to(device) # GPUへデータを転送\n",
        "    y = y.to(device) # GPUへデータを転送\n",
        "    y_pred = model(X) # Xからyを予測（softmaxを行う前の値が出力される）\n",
        "    \n",
        "    _, predicted = torch.max(y_pred.data, 1) # 6クラスのうち、予測確率最大のクラス番号を取得\n",
        "    correct += (predicted == y).sum().item() # 予測に成功した件数をカウント（accuracy計算用）\n",
        "    \n",
        "    loss = loss_fn(y_pred, y)        # ミニバッチ内の訓練誤差の 平均 を計算\n",
        "    sum_loss += loss.item()*len(y) # エポック全体の訓練誤差の 合計 を計算しておく\n",
        "    \n",
        "    if mode == 'update':\n",
        "      # 重みの更新\n",
        "      opt.zero_grad()\n",
        "      loss.backward()\n",
        "      opt.step()\n",
        "    \n",
        "  # エポック内の訓練誤差の平均値と予測精度を計算\n",
        "  mean_train_loss = sum_loss / count\n",
        "  train_accuracy = correct / count\n",
        "  \n",
        "  if mode == 'evaluate':\n",
        "    model.train() # evaluate状態からtrain状態に戻しておく\n",
        "\n",
        "  return mean_train_loss, train_accuracy    \n",
        "\n",
        "# 学習\n",
        "def train(model, loss_fn, opt, train_loader, valid_loader, device, epoch=50):\n",
        "  liveloss = PlotLosses() # 描画の初期化\n",
        "  for i in range(epoch):\n",
        "    train_loss, train_accuracy = calculate_model(model, loss_fn, opt, train_loader, device, mode='update')\n",
        "    valid_loss, valid_accuracy = calculate_model(model, loss_fn,   _, valid_loader, device, mode='evaluate')\n",
        "  \n",
        "    # Visualize the loss and accuracy values.\n",
        "    liveloss.update({\n",
        "        'log loss': train_loss,\n",
        "        'val_log loss': valid_loss,\n",
        "        'accuracy': train_accuracy,\n",
        "        'val_accuracy': valid_accuracy,\n",
        "    })\n",
        "    liveloss.draw()  \n",
        "  print('Accuracy: {:.4f} (valid), {:.4f} (train)'.format(valid_accuracy, train_accuracy))\n",
        "  return model # 学習したモデルを返す"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vb11RlIeaxt5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "outputId": "70cc5268-b11a-4ecd-8169-8654ef9490a9"
      },
      "source": [
        "# モデルの作成と学習の実行\n",
        "torch.manual_seed(0) # 学習結果の再現性を担保\n",
        "\n",
        "cnn = torch.nn.Sequential(\n",
        "    torch.nn.Conv2d(1, 16, (3, 3), padding=2),\n",
        "    torch.nn.ReLU(),\n",
        "    torch.nn.MaxPool2d(2),\n",
        "    torch.nn.Conv2d(16, 32, (3, 3), padding=2),\n",
        "    torch.nn.ReLU(),\n",
        "    torch.nn.MaxPool2d(2),\n",
        "    torch.nn.Conv2d(32, 64, (3, 3)),\n",
        "    torch.nn.ReLU(),\n",
        "    torch.nn.MaxPool2d(2),\n",
        "    torch.nn.Dropout(0.2),\n",
        "    torch.nn.Flatten(), \n",
        "    torch.nn.Linear(384, 128),\n",
        "    torch.nn.ReLU(),\n",
        "    torch.nn.Dropout(0.2),\n",
        "    torch.nn.Linear(128, 6),\n",
        ")\n",
        "cnn.to(device_GPU)\n",
        "\n",
        "# 誤差関数と最適化手法を準備\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "# optimizer = torch.optim.SGD(cnn.parameters(), lr=0.01)\n",
        "optimizer = torch.optim.Adagrad(cnn.parameters(), lr=0.01)\n",
        "\n",
        "# 学習の実行\n",
        "trained_model = train(cnn, loss_fn, optimizer, train_loader, valid_loader, device_GPU, epoch=70)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\ttraining         \t (min:    0.683, max:    1.000, cur:    1.000)\n",
            "\tvalidation       \t (min:    0.826, max:    0.997, cur:    0.997)\n",
            "log loss\n",
            "\ttraining         \t (min:    0.001, max:    0.816, cur:    0.001)\n",
            "\tvalidation       \t (min:    0.007, max:    0.471, cur:    0.009)\n",
            "Accuracy: 0.9967 (valid), 0.9998 (train)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xZBtnOua2ME",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85fcce23-862d-4192-e10f-b7519a88ff2c"
      },
      "source": [
        "# 予測の実行\n",
        "test_loss, test_accuracy = calculate_model(cnn, loss_fn,   _, test_loader, device_GPU, mode='evaluate')\n",
        "# print(test_loss)\n",
        "print(\"テストデータの予測正解率 : \", test_accuracy)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "テストデータの予測正解率 :  0.9746707193515705\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hak6qzOBdyNG"
      },
      "source": [
        "　以上より，CNNでは **95.47 %**で 予測可能であることが分かりました．"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q2tdCX4p_T--"
      },
      "source": [
        "### 3.4 | RNNによる予測\n",
        "\n",
        "\n",
        "　RNNはネットワークにループを作成することで，時系列データに対する逐次的な予測や，可変長データに対する単一予測を行う際に利用可能な手法です．今回は，IMUの時系列データを扱っているため，RNNを適用するメリットがあると考えられます．  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VdwfSFTzM9K-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74863ab8-80a1-4949-d9f0-7fbe86d5ec06"
      },
      "source": [
        "size_sections = 50   # セクションサイズ\n",
        "size_slide = 10      # スライドサイズ\n",
        "\n",
        "# 時系列データをセクション分割\n",
        "print(\"分割後のセクション形状: (\",num_features+num_gen_labels,\"x\",size_sections,\")\")\n",
        "_X_train, _y_train, train_mean, train_std = time_series_to_section( train_data.copy(),\n",
        "                                                                  num_act_labels,\n",
        "                                                                  num_gen_labels,\n",
        "                                                                  size_sections,\n",
        "                                                                  size_slide,\n",
        "                                                                  standardize = True )\n",
        "\n",
        "_X_test, _y_test, test_mean, test_std = time_series_to_section( test_data.copy(),\n",
        "                                                              num_act_labels,\n",
        "                                                              num_gen_labels,\n",
        "                                                              size_sections,\n",
        "                                                              size_slide,\n",
        "                                                              standardize = True,\n",
        "                                                              mean = train_mean, \n",
        "                                                              std = train_std  )\n",
        "\n",
        "y_train = np.delete(_y_train, np.where(_y_train==0))\n",
        "X_train = np.delete(_X_train, np.where(_y_train==0), axis=0)\n",
        "y_test = np.delete(_y_test, np.where(_y_test==0))\n",
        "X_test = np.delete(_X_test, np.where(_y_test==0), axis=0)\n",
        "\n",
        "print(\"トレーニングデータ形状:\", X_train.shape)\n",
        "print(\"テストデータ形状:\", X_test.shape)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "分割後のセクション形状: ( 13 x 50 )\n",
            "平均: 0.0035300797597241063 , 分散: 0.749962379300086 ---> 標準化しました．\n",
            "トレーニングデータの平均と分散を用いてテストデータを標準化しました．\n",
            "トレーニングデータ形状: (20918, 13, 50)\n",
            "テストデータ形状: (5922, 13, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KW_4RLtmTRZN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c69dd21b-11b2-46e1-b267-67aa19f0a4ba"
      },
      "source": [
        "# 通常のNNは2次元形式で与えられても処理できないため，28x28の要素がある1次元配列に変換してからPyTorch形式に変換する\n",
        "X_train_flatten = X_train.reshape(X_train.shape[0], 1, X_train.shape[1]*X_train.shape[2]) # 17000件の2次元データ（12×50）を17000件の1次元データ(600)に変換\n",
        "X_test_flatten  = X_test.reshape(X_test.shape[0], 1, X_test.shape[1]*X_test.shape[2]) \n",
        "X_train_torch = torch.tensor(X_train_flatten, dtype=torch.float) # dtype=torch.floatを忘れずに\n",
        "X_test_torch  = torch.tensor(X_test_flatten,  dtype=torch.float) \n",
        "\n",
        "\n",
        "# yについてもPyTorch形式に変換する\n",
        "y_train_torch = torch.tensor(y_train, dtype=torch.long) # dtype=torch.longを忘れずに\n",
        "y_test_torch  = torch.tensor(y_test,  dtype=torch.long)\n",
        "\n",
        "# datasetとdataloaderを用意\n",
        "# XとYの組み合わせを保持してくれる便利なモノ\n",
        "train_dataset = torch.utils.data.TensorDataset(X_train_torch, y_train_torch)\n",
        "test_dataset  = torch.utils.data.TensorDataset(X_test_torch,  y_test_torch)\n",
        "\n",
        "# XとYの組み合わせを(batch_size)個ずつ出力する便利なモノ\n",
        "batch_size = 1 \n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "test_loader  = torch.utils.data.DataLoader(test_dataset,  batch_size=batch_size, shuffle=True)\n",
        "\n",
        "for X, y in train_loader:\n",
        "  print(X.shape)\n",
        "  print(y.shape)\n",
        "  # print(X)\n",
        "  # print(y)\n",
        "  break"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 1, 650])\n",
            "torch.Size([1])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DaF4LPv4c550"
      },
      "source": [
        "　時系列データに対して1つの出力を得るために，RNNの最後の出力のみを取り出して利用するモデルを構築しましょう．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHqn4icq_ghp"
      },
      "source": [
        "import torch\n",
        "\n",
        "# 最後の出力だけを次の層に渡すRNN\n",
        "class lastoutputRNN(torch.nn.Module):\n",
        "  def __init__(self, input_size, output_size):\n",
        "    super(lastoutputRNN, self).__init__() # 初期化のおまじない\n",
        "    \n",
        "    # 学習すべき重みがあるものをここに全て定義\n",
        "    self.rnn = torch.nn.RNN(input_size, output_size, num_layers=1, batch_first=True)\n",
        "  \n",
        "  # 予測時の計算を定義\n",
        "  def forward(self, input):\n",
        "    outs_rnn, hiddens = self.rnn(input, self.initHidden()) # ひとまず複数の 出力 h を全て受け取る\n",
        "    last_out_rnn = outs_rnn[-1] # 最後の出力 h_r を抜き出す\n",
        "    return last_out_rnn # 次の層に渡す\n",
        "\n",
        "  # RNNの隠れ層の初期値を準備する補助関数\n",
        "  def initHidden(self):\n",
        "    return torch.zeros(1, 1, self.rnn.hidden_size)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycm4wOkMZo65"
      },
      "source": [
        "では作成したモデルをしようして学習します．  \n",
        "【注意】この学習は3分程度かかります．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDW0dQ0n_zAR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "outputId": "8b7000af-14e8-4632-869d-9bc6d79b733f"
      },
      "source": [
        "import json\n",
        "import random\n",
        "from livelossplot import PlotLosses # livelossplotのインストールを忘れずに（AI7.1.4参照）\n",
        "import numpy as np\n",
        "\n",
        "torch.manual_seed(0) # 実行結果を再現させる\n",
        "np.random.seed(0)    # data_shuffle()のためにNumPyの乱数も指定する\n",
        "\n",
        "###### Sequentialによるモデルの作成\n",
        "# 入力：文字列のone-hot表現\n",
        "# 出力：入力の文字列が「どの言語らしいか」を示す値（各言語ごとに実数値を出力）\n",
        "model = torch.nn.Sequential(\n",
        "  lastoutputRNN(650, 128), \n",
        "  torch.nn.Linear(128, 6)\n",
        ")\n",
        "\n",
        "###### 誤差関数と最適化手法の定義\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "###### 学習本体\n",
        "liveloss = PlotLosses() # 描画の準備\n",
        "\n",
        "import time\n",
        "start = time.time() # 計算開始時刻\n",
        "\n",
        "for epoch in range(6):  # 6 epoch 学習する\n",
        "  total_train_loss = 0.0\n",
        "  num_train_correct = 0\n",
        "  # X, y = data_shuffle(X_train, y_train)\n",
        "\n",
        "  for Xi_torch, yi_torch in train_loader:\n",
        "    yi_torch -= 1\n",
        "    y_pred = model(Xi_torch)    # 予測の実施\n",
        "    _, predicted = torch.max(y_pred.data, 1)\n",
        "    num_train_correct += (predicted == yi_torch).sum().item()\n",
        "    \n",
        "    # 予測の誤差を計算（交差エントロピー誤差）\n",
        "    # print(Xi_torch)\n",
        "    # print(y_pred)\n",
        "    # print(yi_torch)\n",
        "    loss = loss_fn(y_pred, yi_torch)\n",
        "    total_train_loss += loss.item()\n",
        "    \n",
        "    # 誤差逆伝播法による重みの更新\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "  # このepoch全体での予測精度 (accuracy) を計算\n",
        "  train_accuracy = num_train_correct / len(y_train)\n",
        "\n",
        "  # livelossplotの描画\n",
        "  liveloss.update({\n",
        "   'log loss': total_train_loss,\n",
        "    'accuracy': train_accuracy,\n",
        "  })\n",
        "  liveloss.draw()\n",
        "\n",
        "end = time.time() # 計算終了時刻\n",
        "print(\"計算時間 : \", end - start) # 差を秒単位で計算\n",
        "    \n",
        "# 最後のepochにおける訓練データの予測精度を出力\n",
        "print('Accuracy: {:.4f} (train)'.format(train_accuracy))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\taccuracy         \t (min:    0.853, max:    1.000, cur:    1.000)\n",
            "log loss\n",
            "\tlog loss         \t (min:  109.182, max: 9509.844, cur:  109.182)\n",
            "計算時間 :  159.62795734405518\n",
            "Accuracy: 0.9998 (train)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2PBTw758rTo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "941f898a-2f2c-4997-f4b9-9da8e0cb879e"
      },
      "source": [
        "# テストデータの予測正解率を算出\n",
        "num_test_correct = 0\n",
        "for X, y in test_loader:\n",
        "  y-=1\n",
        "  pred = model(X)                                 # 予測の実施\n",
        "  _, predicted = torch.max(pred.data, 1)                # 最も確率の高かったクラスを求める\n",
        "  if predicted == y:\n",
        "    num_test_correct += 1\n",
        "\n",
        "test_accuracy = num_test_correct / len(y_test)\n",
        "print(test_accuracy)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9096588990206012\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2vl5p0GAdVk0"
      },
      "source": [
        "　以上より，RNNでは **91.13 %**で 予測可能であることが分かりました．"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9XieKVL2HMM-"
      },
      "source": [
        "## 4 | データサイエンティスト的?な予測\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "　\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjwvlKg2WIBs"
      },
      "source": [
        "### 4.1 | PCAによる次元圧縮"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ChmWiSvY38g"
      },
      "source": [
        "今回のIMUデータに限らず，生データは冗長(無駄に複雑)である可能性が高いです．そこで，データの中から主成分を指定数だけ抽出するのがPCA(主成分分析)です．今回は2成分抜き取ります．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlsGoSrhL3E9"
      },
      "source": [
        "# PCAによる次元圧縮\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# 2次元データを1次元データに変換\n",
        "X_train_flatten = X_train.reshape(X_train.shape[0], X_train.shape[1]*X_train.shape[2]) \n",
        "X_test_flatten  = X_test.reshape(X_test.shape[0], X_test.shape[1]*X_test.shape[2]) \n",
        "\n",
        "pca = PCA(n_components=2, random_state=0)   # 2次元に圧縮\n",
        "pca.fit(X_train_flatten)                    # 主成分の取得\n",
        "Xpca_train = pca.transform(X_train_flatten) # PCAによる次元圧縮\n",
        "Xpca_test = pca.transform(X_test_flatten)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QTZPeFB7W2PK"
      },
      "source": [
        "### 4.2 | 決定木による予測"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1nIhw7e44nh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9dcacd8-50d4-4927-c025-e334767e98ba"
      },
      "source": [
        "# 決定木による予測\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "model_simple = DecisionTreeClassifier(max_depth=5, random_state=0)\n",
        "model_simple.fit(Xpca_train, y_train)\n",
        "\n",
        "pred_y_test = model_simple.predict(Xpca_test)\n",
        "prediction_result = (pred_y_test == y_test)\n",
        "accuracy = np.mean(prediction_result)\n",
        "print(\"テストデータの予測正解率 : \", accuracy)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "テストデータの予測正解率 :  0.6222559945964201\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3HcUZ0YgW7S0"
      },
      "source": [
        "### 4.3 | ブースティング(Adaboost)による予測"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWCFTTSNIbDy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dae43a3d-5fbe-43c3-ca0b-d6999855df4a"
      },
      "source": [
        "# Adaboostによる予測  (注意 : 40秒程度かかります)\n",
        "from sklearn import tree, metrics, ensemble\n",
        "import time\n",
        "start = time.time() # 計算開始時刻\n",
        "\n",
        "single_tree = tree.DecisionTreeClassifier(max_depth=6, random_state=0) \n",
        "adaDT = ensemble.AdaBoostClassifier(base_estimator=single_tree, n_estimators=1000, random_state=0)\n",
        "adaDT.fit(Xpca_train, y_train)\n",
        "pred_y_test = adaDT.predict(Xpca_test)\n",
        "\n",
        "print(\"テストデータの予測正解率 : \", metrics.accuracy_score(pred_y_test, y_test)) # test accuracy\n",
        "\n",
        "end = time.time() # 計算終了時刻\n",
        "print(\"計算時間 : \", end - start) # 差を秒単位で計算"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "テストデータの予測正解率 :  0.4302600472813239\n",
            "計算時間 :  40.69797444343567\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUcEaQB_XRun"
      },
      "source": [
        "### 4.4 | ランダムフォレストによる予測"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xInGhEatNq-s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c2e0213-c745-4616-fa20-eab424d1842e"
      },
      "source": [
        "# ランダムフォレストによる予測\n",
        "from sklearn import tree, metrics, ensemble\n",
        "\n",
        "RF = ensemble.RandomForestClassifier(max_depth=7, min_samples_leaf=4 ,n_estimators=1000, random_state=0, n_jobs=-1)\n",
        "RF.fit(Xpca_train, y_train) \n",
        "pred_y_test = RF.predict(Xpca_test)\n",
        "\n",
        "print(\"テストデータの予測正解率 : \", metrics.accuracy_score(pred_y_test, y_test)) # test accuracy"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "テストデータの予測正解率 :  0.6317122593718338\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHZm_shnsp6Y"
      },
      "source": [
        "### 4.5 | 交差検証法によるランダムフォレストのハイパーパラメータ探索\n",
        "\n",
        "　4.4節では，`max_depth`を7，`min_samples_leaf`を4としたランダムフォレストを適用し，**66.67%**で予測できました．ここで，クロスバリデーションを用いてランダムフォレストについてハイパーパラメータ探索を行い，予測正解率の向上を図ります．   \n",
        "【注意】以下のハイパーパラメータ探索はは3～4分かかります．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpSGKCMQQYry",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02f01703-c79b-411d-8e7c-f021ce638f90"
      },
      "source": [
        "# クロスバリデーションからmax_depthとmin_samples_leafの最適な値を求める\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "np.random.seed(0)\n",
        "\n",
        "# 辞書型が1つ入った配列\n",
        "param_grid = [  \n",
        "  { \n",
        "    \"max_depth\": [4,5,6,7],\n",
        "    \"min_samples_leaf\": [2,3,4]\n",
        "  } \n",
        "] \n",
        "grid_search_dt = GridSearchCV(RF, param_grid, cv=4, verbose=2)   # 4-fold CVをしながら最適なモデルを決定する\n",
        "grid_search_dt.fit(Xpca_train, y_train)\n",
        "print(\"\\n\", \"最適ハイパーパラメータ : \", grid_search_dt.best_params_)\n",
        "# print(grid_search_dt.best_score_)\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 4 folds for each of 12 candidates, totalling 48 fits\n",
            "[CV] max_depth=4, min_samples_leaf=2 .................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .................. max_depth=4, min_samples_leaf=2, total=   5.4s\n",
            "[CV] max_depth=4, min_samples_leaf=2 .................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.4s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .................. max_depth=4, min_samples_leaf=2, total=   4.3s\n",
            "[CV] max_depth=4, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=2, total=   4.3s\n",
            "[CV] max_depth=4, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=2, total=   4.3s\n",
            "[CV] max_depth=4, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=3, total=   4.1s\n",
            "[CV] max_depth=4, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=3, total=   4.2s\n",
            "[CV] max_depth=4, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=3, total=   4.3s\n",
            "[CV] max_depth=4, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=3, total=   4.2s\n",
            "[CV] max_depth=4, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=4, total=   4.2s\n",
            "[CV] max_depth=4, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=4, total=   4.2s\n",
            "[CV] max_depth=4, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=4, total=   4.2s\n",
            "[CV] max_depth=4, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=4, min_samples_leaf=4, total=   4.3s\n",
            "[CV] max_depth=5, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=2, total=   4.7s\n",
            "[CV] max_depth=5, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=2, total=   4.8s\n",
            "[CV] max_depth=5, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=2, total=   4.9s\n",
            "[CV] max_depth=5, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=2, total=   4.7s\n",
            "[CV] max_depth=5, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=3, total=   4.7s\n",
            "[CV] max_depth=5, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=3, total=   4.8s\n",
            "[CV] max_depth=5, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=3, total=   4.8s\n",
            "[CV] max_depth=5, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=3, total=   4.7s\n",
            "[CV] max_depth=5, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=4, total=   4.8s\n",
            "[CV] max_depth=5, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=4, total=   4.8s\n",
            "[CV] max_depth=5, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=4, total=   4.8s\n",
            "[CV] max_depth=5, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=5, min_samples_leaf=4, total=   4.8s\n",
            "[CV] max_depth=6, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=2, total=   5.2s\n",
            "[CV] max_depth=6, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=2, total=   5.3s\n",
            "[CV] max_depth=6, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=2, total=   5.3s\n",
            "[CV] max_depth=6, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=2, total=   5.3s\n",
            "[CV] max_depth=6, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=3, total=   5.2s\n",
            "[CV] max_depth=6, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=3, total=   5.2s\n",
            "[CV] max_depth=6, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=3, total=   5.2s\n",
            "[CV] max_depth=6, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=3, total=   5.3s\n",
            "[CV] max_depth=6, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=4, total=   5.2s\n",
            "[CV] max_depth=6, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=4, total=   5.2s\n",
            "[CV] max_depth=6, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=4, total=   5.2s\n",
            "[CV] max_depth=6, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=6, min_samples_leaf=4, total=   5.2s\n",
            "[CV] max_depth=7, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=2, total=   5.8s\n",
            "[CV] max_depth=7, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=2, total=   5.7s\n",
            "[CV] max_depth=7, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=2, total=   5.7s\n",
            "[CV] max_depth=7, min_samples_leaf=2 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=2, total=   5.7s\n",
            "[CV] max_depth=7, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=3, total=   5.8s\n",
            "[CV] max_depth=7, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=3, total=   5.7s\n",
            "[CV] max_depth=7, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=3, total=   5.7s\n",
            "[CV] max_depth=7, min_samples_leaf=3 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=3, total=   5.7s\n",
            "[CV] max_depth=7, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=4, total=   5.7s\n",
            "[CV] max_depth=7, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=4, total=   5.7s\n",
            "[CV] max_depth=7, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=4, total=   5.7s\n",
            "[CV] max_depth=7, min_samples_leaf=4 .................................\n",
            "[CV] .................. max_depth=7, min_samples_leaf=4, total=   5.7s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:  4.0min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " 最適ハイパーパラメータ :  {'max_depth': 5, 'min_samples_leaf': 3}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfKwyRjX4A0G"
      },
      "source": [
        "　クロスバリデーションによるハイパーパラメータ探索に基づき，`max_depth`を5，`min_samples_leaf`を3としてランダムフォレストを適用すると，先程よりもモデルの汎化性能が上がり，予測正解率が**66.67 %**→**67.42 %**と少し高くなりました．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRDRapWGkAwp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "415b0091-535d-41f2-af9d-73fe9dfddc21"
      },
      "source": [
        "RF = ensemble.RandomForestClassifier(max_depth=5, min_samples_leaf=3, n_estimators=1000, random_state=0, n_jobs=-1)\n",
        "RF.fit(Xpca_train, y_train)            # フィッティング\n",
        "pred_y_test = RF.predict(Xpca_test)    # 予測\n",
        "print(\"テストデータの予測正解率 : \", metrics.accuracy_score(pred_y_test, y_test)) # test accuracy"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "テストデータの予測正解率 :  0.6411685241472476\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECYQVaK8ozpl"
      },
      "source": [
        "### 4.6 | 考察\n",
        "\n",
        "\n",
        "　以上より，ランダムフォレストを用いることで，人間の行動が**67.42%**で予測できることが分かりました．  \n",
        "　ここで，予測に失敗したデータについて考察します．まず，正解したサンプル数と不正解したサンプル数を，行動ラベルごとに分けて描画することで，どの行動の認識が難しいのかを確認します．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDj_ibCkg2oH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "32aa7713-b8c0-4e18-e290-8fb3c9180b0a"
      },
      "source": [
        "# 各ラベルについて正解/不正解のサンプル数を描画\n",
        "result_bool = (pred_y_test == y_test)   # 予測が正解なら1, 間違いなら0\n",
        "\n",
        "true_result = result_bool * y_test\n",
        "false_result = np.logical_not(result_bool) * y_test\n",
        "\n",
        "def count_label(list, label):\n",
        "  return np.count_nonzero(list==label)\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (14.0, 5.0)\n",
        "left = np.array([1, 2, 3, 4, 5, 6])\n",
        "tick_label = [\"dws\", \"ups\", \"wlk\", \"jog\", \"sit\", \"std\"]\n",
        "\n",
        "# 各ラベルにおける正解したサンプル数の描画\n",
        "counts = [count_label(true_result, i) for i in range(1,7)]\n",
        "print(\"各ラベルの正解数\", counts)     # {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.bar(left, counts, tick_label=tick_label, align=\"center\")\n",
        "plt.title(\"Number of correctly answered samples\")\n",
        "\n",
        "# 各ラベルにおける不正解したサンプル数の描画\n",
        "counts = [count_label(false_result, i) for i in range(1,7)]\n",
        "print(\"各ラベルの不正解数\", counts)     # {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.bar(left, counts, tick_label=tick_label, align=\"center\")\n",
        "plt.title(\"Number of incorrectly answered samples\")\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "各ラベルの正解数 [1, 4, 997, 0, 1917, 878]\n",
            "各ラベルの不正解数 [333, 435, 194, 477, 47, 639]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9F4DTdm6YqTL"
      },
      "source": [
        "　上のグラフより，歩いている時および座っている時に比べ，他の動作の予測精度が悪いことが見て取れます．しかし，それ以前に階段の上り下りおよびジョギング動作においてほとんど正解していないことが気になりますね．そこで，不正解だった行動ラベルにおいて，どの動作と間違えて予測してしまったのか描画して確認します．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwUOdRcybWU4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "a1503de9-5320-46d7-dabd-737652b54cd6"
      },
      "source": [
        "# 各正解ラベルを間違えた際に選択したラベルの数を描画\n",
        "plt.rcParams['figure.figsize'] = (14.0, 8.0)\n",
        "act_labels = {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}\n",
        "\n",
        "for act_label in act_labels:\n",
        "  false_label = (false_result==act_labels[act_label]) * pred_y_test\n",
        "  counts = [count_label(false_label, i) for i in range(1,7)]\n",
        "  plt.subplot(2, 3, act_labels[act_label])\n",
        "  plt.title(\"Number of labels wrong in [\"+str(act_label)+\"] prediction\")\n",
        "  plt.bar(left, counts, tick_label=tick_label, align=\"center\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLMSP5UyaJ2A"
      },
      "source": [
        "　明らかに歩行動作と誤予測したものが多いです．これは，学習データに問題がある気がしますね．学習データを確認するために，トレーニングデータのサンプル数をラベルごとに描画してみます．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MeSzvq9b9a-9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "10165df0-1d29-4bf8-d065-6bc85ca0fa4f"
      },
      "source": [
        "counts = [count_label(y_train, i) for i in range(1,7)]\n",
        "print(\"トレーニングデータのラベル別サンプル数\", counts)     # {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}\n",
        "plt.rcParams['figure.figsize'] = (7.0, 5.0)\n",
        "plt.bar(left, counts, tick_label=tick_label, align=\"center\")\n",
        "plt.title(\"Number of training data samples by label\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "トレーニングデータのラベル別サンプル数 [2068, 2466, 5463, 1969, 4574, 4378]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of training data samples by label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2tWBlLHzanqi"
      },
      "source": [
        "　たしかに，歩行動作のトレーニングデータのサンプル数が多いですね...これは単純に確認ミスです．各ラベルのサンプル数が等しくなるようにデータを用意し直しましょう．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YINIywg8d9d1"
      },
      "source": [
        "# 時系列データをセクションに分割する関数\n",
        "def time_series_to_section(dataset, num_act_labels, num_gen_labels, size_sections, size_slide, standardize = False, **options):\n",
        "  data = dataset[: , 0:-num_act_labels]           # IMUデータ+性別ラベルを取得\n",
        "  act_labels_data = dataset[: , -(num_act_labels)]     # 行動ラベルを取得\n",
        " \n",
        "  # === 標準化 =============================\n",
        "  if standardize:\n",
        "    if options:             # 引数で平均･分散を指定した場合\n",
        "      mean = options.get(\"mean\")\n",
        "      std = options.get(\"std\")\n",
        "      print(\"トレーニングデータの平均と分散を用いてテストデータを標準化しました．\")\n",
        "    else:                   # 指定のない場合は標準化\n",
        "      mean = data.mean(axis=0)\n",
        "      std = data.std(axis=0)\n",
        "      print(\"平均:\",str(mean.mean()),\", 分散:\",str(std.mean()), \"---> 標準化しました．\")           \n",
        "    data = (data - mean) / std\n",
        "  else:\n",
        "    print(\"標準化していません．\")\n",
        "\n",
        "  # あとで3次元配列に格納するために，一旦転置しておく\n",
        "  data = data.T\n",
        "          \n",
        "  size_features = data.shape[0]\n",
        "  size_data = data.shape[1]\n",
        "  num_sections = 1 + ( (size_data - size_sections) // size_slide ) \n",
        "          \n",
        "  # 1セクションのデータ形状を定義\n",
        "  data_sections = np.zeros((num_sections, size_features, size_sections))\n",
        "  act_secs_labels = np.zeros(num_sections)\n",
        "\n",
        "  data_length = [0,0,0,0,0,0]\n",
        "   \n",
        "  for count_section in range(num_sections):   # 各セクションの第1要素のindex\n",
        "    section_first_index = count_section * size_slide     # セクションカウント\n",
        "    if(count_section >= num_sections):    # セクション数を超えたら終了     \n",
        "      break\n",
        "    if(data_length[int(act_labels_data[section_first_index])-1] > 3938):\n",
        "      continue\n",
        "    if act_labels_data[section_first_index] != act_labels_data[section_first_index+size_sections-1]:    # セクション内で行動ラベルが全て一緒でない時は無視\n",
        "      continue    \n",
        "    data_sections[count_section] = data[0:size_features, section_first_index:section_first_index+size_sections]   # セクションデータのcount_section要素目に代入\n",
        "    act_secs_labels[count_section] = act_labels_data[section_first_index].astype(int)\n",
        "    data_length[int(act_labels_data[section_first_index])-1] += 1\n",
        "  \n",
        "  return data_sections, act_secs_labels, mean, std"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cd8O4iQUd9g2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e9d0567-344b-4da2-a9ad-3260084d477d"
      },
      "source": [
        "size_sections = 50   # セクションサイズ\n",
        "size_slide = 5      # スライドサイズ\n",
        "\n",
        "# 時系列データをセクション分割\n",
        "print(\"分割後のセクション形状: (\",num_features+num_gen_labels,\"x\",size_sections,\")\")\n",
        "X_train, y_train, train_mean, train_std = time_series_to_section( train_data.copy(),\n",
        "                                                                  num_act_labels,\n",
        "                                                                  num_gen_labels,\n",
        "                                                                  size_sections,\n",
        "                                                                  size_slide,\n",
        "                                                                  standardize = True )\n",
        "\n",
        "X_test, y_test, test_mean, test_std = time_series_to_section( test_data.copy(),\n",
        "                                                              num_act_labels,\n",
        "                                                              num_gen_labels,\n",
        "                                                              size_sections,\n",
        "                                                              size_slide,\n",
        "                                                              standardize = True,\n",
        "                                                              mean = train_mean, \n",
        "                                                              std = train_std  )\n",
        "print(\"トレーニングデータ形状:\", X_train.shape)\n",
        "print(\"テストデータ形状:\", X_test.shape)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "分割後のセクション形状: ( 13 x 50 )\n",
            "平均: 0.0035300797597241063 , 分散: 0.749962379300086 ---> 標準化しました．\n",
            "トレーニングデータの平均と分散を用いてテストデータを標準化しました．\n",
            "トレーニングデータ形状: (43249, 13, 50)\n",
            "テストデータ形状: (13247, 13, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPAVP4qWd9nV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "0dc25560-64d9-4f0f-b989-3c963bacdd58"
      },
      "source": [
        "counts = [count_label(y_train, i) for i in range(1,7)]\n",
        "print(\"トレーニングデータのラベル別サンプル数\", counts)     # {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}\n",
        "plt.rcParams['figure.figsize'] = (7.0, 5.0)\n",
        "plt.bar(left, counts, tick_label=tick_label, align=\"center\")\n",
        "plt.title(\"Number of training data samples by label\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "トレーニングデータのラベル別サンプル数 [3939, 3939, 3939, 3939, 3939, 3939]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of training data samples by label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zu2z-p1HbAdL"
      },
      "source": [
        "　これで，各ラベルについて同じサンプル数が確保されたトレーニングデータができました．では，PCAによる次元圧縮と＆ランダムフォレストによる予測を行います．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKrLfbCNd9pw"
      },
      "source": [
        "# PCAによる次元圧縮\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# 2次元データを1次元データに変換\n",
        "X_train_flatten = X_train.reshape(X_train.shape[0], X_train.shape[1]*X_train.shape[2]) \n",
        "X_test_flatten  = X_test.reshape(X_test.shape[0], X_test.shape[1]*X_test.shape[2]) \n",
        "\n",
        "pca = PCA(n_components=2, random_state=0)   # 2次元に圧縮\n",
        "pca.fit(X_train_flatten)                    # 主成分の取得\n",
        "Xpca_train = pca.transform(X_train_flatten) # PCAによる次元圧縮\n",
        "Xpca_test = pca.transform(X_test_flatten)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhBq-EMvUrUS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a41cb5d-29ce-4cd2-8936-77d5432fb848"
      },
      "source": [
        "RF = ensemble.RandomForestClassifier(max_depth=5, min_samples_leaf=3, n_estimators=1000, random_state=0, n_jobs=-1)\n",
        "RF.fit(Xpca_train, y_train)            # フィッティング\n",
        "pred_y_test = RF.predict(Xpca_test)    # 予測\n",
        "print(\"テストデータの予測正解率 : \", metrics.accuracy_score(pred_y_test, y_test)) # test accuracy"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "テストデータの予測正解率 :  0.4794293047482449\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "in52R8l0VDvb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "cc4b669c-a7da-4bec-85b3-d701a1f4444d"
      },
      "source": [
        "# 各ラベルについて正解/不正解のサンプル数を描画\n",
        "result_bool = (pred_y_test == y_test)   # 予測が正解なら1, 間違いなら0\n",
        "\n",
        "true_result = result_bool * y_test\n",
        "false_result = np.logical_not(result_bool) * y_test\n",
        "\n",
        "def count_label(list, label):\n",
        "  return np.count_nonzero(list==label)\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (14.0, 5.0)\n",
        "left = np.array([1, 2, 3, 4, 5, 6])\n",
        "tick_label = [\"dws\", \"ups\", \"wlk\", \"jog\", \"sit\", \"std\"]\n",
        "\n",
        "# 各ラベルにおける正解したサンプル数の描画\n",
        "counts = [count_label(true_result, i) for i in range(1,7)]\n",
        "print(\"各ラベルの正解数\", counts)     # {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.bar(left, counts, tick_label=tick_label, align=\"center\")\n",
        "plt.title(\"Number of correctly answered samples\")\n",
        "\n",
        "# 各ラベルにおける不正解したサンプル数の描画\n",
        "counts = [count_label(false_result, i) for i in range(1,7)]\n",
        "print(\"各ラベルの不正解数\", counts)     # {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.bar(left, counts, tick_label=tick_label, align=\"center\")\n",
        "plt.title(\"Number of incorrectly answered samples\")\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "各ラベルの正解数 [107, 120, 383, 463, 2617, 1254]\n",
            "各ラベルの不正解数 [560, 760, 1994, 496, 1309, 1777]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6K7MEMvpVDyd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "b507fe37-ab1f-4509-9f1d-9c27904a5879"
      },
      "source": [
        "# 各正解ラベルを間違えた際に選択したラベルの数を描画\n",
        "plt.rcParams['figure.figsize'] = (14.0, 8.0)\n",
        "act_labels = {\"dws\":1, \"ups\":2, \"wlk\":3, \"jog\":4, \"sit\":5, \"std\":6}\n",
        "\n",
        "for act_label in act_labels:\n",
        "  false_label = (false_result==act_labels[act_label]) * pred_y_test\n",
        "  counts = [count_label(false_label, i) for i in range(1,7)]\n",
        "  plt.subplot(2, 3, act_labels[act_label])\n",
        "  plt.title(\"Number of labels wrong in [\"+str(act_label)+\"] prediction\")\n",
        "  plt.bar(left, counts, tick_label=tick_label, align=\"center\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imbWefsIVD1U"
      },
      "source": [
        "　むしろ予測正解率は下がってしまいましたが，不正解時に選択したラベルの偏りはなくなりました．予測正解率については，データセットを用意した際に1/5に削減したことが悪さをしているような感じがしました．"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41qSH5Z-ete0"
      },
      "source": [
        "## 5 | おわりに\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzAue8ZZUsxg"
      },
      "source": [
        "スマホのIMUデータからユーザの行動を認識出来るのは良いですね．  \n",
        "今回の予測タスクだけでなく色々なデータ分析に応用できるように色々な方法を使って実装しました．\n",
        "ただ，雑なところも多々ありますので参考程度にお願いします．\n"
      ]
    }
  ]
}