{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# tsfreshを用いた特徴量自動生成と分類\n"
      ],
      "metadata": {
        "id": "uj7jI2g5cZlt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## tsfreshのインストール"
      ],
      "metadata": {
        "id": "Hdqe-M98ckxa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7K_4Xvt8Vrp",
        "outputId": "b3913144-9d5c-4b26-a603-a4b57ec1bdfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tsfresh in /usr/local/lib/python3.12/dist-packages (0.21.1)\n",
            "Requirement already satisfied: requests>=2.9.1 in /usr/local/lib/python3.12/dist-packages (from tsfresh) (2.32.4)\n",
            "Requirement already satisfied: numpy>=1.15.1 in /usr/local/lib/python3.12/dist-packages (from tsfresh) (2.0.2)\n",
            "Requirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.12/dist-packages (from tsfresh) (2.2.2)\n",
            "Requirement already satisfied: statsmodels>=0.13 in /usr/local/lib/python3.12/dist-packages (from tsfresh) (0.14.6)\n",
            "Requirement already satisfied: patsy>=0.4.1 in /usr/local/lib/python3.12/dist-packages (from tsfresh) (1.0.2)\n",
            "Requirement already satisfied: pywavelets in /usr/local/lib/python3.12/dist-packages (from tsfresh) (1.9.0)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from tsfresh) (1.6.1)\n",
            "Requirement already satisfied: tqdm>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from tsfresh) (4.67.1)\n",
            "Requirement already satisfied: stumpy>=1.7.2 in /usr/local/lib/python3.12/dist-packages (from tsfresh) (1.13.0)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.12/dist-packages (from tsfresh) (3.1.2)\n",
            "Requirement already satisfied: scipy>=1.14.0 in /usr/local/lib/python3.12/dist-packages (from tsfresh) (1.16.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas>=0.25.0->tsfresh) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas>=0.25.0->tsfresh) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas>=0.25.0->tsfresh) (2025.2)\n",
            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.9.1->tsfresh) (3.4.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.9.1->tsfresh) (3.11)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.9.1->tsfresh) (2.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests>=2.9.1->tsfresh) (2025.11.12)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=0.22.0->tsfresh) (1.5.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=0.22.0->tsfresh) (3.6.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.12/dist-packages (from statsmodels>=0.13->tsfresh) (25.0)\n",
            "Requirement already satisfied: numba>=0.57.1 in /usr/local/lib/python3.12/dist-packages (from stumpy>=1.7.2->tsfresh) (0.60.0)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba>=0.57.1->stumpy>=1.7.2->tsfresh) (0.43.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas>=0.25.0->tsfresh) (1.17.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install tsfresh"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ライブラリのインポート"
      ],
      "metadata": {
        "id": "9C81BMEmcuAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "sjJBSdZO-ZUO"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "tsfresh関連のライブラリのインポート"
      ],
      "metadata": {
        "id": "zRQS4qo2g59R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, load_robot_execution_failures\n",
        "from tsfresh import select_features\n",
        "from tsfresh.utilities.dataframe_functions import impute\n",
        "from tsfresh import extract_features"
      ],
      "metadata": {
        "id": "ibgmPJMm8XS9"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## tsfreshに含まれるロボットの誤動作データの読み込み"
      ],
      "metadata": {
        "id": "xnwRI0sWcxBG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "download_robot_execution_failures()\n",
        "timeseries, y = load_robot_execution_failures()"
      ],
      "metadata": {
        "id": "QNdp9aw8g1ec"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timeseries"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "tSHH0nKVhHHK",
        "outputId": "47f3da49-aa1e-44af-cff9-1491aa17964d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  time  F_x  F_y  F_z  T_x  T_y  T_z\n",
              "0      1     0   -1   -1   63   -3   -1    0\n",
              "1      1     1    0    0   62   -3   -1    0\n",
              "2      1     2   -1   -1   61   -3    0    0\n",
              "3      1     3   -1   -1   63   -2   -1    0\n",
              "4      1     4   -1   -1   63   -3   -1    0\n",
              "...   ..   ...  ...  ...  ...  ...  ...  ...\n",
              "1315  88    10  -10    2   39  -21  -24    5\n",
              "1316  88    11  -11    2   38  -24  -22    6\n",
              "1317  88    12  -12    3   23  -24  -24    5\n",
              "1318  88    13  -13    4   26  -29  -27    5\n",
              "1319  88    14  -13    2   15  -25  -25    6\n",
              "\n",
              "[1320 rows x 8 columns]"
            ],
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>62</td>\n",
              "      <td>-3</td>\n",
              "      <td>-1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
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              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>61</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>3</td>\n",
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              "      <td>-1</td>\n",
              "      <td>63</td>\n",
              "      <td>-2</td>\n",
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              "      <th>4</th>\n",
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              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>63</td>\n",
              "      <td>-3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>1315</th>\n",
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              "    <tr>\n",
              "      <th>1316</th>\n",
              "      <td>88</td>\n",
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              "      <td>-11</td>\n",
              "      <td>2</td>\n",
              "      <td>38</td>\n",
              "      <td>-24</td>\n",
              "      <td>-22</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1317</th>\n",
              "      <td>88</td>\n",
              "      <td>12</td>\n",
              "      <td>-12</td>\n",
              "      <td>3</td>\n",
              "      <td>23</td>\n",
              "      <td>-24</td>\n",
              "      <td>-24</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1318</th>\n",
              "      <td>88</td>\n",
              "      <td>13</td>\n",
              "      <td>-13</td>\n",
              "      <td>4</td>\n",
              "      <td>26</td>\n",
              "      <td>-29</td>\n",
              "      <td>-27</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1319</th>\n",
              "      <td>88</td>\n",
              "      <td>14</td>\n",
              "      <td>-13</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>-25</td>\n",
              "      <td>-25</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1320 rows × 8 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          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",
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              "\n",
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              "\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-33686888-60c9-454f-8e57-5c17df0409a2 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_0541421d-fb07-4e35-8425-88053fa1ae01\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('timeseries')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0541421d-fb07-4e35-8425-88053fa1ae01 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('timeseries');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "timeseries",
              "summary": "{\n  \"name\": \"timeseries\",\n  \"rows\": 1320,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 88,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          77,\n          1,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 14,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          9,\n          11,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50,\n        \"min\": -260,\n        \"max\": 342,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          29,\n          -226,\n          -51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F_y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": -353,\n        \"max\": 236,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          82,\n          -8,\n          188\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F_z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 346,\n        \"min\": -1547,\n        \"max\": 157,\n        \"num_unique_values\": 429,\n        \"samples\": [\n          -780,\n          -1360,\n          -966\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147,\n        \"min\": -672,\n        \"max\": 686,\n        \"num_unique_values\": 393,\n        \"samples\": [\n          -129,\n          -106,\n          291\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T_y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101,\n        \"min\": -646,\n        \"max\": 601,\n        \"num_unique_values\": 307,\n        \"samples\": [\n          178,\n          -77,\n          -284\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T_z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": -137,\n        \"max\": 123,\n        \"num_unique_values\": 120,\n        \"samples\": [\n          21,\n          52,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "eO7k4WHAhIsl",
        "outputId": "5b603db6-04e9-4526-a7a2-a4d061ed5c96"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1      True\n",
              "2      True\n",
              "3      True\n",
              "4      True\n",
              "5      True\n",
              "      ...  \n",
              "84    False\n",
              "85    False\n",
              "86    False\n",
              "87    False\n",
              "88    False\n",
              "Length: 88, dtype: bool"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "      <th>4</th>\n",
              "      <td>True</td>\n",
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              "      <th>5</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>84</th>\n",
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              "      <th>88</th>\n",
              "      <td>False</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>88 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> bool</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 学習データとテストデータの分割"
      ],
      "metadata": {
        "id": "6iuLsCEAevHk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "split_id = np.zeros(len(y))\n",
        "split_id[0:int(len(y)/4)] = 1\n",
        "np.random.shuffle(split_id)"
      ],
      "metadata": {
        "id": "2CBRjmWx8mht"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train_timeseries = pd.DataFrame()\n",
        "df_train_label = pd.Series()\n",
        "df_test_timeseries = pd.DataFrame()\n",
        "df_test_label = pd.Series()\n",
        "\n",
        "for i,j in enumerate(split_id):\n",
        "  if j==0:\n",
        "    df_train_timeseries = pd.concat([df_train_timeseries,timeseries[timeseries['id']==i+1]])\n",
        "    df_train_label.loc[i+1] = y[i+1]\n",
        "  else:\n",
        "    df_test_timeseries = pd.concat([df_test_timeseries,timeseries[timeseries['id']==i+1]])\n",
        "    df_test_label.loc[i+1] = y[i+1]\n"
      ],
      "metadata": {
        "id": "cI0YSghB8sc7"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"訓練データの数：\", len(df_train_label))\n",
        "print(\"テストデータの数：\", len(df_test_label))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eL3IRBz1WIEj",
        "outputId": "23828abd-8b4d-4218-9aaf-e140f49287cd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "訓練データの数： 66\n",
            "テストデータの数： 22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 正常データと失敗データの表示\n",
        "\n",
        "True:正常データ<br>\n",
        "False:失敗データ"
      ],
      "metadata": {
        "id": "uu5QpB5me9jM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"id=3のラベル表示\",y[3])\n",
        "print(\"id=20のラベル表示\",y[20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QL-5it-7fOkF",
        "outputId": "163cc9cd-cdb0-454e-c74f-02aee9b05503"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id=3のラベル表示 True\n",
            "id=20のラベル表示 False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#正常データの表示\n",
        "timeseries[timeseries['id'] == 3].plot(subplots=True, sharex=True, figsize=(10,10),color='k')\n",
        "plt.savefig('tsfresh_Normal.png')\n",
        "plt.savefig('tsfresh_Normal.pdf')\n",
        "plt.savefig('tsfresh_Normal.svg')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "wJwkhrua8w4f",
        "outputId": "9f9e8d2d-1210-4626-9092-9e9033d021a1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 8 Axes>"
            ],
            "image/png": "[encoded data, 100792 characters]\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#失敗データの表示\n",
        "timeseries[timeseries['id'] == 20].plot(subplots=True, sharex=True, figsize=(10,10),color='k')\n",
        "plt.savefig('tsfresh_Fail.png')\n",
        "plt.savefig('tsfresh_Fail.pdf')\n",
        "plt.savefig('tsfresh_Fail.svg')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "cvxk3dde9ANw",
        "outputId": "e7dbd106-7363-4f5e-b2d6-e7d26071eddc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 8 Axes>"
            ],
            "image/png": "[encoded data, 58740 characters]\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 特徴量の抽出"
      ],
      "metadata": {
        "id": "LoLyZWCBf9_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "extracted_features_train = extract_features(df_train_timeseries, column_id=\"id\", column_sort=\"time\")\n",
        "extracted_features_test = extract_features(df_test_timeseries, column_id=\"id\", column_sort=\"time\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4n8EYJuCSxB",
        "outputId": "7d542c7e-6519-4e60-9d3c-bd2efb096ac3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tsfresh.feature_extraction.settings:Dependency not available for matrix_profile, this feature will be disabled!\n",
            "Feature Extraction: 100%|██████████| 396/396 [00:38<00:00, 10.22it/s]\n",
            "WARNING:tsfresh.feature_extraction.settings:Dependency not available for matrix_profile, this feature will be disabled!\n",
            "Feature Extraction: 100%|██████████| 132/132 [00:12<00:00, 10.22it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "extracted_features_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "rHdIXM5sgVY8",
        "outputId": "42445e45-732e-4871-a535-61e0d940bb95"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    F_x__variance_larger_than_standard_deviation  F_x__has_duplicate_max  \\\n",
              "1                                            0.0                     0.0   \n",
              "3                                            0.0                     0.0   \n",
              "4                                            0.0                     1.0   \n",
              "6                                            1.0                     1.0   \n",
              "8                                            1.0                     0.0   \n",
              "..                                           ...                     ...   \n",
              "83                                           1.0                     1.0   \n",
              "84                                           1.0                     1.0   \n",
              "85                                           1.0                     0.0   \n",
              "86                                           1.0                     0.0   \n",
              "88                                           1.0                     0.0   \n",
              "\n",
              "    F_x__has_duplicate_min  F_x__has_duplicate  F_x__sum_values  \\\n",
              "1                      1.0                 1.0            -14.0   \n",
              "3                      1.0                 1.0            -10.0   \n",
              "4                      1.0                 1.0             -6.0   \n",
              "6                      1.0                 1.0            -15.0   \n",
              "8                      1.0                 1.0            -10.0   \n",
              "..                     ...                 ...              ...   \n",
              "83                     0.0                 1.0           -285.0   \n",
              "84                     0.0                 1.0          -1073.0   \n",
              "85                     1.0                 1.0            143.0   \n",
              "86                     0.0                 0.0            961.0   \n",
              "88                     1.0                 1.0           -143.0   \n",
              "\n",
              "    F_x__abs_energy  F_x__mean_abs_change  F_x__mean_change  \\\n",
              "1              14.0              0.142857          0.000000   \n",
              "3              12.0              0.714286          0.000000   \n",
              "4              16.0              1.214286         -0.071429   \n",
              "6              39.0              1.714286          0.142857   \n",
              "8              26.0              1.142857         -0.142857   \n",
              "..              ...                   ...               ...   \n",
              "83           5841.0              1.642857         -0.928571   \n",
              "84          96833.0              7.142857         -5.428571   \n",
              "85           1683.0              1.357143          1.071429   \n",
              "86          83497.0              9.071429          9.071429   \n",
              "88           1427.0              0.785714         -0.500000   \n",
              "\n",
              "    F_x__mean_second_derivative_central  F_x__median  ...  \\\n",
              "1                             -0.038462         -1.0  ...   \n",
              "3                             -0.038462         -1.0  ...   \n",
              "4                             -0.038462          0.0  ...   \n",
              "6                              0.153846         -1.0  ...   \n",
              "8                              0.192308         -1.0  ...   \n",
              "..                                  ...          ...  ...   \n",
              "83                            -0.076923        -16.0  ...   \n",
              "84                            -0.038462        -98.0  ...   \n",
              "85                             0.076923          8.0  ...   \n",
              "86                             0.807692         52.0  ...   \n",
              "88                             0.038462         -9.0  ...   \n",
              "\n",
              "    T_z__fourier_entropy__bins_5  T_z__fourier_entropy__bins_10  \\\n",
              "1                            NaN                            NaN   \n",
              "3                       1.386294                       1.732868   \n",
              "4                       1.073543                       1.494175   \n",
              "6                       0.974315                       1.213008   \n",
              "8                       1.494175                       1.732868   \n",
              "..                           ...                            ...   \n",
              "83                      0.735622                       0.735622   \n",
              "84                      0.735622                       0.735622   \n",
              "85                      0.735622                       0.735622   \n",
              "86                      0.735622                       1.073543   \n",
              "88                      1.255482                       1.494175   \n",
              "\n",
              "    T_z__fourier_entropy__bins_100  \\\n",
              "1                              NaN   \n",
              "3                         2.079442   \n",
              "4                         2.079442   \n",
              "6                         1.732868   \n",
              "8                         2.079442   \n",
              "..                             ...   \n",
              "83                        1.213008   \n",
              "84                        1.386294   \n",
              "85                        1.667462   \n",
              "86                        1.732868   \n",
              "88                        2.079442   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_3__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       1.265857   \n",
              "4                                       1.156988   \n",
              "6                                       1.438883   \n",
              "8                                       1.412745   \n",
              "..                                           ...   \n",
              "83                                      1.778233   \n",
              "84                                      1.585771   \n",
              "85                                      1.332245   \n",
              "86                                      0.687092   \n",
              "88                                      0.830518   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_4__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       1.704551   \n",
              "4                                       1.907284   \n",
              "6                                       1.863680   \n",
              "8                                       2.022809   \n",
              "..                                           ...   \n",
              "83                                      2.022809   \n",
              "84                                      1.907284   \n",
              "85                                      1.308605   \n",
              "86                                      0.983088   \n",
              "88                                      1.242453   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_5__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       2.019815   \n",
              "4                                       2.397895   \n",
              "6                                       2.145842   \n",
              "8                                       2.397895   \n",
              "..                                           ...   \n",
              "83                                      2.098274   \n",
              "84                                      2.098274   \n",
              "85                                      1.893788   \n",
              "86                                      1.159589   \n",
              "88                                      1.414279   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_6__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       2.163956   \n",
              "4                                       2.302585   \n",
              "6                                       2.163956   \n",
              "8                                       2.302585   \n",
              "..                                           ...   \n",
              "83                                      2.302585   \n",
              "84                                      2.302585   \n",
              "85                                      2.163956   \n",
              "86                                      1.227529   \n",
              "88                                      1.609438   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_7__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       2.197225   \n",
              "4                                       2.197225   \n",
              "6                                       2.197225   \n",
              "8                                       2.197225   \n",
              "..                                           ...   \n",
              "83                                      2.197225   \n",
              "84                                      2.197225   \n",
              "85                                      2.197225   \n",
              "86                                      1.303092   \n",
              "88                                      1.831020   \n",
              "\n",
              "    T_z__query_similarity_count__query_None__threshold_0.0  \\\n",
              "1                                                 NaN        \n",
              "3                                                 NaN        \n",
              "4                                                 NaN        \n",
              "6                                                 NaN        \n",
              "8                                                 NaN        \n",
              "..                                                ...        \n",
              "83                                                NaN        \n",
              "84                                                NaN        \n",
              "85                                                NaN        \n",
              "86                                                NaN        \n",
              "88                                                NaN        \n",
              "\n",
              "    T_z__mean_n_absolute_max__number_of_maxima_7  \n",
              "1                                       0.000000  \n",
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              "..                                           ...  \n",
              "83                                     14.571429  \n",
              "84                                     24.285714  \n",
              "85                                      5.571429  \n",
              "86                                      9.285714  \n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-54044969-5827-40a6-9b43-b1b329e0fde5 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_efb00e22-ad34-4736-9782-c546a22ea924\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('extracted_features_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_efb00e22-ad34-4736-9782-c546a22ea924 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('extracted_features_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "extracted_features_train"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6種類の時系列データに対して、783個ずつの特徴量が計算された。<br>\n",
        "合計4698個（=6種類x783個）の特徴量"
      ],
      "metadata": {
        "id": "LqlgCrkvhNOs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## すべてが0であったり、値がなかったりする特徴量を削除"
      ],
      "metadata": {
        "id": "wqnzLuJohpm8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "extracted_features_train_impute = impute(extracted_features_train)\n",
        "extracted_features_test_impute = impute(extracted_features_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6KDEMa7Wwo7",
        "outputId": "e5d375b0-e0ad-4ab8-a73e-d44e0c8e71ee"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/tsfresh/utilities/dataframe_functions.py:198: RuntimeWarning: The columns ['F_x__partial_autocorrelation__lag_7'\n",
            " 'F_x__partial_autocorrelation__lag_8'\n",
            " 'F_x__partial_autocorrelation__lag_9' ...\n",
            " 'T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"mean\"'\n",
            " 'T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"var\"'\n",
            " 'T_z__query_similarity_count__query_None__threshold_0.0'] did not have any finite values. Filling with zeros.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.12/dist-packages/tsfresh/utilities/dataframe_functions.py:198: RuntimeWarning: The columns ['F_x__partial_autocorrelation__lag_7'\n",
            " 'F_x__partial_autocorrelation__lag_8'\n",
            " 'F_x__partial_autocorrelation__lag_9' ...\n",
            " 'T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"mean\"'\n",
            " 'T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"var\"'\n",
            " 'T_z__query_similarity_count__query_None__threshold_0.0'] did not have any finite values. Filling with zeros.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "extracted_features_train_impute"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "TzwDFOxsh1Ny",
        "outputId": "25f57f0b-1f35-4ced-fb3b-3ced3d46dc90"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    F_x__variance_larger_than_standard_deviation  F_x__has_duplicate_max  \\\n",
              "1                                            0.0                     0.0   \n",
              "3                                            0.0                     0.0   \n",
              "4                                            0.0                     1.0   \n",
              "6                                            1.0                     1.0   \n",
              "8                                            1.0                     0.0   \n",
              "..                                           ...                     ...   \n",
              "83                                           1.0                     1.0   \n",
              "84                                           1.0                     1.0   \n",
              "85                                           1.0                     0.0   \n",
              "86                                           1.0                     0.0   \n",
              "88                                           1.0                     0.0   \n",
              "\n",
              "    F_x__has_duplicate_min  F_x__has_duplicate  F_x__sum_values  \\\n",
              "1                      1.0                 1.0            -14.0   \n",
              "3                      1.0                 1.0            -10.0   \n",
              "4                      1.0                 1.0             -6.0   \n",
              "6                      1.0                 1.0            -15.0   \n",
              "8                      1.0                 1.0            -10.0   \n",
              "..                     ...                 ...              ...   \n",
              "83                     0.0                 1.0           -285.0   \n",
              "84                     0.0                 1.0          -1073.0   \n",
              "85                     1.0                 1.0            143.0   \n",
              "86                     0.0                 0.0            961.0   \n",
              "88                     1.0                 1.0           -143.0   \n",
              "\n",
              "    F_x__abs_energy  F_x__mean_abs_change  F_x__mean_change  \\\n",
              "1              14.0              0.142857          0.000000   \n",
              "3              12.0              0.714286          0.000000   \n",
              "4              16.0              1.214286         -0.071429   \n",
              "6              39.0              1.714286          0.142857   \n",
              "8              26.0              1.142857         -0.142857   \n",
              "..              ...                   ...               ...   \n",
              "83           5841.0              1.642857         -0.928571   \n",
              "84          96833.0              7.142857         -5.428571   \n",
              "85           1683.0              1.357143          1.071429   \n",
              "86          83497.0              9.071429          9.071429   \n",
              "88           1427.0              0.785714         -0.500000   \n",
              "\n",
              "    F_x__mean_second_derivative_central  F_x__median  ...  \\\n",
              "1                             -0.038462         -1.0  ...   \n",
              "3                             -0.038462         -1.0  ...   \n",
              "4                             -0.038462          0.0  ...   \n",
              "6                              0.153846         -1.0  ...   \n",
              "8                              0.192308         -1.0  ...   \n",
              "..                                  ...          ...  ...   \n",
              "83                            -0.076923        -16.0  ...   \n",
              "84                            -0.038462        -98.0  ...   \n",
              "85                             0.076923          8.0  ...   \n",
              "86                             0.807692         52.0  ...   \n",
              "88                             0.038462         -9.0  ...   \n",
              "\n",
              "    T_z__fourier_entropy__bins_5  T_z__fourier_entropy__bins_10  \\\n",
              "1                       1.007018                       1.320888   \n",
              "3                       1.386294                       1.732868   \n",
              "4                       1.073543                       1.494175   \n",
              "6                       0.974315                       1.213008   \n",
              "8                       1.494175                       1.732868   \n",
              "..                           ...                            ...   \n",
              "83                      0.735622                       0.735622   \n",
              "84                      0.735622                       0.735622   \n",
              "85                      0.735622                       0.735622   \n",
              "86                      0.735622                       1.073543   \n",
              "88                      1.255482                       1.494175   \n",
              "\n",
              "    T_z__fourier_entropy__bins_100  \\\n",
              "1                         1.906155   \n",
              "3                         2.079442   \n",
              "4                         2.079442   \n",
              "6                         1.732868   \n",
              "8                         2.079442   \n",
              "..                             ...   \n",
              "83                        1.213008   \n",
              "84                        1.386294   \n",
              "85                        1.667462   \n",
              "86                        1.732868   \n",
              "88                        2.079442   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_3__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       1.265857   \n",
              "4                                       1.156988   \n",
              "6                                       1.438883   \n",
              "8                                       1.412745   \n",
              "..                                           ...   \n",
              "83                                      1.778233   \n",
              "84                                      1.585771   \n",
              "85                                      1.332245   \n",
              "86                                      0.687092   \n",
              "88                                      0.830518   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_4__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       1.704551   \n",
              "4                                       1.907284   \n",
              "6                                       1.863680   \n",
              "8                                       2.022809   \n",
              "..                                           ...   \n",
              "83                                      2.022809   \n",
              "84                                      1.907284   \n",
              "85                                      1.308605   \n",
              "86                                      0.983088   \n",
              "88                                      1.242453   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_5__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       2.019815   \n",
              "4                                       2.397895   \n",
              "6                                       2.145842   \n",
              "8                                       2.397895   \n",
              "..                                           ...   \n",
              "83                                      2.098274   \n",
              "84                                      2.098274   \n",
              "85                                      1.893788   \n",
              "86                                      1.159589   \n",
              "88                                      1.414279   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_6__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       2.163956   \n",
              "4                                       2.302585   \n",
              "6                                       2.163956   \n",
              "8                                       2.302585   \n",
              "..                                           ...   \n",
              "83                                      2.302585   \n",
              "84                                      2.302585   \n",
              "85                                      2.163956   \n",
              "86                                      1.227529   \n",
              "88                                      1.609438   \n",
              "\n",
              "    T_z__permutation_entropy__dimension_7__tau_1  \\\n",
              "1                                      -0.000000   \n",
              "3                                       2.197225   \n",
              "4                                       2.197225   \n",
              "6                                       2.197225   \n",
              "8                                       2.197225   \n",
              "..                                           ...   \n",
              "83                                      2.197225   \n",
              "84                                      2.197225   \n",
              "85                                      2.197225   \n",
              "86                                      1.303092   \n",
              "88                                      1.831020   \n",
              "\n",
              "    T_z__query_similarity_count__query_None__threshold_0.0  \\\n",
              "1                                                 0.0        \n",
              "3                                                 0.0        \n",
              "4                                                 0.0        \n",
              "6                                                 0.0        \n",
              "8                                                 0.0        \n",
              "..                                                ...        \n",
              "83                                                0.0        \n",
              "84                                                0.0        \n",
              "85                                                0.0        \n",
              "86                                                0.0        \n",
              "88                                                0.0        \n",
              "\n",
              "    T_z__mean_n_absolute_max__number_of_maxima_7  \n",
              "1                                       0.000000  \n",
              "3                                       0.571429  \n",
              "4                                       1.000000  \n",
              "6                                       0.714286  \n",
              "8                                       1.285714  \n",
              "..                                           ...  \n",
              "83                                     14.571429  \n",
              "84                                     24.285714  \n",
              "85                                      5.571429  \n",
              "86                                      9.285714  \n",
              "88                                      5.428571  \n",
              "\n",
              "[66 rows x 4698 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6b335b7-bd8f-4208-9cdf-ba674f42d5c8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>F_x__variance_larger_than_standard_deviation</th>\n",
              "      <th>F_x__has_duplicate_max</th>\n",
              "      <th>F_x__has_duplicate_min</th>\n",
              "      <th>F_x__has_duplicate</th>\n",
              "      <th>F_x__sum_values</th>\n",
              "      <th>F_x__abs_energy</th>\n",
              "      <th>F_x__mean_abs_change</th>\n",
              "      <th>F_x__mean_change</th>\n",
              "      <th>F_x__mean_second_derivative_central</th>\n",
              "      <th>F_x__median</th>\n",
              "      <th>...</th>\n",
              "      <th>T_z__fourier_entropy__bins_5</th>\n",
              "      <th>T_z__fourier_entropy__bins_10</th>\n",
              "      <th>T_z__fourier_entropy__bins_100</th>\n",
              "      <th>T_z__permutation_entropy__dimension_3__tau_1</th>\n",
              "      <th>T_z__permutation_entropy__dimension_4__tau_1</th>\n",
              "      <th>T_z__permutation_entropy__dimension_5__tau_1</th>\n",
              "      <th>T_z__permutation_entropy__dimension_6__tau_1</th>\n",
              "      <th>T_z__permutation_entropy__dimension_7__tau_1</th>\n",
              "      <th>T_z__query_similarity_count__query_None__threshold_0.0</th>\n",
              "      <th>T_z__mean_n_absolute_max__number_of_maxima_7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.038462</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.007018</td>\n",
              "      <td>1.320888</td>\n",
              "      <td>1.906155</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.038462</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>1.732868</td>\n",
              "      <td>2.079442</td>\n",
              "      <td>1.265857</td>\n",
              "      <td>1.704551</td>\n",
              "      <td>2.019815</td>\n",
              "      <td>2.163956</td>\n",
              "      <td>2.197225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.214286</td>\n",
              "      <td>-0.071429</td>\n",
              "      <td>-0.038462</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.073543</td>\n",
              "      <td>1.494175</td>\n",
              "      <td>2.079442</td>\n",
              "      <td>1.156988</td>\n",
              "      <td>1.907284</td>\n",
              "      <td>2.397895</td>\n",
              "      <td>2.302585</td>\n",
              "      <td>2.197225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-15.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1.714286</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.974315</td>\n",
              "      <td>1.213008</td>\n",
              "      <td>1.732868</td>\n",
              "      <td>1.438883</td>\n",
              "      <td>1.863680</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.714286</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1.142857</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>0.192308</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.494175</td>\n",
              "      <td>1.732868</td>\n",
              "      <td>2.079442</td>\n",
              "      <td>1.412745</td>\n",
              "      <td>2.022809</td>\n",
              "      <td>2.397895</td>\n",
              "      <td>2.302585</td>\n",
              "      <td>2.197225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-285.0</td>\n",
              "      <td>5841.0</td>\n",
              "      <td>1.642857</td>\n",
              "      <td>-0.928571</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.735622</td>\n",
              "      <td>0.735622</td>\n",
              "      <td>1.213008</td>\n",
              "      <td>1.778233</td>\n",
              "      <td>2.022809</td>\n",
              "      <td>2.098274</td>\n",
              "      <td>2.302585</td>\n",
              "      <td>2.197225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1073.0</td>\n",
              "      <td>96833.0</td>\n",
              "      <td>7.142857</td>\n",
              "      <td>-5.428571</td>\n",
              "      <td>-0.038462</td>\n",
              "      <td>-98.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.735622</td>\n",
              "      <td>0.735622</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>1.585771</td>\n",
              "      <td>1.907284</td>\n",
              "      <td>2.098274</td>\n",
              "      <td>2.302585</td>\n",
              "      <td>2.197225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>1683.0</td>\n",
              "      <td>1.357143</td>\n",
              "      <td>1.071429</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>8.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.735622</td>\n",
              "      <td>0.735622</td>\n",
              "      <td>1.667462</td>\n",
              "      <td>1.332245</td>\n",
              "      <td>1.308605</td>\n",
              "      <td>1.893788</td>\n",
              "      <td>2.163956</td>\n",
              "      <td>2.197225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>961.0</td>\n",
              "      <td>83497.0</td>\n",
              "      <td>9.071429</td>\n",
              "      <td>9.071429</td>\n",
              "      <td>0.807692</td>\n",
              "      <td>52.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.735622</td>\n",
              "      <td>1.073543</td>\n",
              "      <td>1.732868</td>\n",
              "      <td>0.687092</td>\n",
              "      <td>0.983088</td>\n",
              "      <td>1.159589</td>\n",
              "      <td>1.227529</td>\n",
              "      <td>1.303092</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.285714</td>\n",
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              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-143.0</td>\n",
              "      <td>1427.0</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.255482</td>\n",
              "      <td>1.494175</td>\n",
              "      <td>2.079442</td>\n",
              "      <td>0.830518</td>\n",
              "      <td>1.242453</td>\n",
              "      <td>1.414279</td>\n",
              "      <td>1.609438</td>\n",
              "      <td>1.831020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.428571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66 rows × 4698 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "      <script>\n",
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              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
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              "            console.error('Error during call to suggestCharts:', error);\n",
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              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "  <div id=\"id_39ddc9e3-df2f-4d2f-a339-ed51ab4bb9b4\">\n",
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              "      .colab-df-generate {\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('extracted_features_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('extracted_features_train');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "extracted_features_train"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## すべての特徴量を使ってランダムフォレストにより分類テスト"
      ],
      "metadata": {
        "id": "RaTesuPZh8pW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "Ny5R7b-jlVXu"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestClassifier(random_state=0)\n",
        "clf.fit(extracted_features_train_impute, df_train_label)\n",
        "print(\"訓練データの正答率: {:.3f}\".format(clf.score(extracted_features_train_impute, df_train_label.to_numpy())))\n",
        "print(\"テストデータの正答率:{:.3f}\".format(clf.score(extracted_features_test_impute, df_test_label.to_numpy())))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wlgvn33mWq4r",
        "outputId": "c7f0a242-a0d2-4051-e4fe-f456795a0356"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "訓練データの正答率: 1.000\n",
            "テストデータの正答率:1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## すべての特徴量を使ってSVM（サポート・ベクタ・マシン）により分類テスト"
      ],
      "metadata": {
        "id": "r3NkTwjyJdvj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "clf = SVC(kernel='rbf', gamma='auto', random_state=0)\n",
        "\n",
        "clf.fit(extracted_features_train_impute, df_train_label)\n",
        "pred_train = clf.predict(extracted_features_train_impute)\n",
        "print(\"訓練スコア: {:.3f}\".format(clf.score(extracted_features_train, df_train_label.to_numpy())))\n",
        "pred_test = clf.predict(extracted_features_test_impute)\n",
        "print(\"テストスコア:{:.3f}\".format(clf.score(extracted_features_test, df_test_label.to_numpy())))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PjY98C_TJYIj",
        "outputId": "0d05909e-0272-406e-8b30-1cc318c49cb9"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "訓練スコア: 1.000\n",
            "テストスコア:0.727\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##【発展】特徴量を減らす\n",
        "\n",
        "得られた特徴量をもとにした場合に統計的な有意差がありそうな特徴量のみになるように、統計的仮説検定を用いて特徴量を選択する。\n",
        "\n",
        "この例では4698個の特徴量が671個の特徴量まで減る。\n"
      ],
      "metadata": {
        "id": "D951z6UgimBi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features_filtered_train = select_features(extracted_features_train_impute, df_train_label)"
      ],
      "metadata": {
        "id": "YW-0L5zmWq9x"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features_filtered_train"
      ],
      "metadata": {
        "id": "74H5BlqBWq_d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "1cc60e73-dbe2-4264-8670-3dbaa8915e80"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    F_x__value_count__value_-1  F_x__absolute_maximum  \\\n",
              "1                         14.0                    1.0   \n",
              "3                         11.0                    1.0   \n",
              "4                          5.0                    2.0   \n",
              "6                          6.0                    3.0   \n",
              "8                          8.0                    2.0   \n",
              "..                         ...                    ...   \n",
              "83                         0.0                   28.0   \n",
              "84                         0.0                  110.0   \n",
              "85                         0.0                   19.0   \n",
              "86                         0.0                  148.0   \n",
              "88                         0.0                   13.0   \n",
              "\n",
              "    F_x__mean_n_absolute_max__number_of_maxima_7  F_x__root_mean_square  \\\n",
              "1                                       1.000000               0.966092   \n",
              "3                                       1.000000               0.894427   \n",
              "4                                       1.285714               1.032796   \n",
              "6                                       2.000000               1.612452   \n",
              "8                                       1.571429               1.316561   \n",
              "..                                           ...                    ...   \n",
              "83                                     23.857143              19.733221   \n",
              "84                                    105.285714              80.346334   \n",
              "85                                     13.714286              10.592450   \n",
              "86                                     98.142857              74.608757   \n",
              "88                                     11.428571               9.753632   \n",
              "\n",
              "    F_x__abs_energy  F_x__standard_deviation  \\\n",
              "1              14.0                 0.249444   \n",
              "3              12.0                 0.596285   \n",
              "4              16.0                 0.952190   \n",
              "6              39.0                 1.264911   \n",
              "8              26.0                 1.135292   \n",
              "..              ...                      ...   \n",
              "83           5841.0                 5.329165   \n",
              "84          96833.0                36.585729   \n",
              "85           1683.0                 4.616877   \n",
              "86          83497.0                38.235179   \n",
              "88           1427.0                 2.061283   \n",
              "\n",
              "    F_x__fft_coefficient__attr_\"abs\"__coeff_1  F_x__variance  \\\n",
              "1                                    1.000000       0.062222   \n",
              "3                                    2.203858       0.355556   \n",
              "4                                    0.844394       0.906667   \n",
              "6                                    2.001820       1.600000   \n",
              "8                                    2.090520       1.288889   \n",
              "..                                        ...            ...   \n",
              "83                                  49.746706      28.400000   \n",
              "84                                 359.248162    1338.515556   \n",
              "85                                  36.770027      21.315556   \n",
              "86                                 312.044052    1461.928889   \n",
              "88                                  15.524355       4.248889   \n",
              "\n",
              "    T_y__absolute_maximum  F_y__abs_energy  ...  T_x__fourier_entropy__bins_2  \\\n",
              "1                     1.0             13.0  ...                      0.376770   \n",
              "3                     5.0             40.0  ...                      0.661563   \n",
              "4                     6.0             60.0  ...                      0.661563   \n",
              "6                     6.0             88.0  ...                      0.562335   \n",
              "8                     6.0             24.0  ...                      0.661563   \n",
              "..                    ...              ...  ...                           ...   \n",
              "83                   23.0           1801.0  ...                      0.376770   \n",
              "84                  167.0          42780.0  ...                      0.376770   \n",
              "85                   14.0           1523.0  ...                      0.376770   \n",
              "86                  191.0          21064.0  ...                      0.376770   \n",
              "88                   27.0            113.0  ...                      0.376770   \n",
              "\n",
              "    T_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4  \\\n",
              "1                                            0.000000                 \n",
              "3                                            0.000000                 \n",
              "4                                            0.000000                 \n",
              "6                                            0.000000                 \n",
              "8                                            0.000000                 \n",
              "..                                                ...                 \n",
              "83                                           0.500000                 \n",
              "84                                           5.000000                 \n",
              "85                                           0.333333                 \n",
              "86                                           0.000000                 \n",
              "88                                           0.000000                 \n",
              "\n",
              "    F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0  \\\n",
              "1                                            0.000000                \n",
              "3                                            1.061224                \n",
              "4                                            1.785124                \n",
              "6                                            1.816327                \n",
              "8                                            0.640000                \n",
              "..                                                ...                \n",
              "83                                           1.388889                \n",
              "84                                         106.244898                \n",
              "85                                           0.859375                \n",
              "86                                           2.750000                \n",
              "88                                           0.800000                \n",
              "\n",
              "    F_z__cwt_coefficients__coeff_11__w_2__widths_(2, 5, 10, 20)  \\\n",
              "1                                           20.334937             \n",
              "3                                           27.838034             \n",
              "4                                           25.781589             \n",
              "6                                           21.897796             \n",
              "8                                           23.823194             \n",
              "..                                                ...             \n",
              "83                                         -47.615765             \n",
              "84                                        -399.160911             \n",
              "85                                           1.798937             \n",
              "86                                         -98.145684             \n",
              "88                                          14.911760             \n",
              "\n",
              "    T_y__has_duplicate_min  \\\n",
              "1                      1.0   \n",
              "3                      0.0   \n",
              "4                      0.0   \n",
              "6                      1.0   \n",
              "8                      1.0   \n",
              "..                     ...   \n",
              "83                     0.0   \n",
              "84                     0.0   \n",
              "85                     0.0   \n",
              "86                     0.0   \n",
              "88                     1.0   \n",
              "\n",
              "    F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0  \\\n",
              "1                                                0.00                \n",
              "3                                                0.00                \n",
              "4                                                0.00                \n",
              "6                                                0.00                \n",
              "8                                                0.00                \n",
              "..                                                ...                \n",
              "83                                               1.00                \n",
              "84                                               6.25                \n",
              "85                                               0.00                \n",
              "86                                               0.25                \n",
              "88                                               0.80                \n",
              "\n",
              "    T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"mean\"  \\\n",
              "1                                            0.461880                 \n",
              "3                                            0.057735                 \n",
              "4                                            0.288675                 \n",
              "6                                            0.173205                 \n",
              "8                                            0.692820                 \n",
              "..                                                ...                 \n",
              "83                                           0.866025                 \n",
              "84                                           0.288675                 \n",
              "85                                           0.404145                 \n",
              "86                                           2.944486                 \n",
              "88                                           0.288675                 \n",
              "\n",
              "    F_z__time_reversal_asymmetry_statistic__lag_2  \\\n",
              "1                                    2.181909e+03   \n",
              "3                                    3.876455e+03   \n",
              "4                                    1.167173e+04   \n",
              "6                                   -6.112455e+03   \n",
              "8                                   -5.671545e+03   \n",
              "..                                            ...   \n",
              "83                                  -7.895450e+05   \n",
              "84                                  -1.825377e+08   \n",
              "85                                  -1.953582e+04   \n",
              "86                                  -8.295896e+06   \n",
              "88                                  -2.681191e+04   \n",
              "\n",
              "    F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6  \\\n",
              "1                                            0.000000                 \n",
              "3                                            2.000000                 \n",
              "4                                            3.666667                 \n",
              "6                                            2.666667                 \n",
              "8                                            0.800000                 \n",
              "..                                                ...                 \n",
              "83                                           3.800000                 \n",
              "84                                          87.200000                 \n",
              "85                                           2.750000                 \n",
              "86                                          11.200000                 \n",
              "88                                           3.250000                 \n",
              "\n",
              "    T_y__kurtosis  \n",
              "1       -1.615385  \n",
              "3       -0.781200  \n",
              "4       -0.416451  \n",
              "6       -1.161601  \n",
              "8       -0.734509  \n",
              "..            ...  \n",
              "83      -0.609039  \n",
              "84       0.853567  \n",
              "85       1.171031  \n",
              "86      -0.019458  \n",
              "88       0.060082  \n",
              "\n",
              "[66 rows x 537 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0a694ab-6dcf-4d63-89ce-5eaee688eb25\" 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>F_x__value_count__value_-1</th>\n",
              "      <th>F_x__absolute_maximum</th>\n",
              "      <th>F_x__mean_n_absolute_max__number_of_maxima_7</th>\n",
              "      <th>F_x__root_mean_square</th>\n",
              "      <th>F_x__abs_energy</th>\n",
              "      <th>F_x__standard_deviation</th>\n",
              "      <th>F_x__fft_coefficient__attr_\"abs\"__coeff_1</th>\n",
              "      <th>F_x__variance</th>\n",
              "      <th>T_y__absolute_maximum</th>\n",
              "      <th>F_y__abs_energy</th>\n",
              "      <th>...</th>\n",
              "      <th>T_x__fourier_entropy__bins_2</th>\n",
              "      <th>T_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4</th>\n",
              "      <th>F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0</th>\n",
              "      <th>F_z__cwt_coefficients__coeff_11__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>T_y__has_duplicate_min</th>\n",
              "      <th>F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0</th>\n",
              "      <th>T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"mean\"</th>\n",
              "      <th>F_z__time_reversal_asymmetry_statistic__lag_2</th>\n",
              "      <th>F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6</th>\n",
              "      <th>T_y__kurtosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.966092</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.249444</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.062222</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.334937</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.461880</td>\n",
              "      <td>2.181909e+03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.615385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.894427</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.596285</td>\n",
              "      <td>2.203858</td>\n",
              "      <td>0.355556</td>\n",
              "      <td>5.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.661563</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.061224</td>\n",
              "      <td>27.838034</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.057735</td>\n",
              "      <td>3.876455e+03</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-0.781200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.285714</td>\n",
              "      <td>1.032796</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.952190</td>\n",
              "      <td>0.844394</td>\n",
              "      <td>0.906667</td>\n",
              "      <td>6.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.661563</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.785124</td>\n",
              "      <td>25.781589</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.288675</td>\n",
              "      <td>1.167173e+04</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>-0.416451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.612452</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1.264911</td>\n",
              "      <td>2.001820</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.816327</td>\n",
              "      <td>21.897796</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.173205</td>\n",
              "      <td>-6.112455e+03</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>-1.161601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>1.316561</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1.135292</td>\n",
              "      <td>2.090520</td>\n",
              "      <td>1.288889</td>\n",
              "      <td>6.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.661563</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>23.823194</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.692820</td>\n",
              "      <td>-5.671545e+03</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>-0.734509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>0.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>23.857143</td>\n",
              "      <td>19.733221</td>\n",
              "      <td>5841.0</td>\n",
              "      <td>5.329165</td>\n",
              "      <td>49.746706</td>\n",
              "      <td>28.400000</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1801.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>-47.615765</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>3.800000</td>\n",
              "      <td>-0.609039</td>\n",
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              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>0.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>105.285714</td>\n",
              "      <td>80.346334</td>\n",
              "      <td>96833.0</td>\n",
              "      <td>36.585729</td>\n",
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              "      <td>1338.515556</td>\n",
              "      <td>167.0</td>\n",
              "      <td>42780.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>106.244898</td>\n",
              "      <td>-399.160911</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.25</td>\n",
              "      <td>0.288675</td>\n",
              "      <td>-1.825377e+08</td>\n",
              "      <td>87.200000</td>\n",
              "      <td>0.853567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>13.714286</td>\n",
              "      <td>10.592450</td>\n",
              "      <td>1683.0</td>\n",
              "      <td>4.616877</td>\n",
              "      <td>36.770027</td>\n",
              "      <td>21.315556</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1523.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.859375</td>\n",
              "      <td>1.798937</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.404145</td>\n",
              "      <td>-1.953582e+04</td>\n",
              "      <td>2.750000</td>\n",
              "      <td>1.171031</td>\n",
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              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>0.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>98.142857</td>\n",
              "      <td>74.608757</td>\n",
              "      <td>83497.0</td>\n",
              "      <td>38.235179</td>\n",
              "      <td>312.044052</td>\n",
              "      <td>1461.928889</td>\n",
              "      <td>191.0</td>\n",
              "      <td>21064.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.750000</td>\n",
              "      <td>-98.145684</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>2.944486</td>\n",
              "      <td>-8.295896e+06</td>\n",
              "      <td>11.200000</td>\n",
              "      <td>-0.019458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>11.428571</td>\n",
              "      <td>9.753632</td>\n",
              "      <td>1427.0</td>\n",
              "      <td>2.061283</td>\n",
              "      <td>15.524355</td>\n",
              "      <td>4.248889</td>\n",
              "      <td>27.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>14.911760</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.288675</td>\n",
              "      <td>-2.681191e+04</td>\n",
              "      <td>3.250000</td>\n",
              "      <td>0.060082</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66 rows × 537 columns</p>\n",
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              "    80% {\n",
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              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
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              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('features_filtered_train');\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features_filtered_train"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "学習データの特徴量とテストデータの特徴量が同じとは限らないので、抽出した特徴量と同じ特徴量だけ抜き出す。"
      ],
      "metadata": {
        "id": "KFmWPtCmjteG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features_filtered_test = pd.DataFrame()\n",
        "for fn in features_filtered_train.columns:\n",
        "  features_filtered_test[fn] = extracted_features_test[fn]\n",
        "features_filtered_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "DeZK5rZJkQAt",
        "outputId": "aaa27258-5ffe-4e93-8463-be3d7119f349"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n",
            "/tmp/ipython-input-4249292536.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  features_filtered_test[fn] = extracted_features_test[fn]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    F_x__value_count__value_-1  F_x__absolute_maximum  \\\n",
              "2                          7.0                    3.0   \n",
              "5                          9.0                    2.0   \n",
              "7                          8.0                    3.0   \n",
              "12                         5.0                    3.0   \n",
              "14                         5.0                    2.0   \n",
              "17                         6.0                    2.0   \n",
              "19                         0.0                   69.0   \n",
              "21                         2.0                  119.0   \n",
              "32                         3.0                   20.0   \n",
              "33                         5.0                   32.0   \n",
              "35                         1.0                   93.0   \n",
              "36                         0.0                   38.0   \n",
              "40                         2.0                   88.0   \n",
              "46                         0.0                  102.0   \n",
              "47                         0.0                   36.0   \n",
              "53                         0.0                   16.0   \n",
              "66                         4.0                   21.0   \n",
              "67                         1.0                    4.0   \n",
              "72                         1.0                    4.0   \n",
              "78                         3.0                   15.0   \n",
              "79                         0.0                    3.0   \n",
              "87                         0.0                  342.0   \n",
              "\n",
              "    F_x__mean_n_absolute_max__number_of_maxima_7  F_x__root_mean_square  \\\n",
              "2                                       1.571429               1.290994   \n",
              "5                                       1.285714               1.064581   \n",
              "7                                       1.428571               1.183216   \n",
              "12                                      1.714286               1.414214   \n",
              "14                                      1.285714               1.064581   \n",
              "17                                      1.571429               1.211060   \n",
              "19                                     21.428571              20.180849   \n",
              "21                                     30.714286              33.161222   \n",
              "32                                      5.142857               5.483308   \n",
              "33                                      7.285714               8.563488   \n",
              "35                                     22.285714              25.139610   \n",
              "36                                     20.285714              16.180235   \n",
              "40                                     33.285714              32.253165   \n",
              "46                                     38.428571              37.549967   \n",
              "47                                     21.857143              16.417470   \n",
              "53                                     15.000000              13.517889   \n",
              "66                                     10.571429               8.820431   \n",
              "67                                      3.428571               2.658320   \n",
              "72                                      3.428571               2.620433   \n",
              "78                                     12.285714               8.778762   \n",
              "79                                      2.428571               2.236068   \n",
              "87                                    340.000000             306.097697   \n",
              "\n",
              "    F_x__abs_energy  F_x__standard_deviation  \\\n",
              "2              25.0                 0.956847   \n",
              "5              17.0                 0.879394   \n",
              "7              21.0                 0.805536   \n",
              "12             30.0                 1.356466   \n",
              "14             17.0                 1.011050   \n",
              "17             22.0                 0.771722   \n",
              "19           6109.0                19.071095   \n",
              "21          16495.0                31.140113   \n",
              "32            451.0                 5.391351   \n",
              "33           1100.0                 8.436956   \n",
              "35           9480.0                24.538043   \n",
              "36           3927.0                11.482160   \n",
              "40          15604.0                29.718382   \n",
              "46          21150.0                37.149907   \n",
              "47           4043.0                15.408511   \n",
              "53           2741.0                 1.781385   \n",
              "66           1167.0                 8.634813   \n",
              "67            106.0                 1.751190   \n",
              "72            103.0                 2.293953   \n",
              "78           1156.0                 5.711587   \n",
              "79             75.0                 0.400000   \n",
              "87        1405437.0                57.753268   \n",
              "\n",
              "    F_x__fft_coefficient__attr_\"abs\"__coeff_1  F_x__variance  \\\n",
              "2                                    0.624118       0.915556   \n",
              "5                                    2.730599       0.773333   \n",
              "7                                    1.133819       0.648889   \n",
              "12                                   4.370513       1.840000   \n",
              "14                                   3.091721       1.022222   \n",
              "17                                   4.025810       0.595556   \n",
              "19                                 108.337689     363.706667   \n",
              "21                                 173.655184     969.706667   \n",
              "32                                  23.865258      29.066667   \n",
              "33                                  36.374106      71.182222   \n",
              "35                                 110.899588     602.115556   \n",
              "36                                  88.900064     131.840000   \n",
              "40                                 178.705792     883.182222   \n",
              "46                                 149.494632    1380.115556   \n",
              "47                                  98.974989     237.422222   \n",
              "53                                  12.912141       3.173333   \n",
              "66                                  22.409305      74.560000   \n",
              "67                                  11.057600       3.066667   \n",
              "72                                  22.159380       5.262222   \n",
              "78                                  58.390222      32.622222   \n",
              "79                                   2.827091       0.160000   \n",
              "87                                 481.046930    3335.440000   \n",
              "\n",
              "    T_y__absolute_maximum  F_y__abs_energy  ...  T_x__fourier_entropy__bins_2  \\\n",
              "2                     5.0             76.0  ...                      0.376770   \n",
              "5                     5.0             46.0  ...                      0.562335   \n",
              "7                     7.0             27.0  ...                      0.661563   \n",
              "12                    6.0             70.0  ...                      0.693147   \n",
              "14                    6.0             52.0  ...                      0.562335   \n",
              "17                    6.0             26.0  ...                      0.562335   \n",
              "19                  109.0           3766.0  ...                      0.376770   \n",
              "21                  266.0           2867.0  ...                      0.562335   \n",
              "32                   41.0            174.0  ...                      0.661563   \n",
              "33                   85.0            382.0  ...                      0.562335   \n",
              "35                  151.0          12028.0  ...                      0.562335   \n",
              "36                  601.0          39593.0  ...                      0.693147   \n",
              "40                  137.0          21086.0  ...                      0.562335   \n",
              "46                   91.0          55874.0  ...                      0.562335   \n",
              "47                  232.0         120878.0  ...                      0.562335   \n",
              "53                   79.0          52217.0  ...                      0.376770   \n",
              "66                  275.0           1783.0  ...                      0.562335   \n",
              "67                   80.0            370.0  ...                      0.376770   \n",
              "72                   26.0            166.0  ...                      0.376770   \n",
              "78                  245.0          60380.0  ...                      0.376770   \n",
              "79                  227.0          70132.0  ...                      0.376770   \n",
              "87                  471.0         308658.0  ...                      0.376770   \n",
              "\n",
              "    T_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4  \\\n",
              "2                                            0.000000                 \n",
              "5                                            0.000000                 \n",
              "7                                            0.000000                 \n",
              "12                                           0.000000                 \n",
              "14                                           0.428571                 \n",
              "17                                           0.727273                 \n",
              "19                                           0.000000                 \n",
              "21                                           0.000000                 \n",
              "32                                           0.666667                 \n",
              "33                                           0.000000                 \n",
              "35                                           0.416667                 \n",
              "36                                           1.000000                 \n",
              "40                                           0.000000                 \n",
              "46                                           0.500000                 \n",
              "47                                           0.500000                 \n",
              "53                                           0.666667                 \n",
              "66                                           0.000000                 \n",
              "67                                           0.166667                 \n",
              "72                                           0.000000                 \n",
              "78                                           0.000000                 \n",
              "79                                           0.250000                 \n",
              "87                                           2.500000                 \n",
              "\n",
              "    F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0  \\\n",
              "2                                            1.346939                \n",
              "5                                            0.666667                \n",
              "7                                            0.204082                \n",
              "12                                           1.500000                \n",
              "14                                           0.693878                \n",
              "17                                           0.750000                \n",
              "19                                          19.346939                \n",
              "21                                           0.138889                \n",
              "32                                           4.138889                \n",
              "33                                           0.472222                \n",
              "35                                           0.859375                \n",
              "36                                           3.714286                \n",
              "40                                         720.122449                \n",
              "46                                         486.285714                \n",
              "47                                        2014.560000                \n",
              "53                                           3.937500                \n",
              "66                                           0.640000                \n",
              "67                                           1.673469                \n",
              "72                                           1.583333                \n",
              "78                                           4.437500                \n",
              "79                                           0.250000                \n",
              "87                                          43.187500                \n",
              "\n",
              "    F_z__cwt_coefficients__coeff_11__w_2__widths_(2, 5, 10, 20)  \\\n",
              "2                                           23.167338             \n",
              "5                                           28.322770             \n",
              "7                                           27.058340             \n",
              "12                                          15.069831             \n",
              "14                                          13.362728             \n",
              "17                                          13.125617             \n",
              "19                                          24.279952             \n",
              "21                                          19.502536             \n",
              "32                                          23.482919             \n",
              "33                                          19.125002             \n",
              "35                                          24.670689             \n",
              "36                                         334.041870             \n",
              "40                                           7.281550             \n",
              "46                                         -24.488800             \n",
              "47                                        -172.893012             \n",
              "53                                        -121.494836             \n",
              "66                                          69.865607             \n",
              "67                                        -198.645327             \n",
              "72                                           5.909235             \n",
              "78                                        -261.869446             \n",
              "79                                        -136.068835             \n",
              "87                                        -315.854957             \n",
              "\n",
              "    T_y__has_duplicate_min  \\\n",
              "2                      0.0   \n",
              "5                      1.0   \n",
              "7                      0.0   \n",
              "12                     0.0   \n",
              "14                     1.0   \n",
              "17                     1.0   \n",
              "19                     0.0   \n",
              "21                     0.0   \n",
              "32                     0.0   \n",
              "33                     0.0   \n",
              "35                     0.0   \n",
              "36                     1.0   \n",
              "40                     0.0   \n",
              "46                     0.0   \n",
              "47                     0.0   \n",
              "53                     0.0   \n",
              "66                     0.0   \n",
              "67                     0.0   \n",
              "72                     1.0   \n",
              "78                     0.0   \n",
              "79                     0.0   \n",
              "87                     0.0   \n",
              "\n",
              "    F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0  \\\n",
              "2                                            0.000000                \n",
              "5                                            0.000000                \n",
              "7                                            0.000000                \n",
              "12                                           0.250000                \n",
              "14                                           0.000000                \n",
              "17                                           0.000000                \n",
              "19                                           9.000000                \n",
              "21                                           0.000000                \n",
              "32                                           0.750000                \n",
              "33                                           0.000000                \n",
              "35                                           0.250000                \n",
              "36                                           0.888889                \n",
              "40                                         841.000000                \n",
              "46                                           0.000000                \n",
              "47                                          36.000000                \n",
              "53                                           0.000000                \n",
              "66                                           0.000000                \n",
              "67                                           0.000000                \n",
              "72                                           0.000000                \n",
              "78                                           0.000000                \n",
              "79                                           0.000000                \n",
              "87                                           9.000000                \n",
              "\n",
              "    T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"mean\"  \\\n",
              "2                                        1.154701e-01                 \n",
              "5                                        4.618802e-01                 \n",
              "7                                        5.773503e-02                 \n",
              "12                                       2.309401e-01                 \n",
              "14                                       5.773503e-02                 \n",
              "17                                       1.154701e-01                 \n",
              "19                                       1.685874e-08                 \n",
              "21                                       4.445597e+00                 \n",
              "32                                       6.928203e-01                 \n",
              "33                                       1.096966e+00                 \n",
              "35                                       5.773503e-02                 \n",
              "36                                       9.641749e+00                 \n",
              "40                                       2.886751e+00                 \n",
              "46                                       3.002221e+00                 \n",
              "47                                       8.140639e+00                 \n",
              "53                                       1.154701e-01                 \n",
              "66                                       6.928203e-01                 \n",
              "67                                       4.099187e+00                 \n",
              "72                                       7.505553e-01                 \n",
              "78                                       4.618802e-01                 \n",
              "79                                       4.041452e-01                 \n",
              "87                                       2.367136e+00                 \n",
              "\n",
              "    F_z__time_reversal_asymmetry_statistic__lag_2  \\\n",
              "2                                    6.051364e+03   \n",
              "5                                    7.744000e+03   \n",
              "7                                    1.901545e+03   \n",
              "12                                  -4.814000e+03   \n",
              "14                                  -5.647091e+03   \n",
              "17                                  -1.054009e+04   \n",
              "19                                   6.569091e+02   \n",
              "21                                  -8.460645e+04   \n",
              "32                                  -1.315627e+04   \n",
              "33                                   4.313182e+03   \n",
              "35                                  -1.617464e+04   \n",
              "36                                   2.080792e+06   \n",
              "40                                   1.797666e+07   \n",
              "46                                   2.857063e+07   \n",
              "47                                   9.245148e+07   \n",
              "53                                  -9.386510e+06   \n",
              "66                                  -2.098632e+07   \n",
              "67                                  -3.637048e+08   \n",
              "72                                  -1.485000e+04   \n",
              "78                                  -3.301029e+07   \n",
              "79                                   6.741841e+06   \n",
              "87                                  -2.082176e+08   \n",
              "\n",
              "    F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6  \\\n",
              "2                                            0.666667                 \n",
              "5                                            6.750000                 \n",
              "7                                            3.000000                 \n",
              "12                                           2.500000                 \n",
              "14                                           2.000000                 \n",
              "17                                           3.000000                 \n",
              "19                                           4.500000                 \n",
              "21                                           0.000000                 \n",
              "32                                           0.666667                 \n",
              "33                                           0.500000                 \n",
              "35                                          18.000000                 \n",
              "36                                           1.800000                 \n",
              "40                                           3.250000                 \n",
              "46                                          12.600000                 \n",
              "47                                           2.333333                 \n",
              "53                                           9.800000                 \n",
              "66                                           2.000000                 \n",
              "67                                          10.400000                 \n",
              "72                                           4.250000                 \n",
              "78                                          28.200000                 \n",
              "79                                           2.200000                 \n",
              "87                                          74.000000                 \n",
              "\n",
              "    T_y__kurtosis  \n",
              "2        1.971575  \n",
              "5        3.048096  \n",
              "7       -0.532580  \n",
              "12      -1.257302  \n",
              "14      -0.622892  \n",
              "17      -0.717895  \n",
              "19       3.603345  \n",
              "21      10.153373  \n",
              "32       8.636339  \n",
              "33       6.894147  \n",
              "35      12.477101  \n",
              "36       7.949064  \n",
              "40       8.973204  \n",
              "46       2.610962  \n",
              "47       0.964900  \n",
              "53      -0.974683  \n",
              "66       5.601432  \n",
              "67      -0.895912  \n",
              "72      -1.272667  \n",
              "78      -0.963181  \n",
              "79      -0.054792  \n",
              "87      -0.315271  \n",
              "\n",
              "[22 rows x 537 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09de556e-30a9-4185-9ede-cdadf7dc2304\" 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>F_x__value_count__value_-1</th>\n",
              "      <th>F_x__absolute_maximum</th>\n",
              "      <th>F_x__mean_n_absolute_max__number_of_maxima_7</th>\n",
              "      <th>F_x__root_mean_square</th>\n",
              "      <th>F_x__abs_energy</th>\n",
              "      <th>F_x__standard_deviation</th>\n",
              "      <th>F_x__fft_coefficient__attr_\"abs\"__coeff_1</th>\n",
              "      <th>F_x__variance</th>\n",
              "      <th>T_y__absolute_maximum</th>\n",
              "      <th>F_y__abs_energy</th>\n",
              "      <th>...</th>\n",
              "      <th>T_x__fourier_entropy__bins_2</th>\n",
              "      <th>T_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4</th>\n",
              "      <th>F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0</th>\n",
              "      <th>F_z__cwt_coefficients__coeff_11__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>T_y__has_duplicate_min</th>\n",
              "      <th>F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0</th>\n",
              "      <th>T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"mean\"</th>\n",
              "      <th>F_z__time_reversal_asymmetry_statistic__lag_2</th>\n",
              "      <th>F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6</th>\n",
              "      <th>T_y__kurtosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>1.290994</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.956847</td>\n",
              "      <td>0.624118</td>\n",
              "      <td>0.915556</td>\n",
              "      <td>5.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.346939</td>\n",
              "      <td>23.167338</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.154701e-01</td>\n",
              "      <td>6.051364e+03</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.971575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.285714</td>\n",
              "      <td>1.064581</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.879394</td>\n",
              "      <td>2.730599</td>\n",
              "      <td>0.773333</td>\n",
              "      <td>5.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>28.322770</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.618802e-01</td>\n",
              "      <td>7.744000e+03</td>\n",
              "      <td>6.750000</td>\n",
              "      <td>3.048096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.428571</td>\n",
              "      <td>1.183216</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.805536</td>\n",
              "      <td>1.133819</td>\n",
              "      <td>0.648889</td>\n",
              "      <td>7.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.661563</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.204082</td>\n",
              "      <td>27.058340</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.773503e-02</td>\n",
              "      <td>1.901545e+03</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-0.532580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.714286</td>\n",
              "      <td>1.414214</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.356466</td>\n",
              "      <td>4.370513</td>\n",
              "      <td>1.840000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>15.069831</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>2.309401e-01</td>\n",
              "      <td>-4.814000e+03</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>-1.257302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.285714</td>\n",
              "      <td>1.064581</td>\n",
              "      <td>17.0</td>\n",
              "      <td>1.011050</td>\n",
              "      <td>3.091721</td>\n",
              "      <td>1.022222</td>\n",
              "      <td>6.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.693878</td>\n",
              "      <td>13.362728</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.773503e-02</td>\n",
              "      <td>-5.647091e+03</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-0.622892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>1.211060</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.771722</td>\n",
              "      <td>4.025810</td>\n",
              "      <td>0.595556</td>\n",
              "      <td>6.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>13.125617</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.154701e-01</td>\n",
              "      <td>-1.054009e+04</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-0.717895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>21.428571</td>\n",
              "      <td>20.180849</td>\n",
              "      <td>6109.0</td>\n",
              "      <td>19.071095</td>\n",
              "      <td>108.337689</td>\n",
              "      <td>363.706667</td>\n",
              "      <td>109.0</td>\n",
              "      <td>3766.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.346939</td>\n",
              "      <td>24.279952</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.685874e-08</td>\n",
              "      <td>6.569091e+02</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>3.603345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2.0</td>\n",
              "      <td>119.0</td>\n",
              "      <td>30.714286</td>\n",
              "      <td>33.161222</td>\n",
              "      <td>16495.0</td>\n",
              "      <td>31.140113</td>\n",
              "      <td>173.655184</td>\n",
              "      <td>969.706667</td>\n",
              "      <td>266.0</td>\n",
              "      <td>2867.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.138889</td>\n",
              "      <td>19.502536</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.445597e+00</td>\n",
              "      <td>-8.460645e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.153373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>3.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.142857</td>\n",
              "      <td>5.483308</td>\n",
              "      <td>451.0</td>\n",
              "      <td>5.391351</td>\n",
              "      <td>23.865258</td>\n",
              "      <td>29.066667</td>\n",
              "      <td>41.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.661563</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>4.138889</td>\n",
              "      <td>23.482919</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>6.928203e-01</td>\n",
              "      <td>-1.315627e+04</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>8.636339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>7.285714</td>\n",
              "      <td>8.563488</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>8.436956</td>\n",
              "      <td>36.374106</td>\n",
              "      <td>71.182222</td>\n",
              "      <td>85.0</td>\n",
              "      <td>382.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.472222</td>\n",
              "      <td>19.125002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.096966e+00</td>\n",
              "      <td>4.313182e+03</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>6.894147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>22.285714</td>\n",
              "      <td>25.139610</td>\n",
              "      <td>9480.0</td>\n",
              "      <td>24.538043</td>\n",
              "      <td>110.899588</td>\n",
              "      <td>602.115556</td>\n",
              "      <td>151.0</td>\n",
              "      <td>12028.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.859375</td>\n",
              "      <td>24.670689</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>5.773503e-02</td>\n",
              "      <td>-1.617464e+04</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>12.477101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>20.285714</td>\n",
              "      <td>16.180235</td>\n",
              "      <td>3927.0</td>\n",
              "      <td>11.482160</td>\n",
              "      <td>88.900064</td>\n",
              "      <td>131.840000</td>\n",
              "      <td>601.0</td>\n",
              "      <td>39593.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.714286</td>\n",
              "      <td>334.041870</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>9.641749e+00</td>\n",
              "      <td>2.080792e+06</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>7.949064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>33.285714</td>\n",
              "      <td>32.253165</td>\n",
              "      <td>15604.0</td>\n",
              "      <td>29.718382</td>\n",
              "      <td>178.705792</td>\n",
              "      <td>883.182222</td>\n",
              "      <td>137.0</td>\n",
              "      <td>21086.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>720.122449</td>\n",
              "      <td>7.281550</td>\n",
              "      <td>0.0</td>\n",
              "      <td>841.000000</td>\n",
              "      <td>2.886751e+00</td>\n",
              "      <td>1.797666e+07</td>\n",
              "      <td>3.250000</td>\n",
              "      <td>8.973204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>38.428571</td>\n",
              "      <td>37.549967</td>\n",
              "      <td>21150.0</td>\n",
              "      <td>37.149907</td>\n",
              "      <td>149.494632</td>\n",
              "      <td>1380.115556</td>\n",
              "      <td>91.0</td>\n",
              "      <td>55874.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>486.285714</td>\n",
              "      <td>-24.488800</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.002221e+00</td>\n",
              "      <td>2.857063e+07</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>2.610962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>21.857143</td>\n",
              "      <td>16.417470</td>\n",
              "      <td>4043.0</td>\n",
              "      <td>15.408511</td>\n",
              "      <td>98.974989</td>\n",
              "      <td>237.422222</td>\n",
              "      <td>232.0</td>\n",
              "      <td>120878.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>2014.560000</td>\n",
              "      <td>-172.893012</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>8.140639e+00</td>\n",
              "      <td>9.245148e+07</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>0.964900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>13.517889</td>\n",
              "      <td>2741.0</td>\n",
              "      <td>1.781385</td>\n",
              "      <td>12.912141</td>\n",
              "      <td>3.173333</td>\n",
              "      <td>79.0</td>\n",
              "      <td>52217.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>3.937500</td>\n",
              "      <td>-121.494836</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.154701e-01</td>\n",
              "      <td>-9.386510e+06</td>\n",
              "      <td>9.800000</td>\n",
              "      <td>-0.974683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>4.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>10.571429</td>\n",
              "      <td>8.820431</td>\n",
              "      <td>1167.0</td>\n",
              "      <td>8.634813</td>\n",
              "      <td>22.409305</td>\n",
              "      <td>74.560000</td>\n",
              "      <td>275.0</td>\n",
              "      <td>1783.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>69.865607</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.928203e-01</td>\n",
              "      <td>-2.098632e+07</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.601432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.428571</td>\n",
              "      <td>2.658320</td>\n",
              "      <td>106.0</td>\n",
              "      <td>1.751190</td>\n",
              "      <td>11.057600</td>\n",
              "      <td>3.066667</td>\n",
              "      <td>80.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1.673469</td>\n",
              "      <td>-198.645327</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.099187e+00</td>\n",
              "      <td>-3.637048e+08</td>\n",
              "      <td>10.400000</td>\n",
              "      <td>-0.895912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.428571</td>\n",
              "      <td>2.620433</td>\n",
              "      <td>103.0</td>\n",
              "      <td>2.293953</td>\n",
              "      <td>22.159380</td>\n",
              "      <td>5.262222</td>\n",
              "      <td>26.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.583333</td>\n",
              "      <td>5.909235</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.505553e-01</td>\n",
              "      <td>-1.485000e+04</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>-1.272667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>3.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>12.285714</td>\n",
              "      <td>8.778762</td>\n",
              "      <td>1156.0</td>\n",
              "      <td>5.711587</td>\n",
              "      <td>58.390222</td>\n",
              "      <td>32.622222</td>\n",
              "      <td>245.0</td>\n",
              "      <td>60380.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.437500</td>\n",
              "      <td>-261.869446</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.618802e-01</td>\n",
              "      <td>-3.301029e+07</td>\n",
              "      <td>28.200000</td>\n",
              "      <td>-0.963181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.428571</td>\n",
              "      <td>2.236068</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>2.827091</td>\n",
              "      <td>0.160000</td>\n",
              "      <td>227.0</td>\n",
              "      <td>70132.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-136.068835</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.041452e-01</td>\n",
              "      <td>6.741841e+06</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>-0.054792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>0.0</td>\n",
              "      <td>342.0</td>\n",
              "      <td>340.000000</td>\n",
              "      <td>306.097697</td>\n",
              "      <td>1405437.0</td>\n",
              "      <td>57.753268</td>\n",
              "      <td>481.046930</td>\n",
              "      <td>3335.440000</td>\n",
              "      <td>471.0</td>\n",
              "      <td>308658.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>43.187500</td>\n",
              "      <td>-315.854957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.367136e+00</td>\n",
              "      <td>-2.082176e+08</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>-0.315271</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22 rows × 537 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09de556e-30a9-4185-9ede-cdadf7dc2304')\"\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-09de556e-30a9-4185-9ede-cdadf7dc2304 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-09de556e-30a9-4185-9ede-cdadf7dc2304');\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",
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              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-bd344c81-6299-4387-af25-5a3395994b19 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_a7bd6b45-5246-4932-9e6a-d2dafe9a3fab\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('features_filtered_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a7bd6b45-5246-4932-9e6a-d2dafe9a3fab button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('features_filtered_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features_filtered_test"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_filtered_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "id": "9NxFfON7l2aO",
        "outputId": "29cdfe23-d67d-4dcd-ef02-69170a029de3"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    F_x__value_count__value_-1  F_x__absolute_maximum  \\\n",
              "2                          7.0                    3.0   \n",
              "5                          9.0                    2.0   \n",
              "7                          8.0                    3.0   \n",
              "12                         5.0                    3.0   \n",
              "14                         5.0                    2.0   \n",
              "17                         6.0                    2.0   \n",
              "19                         0.0                   69.0   \n",
              "21                         2.0                  119.0   \n",
              "32                         3.0                   20.0   \n",
              "33                         5.0                   32.0   \n",
              "35                         1.0                   93.0   \n",
              "36                         0.0                   38.0   \n",
              "40                         2.0                   88.0   \n",
              "46                         0.0                  102.0   \n",
              "47                         0.0                   36.0   \n",
              "53                         0.0                   16.0   \n",
              "66                         4.0                   21.0   \n",
              "67                         1.0                    4.0   \n",
              "72                         1.0                    4.0   \n",
              "78                         3.0                   15.0   \n",
              "79                         0.0                    3.0   \n",
              "87                         0.0                  342.0   \n",
              "\n",
              "    F_x__mean_n_absolute_max__number_of_maxima_7  F_x__root_mean_square  \\\n",
              "2                                       1.571429               1.290994   \n",
              "5                                       1.285714               1.064581   \n",
              "7                                       1.428571               1.183216   \n",
              "12                                      1.714286               1.414214   \n",
              "14                                      1.285714               1.064581   \n",
              "17                                      1.571429               1.211060   \n",
              "19                                     21.428571              20.180849   \n",
              "21                                     30.714286              33.161222   \n",
              "32                                      5.142857               5.483308   \n",
              "33                                      7.285714               8.563488   \n",
              "35                                     22.285714              25.139610   \n",
              "36                                     20.285714              16.180235   \n",
              "40                                     33.285714              32.253165   \n",
              "46                                     38.428571              37.549967   \n",
              "47                                     21.857143              16.417470   \n",
              "53                                     15.000000              13.517889   \n",
              "66                                     10.571429               8.820431   \n",
              "67                                      3.428571               2.658320   \n",
              "72                                      3.428571               2.620433   \n",
              "78                                     12.285714               8.778762   \n",
              "79                                      2.428571               2.236068   \n",
              "87                                    340.000000             306.097697   \n",
              "\n",
              "    F_x__abs_energy  F_x__standard_deviation  \\\n",
              "2              25.0                 0.956847   \n",
              "5              17.0                 0.879394   \n",
              "7              21.0                 0.805536   \n",
              "12             30.0                 1.356466   \n",
              "14             17.0                 1.011050   \n",
              "17             22.0                 0.771722   \n",
              "19           6109.0                19.071095   \n",
              "21          16495.0                31.140113   \n",
              "32            451.0                 5.391351   \n",
              "33           1100.0                 8.436956   \n",
              "35           9480.0                24.538043   \n",
              "36           3927.0                11.482160   \n",
              "40          15604.0                29.718382   \n",
              "46          21150.0                37.149907   \n",
              "47           4043.0                15.408511   \n",
              "53           2741.0                 1.781385   \n",
              "66           1167.0                 8.634813   \n",
              "67            106.0                 1.751190   \n",
              "72            103.0                 2.293953   \n",
              "78           1156.0                 5.711587   \n",
              "79             75.0                 0.400000   \n",
              "87        1405437.0                57.753268   \n",
              "\n",
              "    F_x__fft_coefficient__attr_\"abs\"__coeff_1  F_x__variance  \\\n",
              "2                                    0.624118       0.915556   \n",
              "5                                    2.730599       0.773333   \n",
              "7                                    1.133819       0.648889   \n",
              "12                                   4.370513       1.840000   \n",
              "14                                   3.091721       1.022222   \n",
              "17                                   4.025810       0.595556   \n",
              "19                                 108.337689     363.706667   \n",
              "21                                 173.655184     969.706667   \n",
              "32                                  23.865258      29.066667   \n",
              "33                                  36.374106      71.182222   \n",
              "35                                 110.899588     602.115556   \n",
              "36                                  88.900064     131.840000   \n",
              "40                                 178.705792     883.182222   \n",
              "46                                 149.494632    1380.115556   \n",
              "47                                  98.974989     237.422222   \n",
              "53                                  12.912141       3.173333   \n",
              "66                                  22.409305      74.560000   \n",
              "67                                  11.057600       3.066667   \n",
              "72                                  22.159380       5.262222   \n",
              "78                                  58.390222      32.622222   \n",
              "79                                   2.827091       0.160000   \n",
              "87                                 481.046930    3335.440000   \n",
              "\n",
              "    T_y__absolute_maximum  F_y__abs_energy  ...  T_x__fourier_entropy__bins_2  \\\n",
              "2                     5.0             76.0  ...                      0.376770   \n",
              "5                     5.0             46.0  ...                      0.562335   \n",
              "7                     7.0             27.0  ...                      0.661563   \n",
              "12                    6.0             70.0  ...                      0.693147   \n",
              "14                    6.0             52.0  ...                      0.562335   \n",
              "17                    6.0             26.0  ...                      0.562335   \n",
              "19                  109.0           3766.0  ...                      0.376770   \n",
              "21                  266.0           2867.0  ...                      0.562335   \n",
              "32                   41.0            174.0  ...                      0.661563   \n",
              "33                   85.0            382.0  ...                      0.562335   \n",
              "35                  151.0          12028.0  ...                      0.562335   \n",
              "36                  601.0          39593.0  ...                      0.693147   \n",
              "40                  137.0          21086.0  ...                      0.562335   \n",
              "46                   91.0          55874.0  ...                      0.562335   \n",
              "47                  232.0         120878.0  ...                      0.562335   \n",
              "53                   79.0          52217.0  ...                      0.376770   \n",
              "66                  275.0           1783.0  ...                      0.562335   \n",
              "67                   80.0            370.0  ...                      0.376770   \n",
              "72                   26.0            166.0  ...                      0.376770   \n",
              "78                  245.0          60380.0  ...                      0.376770   \n",
              "79                  227.0          70132.0  ...                      0.376770   \n",
              "87                  471.0         308658.0  ...                      0.376770   \n",
              "\n",
              "    T_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4  \\\n",
              "2                                            0.000000                 \n",
              "5                                            0.000000                 \n",
              "7                                            0.000000                 \n",
              "12                                           0.000000                 \n",
              "14                                           0.428571                 \n",
              "17                                           0.727273                 \n",
              "19                                           0.000000                 \n",
              "21                                           0.000000                 \n",
              "32                                           0.666667                 \n",
              "33                                           0.000000                 \n",
              "35                                           0.416667                 \n",
              "36                                           1.000000                 \n",
              "40                                           0.000000                 \n",
              "46                                           0.500000                 \n",
              "47                                           0.500000                 \n",
              "53                                           0.666667                 \n",
              "66                                           0.000000                 \n",
              "67                                           0.166667                 \n",
              "72                                           0.000000                 \n",
              "78                                           0.000000                 \n",
              "79                                           0.250000                 \n",
              "87                                           2.500000                 \n",
              "\n",
              "    F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0  \\\n",
              "2                                            1.346939                \n",
              "5                                            0.666667                \n",
              "7                                            0.204082                \n",
              "12                                           1.500000                \n",
              "14                                           0.693878                \n",
              "17                                           0.750000                \n",
              "19                                          19.346939                \n",
              "21                                           0.138889                \n",
              "32                                           4.138889                \n",
              "33                                           0.472222                \n",
              "35                                           0.859375                \n",
              "36                                           3.714286                \n",
              "40                                         720.122449                \n",
              "46                                         486.285714                \n",
              "47                                        2014.560000                \n",
              "53                                           3.937500                \n",
              "66                                           0.640000                \n",
              "67                                           1.673469                \n",
              "72                                           1.583333                \n",
              "78                                           4.437500                \n",
              "79                                           0.250000                \n",
              "87                                          43.187500                \n",
              "\n",
              "    F_z__cwt_coefficients__coeff_11__w_2__widths_(2, 5, 10, 20)  \\\n",
              "2                                           23.167338             \n",
              "5                                           28.322770             \n",
              "7                                           27.058340             \n",
              "12                                          15.069831             \n",
              "14                                          13.362728             \n",
              "17                                          13.125617             \n",
              "19                                          24.279952             \n",
              "21                                          19.502536             \n",
              "32                                          23.482919             \n",
              "33                                          19.125002             \n",
              "35                                          24.670689             \n",
              "36                                         334.041870             \n",
              "40                                           7.281550             \n",
              "46                                         -24.488800             \n",
              "47                                        -172.893012             \n",
              "53                                        -121.494836             \n",
              "66                                          69.865607             \n",
              "67                                        -198.645327             \n",
              "72                                           5.909235             \n",
              "78                                        -261.869446             \n",
              "79                                        -136.068835             \n",
              "87                                        -315.854957             \n",
              "\n",
              "    T_y__has_duplicate_min  \\\n",
              "2                      0.0   \n",
              "5                      1.0   \n",
              "7                      0.0   \n",
              "12                     0.0   \n",
              "14                     1.0   \n",
              "17                     1.0   \n",
              "19                     0.0   \n",
              "21                     0.0   \n",
              "32                     0.0   \n",
              "33                     0.0   \n",
              "35                     0.0   \n",
              "36                     1.0   \n",
              "40                     0.0   \n",
              "46                     0.0   \n",
              "47                     0.0   \n",
              "53                     0.0   \n",
              "66                     0.0   \n",
              "67                     0.0   \n",
              "72                     1.0   \n",
              "78                     0.0   \n",
              "79                     0.0   \n",
              "87                     0.0   \n",
              "\n",
              "    F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0  \\\n",
              "2                                            0.000000                \n",
              "5                                            0.000000                \n",
              "7                                            0.000000                \n",
              "12                                           0.250000                \n",
              "14                                           0.000000                \n",
              "17                                           0.000000                \n",
              "19                                           9.000000                \n",
              "21                                           0.000000                \n",
              "32                                           0.750000                \n",
              "33                                           0.000000                \n",
              "35                                           0.250000                \n",
              "36                                           0.888889                \n",
              "40                                         841.000000                \n",
              "46                                           0.000000                \n",
              "47                                          36.000000                \n",
              "53                                           0.000000                \n",
              "66                                           0.000000                \n",
              "67                                           0.000000                \n",
              "72                                           0.000000                \n",
              "78                                           0.000000                \n",
              "79                                           0.000000                \n",
              "87                                           9.000000                \n",
              "\n",
              "    T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"mean\"  \\\n",
              "2                                        1.154701e-01                 \n",
              "5                                        4.618802e-01                 \n",
              "7                                        5.773503e-02                 \n",
              "12                                       2.309401e-01                 \n",
              "14                                       5.773503e-02                 \n",
              "17                                       1.154701e-01                 \n",
              "19                                       1.685874e-08                 \n",
              "21                                       4.445597e+00                 \n",
              "32                                       6.928203e-01                 \n",
              "33                                       1.096966e+00                 \n",
              "35                                       5.773503e-02                 \n",
              "36                                       9.641749e+00                 \n",
              "40                                       2.886751e+00                 \n",
              "46                                       3.002221e+00                 \n",
              "47                                       8.140639e+00                 \n",
              "53                                       1.154701e-01                 \n",
              "66                                       6.928203e-01                 \n",
              "67                                       4.099187e+00                 \n",
              "72                                       7.505553e-01                 \n",
              "78                                       4.618802e-01                 \n",
              "79                                       4.041452e-01                 \n",
              "87                                       2.367136e+00                 \n",
              "\n",
              "    F_z__time_reversal_asymmetry_statistic__lag_2  \\\n",
              "2                                    6.051364e+03   \n",
              "5                                    7.744000e+03   \n",
              "7                                    1.901545e+03   \n",
              "12                                  -4.814000e+03   \n",
              "14                                  -5.647091e+03   \n",
              "17                                  -1.054009e+04   \n",
              "19                                   6.569091e+02   \n",
              "21                                  -8.460645e+04   \n",
              "32                                  -1.315627e+04   \n",
              "33                                   4.313182e+03   \n",
              "35                                  -1.617464e+04   \n",
              "36                                   2.080792e+06   \n",
              "40                                   1.797666e+07   \n",
              "46                                   2.857063e+07   \n",
              "47                                   9.245148e+07   \n",
              "53                                  -9.386510e+06   \n",
              "66                                  -2.098632e+07   \n",
              "67                                  -3.637048e+08   \n",
              "72                                  -1.485000e+04   \n",
              "78                                  -3.301029e+07   \n",
              "79                                   6.741841e+06   \n",
              "87                                  -2.082176e+08   \n",
              "\n",
              "    F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6  \\\n",
              "2                                            0.666667                 \n",
              "5                                            6.750000                 \n",
              "7                                            3.000000                 \n",
              "12                                           2.500000                 \n",
              "14                                           2.000000                 \n",
              "17                                           3.000000                 \n",
              "19                                           4.500000                 \n",
              "21                                           0.000000                 \n",
              "32                                           0.666667                 \n",
              "33                                           0.500000                 \n",
              "35                                          18.000000                 \n",
              "36                                           1.800000                 \n",
              "40                                           3.250000                 \n",
              "46                                          12.600000                 \n",
              "47                                           2.333333                 \n",
              "53                                           9.800000                 \n",
              "66                                           2.000000                 \n",
              "67                                          10.400000                 \n",
              "72                                           4.250000                 \n",
              "78                                          28.200000                 \n",
              "79                                           2.200000                 \n",
              "87                                          74.000000                 \n",
              "\n",
              "    T_y__kurtosis  \n",
              "2        1.971575  \n",
              "5        3.048096  \n",
              "7       -0.532580  \n",
              "12      -1.257302  \n",
              "14      -0.622892  \n",
              "17      -0.717895  \n",
              "19       3.603345  \n",
              "21      10.153373  \n",
              "32       8.636339  \n",
              "33       6.894147  \n",
              "35      12.477101  \n",
              "36       7.949064  \n",
              "40       8.973204  \n",
              "46       2.610962  \n",
              "47       0.964900  \n",
              "53      -0.974683  \n",
              "66       5.601432  \n",
              "67      -0.895912  \n",
              "72      -1.272667  \n",
              "78      -0.963181  \n",
              "79      -0.054792  \n",
              "87      -0.315271  \n",
              "\n",
              "[22 rows x 537 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-159a9efa-a036-485f-9cde-f5876e818b8f\" 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>F_x__value_count__value_-1</th>\n",
              "      <th>F_x__absolute_maximum</th>\n",
              "      <th>F_x__mean_n_absolute_max__number_of_maxima_7</th>\n",
              "      <th>F_x__root_mean_square</th>\n",
              "      <th>F_x__abs_energy</th>\n",
              "      <th>F_x__standard_deviation</th>\n",
              "      <th>F_x__fft_coefficient__attr_\"abs\"__coeff_1</th>\n",
              "      <th>F_x__variance</th>\n",
              "      <th>T_y__absolute_maximum</th>\n",
              "      <th>F_y__abs_energy</th>\n",
              "      <th>...</th>\n",
              "      <th>T_x__fourier_entropy__bins_2</th>\n",
              "      <th>T_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4</th>\n",
              "      <th>F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0</th>\n",
              "      <th>F_z__cwt_coefficients__coeff_11__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>T_y__has_duplicate_min</th>\n",
              "      <th>F_y__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0</th>\n",
              "      <th>T_z__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"mean\"</th>\n",
              "      <th>F_z__time_reversal_asymmetry_statistic__lag_2</th>\n",
              "      <th>F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6</th>\n",
              "      <th>T_y__kurtosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>1.290994</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.956847</td>\n",
              "      <td>0.624118</td>\n",
              "      <td>0.915556</td>\n",
              "      <td>5.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.346939</td>\n",
              "      <td>23.167338</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.154701e-01</td>\n",
              "      <td>6.051364e+03</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.971575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.285714</td>\n",
              "      <td>1.064581</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.879394</td>\n",
              "      <td>2.730599</td>\n",
              "      <td>0.773333</td>\n",
              "      <td>5.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>28.322770</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.618802e-01</td>\n",
              "      <td>7.744000e+03</td>\n",
              "      <td>6.750000</td>\n",
              "      <td>3.048096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.428571</td>\n",
              "      <td>1.183216</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.805536</td>\n",
              "      <td>1.133819</td>\n",
              "      <td>0.648889</td>\n",
              "      <td>7.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.661563</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.204082</td>\n",
              "      <td>27.058340</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.773503e-02</td>\n",
              "      <td>1.901545e+03</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-0.532580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.714286</td>\n",
              "      <td>1.414214</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.356466</td>\n",
              "      <td>4.370513</td>\n",
              "      <td>1.840000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>15.069831</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>2.309401e-01</td>\n",
              "      <td>-4.814000e+03</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>-1.257302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.285714</td>\n",
              "      <td>1.064581</td>\n",
              "      <td>17.0</td>\n",
              "      <td>1.011050</td>\n",
              "      <td>3.091721</td>\n",
              "      <td>1.022222</td>\n",
              "      <td>6.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.693878</td>\n",
              "      <td>13.362728</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.773503e-02</td>\n",
              "      <td>-5.647091e+03</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-0.622892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>1.211060</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.771722</td>\n",
              "      <td>4.025810</td>\n",
              "      <td>0.595556</td>\n",
              "      <td>6.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>13.125617</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.154701e-01</td>\n",
              "      <td>-1.054009e+04</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-0.717895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>21.428571</td>\n",
              "      <td>20.180849</td>\n",
              "      <td>6109.0</td>\n",
              "      <td>19.071095</td>\n",
              "      <td>108.337689</td>\n",
              "      <td>363.706667</td>\n",
              "      <td>109.0</td>\n",
              "      <td>3766.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.346939</td>\n",
              "      <td>24.279952</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.685874e-08</td>\n",
              "      <td>6.569091e+02</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>3.603345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2.0</td>\n",
              "      <td>119.0</td>\n",
              "      <td>30.714286</td>\n",
              "      <td>33.161222</td>\n",
              "      <td>16495.0</td>\n",
              "      <td>31.140113</td>\n",
              "      <td>173.655184</td>\n",
              "      <td>969.706667</td>\n",
              "      <td>266.0</td>\n",
              "      <td>2867.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.138889</td>\n",
              "      <td>19.502536</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.445597e+00</td>\n",
              "      <td>-8.460645e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.153373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>3.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.142857</td>\n",
              "      <td>5.483308</td>\n",
              "      <td>451.0</td>\n",
              "      <td>5.391351</td>\n",
              "      <td>23.865258</td>\n",
              "      <td>29.066667</td>\n",
              "      <td>41.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.661563</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>4.138889</td>\n",
              "      <td>23.482919</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>6.928203e-01</td>\n",
              "      <td>-1.315627e+04</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>8.636339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>7.285714</td>\n",
              "      <td>8.563488</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>8.436956</td>\n",
              "      <td>36.374106</td>\n",
              "      <td>71.182222</td>\n",
              "      <td>85.0</td>\n",
              "      <td>382.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.472222</td>\n",
              "      <td>19.125002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.096966e+00</td>\n",
              "      <td>4.313182e+03</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>6.894147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>22.285714</td>\n",
              "      <td>25.139610</td>\n",
              "      <td>9480.0</td>\n",
              "      <td>24.538043</td>\n",
              "      <td>110.899588</td>\n",
              "      <td>602.115556</td>\n",
              "      <td>151.0</td>\n",
              "      <td>12028.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.859375</td>\n",
              "      <td>24.670689</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>5.773503e-02</td>\n",
              "      <td>-1.617464e+04</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>12.477101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>20.285714</td>\n",
              "      <td>16.180235</td>\n",
              "      <td>3927.0</td>\n",
              "      <td>11.482160</td>\n",
              "      <td>88.900064</td>\n",
              "      <td>131.840000</td>\n",
              "      <td>601.0</td>\n",
              "      <td>39593.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>334.041870</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>9.641749e+00</td>\n",
              "      <td>2.080792e+06</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>7.949064</td>\n",
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              "    <tr>\n",
              "      <th>40</th>\n",
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              "      <td>8.973204</td>\n",
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              "      <th>46</th>\n",
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              "      <td>3.002221e+00</td>\n",
              "      <td>2.857063e+07</td>\n",
              "      <td>12.600000</td>\n",
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              "    <tr>\n",
              "      <th>47</th>\n",
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              "      <td>15.408511</td>\n",
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              "      <td>232.0</td>\n",
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              "      <td>...</td>\n",
              "      <td>0.562335</td>\n",
              "      <td>0.500000</td>\n",
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              "      <th>66</th>\n",
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              "      <td>275.0</td>\n",
              "      <td>1783.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>69.865607</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>5.601432</td>\n",
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              "    <tr>\n",
              "      <th>67</th>\n",
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              "      <td>2.658320</td>\n",
              "      <td>106.0</td>\n",
              "      <td>1.751190</td>\n",
              "      <td>11.057600</td>\n",
              "      <td>3.066667</td>\n",
              "      <td>80.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1.673469</td>\n",
              "      <td>-198.645327</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>4.099187e+00</td>\n",
              "      <td>-3.637048e+08</td>\n",
              "      <td>10.400000</td>\n",
              "      <td>-0.895912</td>\n",
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              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.428571</td>\n",
              "      <td>2.620433</td>\n",
              "      <td>103.0</td>\n",
              "      <td>2.293953</td>\n",
              "      <td>22.159380</td>\n",
              "      <td>5.262222</td>\n",
              "      <td>26.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.583333</td>\n",
              "      <td>5.909235</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.505553e-01</td>\n",
              "      <td>-1.485000e+04</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>-1.272667</td>\n",
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              "    <tr>\n",
              "      <th>78</th>\n",
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              "      <td>15.0</td>\n",
              "      <td>12.285714</td>\n",
              "      <td>8.778762</td>\n",
              "      <td>1156.0</td>\n",
              "      <td>5.711587</td>\n",
              "      <td>58.390222</td>\n",
              "      <td>32.622222</td>\n",
              "      <td>245.0</td>\n",
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              "      <td>0.376770</td>\n",
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              "      <th>79</th>\n",
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              "      <td>481.046930</td>\n",
              "      <td>3335.440000</td>\n",
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              "      <td>308658.0</td>\n",
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              "      <td>-315.854957</td>\n",
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              "      <td>-0.315271</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22 rows × 537 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_44fcfa99-20dc-4de3-858c-b00995dc3861 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('features_filtered_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features_filtered_test"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestClassifier(random_state=0)\n",
        "clf.fit(extracted_features_train_impute, df_train_label)\n",
        "print(\"訓練データの正答率: {:.3f}\".format(clf.score(extracted_features_train_impute, df_train_label.to_numpy())))\n",
        "print(\"テストデータの正答率:{:.3f}\".format(clf.score(extracted_features_test_impute, df_test_label.to_numpy())))"
      ],
      "metadata": {
        "id": "P4tV-SZ0kQLp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b750c231-f39d-4457-b253-22bc61d00515"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "訓練データの正答率: 1.000\n",
            "テストデータの正答率:1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = SVC(kernel='rbf', gamma='auto', random_state=0)\n",
        "\n",
        "clf.fit(extracted_features_train_impute, df_train_label)\n",
        "pred_train = clf.predict(extracted_features_train_impute)\n",
        "print(\"訓練スコア: {:.3f}\".format(clf.score(extracted_features_train, df_train_label.to_numpy())))\n",
        "pred_test = clf.predict(extracted_features_test_impute)\n",
        "print(\"テストスコア:{:.3f}\".format(clf.score(extracted_features_test, df_test_label.to_numpy())))"
      ],
      "metadata": {
        "id": "-iB0s_I2Qh4Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cd3e2ed-c1ff-488a-eb55-3a562d0029a3"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "訓練スコア: 1.000\n",
            "テストスコア:0.727\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JkpU9dy7KV5T"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}