Drive_PCA.ipynb

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  "metadata": {
    "colab": {
      "name": "Drive_PCA.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Irk2F014xwS-M0P6fzogZRWtRyAeLH1j",
      "authorship_tag": "ABX9TyPqJ2SBdP4rlz4Q/GSddMxb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CarveTheFuture/Hard_Drive_Reliability/blob/main/Drive_PCA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvQiWkE0Cd7l",
        "outputId": "757dc791-979c-43e9-d1d0-e6c9ea63ff9e"
      },
      "source": [
        "pip install fdasrsf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJq75Xl_Gr2Y"
      },
      "source": [
        "import pandas as pd\r\n",
        "import csv, sqlite3\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v0q2IhKNGob3"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cpv6zmVICjSH"
      },
      "source": [
        "from fdasrsf import fPCA, time_warping, fdawarp, fdahpca"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bsbWiM4DUAB",
        "outputId": "ffe02546-8afa-4332-d982-c2f7aea921aa"
      },
      "source": [
        " pip  install  numba==0.52.0 --upgrade"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: numba==0.52.0 in /usr/local/lib/python3.6/dist-packages (0.52.0)\n",
            "Requirement already satisfied, skipping upgrade: llvmlite<0.36,>=0.35.0 in /usr/local/lib/python3.6/dist-packages (from numba==0.52.0) (0.35.0)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bafV-km8Gc00"
      },
      "source": [
        "X_train=pd.read_csv('/content/drive/MyDrive/Analysis_Deta/2019/Model_Inputs/X_train_hours.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RvwNvaSfGf1X"
      },
      "source": [
        "X_test=pd.read_csv('/content/drive/MyDrive/Analysis_Deta/2019/Model_Inputs/X_test_hours.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "QSqeEB3lmtUj",
        "outputId": "0e2332f5-353c-4977-fad7-9c03ccfe98dc"
      },
      "source": [
        "X_train.head(2)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Unnamed: 0  Unnamed: 0.1  ...   date_fail perform\n",
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          "metadata": {
            "tags": []
          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Flav01LvHQjd",
        "outputId": "1516cd91-4d06-444b-cc8d-81f3c47054fe"
      },
      "source": [
        "X_train.index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=95254, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cltBfB1IRdWG",
        "outputId": "4571f1f8-2388-41f3-adc3-3b485f221c8c"
      },
      "source": [
        "pip install scikit-fda"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSWer9HsRY6S"
      },
      "source": [
        "\r\n",
        "import skfda\r\n",
        "from skfda.datasets import fetch_growth\r\n",
        "from skfda.exploratory.visualization import plot_fpca_perturbation_graphs\r\n",
        "from skfda.preprocessing.dim_reduction.projection import FPCA\r\n",
        "from skfda.representation.basis import BSpline, Fourier, Monomial\r\n",
        "\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "ZI9rz_NJSZ3q",
        "outputId": "7224cbd2-ced5-4f7f-b268-ec23be99e380"
      },
      "source": [
        "pip install --upgrade scikit-learn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting scikit-learn\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/aa/db462d385c56905b731403885454188683f63c86ea68900f6f7e7558b5fa/scikit_learn-0.24.0-cp36-cp36m-manylinux2010_x86_64.whl (22.2MB)\n",
            "\u001b[K     |████████████████████████████████| 22.2MB 1.7MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: scipy>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.4.1)\n",
            "Collecting threadpoolctl>=2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.0.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.19.5)\n",
            "Installing collected packages: threadpoolctl, scikit-learn\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "Successfully installed scikit-learn-0.24.0 threadpoolctl-2.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "sklearn"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chqlYshVUOSI"
      },
      "source": [
        "dataset = skfda.datasets.fetch_growth()\r\n",
        "fd = dataset['data']\r\n",
        "y = dataset['target']\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdiOorb0fVw2"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\r\n",
        "scaler = StandardScaler()\r\n",
        "\r\n",
        "# Fit on training set only.\r\n",
        "scaler.fit(X_train.iloc[:,2:36])\r\n",
        "\r\n",
        "# Apply transform to both the training set and the test set.\r\n",
        "X_train_scaled = scaler.transform(X_train.iloc[:,2:36])\r\n",
        "X_test_scaled = scaler.transform(X_test.iloc[:,2:36])"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "jM0B3-CbjLw9",
        "outputId": "4356ad91-e68c-4b4d-990b-ec5acc12b355"
      },
      "source": [
        "X_train.iloc[:,2:44].head(5)"
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>smart_242_raw</th>\n",
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              "      <th>serial</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>date\\n2019-06-10   2019-06-10\\n2019-06-10   20...</td>\n",
              "      <td>252</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       date.1 serial_number  ... serial  life_left\n",
              "0  2019-01-01      Z302BV7C  ...    142      316.0\n",
              "1  2019-01-01      Z304JN7J  ...    271       26.0\n",
              "2  2019-01-01      Z305D2NT  ...    366      178.0\n",
              "3  2019-01-01      Z305D2NT  ...    366      178.0\n",
              "4  2019-01-01      Z304JDL3  ...    252      160.0\n",
              "\n",
              "[5 rows x 42 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 218
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    {
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      "metadata": {
        "id": "j4ccZ6C07N6B"
      },
      "source": [
        "col_select=list(range(6,41))"
      ],
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lLgb5WO6yIB"
      },
      "source": [
        "col_select.insert(0,2)"
      ],
      "execution_count": 228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "aIGZxUT87zgS",
        "outputId": "d2e36167-5224-406d-929b-06690e7719f3"
      },
      "source": [
        "col_select"
      ],
      "execution_count": 229,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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    {
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      "metadata": {
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      },
      "source": [
        "col_select.append(42)"
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      "execution_count": 230,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
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        "id": "eCz0n1Yjiz3p",
        "outputId": "0c58da05-b4b4-4dd4-dbc5-0039373b3dd5"
      },
      "source": [
        "X_test.head()"
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>38399.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30503.0</td>\n",
              "      <td>5.403677e+10</td>\n",
              "      <td>1.219510e+11</td>\n",
              "      <td>2019-11-13</td>\n",
              "      <td>316 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68.0</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>Z304JN7J</td>\n",
              "      <td>208663312.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>429695685.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>28392.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>7273.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28300.0</td>\n",
              "      <td>4.796113e+10</td>\n",
              "      <td>1.341161e+11</td>\n",
              "      <td>2019-01-27</td>\n",
              "      <td>26 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>72.0</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>Z305D2NT</td>\n",
              "      <td>233925808.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>143691431.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>25356.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>9335.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25274.0</td>\n",
              "      <td>4.198461e+10</td>\n",
              "      <td>1.190565e+11</td>\n",
              "      <td>2019-06-28</td>\n",
              "      <td>178 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>68.0</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>Z304JDL3</td>\n",
              "      <td>162816872.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>377868052.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>28373.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>8895.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28276.0</td>\n",
              "      <td>4.868509e+10</td>\n",
              "      <td>1.373655e+11</td>\n",
              "      <td>2019-06-10</td>\n",
              "      <td>160 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>66.0</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>S300YSEP</td>\n",
              "      <td>42920808.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>425097444.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>30307.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>51200.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29949.0</td>\n",
              "      <td>4.892523e+10</td>\n",
              "      <td>1.212411e+11</td>\n",
              "      <td>2019-07-12</td>\n",
              "      <td>192 days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   smart_9_normalized       date  ...  fail_date  life_left\n",
              "0                65.0 2019-01-01  ... 2019-11-13   316 days\n",
              "1                68.0 2019-01-01  ... 2019-01-27    26 days\n",
              "2                72.0 2019-01-01  ... 2019-06-28   178 days\n",
              "3                68.0 2019-01-01  ... 2019-06-10   160 days\n",
              "4                66.0 2019-01-01  ... 2019-07-12   192 days\n",
              "\n",
              "[5 rows x 39 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnUr6ReV92hQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkdgTRbF8oFg"
      },
      "source": [
        "X_tr=X_train.iloc[:,col_select]\r\n",
        "X_te=X_test.iloc[:,col_select]"
      ],
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3CLUjlUj2W0",
        "outputId": "66838279-bd2f-4672-8871-3b91764bd97e"
      },
      "source": [
        "X_train_scaled"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.5548562 , -0.160029  , -0.3335342 , ..., -0.02606995,\n",
              "         0.74141878, -0.67079206],\n",
              "       [ 1.23455744, -0.64730332, -0.01224556, ..., -0.66339685,\n",
              "        -0.02890305, -0.25142493],\n",
              "       [ 1.59367148, -1.13457763,  0.14839876, ..., -1.53881727,\n",
              "        -0.78665822, -0.77057415],\n",
              "       ...,\n",
              "       [-1.30837897,  2.27634258, -1.13675582, ...,  3.30145342,\n",
              "         2.15623227,  1.91599899],\n",
              "       [-1.68284725, -0.64730332, -0.01224556, ...,  1.49969585,\n",
              "         0.71340683,  0.6995309 ],\n",
              "       [ 1.09564378, -0.64730332,  0.63033173, ...,  0.77731489,\n",
              "         0.00352083,  1.49979862]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fdnBi-DpT1w"
      },
      "source": [
        "names=X_train.columns[2:36]"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5XxbDs9jXGj"
      },
      "source": [
        "Xtr=pd.DataFrame(X_train_scaled, columns=names)\r\n",
        "Xte=pd.DataFrame(X_test_scaled, columns=names)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Aa0GB4X9-xG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "KadOUKIcps5j",
        "outputId": "6e3556f4-e945-4413-f5eb-80a5bdb40f75"
      },
      "source": [
        "Xtr.head()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>smart_1_raw</th>\n",
              "      <th>smart_3_normalized</th>\n",
              "      <th>smart_4_raw</th>\n",
              "      <th>smart_5_normalized</th>\n",
              "      <th>smart_5_raw</th>\n",
              "      <th>smart_7_normalized</th>\n",
              "      <th>smart_7_raw</th>\n",
              "      <th>smart_9_normalized</th>\n",
              "      <th>smart_9_raw</th>\n",
              "      <th>smart_12_raw</th>\n",
              "      <th>smart_183_normalized</th>\n",
              "      <th>smart_183_raw</th>\n",
              "      <th>smart_184_normalized</th>\n",
              "      <th>smart_184_raw</th>\n",
              "      <th>smart_187_normalized</th>\n",
              "      <th>smart_187_raw</th>\n",
              "      <th>smart_188_raw</th>\n",
              "      <th>smart_189_normalized</th>\n",
              "      <th>smart_189_raw</th>\n",
              "      <th>smart_190_normalized</th>\n",
              "      <th>smart_190_raw</th>\n",
              "      <th>smart_192_raw</th>\n",
              "      <th>smart_193_normalized</th>\n",
              "      <th>smart_193_raw</th>\n",
              "      <th>smart_194_normalized</th>\n",
              "      <th>smart_194_raw</th>\n",
              "      <th>smart_197_normalized</th>\n",
              "      <th>smart_197_raw</th>\n",
              "      <th>smart_198_normalized</th>\n",
              "      <th>smart_198_raw</th>\n",
              "      <th>smart_199_raw</th>\n",
              "      <th>smart_240_raw</th>\n",
              "      <th>smart_241_raw</th>\n",
              "      <th>smart_242_raw</th>\n",
              "      <th>serial_number</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.554856</td>\n",
              "      <td>-0.160029</td>\n",
              "      <td>-0.333534</td>\n",
              "      <td>0.062772</td>\n",
              "      <td>-0.062867</td>\n",
              "      <td>0.297073</td>\n",
              "      <td>-0.089871</td>\n",
              "      <td>-0.092556</td>\n",
              "      <td>0.028341</td>\n",
              "      <td>-0.351567</td>\n",
              "      <td>0.248836</td>\n",
              "      <td>-0.248149</td>\n",
              "      <td>0.067157</td>\n",
              "      <td>-0.067157</td>\n",
              "      <td>0.402288</td>\n",
              "      <td>-0.402063</td>\n",
              "      <td>-0.066566</td>\n",
              "      <td>0.048561</td>\n",
              "      <td>-0.048561</td>\n",
              "      <td>-0.029835</td>\n",
              "      <td>0.029835</td>\n",
              "      <td>-0.300755</td>\n",
              "      <td>-0.364703</td>\n",
              "      <td>0.312988</td>\n",
              "      <td>0.029835</td>\n",
              "      <td>0.029835</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>-0.064143</td>\n",
              "      <td>-0.026070</td>\n",
              "      <td>0.741419</td>\n",
              "      <td>-0.670792</td>\n",
              "      <td>Z302BV7C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.234557</td>\n",
              "      <td>-0.647303</td>\n",
              "      <td>-0.012246</td>\n",
              "      <td>0.062772</td>\n",
              "      <td>-0.062867</td>\n",
              "      <td>-0.047996</td>\n",
              "      <td>-0.148473</td>\n",
              "      <td>0.659437</td>\n",
              "      <td>-0.683523</td>\n",
              "      <td>0.067441</td>\n",
              "      <td>0.018372</td>\n",
              "      <td>-0.018677</td>\n",
              "      <td>0.067157</td>\n",
              "      <td>-0.067157</td>\n",
              "      <td>0.402288</td>\n",
              "      <td>-0.402063</td>\n",
              "      <td>-0.066566</td>\n",
              "      <td>0.048561</td>\n",
              "      <td>-0.048561</td>\n",
              "      <td>0.196231</td>\n",
              "      <td>-0.196231</td>\n",
              "      <td>-0.300755</td>\n",
              "      <td>0.471652</td>\n",
              "      <td>-0.445624</td>\n",
              "      <td>-0.196231</td>\n",
              "      <td>-0.196231</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>-0.064143</td>\n",
              "      <td>-0.663397</td>\n",
              "      <td>-0.028903</td>\n",
              "      <td>-0.251425</td>\n",
              "      <td>Z304JN7J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.593671</td>\n",
              "      <td>-1.134578</td>\n",
              "      <td>0.148399</td>\n",
              "      <td>0.062772</td>\n",
              "      <td>-0.062867</td>\n",
              "      <td>-1.773340</td>\n",
              "      <td>-0.321326</td>\n",
              "      <td>1.662096</td>\n",
              "      <td>-1.554631</td>\n",
              "      <td>0.276945</td>\n",
              "      <td>0.248836</td>\n",
              "      <td>-0.248149</td>\n",
              "      <td>0.067157</td>\n",
              "      <td>-0.067157</td>\n",
              "      <td>0.402288</td>\n",
              "      <td>-0.402063</td>\n",
              "      <td>-0.066566</td>\n",
              "      <td>0.048561</td>\n",
              "      <td>-0.048561</td>\n",
              "      <td>-0.934099</td>\n",
              "      <td>0.934099</td>\n",
              "      <td>-0.300755</td>\n",
              "      <td>0.419380</td>\n",
              "      <td>-0.395368</td>\n",
              "      <td>0.934099</td>\n",
              "      <td>0.934099</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>-0.064143</td>\n",
              "      <td>-1.538817</td>\n",
              "      <td>-0.786658</td>\n",
              "      <td>-0.770574</td>\n",
              "      <td>Z305D2NT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.582836</td>\n",
              "      <td>0.327245</td>\n",
              "      <td>-0.494179</td>\n",
              "      <td>0.062772</td>\n",
              "      <td>-0.062867</td>\n",
              "      <td>-0.393065</td>\n",
              "      <td>-0.179796</td>\n",
              "      <td>0.659437</td>\n",
              "      <td>-0.688975</td>\n",
              "      <td>-0.770575</td>\n",
              "      <td>0.248836</td>\n",
              "      <td>-0.248149</td>\n",
              "      <td>0.067157</td>\n",
              "      <td>-0.067157</td>\n",
              "      <td>0.402288</td>\n",
              "      <td>-0.402063</td>\n",
              "      <td>-0.066566</td>\n",
              "      <td>0.048561</td>\n",
              "      <td>-0.048561</td>\n",
              "      <td>0.874430</td>\n",
              "      <td>-0.874430</td>\n",
              "      <td>-0.300755</td>\n",
              "      <td>0.419380</td>\n",
              "      <td>-0.406092</td>\n",
              "      <td>-0.874430</td>\n",
              "      <td>-0.874430</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>-0.064143</td>\n",
              "      <td>-0.670340</td>\n",
              "      <td>0.062887</td>\n",
              "      <td>-0.139408</td>\n",
              "      <td>Z304JDL3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.121523</td>\n",
              "      <td>1.301794</td>\n",
              "      <td>-0.815467</td>\n",
              "      <td>0.062772</td>\n",
              "      <td>-0.062867</td>\n",
              "      <td>-0.047996</td>\n",
              "      <td>-0.151252</td>\n",
              "      <td>0.158108</td>\n",
              "      <td>-0.134059</td>\n",
              "      <td>-0.980079</td>\n",
              "      <td>0.248836</td>\n",
              "      <td>-0.248149</td>\n",
              "      <td>0.067157</td>\n",
              "      <td>-0.067157</td>\n",
              "      <td>0.402288</td>\n",
              "      <td>-0.402063</td>\n",
              "      <td>-0.066566</td>\n",
              "      <td>0.048561</td>\n",
              "      <td>-0.048561</td>\n",
              "      <td>-0.029835</td>\n",
              "      <td>0.029835</td>\n",
              "      <td>-0.300755</td>\n",
              "      <td>-0.678336</td>\n",
              "      <td>0.624978</td>\n",
              "      <td>0.029835</td>\n",
              "      <td>0.029835</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>0.00838</td>\n",
              "      <td>-0.021728</td>\n",
              "      <td>-0.064143</td>\n",
              "      <td>-0.186342</td>\n",
              "      <td>0.093333</td>\n",
              "      <td>-0.695262</td>\n",
              "      <td>S300YSEP</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   smart_1_raw  smart_3_normalized  ...  smart_242_raw  serial_number\n",
              "0     1.554856           -0.160029  ...      -0.670792       Z302BV7C\n",
              "1     1.234557           -0.647303  ...      -0.251425       Z304JN7J\n",
              "2     1.593671           -1.134578  ...      -0.770574       Z305D2NT\n",
              "3     0.582836            0.327245  ...      -0.139408       Z304JDL3\n",
              "4    -1.121523            1.301794  ...      -0.695262       S300YSEP\n",
              "\n",
              "[5 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5pCbVTk-AVx"
      },
      "source": [
        "y_tr=X_train.iloc[:,43]\r\n",
        "y_te=X_test.iloc[:,43]"
      ],
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sA2IVcPFpyjL"
      },
      "source": [
        "Xtr['serial_number']=X_train['serial_number']\r\n",
        "Xte['serial_number']=X_test['serial_number']"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsxlOozQqHyj"
      },
      "source": [
        "Xtr.to_csv('/content/drive/MyDrive/Analysis_Deta/2019/Model_Inputs/X_train_scaled.csv')\r\n",
        "Xte.to_csv('/content/drive/MyDrive/Analysis_Deta/2019/Model_Inputs/X_test_scaled.csv')"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFQYLGyqbx6a"
      },
      "source": [
        "\r\n",
        "X_tr= skfda.representation.grid.FDataGrid(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dxjn4qMvd6gk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "owef0PrdZjHn",
        "outputId": "34f38be9-9f07-4db5-ea57-950d6994653b"
      },
      "source": [
        "fpca_discretized = FPCA(n_components=4)\r\n",
        "fpca_discretized.fit(X_tr)\r\n",
        "fpca_discretized.components_.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 981
        },
        "id": "CvoK2z3LdDCU",
        "outputId": "5615dec6-7ff8-4320-e859-b59b9c2c27aa"
      },
      "source": [
        "plot_fpca_perturbation_graphs(X_tr.mean(),\r\n",
        "                              fpca_discretized.components_,\r\n",
        "                              30,\r\n",
        "                              fig=plt.figure(figsize=(8, 2 * 4)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hj70-ThmuKZP"
      },
      "source": [
        "X = pd.DataFrame(data=[['A', 'A', 'A', 2, 5, 7, 6, 3, 6, 7],['A', 'A', 'A', 4, 4, 4, 2, 4, 4, 3],['B', 'A', 'B', 5, 2, 1, 1, 7, 1, 1],['B', 'A', 'B', 7, 2, 1, 2, 2, 2, 2],['B', 'B', 'B', 3, 5, 6, 5, 2, 6, 6],['B', 'B', 'A', 3, 5, 4, 5, 1, 7, 5]],columns=['E1 fruity', 'E1 woody', 'E1 coffee','E2 red fruit', 'E2 roasted', 'E2 vanillin', 'E2 woody','E3 fruity', 'E3 butter', 'E3 woody'],index=['Wine {}'.format(i+1) for i in range(6)])\r\n",
        "X['Oak type'] = [1, 2, 2, 2, 1, 1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "RuhgRDluup8_",
        "outputId": "b20c1b7d-84d6-4d3a-9c99-02ffe62dcaa2"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>E1 fruity</th>\n",
              "      <th>E1 woody</th>\n",
              "      <th>E1 coffee</th>\n",
              "      <th>E2 red fruit</th>\n",
              "      <th>E2 roasted</th>\n",
              "      <th>E2 vanillin</th>\n",
              "      <th>E2 woody</th>\n",
              "      <th>E3 fruity</th>\n",
              "      <th>E3 butter</th>\n",
              "      <th>E3 woody</th>\n",
              "      <th>Oak type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wine 1</th>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wine 2</th>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wine 3</th>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wine 4</th>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wine 5</th>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       E1 fruity E1 woody E1 coffee  ...  E3 butter  E3 woody  Oak type\n",
              "Wine 1         A        A         A  ...          6         7         1\n",
              "Wine 2         A        A         A  ...          4         3         2\n",
              "Wine 3         B        A         B  ...          1         1         2\n",
              "Wine 4         B        A         B  ...          2         2         2\n",
              "Wine 5         B        B         B  ...          6         6         1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TS6b9l_u--O"
      },
      "source": [
        "import prince\r\n",
        "famd = prince.FAMD(\r\n",
        "    n_components=2,\r\n",
        "...     n_iter=3,\r\n",
        "...     copy=True,\r\n",
        "...     check_input=True,\r\n",
        "...     engine='auto',\r\n",
        "...     random_state=42\r\n",
        "... )\r\n",
        ">>> famd = famd.fit(X.drop('Oak type', axis='columns'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VhdD44xvPHp",
        "outputId": "bacb5407-8650-48a9-f60f-1b8e6f79421d"
      },
      "source": [
        "pip install prince"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting prince\n",
            "  Downloading https://files.pythonhosted.org/packages/94/6c/491a3fabfd1ce75e285a4fe4200fccde5d83664733541a3a74c0b02e77fb/prince-0.7.1-py3-none-any.whl\n",
            "Requirement already satisfied: scipy>=1.3.0 in /usr/local/lib/python3.6/dist-packages (from prince) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.17.1 in /usr/local/lib/python3.6/dist-packages (from prince) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn>=0.22.1 in /usr/local/lib/python3.6/dist-packages (from prince) (0.22.2.post1)\n",
            "Requirement already satisfied: pandas>=1.0.3 in /usr/local/lib/python3.6/dist-packages (from prince) (1.1.5)\n",
            "Requirement already satisfied: matplotlib>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from prince) (3.2.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.22.1->prince) (1.0.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=1.0.3->prince) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=1.0.3->prince) (2018.9)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.2->prince) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.2->prince) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.2->prince) (1.3.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0.3->prince) (1.15.0)\n",
            "Installing collected packages: prince\n",
            "Successfully installed prince-0.7.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4RhpTyjvu50"
      },
      "source": [
        "import prince\r\n",
        "famd = prince.FAMD(n_components=2,n_iter=3,copy=True,check_input=True,engine='auto',random_state=42)\r\n",
        "famd = famd.fit(X.drop('Oak type', axis='columns'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "B2-ez0fMbbFP",
        "outputId": "95ec703c-ed28-4486-d7d1-8338861c0d2a"
      },
      "source": [
        "pip install light_FAMD"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting light_FAMD\n",
            "  Downloading https://files.pythonhosted.org/packages/6c/40/6678217385426fe2d7791df6a56c866e8676f9d75afa26e188d9a2a291a3/light_famd-0.0.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from light_FAMD) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from light_FAMD) (1.4.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from light_FAMD) (1.1.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from light_FAMD) (1.19.5)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->light_FAMD) (1.0.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->light_FAMD) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->light_FAMD) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->light_FAMD) (1.15.0)\n",
            "Installing collected packages: light-FAMD\n",
            "Successfully installed light-FAMD-0.0.3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "math",
                  "numbers"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nwd2KGevbSRw",
        "outputId": "a732559d-8727-40f5-b029-a2a050ce5e25"
      },
      "source": [
        "import pandas as pd\r\n",
        "from light_famd import FAMD\r\n",
        "X_n = pd.DataFrame(data=np.random.randint(0,100,size=(10,2)),columns=list('AB'))\r\n",
        "X_c =pd.DataFrame(np.random.choice(list('abcde'),size=(10,4),replace=True),columns =list('CDEF'))\r\n",
        "X=pd.concat([X_n,X_c],axis=1)\r\n",
        "print(X)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    A   B  C  D  E  F\n",
            "0  48  26  b  c  a  d\n",
            "1  66  29  b  e  a  c\n",
            "2  49  96  e  a  e  d\n",
            "3   2  26  c  d  e  b\n",
            "4  19   4  a  a  b  e\n",
            "5  40  52  a  d  a  b\n",
            "6  28  71  a  e  d  d\n",
            "7  69  13  b  d  b  b\n",
            "8  38  75  a  a  c  c\n",
            "9  32  17  b  d  b  a\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "GDkHFZdabpoj",
        "outputId": "cf6e7fb4-416c-4395-c328-66eff5b72f6c"
      },
      "source": [
        "famd = FAMD(n_components=2)\r\n",
        "famd.fit(X)\r\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-6467fcb8eaf0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfamd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFAMD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfamd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/light_famd/famd.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     43\u001b[0m                              \"you only have numerical data; you should consider using PCA\")\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/light_famd/mfa.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     66\u001b[0m                     \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m                 )\n\u001b[0;32m---> 68\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_factor_analysis_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0;31m# Fit the global PCA\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/light_famd/mca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_hot_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mone_hot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOneHotEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0m_X_t\u001b[0m\u001b[0;34m=\u001b[0m  \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_hot_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0m_0_freq_serie\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_X_t\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_X_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/light_famd/one_hot.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_names_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m             \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m             \u001b[0mdefault_fill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m         )\n",
            "\u001b[0;31mTypeError\u001b[0m: object() takes no parameters"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEYCoxRac9bL"
      },
      "source": [
        "\r\n",
        "import pandas as pd\r\n",
        "import prince\r\n",
        "import pprint\r\n",
        "\r\n",
        "## Import data\r\n",
        "#df = pd.read_csv('https://github.com/nchelaru/data-prep/raw/master/telco_cleaned_renamed.csv')\r\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHZnVNZ0c_P7"
      },
      "source": [
        "\r\n",
        "## Instantiate FAMD object\r\n",
        "famd = prince.FAMD(\r\n",
        "     n_components=25,\r\n",
        "     n_iter=10,\r\n",
        "     copy=True,\r\n",
        "     check_input=True,\r\n",
        "     engine='auto',       ## Can be \"auto\", 'sklearn', 'fbpca'\r\n",
        "     random_state=42)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H85wEElHdTb1"
      },
      "source": [
        "famd=famd.fit(X)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QT-z1rz2dbQr",
        "outputId": "95fbc2d3-f073-4465-8ced-707cf9c39c68"
      },
      "source": [
        "pp = pprint.PrettyPrinter()\r\n",
        "pp.pprint(famd.explained_inertia_) "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "array([2.56418541e-01, 2.18806549e-01, 1.25026703e-01, 1.03309206e-01,\n",
            "       9.13227958e-02, 7.34440378e-02, 5.71551915e-02, 4.82864415e-02,\n",
            "       2.62305336e-02, 1.29973077e-33])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3ObaGs0dfHt"
      },
      "source": [
        "## Instantiate FAMD object\r\n",
        "famd_drive = prince.FAMD(n_components=25,n_iter=10,copy=True,check_input=True,engine='auto',random_state=42) "
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UeUIneqael7E"
      },
      "source": [
        "famd_drive=famd_drive.fit(Xtr)"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkWiimgOrnwg",
        "outputId": "a11916fb-6e3e-43be-c625-51c6a7611035"
      },
      "source": [
        "pp = pprint.PrettyPrinter()\r\n",
        "pp.pprint(famd.explained_inertia_) "
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "array([2.56418541e-01, 2.18806549e-01, 1.25026703e-01, 1.03309206e-01,\n",
            "       9.13227958e-02, 7.34440378e-02, 5.71551915e-02, 4.82864415e-02,\n",
            "       2.62305336e-02, 1.29973077e-33])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s92-eHCMrp_F",
        "outputId": "bcac14b1-4c24-4fd6-83dd-c983b63c85ca"
      },
      "source": [
        "pp = pprint.PrettyPrinter()\r\n",
        "pp.pprint(famd_drive.explained_inertia_) "
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "array([0.00474052, 0.00429224, 0.00389629, 0.00367753, 0.00351053,\n",
            "       0.00347221, 0.00329198, 0.00319103, 0.00283636, 0.00280484,\n",
            "       0.00276309, 0.00267216, 0.00258841, 0.00252   , 0.00249071,\n",
            "       0.00247034, 0.00246317, 0.00245389, 0.00245106, 0.00244986,\n",
            "       0.0024497 , 0.0024496 , 0.00244957, 0.00244948, 0.00244937])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pOfiOIL26_-"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\r\n",
        "from sklearn.datasets import load_boston\r\n",
        "from sklearn.datasets import make_regression\r\n",
        "from sklearn.metrics import mean_squared_error\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.preprocessing import scale\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from sklearn import set_config "
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEAOLKsC3jWR"
      },
      "source": [
        "f=((pd.to_datetime(X_tr['date.1'], yearfirst=True))-start)/ np.timedelta64(1,'D')\r\n",
        "g=((pd.to_datetime(X_te['date.1'], yearfirst=True))-start)/ np.timedelta64(1,'D')"
      ],
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlFNFNHRAMLx"
      },
      "source": [
        "start=X_tr['date.1'][0]"
      ],
      "execution_count": 244,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVHlRGliAlCQ",
        "outputId": "753bf513-3571-4344-e297-dfe964794c9c"
      },
      "source": [
        "f[955]"
      ],
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAoth9MeAsF-",
        "outputId": "91229dba-91d4-4466-b422-aa84a4193726"
      },
      "source": [
        "X_tr['date.1']=f\r\n",
        "X_te['date.1']=g\r\n"
      ],
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P5Iee6QDDdN-"
      },
      "source": [
        "remove=np.where(np.isnan(y_tr))"
      ],
      "execution_count": 261,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LH-ji3ioDyWL"
      },
      "source": [
        "remove=np.array(remove)"
      ],
      "execution_count": 271,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylkMlOmDEcLX",
        "outputId": "7d23a178-81f8-4133-a757-59e786908364"
      },
      "source": [
        "remove.flatten()"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  116,   622,  1126,  1627,  2128,  2629,  3130,  3631,  4132,\n",
              "        4632,  5131,  5629,  6129,  6625,  7118,  7609,  8098,  8586,\n",
              "        9072,  9558, 10043, 10528, 11009, 11489, 11966, 12443, 12918,\n",
              "       13392, 13863, 14333, 14802, 15271])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "id": "jbHct_cCDf51",
        "outputId": "c4f6cf8a-5ceb-4425-942e-0cabb8882b7e"
      },
      "source": [
        "X_trr=X_tr.drop(list(remove[0]))\r\n",
        "X_ter=X_te.drop(list(remove[0]))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-0511c17bd2e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_trr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_tr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mX_ter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_te\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X_tr' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oj-OKDeKPOxn"
      },
      "source": [
        "y_trr=y_tr.drop(list(remove[0]))\r\n",
        "y_ter=y_te.drop(list(remove[0]))"
      ],
      "execution_count": 284,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "135vXYx4DgFu",
        "outputId": "60e9073d-235a-468e-ebac-551e7fe6f445"
      },
      "source": [
        "X.drop([1,2])"
      ],
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>A</th>\n",
              "      <th>B</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.145319</td>\n",
              "      <td>-0.161377</td>\n",
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              "      <td>c</td>\n",
              "      <td>a</td>\n",
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              "      <th>3</th>\n",
              "      <td>-0.605768</td>\n",
              "      <td>-0.161377</td>\n",
              "      <td>c</td>\n",
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              "      <td>e</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.328192</td>\n",
              "      <td>-0.399652</td>\n",
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              "      <td>0.120220</td>\n",
              "      <td>a</td>\n",
              "      <td>d</td>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-0.181240</td>\n",
              "      <td>0.326003</td>\n",
              "      <td>a</td>\n",
              "      <td>e</td>\n",
              "      <td>d</td>\n",
              "      <td>d</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.488206</td>\n",
              "      <td>-0.302176</td>\n",
              "      <td>b</td>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-0.017961</td>\n",
              "      <td>0.369326</td>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>c</td>\n",
              "      <td>c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-0.115929</td>\n",
              "      <td>-0.258853</td>\n",
              "      <td>b</td>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>a</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          A         B  C  D  E  F\n",
              "0  0.145319 -0.161377  b  c  a  d\n",
              "3 -0.605768 -0.161377  c  d  e  b\n",
              "4 -0.328192 -0.399652  a  a  b  e\n",
              "5  0.014695  0.120220  a  d  a  b\n",
              "6 -0.181240  0.326003  a  e  d  d\n",
              "7  0.488206 -0.302176  b  d  b  b\n",
              "8 -0.017961  0.369326  a  a  c  c\n",
              "9 -0.115929 -0.258853  b  d  b  a"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqMl6JoIDgR8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X5KgGEGuDgka"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkSq8PPWDgtu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sp63j6tQDg7y"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZTIBWJBCWCn"
      },
      "source": [
        "X_train['fail_date']=pd.to_datetime(X_train['fail_date'], yearfirst=True)\r\n",
        "X_test['fail_date']=pd.to_datetime(X_test['fail_date'], yearfirst=True)"
      ],
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqmFOVDRAIND"
      },
      "source": [
        "X_train['life_left']=(X_train['fail_date']-X_train['date'])/ np.timedelta64(1,'D')"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Emx1v0oIBM0"
      },
      "source": [
        "X_test['life_left']=X_test['fail_date']-X_test['date']"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hF0qZIJ8G1LV",
        "outputId": "6c2b8c98-be89-4ee2-da9b-cf032e244565"
      },
      "source": [
        "len(X_train.columns)"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LatBpvpRCtl3"
      },
      "source": [
        "X_tr=X_train.iloc[:,3:39].drop('fail_date',axis=1)"
      ],
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEmwJBriSf9k"
      },
      "source": [
        "X_tr=X_tr.drop('life_left',axis=1)"
      ],
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "haPYiWpVRa8Z"
      },
      "source": [
        "X_tr['serial']=X_train['serial']"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjUImsFUJOns"
      },
      "source": [
        "X_te=X_test.iloc[:,2:37]"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "8tmGPZm8G92g",
        "outputId": "22283506-4d95-402d-afbd-c16797c84335"
      },
      "source": [
        "X_tr.head(5)"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>date.1</th>\n",
              "      <th>smart_1_normalized</th>\n",
              "      <th>smart_1_raw</th>\n",
              "      <th>smart_3_normalized</th>\n",
              "      <th>smart_4_raw</th>\n",
              "      <th>smart_5_normalized</th>\n",
              "      <th>smart_5_raw</th>\n",
              "      <th>smart_7_normalized</th>\n",
              "      <th>smart_7_raw</th>\n",
              "      <th>smart_9_normalized</th>\n",
              "      <th>smart_9_raw</th>\n",
              "      <th>smart_12_raw</th>\n",
              "      <th>smart_183_normalized</th>\n",
              "      <th>smart_183_raw</th>\n",
              "      <th>smart_184_normalized</th>\n",
              "      <th>smart_184_raw</th>\n",
              "      <th>smart_187_normalized</th>\n",
              "      <th>smart_187_raw</th>\n",
              "      <th>smart_188_raw</th>\n",
              "      <th>smart_189_normalized</th>\n",
              "      <th>smart_189_raw</th>\n",
              "      <th>smart_190_normalized</th>\n",
              "      <th>smart_190_raw</th>\n",
              "      <th>smart_192_raw</th>\n",
              "      <th>smart_193_normalized</th>\n",
              "      <th>smart_193_raw</th>\n",
              "      <th>smart_194_normalized</th>\n",
              "      <th>smart_194_raw</th>\n",
              "      <th>smart_197_normalized</th>\n",
              "      <th>smart_197_raw</th>\n",
              "      <th>smart_198_normalized</th>\n",
              "      <th>smart_198_raw</th>\n",
              "      <th>smart_199_raw</th>\n",
              "      <th>smart_240_raw</th>\n",
              "      <th>smart_241_raw</th>\n",
              "      <th>smart_242_raw</th>\n",
              "      <th>serial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>119.0</td>\n",
              "      <td>231195280.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>526658972.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>30873.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>38399.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30503.0</td>\n",
              "      <td>5.403677e+10</td>\n",
              "      <td>1.219510e+11</td>\n",
              "      <td>142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>119.0</td>\n",
              "      <td>208663312.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>429695685.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>28392.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>7273.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28300.0</td>\n",
              "      <td>4.796113e+10</td>\n",
              "      <td>1.341161e+11</td>\n",
              "      <td>271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>119.0</td>\n",
              "      <td>233925808.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>143691431.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>25356.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>9335.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25274.0</td>\n",
              "      <td>4.198461e+10</td>\n",
              "      <td>1.190565e+11</td>\n",
              "      <td>366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>119.0</td>\n",
              "      <td>233925808.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>143691431.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>25356.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>9335.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25274.0</td>\n",
              "      <td>4.198461e+10</td>\n",
              "      <td>1.190565e+11</td>\n",
              "      <td>366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>117.0</td>\n",
              "      <td>162816872.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>377868052.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>28373.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>8895.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28276.0</td>\n",
              "      <td>4.868509e+10</td>\n",
              "      <td>1.373655e+11</td>\n",
              "      <td>252</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       date.1  smart_1_normalized  ...  smart_242_raw  serial\n",
              "0  2019-01-01               119.0  ...   1.219510e+11     142\n",
              "1  2019-01-01               119.0  ...   1.341161e+11     271\n",
              "2  2019-01-01               119.0  ...   1.190565e+11     366\n",
              "3  2019-01-01               119.0  ...   1.190565e+11     366\n",
              "4  2019-01-01               117.0  ...   1.373655e+11     252\n",
              "\n",
              "[5 rows x 37 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yp2OxL9QadUN",
        "outputId": "5a0ba046-a1d8-4acb-e7cf-2ff9fd286ec0"
      },
      "source": [
        "X_tr.info()"
      ],
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 95254 entries, 0 to 95253\n",
            "Data columns (total 37 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   date.1                95254 non-null  object \n",
            " 1   smart_1_normalized    95254 non-null  float64\n",
            " 2   smart_1_raw           95254 non-null  float64\n",
            " 3   smart_3_normalized    95254 non-null  float64\n",
            " 4   smart_4_raw           95254 non-null  float64\n",
            " 5   smart_5_normalized    95254 non-null  float64\n",
            " 6   smart_5_raw           95254 non-null  float64\n",
            " 7   smart_7_normalized    95254 non-null  float64\n",
            " 8   smart_7_raw           95254 non-null  float64\n",
            " 9   smart_9_normalized    95254 non-null  float64\n",
            " 10  smart_9_raw           95254 non-null  float64\n",
            " 11  smart_12_raw          95254 non-null  float64\n",
            " 12  smart_183_normalized  95254 non-null  float64\n",
            " 13  smart_183_raw         95254 non-null  float64\n",
            " 14  smart_184_normalized  95254 non-null  float64\n",
            " 15  smart_184_raw         95254 non-null  float64\n",
            " 16  smart_187_normalized  95254 non-null  float64\n",
            " 17  smart_187_raw         95254 non-null  float64\n",
            " 18  smart_188_raw         95254 non-null  float64\n",
            " 19  smart_189_normalized  95254 non-null  float64\n",
            " 20  smart_189_raw         95254 non-null  float64\n",
            " 21  smart_190_normalized  95254 non-null  float64\n",
            " 22  smart_190_raw         95254 non-null  float64\n",
            " 23  smart_192_raw         95254 non-null  float64\n",
            " 24  smart_193_normalized  95254 non-null  float64\n",
            " 25  smart_193_raw         95254 non-null  float64\n",
            " 26  smart_194_normalized  95254 non-null  float64\n",
            " 27  smart_194_raw         95254 non-null  float64\n",
            " 28  smart_197_normalized  95254 non-null  float64\n",
            " 29  smart_197_raw         95254 non-null  float64\n",
            " 30  smart_198_normalized  95254 non-null  float64\n",
            " 31  smart_198_raw         95254 non-null  float64\n",
            " 32  smart_199_raw         95254 non-null  float64\n",
            " 33  smart_240_raw         95254 non-null  float64\n",
            " 34  smart_241_raw         95254 non-null  float64\n",
            " 35  smart_242_raw         95254 non-null  float64\n",
            " 36  serial                95254 non-null  int64  \n",
            "dtypes: float64(35), int64(1), object(1)\n",
            "memory usage: 26.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzfDU9Pq_7bq"
      },
      "source": [
        "y_tr=X_train['life_left']\r\n",
        "y_te=X_test['life_left']\r\n"
      ],
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0xFL-KSVcRg",
        "outputId": "d2ef05fe-573d-4c4c-c6c6-bee2b033328e"
      },
      "source": [
        " y_tr/np.timedelta64(1,'D')"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "smart_9_normalized\n",
              "65.0    316.0\n",
              "68.0     26.0\n",
              "72.0    178.0\n",
              "68.0    160.0\n",
              "66.0    192.0\n",
              "        ...  \n",
              "59.0    -54.0\n",
              "62.0    -14.0\n",
              "52.0   -165.0\n",
              "60.0    -88.0\n",
              "62.0   -281.0\n",
              "Name: life_left, Length: 95254, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bMQSbmT__Gi"
      },
      "source": [
        "col_select.insert(0,2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2BzvnbT3GdG",
        "outputId": "04b005d0-1f18-4924-b4eb-6530930dffc3"
      },
      "source": [
        "set_config(print_changed_only=False) \r\n",
        " \r\n",
        "rfr = RandomForestRegressor()\r\n",
        "print(rfr)\r\n",
        " \r\n",
        "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\r\n",
        "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\r\n",
        "                      max_samples=None, min_impurity_decrease=0.0,\r\n",
        "                      min_impurity_split=None, min_samples_leaf=1,\r\n",
        "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\r\n",
        "                      n_estimators=100, n_jobs=None, oob_score=False,\r\n",
        "                      random_state=None, verbose=0, warm_start=False) \r\n"
      ],
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
            "                      max_samples=None, min_impurity_decrease=0.0,\n",
            "                      min_impurity_split=None, min_samples_leaf=1,\n",
            "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
            "                      random_state=None, verbose=0, warm_start=False)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPaVcM50BAPw",
        "outputId": "8bf899af-8703-4f52-dc7b-d9ac06131ba7"
      },
      "source": [
        "y_tr.isnull().any()"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lp3e8XYzC08d"
      },
      "source": [
        "remove=np.where(np.isnan(y_tr))"
      ],
      "execution_count": 260,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRwiE9XYCSMV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cRm4PlzHPoB",
        "outputId": "73d5693b-bfe4-4e66-888f-9b4f14d94d38"
      },
      "source": [
        "rfr.fit(X_trr, y_trr)\r\n",
        "\r\n",
        "#score = rfr.score(X_tr, y_tr)\r\n",
        "#print(\"R-squared:\", score) \r\n",
        " \r\n"
      ],
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 285
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-ai-uUPUtAv",
        "outputId": "03132b9f-04bc-4678-8f47-80a3c000ce22"
      },
      "source": [
        "score = rfr.score(X_trr, (y_trr))\r\n",
        "print(\"R-squared:\", score) \r\n",
        " "
      ],
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R-squared: 0.9998905498843271\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Unqe50BBIiOk",
        "outputId": "5c07f78a-b08f-4870-c671-4fb1446a3682"
      },
      "source": [
        "ypred = rfr.predict(X_te)\r\n",
        "\r\n",
        "mse = mean_squared_error(y_te, ypred)\r\n",
        "print(\"MSE: \", mse)\r\n",
        "print(\"RMSE: \", mse*(1/2.0)) \r\n",
        "\r\n"
      ],
      "execution_count": 292,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE:  3294.4292179190456\n",
            "RMSE:  1647.2146089595228\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "XmAGmY0IWbox",
        "outputId": "2c25a477-ebf3-4029-f67a-e2c25ecb9141"
      },
      "source": [
        "plt.scatter(y_te,ypred)"
      ],
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fe8b786eb38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 296
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "69VpX8G8X-1G",
        "outputId": "a4b6a0a6-3020-4a79-80b0-7a6de126f1dd"
      },
      "source": [
        "plt.scatter(X_trr['smart_9_raw'],y_trr,c=X_trr['serial'], marker='s',cmap='gray',s=0.05)\r\n",
        "plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)\r\n",
        "plt.minorticks_on()\r\n",
        "plt.show()"
      ],
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdjlWyfTNurx",
        "outputId": "0ae38a5b-b97f-4ea3-d16b-922756b25582"
      },
      "source": [
        "X_train['serial_number'].nunique()"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "402"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LJN25V-PI8M"
      },
      "source": [
        "# import labelencoder\r\n",
        "from sklearn.preprocessing import LabelEncoder\r\n",
        "# instantiate labelencoder object\r\n",
        "le = LabelEncoder()\r\n",
        "#Apply LabelEncoder on each of the categorical columns:\r\n",
        "X_train['serial'] = le.fit_transform(X_train['serial_number'])"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3wqWqqPvhc",
        "outputId": "28ca7577-8eae-4e44-c813-73d699b3ebb6"
      },
      "source": [
        "X_train['serial']"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        142\n",
              "1        271\n",
              "2        366\n",
              "3        252\n",
              "4         13\n",
              "        ... \n",
              "95249    293\n",
              "95250    387\n",
              "95251    128\n",
              "95252     63\n",
              "95253    354\n",
              "Name: serial, Length: 95254, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6Zj_oZgQg0N"
      },
      "source": [
        "X_train = X_train.set_index(X_train['smart_9_normalized'])"
      ],
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SQ_c_2IbRmv"
      },
      "source": [
        ""
      ]
    }
  ]
}