{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f2b11b5e-d796-4f8c-b6d0-c948f68ce5a8",
   "metadata": {
    "id": "f2b11b5e-d796-4f8c-b6d0-c948f68ce5a8"
   },
   "source": [
    "# 演習・交通手段分類（機械学習）"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "154c420b-5784-4f94-8b6d-42387cddbcb8",
   "metadata": {
    "id": "154c420b-5784-4f94-8b6d-42387cddbcb8"
   },
   "source": [
    "## 教師あり"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c3f98b05-36ce-4b75-a4ad-627f88bc3f0d",
   "metadata": {
    "id": "c3f98b05-36ce-4b75-a4ad-627f88bc3f0d"
   },
   "source": [
    "### 分類"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2c82ab82-9038-4878-8f38-7b49337670a1",
   "metadata": {
    "id": "2c82ab82-9038-4878-8f38-7b49337670a1"
   },
   "source": [
    "交通手段をいくつかの特徴で分類します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cea352a-c3d7-4dee-9a85-12f1e43a7e69",
   "metadata": {
    "executionInfo": {
     "elapsed": 1999,
     "status": "ok",
     "timestamp": 1684727762616,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "3cea352a-c3d7-4dee-9a85-12f1e43a7e69"
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "100288f1-9104-45fa-b31b-a3f707a36877",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "executionInfo": {
     "elapsed": 4935,
     "status": "ok",
     "timestamp": 1684727769721,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "100288f1-9104-45fa-b31b-a3f707a36877",
    "outputId": "aa2952eb-0693-4083-be74-815d4c86a193"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-3cdc2b44-33d6-4c68-a7cd-9b7117ec6065\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>speed</th>\n",
       "      <th>accel</th>\n",
       "      <th>angle</th>\n",
       "      <th>angular_velocity</th>\n",
       "      <th>trans_mode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trans_trip</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.920653</td>\n",
       "      <td>55.720618</td>\n",
       "      <td>-0.014007</td>\n",
       "      <td>68.982383</td>\n",
       "      <td>0.354393</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1.082321</td>\n",
       "      <td>65.670647</td>\n",
       "      <td>-0.001027</td>\n",
       "      <td>116.581851</td>\n",
       "      <td>0.568180</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>1.339320</td>\n",
       "      <td>74.683147</td>\n",
       "      <td>-0.003409</td>\n",
       "      <td>215.717754</td>\n",
       "      <td>0.480407</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>3.513432</td>\n",
       "      <td>173.776717</td>\n",
       "      <td>0.233277</td>\n",
       "      <td>174.470829</td>\n",
       "      <td>0.283810</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>4.282399</td>\n",
       "      <td>68.448484</td>\n",
       "      <td>-0.025508</td>\n",
       "      <td>150.209730</td>\n",
       "      <td>0.176290</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cdc2b44-33d6-4c68-a7cd-9b7117ec6065')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-3cdc2b44-33d6-4c68-a7cd-9b7117ec6065 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-3cdc2b44-33d6-4c68-a7cd-9b7117ec6065');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "            distance       speed     accel       angle  angular_velocity  \\\n",
       "trans_trip                                                                 \n",
       "1.0         0.920653   55.720618 -0.014007   68.982383          0.354393   \n",
       "2.0         1.082321   65.670647 -0.001027  116.581851          0.568180   \n",
       "3.0         1.339320   74.683147 -0.003409  215.717754          0.480407   \n",
       "4.0         3.513432  173.776717  0.233277  174.470829          0.283810   \n",
       "5.0         4.282399   68.448484 -0.025508  150.209730          0.176290   \n",
       "\n",
       "           trans_mode  \n",
       "trans_trip             \n",
       "1.0             train  \n",
       "2.0             train  \n",
       "3.0             train  \n",
       "4.0             train  \n",
       "5.0             train  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PLEASE REPLACE THE BELOW PATH WITH YOUR OWN\n",
    "df = pd.read_csv('./traj_010_labeled_with_features.csv', index_col=0)\n",
    "# create dataframe holding features per trip\n",
    "# in this notebook, we focus on the features' mean scores per trip\n",
    "df = df.groupby('trans_trip').agg({\n",
    "    'distance': np.mean,\n",
    "    'speed': np.mean,\n",
    "    'accel': np.mean,\n",
    "    'angle':np.mean,\n",
    "    'angular_velocity':np.mean,\n",
    "    'trans_mode':lambda x: pd.unique(x)[0],\n",
    "})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c5b97fb-fa65-4cdb-968e-f1b6fa116ff8",
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1684727769722,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "5c5b97fb-fa65-4cdb-968e-f1b6fa116ff8"
   },
   "outputs": [],
   "source": [
    "# drop rows holding np.nan\n",
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0c0b5b6-35b3-4bfb-8364-31223290f423",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1684727769722,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "f0c0b5b6-35b3-4bfb-8364-31223290f423",
    "outputId": "6eb26b30-ec5a-4b1d-9eb5-0823972d04ed"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-378d62dc-bce8-486a-a768-6789d4c66a32\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>speed</th>\n",
       "      <th>accel</th>\n",
       "      <th>angle</th>\n",
       "      <th>angular_velocity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>428.000000</td>\n",
       "      <td>428.000000</td>\n",
       "      <td>428.000000</td>\n",
       "      <td>428.000000</td>\n",
       "      <td>428.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.081362</td>\n",
       "      <td>39.136603</td>\n",
       "      <td>-0.068544</td>\n",
       "      <td>171.572185</td>\n",
       "      <td>24.004009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.329114</td>\n",
       "      <td>38.591674</td>\n",
       "      <td>0.115817</td>\n",
       "      <td>64.819410</td>\n",
       "      <td>24.533810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.001203</td>\n",
       "      <td>1.925925</td>\n",
       "      <td>-1.116834</td>\n",
       "      <td>5.303116</td>\n",
       "      <td>0.176290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.002057</td>\n",
       "      <td>6.276831</td>\n",
       "      <td>-0.108822</td>\n",
       "      <td>128.523012</td>\n",
       "      <td>2.852289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.009352</td>\n",
       "      <td>27.671732</td>\n",
       "      <td>-0.045742</td>\n",
       "      <td>173.816135</td>\n",
       "      <td>18.066445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.020917</td>\n",
       "      <td>53.299781</td>\n",
       "      <td>-0.006693</td>\n",
       "      <td>218.588914</td>\n",
       "      <td>35.412731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.282399</td>\n",
       "      <td>175.182973</td>\n",
       "      <td>0.298885</td>\n",
       "      <td>329.530934</td>\n",
       "      <td>114.471672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-378d62dc-bce8-486a-a768-6789d4c66a32')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-378d62dc-bce8-486a-a768-6789d4c66a32 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-378d62dc-bce8-486a-a768-6789d4c66a32');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         distance       speed       accel       angle  angular_velocity\n",
       "count  428.000000  428.000000  428.000000  428.000000        428.000000\n",
       "mean     0.081362   39.136603   -0.068544  171.572185         24.004009\n",
       "std      0.329114   38.591674    0.115817   64.819410         24.533810\n",
       "min      0.001203    1.925925   -1.116834    5.303116          0.176290\n",
       "25%      0.002057    6.276831   -0.108822  128.523012          2.852289\n",
       "50%      0.009352   27.671732   -0.045742  173.816135         18.066445\n",
       "75%      0.020917   53.299781   -0.006693  218.588914         35.412731\n",
       "max      4.282399  175.182973    0.298885  329.530934        114.471672"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d8249b5-8c35-4be6-a4e7-782231cbd7ce",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1684727769722,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "0d8249b5-8c35-4be6-a4e7-782231cbd7ce",
    "outputId": "1d1959c0-2f2c-4327-c8f3-681685339233"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "walk      152\n",
       "train      99\n",
       "taxi       96\n",
       "subway     47\n",
       "bus        34\n",
       "Name: trans_mode, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the counts of the target categorical values in the dataset\n",
    "df.trans_mode.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f8a775a4-1dbd-42f8-96f9-a68d39f5cca9",
   "metadata": {
    "id": "f8a775a4-1dbd-42f8-96f9-a68d39f5cca9"
   },
   "source": [
    "In this notebook, we merge several labels and use three labels: vehicle(car/taxi/bus), walk, and train(train/subway)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8d320df-2f29-4832-b156-5be06713eab4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1684727769722,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "d8d320df-2f29-4832-b156-5be06713eab4",
    "outputId": "1bf9b424-d5fd-4ebc-ae9d-b2afdf19333b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "walk       152\n",
       "train      146\n",
       "vehicle    130\n",
       "Name: trans_mode, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dictionary holding new labels as values and corresponding old labels as keys\n",
    "trans_mode_map = {'bus':'vehicle', 'car':'vehicle','taxi':'vehicle','subway':'train', 'walk':'walk','train':'train'}\n",
    "# map the above dictionary to the current labels and replace them with the three labels\n",
    "df['trans_mode'] = df.trans_mode.map(trans_mode_map)\n",
    "# count each labels\n",
    "df.trans_mode.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b311aed8-bfcd-4dde-918c-40f4a327e334",
   "metadata": {
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1684727769723,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "b311aed8-bfcd-4dde-918c-40f4a327e334"
   },
   "outputs": [],
   "source": [
    "### implementation of some basic classificaition models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0993f3f9-6c35-4227-b95b-b2b72a0cb5a9",
   "metadata": {
    "id": "0993f3f9-6c35-4227-b95b-b2b72a0cb5a9"
   },
   "source": [
    "create `X` holding features to be used in models and `y` holding labels to be predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6490be1-a748-4dae-98f8-f8febd7d9a87",
   "metadata": {
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1684727769723,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "b6490be1-a748-4dae-98f8-f8febd7d9a87"
   },
   "outputs": [],
   "source": [
    "X = df[['speed','accel','angular_velocity']]\n",
    "y = df['trans_mode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dcdb4c7a-5b6c-4221-9472-c57ff3519e69",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1684727769723,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "dcdb4c7a-5b6c-4221-9472-c57ff3519e69",
    "outputId": "6094b5aa-fb53-4ad1-cb3f-2a7fd51b9730"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-b6e6c6a3-eebc-48aa-a453-cb3e0259d648\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speed</th>\n",
       "      <th>accel</th>\n",
       "      <th>angular_velocity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>speed</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159520</td>\n",
       "      <td>-0.530622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accel</th>\n",
       "      <td>0.159520</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.068022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>angular_velocity</th>\n",
       "      <td>-0.530622</td>\n",
       "      <td>-0.068022</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6e6c6a3-eebc-48aa-a453-cb3e0259d648')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-b6e6c6a3-eebc-48aa-a453-cb3e0259d648 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-b6e6c6a3-eebc-48aa-a453-cb3e0259d648');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                     speed     accel  angular_velocity\n",
       "speed             1.000000  0.159520         -0.530622\n",
       "accel             0.159520  1.000000         -0.068022\n",
       "angular_velocity -0.530622 -0.068022          1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check correlations between features\n",
    "X.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c7a059dc-b1de-4cb8-b1ce-4976e3c61525",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1684727769723,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "c7a059dc-b1de-4cb8-b1ce-4976e3c61525",
    "outputId": "1c4d5cc3-4d60-4570-8d0a-50be332426f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((428, 3), (428,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the sizes of X and y\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d893c5b8-6330-4540-8514-f49ce1c601ab",
   "metadata": {
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1684727769723,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "d893c5b8-6330-4540-8514-f49ce1c601ab"
   },
   "outputs": [],
   "source": [
    "# Split into training and validation data.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6eea2bcc-1874-463d-832e-0c6514f33335",
   "metadata": {
    "id": "6eea2bcc-1874-463d-832e-0c6514f33335"
   },
   "source": [
    "Run several classification models to estimate transportation mode (vehicle, train, and walk)\n",
    "\n",
    "In this notebook, we run `logistic regression`, `SVM(support vector machines)`, and `Decision Tree` as examples. \n",
    "To understand how each model works, please check the documents below and other machine learning introductory materials.\n",
    "\n",
    "logistic regression: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
    "\n",
    "SVM: https://scikit-learn.org/stable/modules/svm.html#support-vector-machines\n",
    "\n",
    "Decision Tree: https://scikit-learn.org/stable/modules/tree.html#decision-trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e857a1e-2976-432b-9494-afb9d2a8e60a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "executionInfo": {
     "elapsed": 593,
     "status": "ok",
     "timestamp": 1684727770302,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "7e857a1e-2976-432b-9494-afb9d2a8e60a",
    "outputId": "808c3bd2-98cc-4765-d384-4de69d8cc2a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score (w/ training data): 0.9130434782608695\n",
      "accuracy score (w/ validation data): 0.9069767441860465\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       train       0.92      0.80      0.85        44\n",
      "     vehicle       0.80      0.92      0.86        39\n",
      "        walk       1.00      1.00      1.00        46\n",
      "\n",
      "    accuracy                           0.91       129\n",
      "   macro avg       0.91      0.91      0.90       129\n",
      "weighted avg       0.91      0.91      0.91       129\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-1fb51577-e288-46d4-97ba-440cc8792e07\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train</th>\n",
       "      <th>vehicle</th>\n",
       "      <th>walk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle</th>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>walk</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1fb51577-e288-46d4-97ba-440cc8792e07')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-1fb51577-e288-46d4-97ba-440cc8792e07 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-1fb51577-e288-46d4-97ba-440cc8792e07');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         train  vehicle  walk\n",
       "train       35        9     0\n",
       "vehicle      3       36     0\n",
       "walk         0        0    46"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic regression models (one-vs-rest)\n",
    "lg_model=LogisticRegression()\n",
    "lg_model.fit(X_train, y_train) # Fitting models to training data\n",
    "lg_y_predicted=lg_model.predict(X_test) # Predicting labels with test data.\n",
    "# Assessment of prediction accuracy\n",
    "print('accuracy score (w/ training data): {}'.format(lg_model.score(X_train, y_train)))\n",
    "# Assessment of prediction accuracy\n",
    "print('accuracy score (w/ validation data): {}'.format(lg_model.score(X_test, y_test)))\n",
    "print(classification_report(y_test, lg_y_predicted))\n",
    "# print out confusion matrix\n",
    "pd.DataFrame(confusion_matrix(y_test, lg_y_predicted), \n",
    "             columns = ['train','vehicle','walk'], \n",
    "             index=['train','vehicle','walk'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "64448d5e-7fc5-4685-98f7-b77a1fcc42f8",
   "metadata": {
    "id": "64448d5e-7fc5-4685-98f7-b77a1fcc42f8"
   },
   "source": [
    "In the above cell, we printed out the accuracy score within training data and those within validation data.\n",
    "\n",
    "Also, we printed out the classification reports that hold more information, including precision, recall, and f1-score.\n",
    "\n",
    "* $\\texttt{accuracy}(y, \\hat{y}) = \\frac{1}{n_\\text{samples}} \\sum_{i=0}^{n_\\text{samples}-1} 1(\\hat{y}_i = y_i)$\n",
    "* $\\texttt{precision} = tp / (tp + fp)$ where tp is tp (true positive) is the correct result and fp (false positive) is an unexpected result\n",
    "* $\\texttt{recall} =  tp / (tp + fn)$ where fn (false negative) is missing result\n",
    "* $\\texttt{f1-score} = 2 * (precision * recall) / (precision + recall)$\n",
    "\n",
    "With the confusion matrix, we can easily check which label are misclassified to which."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "685748f0-8a41-49d5-ba4d-f74c0a5db012",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1684727770303,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "685748f0-8a41-49d5-ba4d-f74c0a5db012",
    "outputId": "d9ac9f47-8e79-4c29-a56e-bd80e819827a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score (w/ training data):  0.919732441471572\n",
      "accuracy score (w/ validation data):  0.875968992248062\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       train       0.92      0.82      0.87        44\n",
      "     vehicle       0.79      0.79      0.79        39\n",
      "        walk       0.90      1.00      0.95        46\n",
      "\n",
      "    accuracy                           0.88       129\n",
      "   macro avg       0.87      0.87      0.87       129\n",
      "weighted avg       0.88      0.88      0.87       129\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-d298800d-f93d-4f93-8737-556f79d98ed2\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train</th>\n",
       "      <th>vehicle</th>\n",
       "      <th>walk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>walk</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d298800d-f93d-4f93-8737-556f79d98ed2')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-d298800d-f93d-4f93-8737-556f79d98ed2 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-d298800d-f93d-4f93-8737-556f79d98ed2');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         train  vehicle  walk\n",
       "train       36        8     0\n",
       "vehicle      3       31     5\n",
       "walk         0        0    46"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machine (SVM)\n",
    "svm_clf = svm.SVC()\n",
    "svm_clf.fit(X_train, y_train) \n",
    "svm_y_predicted=svm_clf.predict(X_test) # Predicting labels with test data.\n",
    "print(\"accuracy score (w/ training data): \", svm_clf.score(X_train, y_train)) \n",
    "print(\"accuracy score (w/ validation data): \", svm_clf.score(X_test, y_test)) \n",
    "print(classification_report(y_test, svm_y_predicted))\n",
    "# print out confusion matrix\n",
    "pd.DataFrame(confusion_matrix(y_test, svm_y_predicted), \n",
    "             columns = ['train','vehicle','walk'], \n",
    "             index=['train','vehicle','walk'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "46957626-013d-41ed-ad5b-45ce1cd391ff",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1684727770303,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "46957626-013d-41ed-ad5b-45ce1cd391ff",
    "outputId": "227687a1-87a5-4045-ca5e-53b780e7bc3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score (w/ training data):  0.9431438127090301\n",
      "accuracy score (w/ validation data):  0.9302325581395349\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       train       0.89      0.91      0.90        44\n",
      "     vehicle       0.89      0.87      0.88        39\n",
      "        walk       1.00      1.00      1.00        46\n",
      "\n",
      "    accuracy                           0.93       129\n",
      "   macro avg       0.93      0.93      0.93       129\n",
      "weighted avg       0.93      0.93      0.93       129\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-d979ae4e-bd33-4935-8202-2e61705ff1f5\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train</th>\n",
       "      <th>vehicle</th>\n",
       "      <th>walk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle</th>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>walk</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d979ae4e-bd33-4935-8202-2e61705ff1f5')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-d979ae4e-bd33-4935-8202-2e61705ff1f5 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-d979ae4e-bd33-4935-8202-2e61705ff1f5');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         train  vehicle  walk\n",
       "train       40        4     0\n",
       "vehicle      5       34     0\n",
       "walk         0        0    46"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree Classifier\n",
    "tree_clf = DecisionTreeClassifier(random_state=0, max_depth=3)\n",
    "tree_clf.fit(X_train, y_train) \n",
    "tree_y_predicted=tree_clf.predict(X_test)\n",
    "print(\"accuracy score (w/ training data): \", tree_clf.score(X_train, y_train)) \n",
    "print(\"accuracy score (w/ validation data): \",tree_clf.score(X_test, y_test))\n",
    "print(classification_report(y_test, tree_y_predicted))\n",
    "# print out confusion matrix\n",
    "pd.DataFrame(confusion_matrix(y_test, tree_y_predicted), \n",
    "             columns = ['train','vehicle','walk'], \n",
    "             index=['train','vehicle','walk'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cb5791cf-7388-49ab-9c8a-86678b727a6e",
   "metadata": {
    "id": "cb5791cf-7388-49ab-9c8a-86678b727a6e"
   },
   "source": [
    "Let's visualize how the tree classifes our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2a5542e1-d331-4e36-b8d0-768eef880317",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "executionInfo": {
     "elapsed": 2761,
     "status": "ok",
     "timestamp": 1684727773051,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "2a5542e1-d331-4e36-b8d0-768eef880317",
    "outputId": "eb72c780-12d9-4cdd-9305-a26d8e1e3ae4"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "tree.plot_tree(\n",
    "    tree_clf, ax = ax, fontsize=8, \n",
    "    feature_names= X.columns,  \n",
    "    class_names= y.unique(), \n",
    "    filled=True,\n",
    "    rounded=True,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2185d5d8-cca4-4c8d-9e16-27ef3fde257e",
   "metadata": {
    "id": "2185d5d8-cca4-4c8d-9e16-27ef3fde257e",
    "tags": []
   },
   "source": [
    "## 教師なし\n",
    "\n",
    "ここでは`K-means`と`PCA`を用います\n",
    "\n",
    "* K-means: https://scikit-learn.org/stable/modules/clustering.html#k-means\n",
    "* PCA: https://scikit-learn.org/stable/modules/decomposition.html#pca\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "72f0f28d-0454-4d68-a313-dc889a54a135",
   "metadata": {
    "id": "72f0f28d-0454-4d68-a313-dc889a54a135"
   },
   "source": [
    "### K-means"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8ae6a92c-77a3-49bb-9a76-7f998bc6bdd9",
   "metadata": {
    "id": "8ae6a92c-77a3-49bb-9a76-7f998bc6bdd9"
   },
   "source": [
    "ここでは、特徴量 `angular_velocity` と `speed` を用いて、K-means を用いてクラスタリングを行います。\n",
    "K-meansでは、クラスタの数を指定する必要があるので、ここでは2つのクラスタを生成します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f95c7da0-3464-4c01-a375-926f1974afba",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 400,
     "status": "ok",
     "timestamp": 1684727773438,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "f95c7da0-3464-4c01-a375-926f1974afba",
    "outputId": "971f5fcd-bbf7-4497-95d1-286d168b3779"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "X_kmeans = df[['angular_velocity', 'speed']]\n",
    "model = KMeans(n_clusters=3) # with n_clusters specifying the number of clusters\n",
    "model.fit(X_kmeans) # fitting the data\n",
    "y_km=model.predict(X_kmeans) # predict clusters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "70767db5-f2ab-4923-9bdd-7c0c6671d87d",
   "metadata": {
    "id": "70767db5-f2ab-4923-9bdd-7c0c6671d87d"
   },
   "source": [
    "正解データとクラスタリングの結果を比較してみます"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "75dd2162-560f-4c92-a406-4baa0ee3ca1f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "executionInfo": {
     "elapsed": 902,
     "status": "ok",
     "timestamp": 1684727774337,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "75dd2162-560f-4c92-a406-4baa0ee3ca1f",
    "outputId": "9573f719-5f5c-4683-b81c-54d29d6242cc"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df['trans_mode']\n",
    "y_colors = y.map({'train':'navy', 'vehicle':'turquoise', 'walk':'darkorange'}) \n",
    "fig, axs = plt.subplots(1, 2, figsize=(8, 3))\n",
    "for i, ax in enumerate(axs):\n",
    "    ax.scatter(df['angular_velocity'], df['speed'], c = [y_km, y_colors][i])\n",
    "    ax.set_xlabel('angle rate')\n",
    "    ax.set_ylabel('speed')\n",
    "    ax.set_title(['K-means clustering', 'Ground truth class'][i])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c8d1d528-0eb7-435e-b282-b65f0ff717fb",
   "metadata": {
    "id": "c8d1d528-0eb7-435e-b282-b65f0ff717fb"
   },
   "source": [
    "2つの特徴量しか用いていないこともあり、K-meanではあまりうまく分類ができていないですが、それでも大まかな傾向は捉えているようです。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d6a03ea7-907c-4ce7-b887-048b7fe91fe4",
   "metadata": {
    "id": "d6a03ea7-907c-4ce7-b887-048b7fe91fe4"
   },
   "source": [
    "#### PCA (Principal Component Analysis)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "33803666-1e67-4702-8418-4bba644adcb5",
   "metadata": {
    "id": "33803666-1e67-4702-8418-4bba644adcb5"
   },
   "source": [
    "高次元データの次元削減のために用いられることが多いです。\n",
    "ここでは、GPS軌跡データの5つの特徴量をPCAを用いて2次元に縮小します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "033533da-982e-4810-b4f2-9803c1bfa13b",
   "metadata": {
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1684727774337,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "033533da-982e-4810-b4f2-9803c1bfa13b"
   },
   "outputs": [],
   "source": [
    "X_pca = df[['distance','speed','accel','angle','angular_velocity']]\n",
    "y = df['trans_mode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c15a132-8b40-4f4e-be50-59558ccd62e5",
   "metadata": {
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1684727774337,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "0c15a132-8b40-4f4e-be50-59558ccd62e5"
   },
   "outputs": [],
   "source": [
    "X_pca = StandardScaler().fit_transform(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b4ad696-eea0-49b7-b60b-c1f0f049d65c",
   "metadata": {
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1684727774337,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "7b4ad696-eea0-49b7-b60b-c1f0f049d65c"
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_pca_res = pca.fit_transform(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b20d21ab-517f-45a1-a253-48db5a7d917c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1684727774338,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "b20d21ab-517f-45a1-a253-48db5a7d917c",
    "outputId": "09b5205c-3742-408f-9b75-d86a09eaf69c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.36113444 0.2130784 ]\n"
     ]
    }
   ],
   "source": [
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e2773708-f408-4514-9866-04c7c0a89f0a",
   "metadata": {
    "id": "e2773708-f408-4514-9866-04c7c0a89f0a"
   },
   "source": [
    "第1主成分と第2主成分をプロットします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e3329d52-8a84-4e71-b9e2-9f94ecda9be7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "executionInfo": {
     "elapsed": 1213,
     "status": "ok",
     "timestamp": 1684727775544,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "e3329d52-8a84-4e71-b9e2-9f94ecda9be7",
    "outputId": "93d18a83-932b-460e-ab14-34755a5f92b1",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['navy', 'turquoise', 'darkorange']\n",
    "target_names =  y.unique()\n",
    "for color, target_name in zip(colors, target_names):\n",
    "    plt.scatter(X_pca_res[y == target_name, 0], X_pca_res[y == target_name, 1], color=color, alpha=.8, label=target_name)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1, frameon=False)\n",
    "plt.title('PCA of GPS dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bb41f234-431e-4bd5-a53d-13904b9a39c1",
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1684727775544,
     "user": {
      "displayName": "Yuya Shibuya",
      "userId": "13278923316285788453"
     },
     "user_tz": -540
    },
    "id": "bb41f234-431e-4bd5-a53d-13904b9a39c1"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}