preprocessing_final_appliance_classification.ipynb

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  "metadata": {
    "colab": {
      "name": "preprocessing_final_appliance_classification.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "oa-Do9AhO94F"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qSeDu0mIiJG",
        "colab_type": "text"
      },
      "source": [
        "**features**\n",
        "\n",
        "\n",
        "*   3,5,7,9,11,13, 15, 17, 19 harmonics [har folder]\n",
        "*   coiflet 17 features [csv_files fold]\n",
        "*   maximum active power  [features fold]\n",
        "*   minimum active power [features fold]\n",
        "*   power factor [features]\n",
        "*   voltage rms value [features]\n",
        "*   current rms value  [features]\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzimLlBkHuFK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "46d0c9f6-7adc-4552-dbeb-3a23570dd8d5"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtNAJjVoH_p8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "65b4a2bc-424e-4c52-87fa-e62afe7c2c0c"
      },
      "source": [
        "%cd /content/gdrive/'My Drive'/'Colab Notebooks'/data/avrio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/Colab Notebooks/data/avrio\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bet_swwHFM9v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pywt\n",
        "import scipy.io.wavfile as wav\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.signal import butter, lfilter\n",
        "from scipy.signal import freqz\n",
        "from sklearn.preprocessing import normalize \n",
        "%matplotlib inline\n",
        "from scipy import signal\n",
        "from scipy.signal import decimate\n",
        "from pywt import  wavedec\n",
        "from scipy.fft import fftshift\n",
        "from scipy.fft import fft\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kaXT9ZUFRj0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "3666216c-8935-49a2-d494-a5c10a50160d"
      },
      "source": [
        "a = os.listdir()\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['v_rms.csv',\n",
              " 'harmonic_features.csv',\n",
              " 'i_rms.csv',\n",
              " 'max_power.csv',\n",
              " 'all_coif2_7_labelled.csv',\n",
              " 'power_factor.csv',\n",
              " 'min_power.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kkXIMhWcIvNT",
        "colab_type": "text"
      },
      "source": [
        "## **preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkMS8nGLFT6N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "8d533613-52c2-42b0-ef52-4d885731eaf9"
      },
      "source": [
        "df0 = pd.read_csv(a[0])\n",
        "df0.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>v_rms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051929.csv</td>\n",
              "      <td>204.791927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051942.csv</td>\n",
              "      <td>204.820971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051951.csv</td>\n",
              "      <td>205.330804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052001.csv</td>\n",
              "      <td>204.802931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052012.csv</td>\n",
              "      <td>204.551234</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                                    filename       v_rms\n",
              "0  Fan_Cyclone3000_r4_MK2_20151102051929.csv  204.791927\n",
              "1  Fan_Cyclone3000_r4_MK2_20151102051942.csv  204.820971\n",
              "2  Fan_Cyclone3000_r4_MK2_20151102051951.csv  205.330804\n",
              "3  Fan_Cyclone3000_r4_MK2_20151102052001.csv  204.802931\n",
              "4  Fan_Cyclone3000_r4_MK2_20151102052012.csv  204.551234"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cDv8ab7FhEr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "b1b1f36e-6ecf-4242-f972-93e7daf6ad7d"
      },
      "source": [
        "df1 = pd.read_csv(a[1])\n",
        "df1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>3_har</th>\n",
              "      <th>5_har</th>\n",
              "      <th>7_har</th>\n",
              "      <th>9_har</th>\n",
              "      <th>11_har</th>\n",
              "      <th>13_har</th>\n",
              "      <th>15_har</th>\n",
              "      <th>17_har</th>\n",
              "      <th>19_har</th>\n",
              "      <th>21_har</th>\n",
              "      <th>23_har</th>\n",
              "      <th>25_har</th>\n",
              "      <th>27_har</th>\n",
              "      <th>29_har</th>\n",
              "      <th>31_har</th>\n",
              "      <th>33_har</th>\n",
              "      <th>35_har</th>\n",
              "      <th>37_har</th>\n",
              "      <th>39_har</th>\n",
              "      <th>41_har</th>\n",
              "      <th>43_har</th>\n",
              "      <th>45_har</th>\n",
              "      <th>47_har</th>\n",
              "      <th>49_har</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.023331</td>\n",
              "      <td>0.017029</td>\n",
              "      <td>0.001326</td>\n",
              "      <td>0.002706</td>\n",
              "      <td>0.001070</td>\n",
              "      <td>0.000483</td>\n",
              "      <td>0.000382</td>\n",
              "      <td>0.000286</td>\n",
              "      <td>0.000227</td>\n",
              "      <td>0.000171</td>\n",
              "      <td>0.000145</td>\n",
              "      <td>0.000132</td>\n",
              "      <td>0.000123</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.000084</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>0.000074</td>\n",
              "      <td>0.000083</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.000065</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>0.000052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.017799</td>\n",
              "      <td>0.008058</td>\n",
              "      <td>0.001346</td>\n",
              "      <td>0.000844</td>\n",
              "      <td>0.001015</td>\n",
              "      <td>0.000425</td>\n",
              "      <td>0.000588</td>\n",
              "      <td>0.000238</td>\n",
              "      <td>0.000295</td>\n",
              "      <td>0.000168</td>\n",
              "      <td>0.000217</td>\n",
              "      <td>0.000166</td>\n",
              "      <td>0.000130</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.000094</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0.000084</td>\n",
              "      <td>0.000078</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>0.000063</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.000067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.006896</td>\n",
              "      <td>0.005013</td>\n",
              "      <td>0.000347</td>\n",
              "      <td>0.001234</td>\n",
              "      <td>0.000414</td>\n",
              "      <td>0.000353</td>\n",
              "      <td>0.000367</td>\n",
              "      <td>0.000265</td>\n",
              "      <td>0.000174</td>\n",
              "      <td>0.000159</td>\n",
              "      <td>0.000123</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0.000083</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.000073</td>\n",
              "      <td>0.000067</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.000045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.008346</td>\n",
              "      <td>0.002116</td>\n",
              "      <td>0.000966</td>\n",
              "      <td>0.001707</td>\n",
              "      <td>0.001421</td>\n",
              "      <td>0.000272</td>\n",
              "      <td>0.000306</td>\n",
              "      <td>0.000234</td>\n",
              "      <td>0.000232</td>\n",
              "      <td>0.000155</td>\n",
              "      <td>0.000153</td>\n",
              "      <td>0.000154</td>\n",
              "      <td>0.000109</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.000078</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>0.000041</td>\n",
              "      <td>0.000045</td>\n",
              "      <td>0.000041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.011693</td>\n",
              "      <td>0.003311</td>\n",
              "      <td>0.000870</td>\n",
              "      <td>0.001272</td>\n",
              "      <td>0.001291</td>\n",
              "      <td>0.000419</td>\n",
              "      <td>0.000343</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0.000184</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.000096</td>\n",
              "      <td>0.000121</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>0.000075</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>0.000058</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.000044</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label     3_har     5_har     7_har  ...    43_har    45_har    47_har    49_har\n",
              "0   Fan  0.023331  0.017029  0.001326  ...  0.000065  0.000061  0.000056  0.000052\n",
              "1   Fan  0.017799  0.008058  0.001346  ...  0.000069  0.000063  0.000059  0.000067\n",
              "2   Fan  0.006896  0.005013  0.000347  ...  0.000047  0.000050  0.000049  0.000045\n",
              "3   Fan  0.008346  0.002116  0.000966  ...  0.000044  0.000041  0.000045  0.000041\n",
              "4   Fan  0.011693  0.003311  0.000870  ...  0.000047  0.000036  0.000042  0.000044\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4rMFGOfVFuGr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "23f10e2b-0665-4db7-ca0f-1a8a834a8879"
      },
      "source": [
        "df2 = pd.read_csv(a[2])\n",
        "df2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>i_rms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051929.csv</td>\n",
              "      <td>1.871610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051942.csv</td>\n",
              "      <td>1.637165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051951.csv</td>\n",
              "      <td>1.648813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052001.csv</td>\n",
              "      <td>1.650404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052012.csv</td>\n",
              "      <td>1.692684</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "                                    filename     i_rms\n",
              "0  Fan_Cyclone3000_r4_MK2_20151102051929.csv  1.871610\n",
              "1  Fan_Cyclone3000_r4_MK2_20151102051942.csv  1.637165\n",
              "2  Fan_Cyclone3000_r4_MK2_20151102051951.csv  1.648813\n",
              "3  Fan_Cyclone3000_r4_MK2_20151102052001.csv  1.650404\n",
              "4  Fan_Cyclone3000_r4_MK2_20151102052012.csv  1.692684"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pAs3a9HF0D7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "b33fa8cb-5f18-42b9-be0f-5595d157e742"
      },
      "source": [
        "df3 = pd.read_csv(a[3])\n",
        "df3.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>max_power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...</td>\n",
              "      <td>1935.744269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161033.csv</td>\n",
              "      <td>83.850171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161054.csv</td>\n",
              "      <td>82.943542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161121.csv</td>\n",
              "      <td>83.127137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161142.csv</td>\n",
              "      <td>83.028246</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename    max_power\n",
              "0  WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...  1935.744269\n",
              "1     Laptop_Schenker-W503_r1_MK1_20160126161033.csv    83.850171\n",
              "2     Laptop_Schenker-W503_r1_MK1_20160126161054.csv    82.943542\n",
              "3     Laptop_Schenker-W503_r1_MK1_20160126161121.csv    83.127137\n",
              "4     Laptop_Schenker-W503_r1_MK1_20160126161142.csv    83.028246"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZmyIUISG1fc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "a5955281-ed79-47fa-8775-982821a18e63"
      },
      "source": [
        "df4 = pd.read_csv(a[4])\n",
        "df4.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>6,4</th>\n",
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              "      <th>7,5</th>\n",
              "      <th>7,6</th>\n",
              "      <th>7,7</th>\n",
              "      <th>7,8</th>\n",
              "      <th>7,9</th>\n",
              "      <th>7,10</th>\n",
              "      <th>7,11</th>\n",
              "      <th>7,12</th>\n",
              "      <th>7,13</th>\n",
              "      <th>7,14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051929.csv</td>\n",
              "      <td>-17.307276</td>\n",
              "      <td>12.425824</td>\n",
              "      <td>-2.066512</td>\n",
              "      <td>-1.173022</td>\n",
              "      <td>-0.572505</td>\n",
              "      <td>-0.163204</td>\n",
              "      <td>0.774277</td>\n",
              "      <td>1.383390</td>\n",
              "      <td>-2.216174</td>\n",
              "      <td>-0.758174</td>\n",
              "      <td>1.981025</td>\n",
              "      <td>0.358803</td>\n",
              "      <td>-0.817273</td>\n",
              "      <td>-0.402471</td>\n",
              "      <td>-43.674182</td>\n",
              "      <td>-22.614261</td>\n",
              "      <td>-15.592891</td>\n",
              "      <td>5.451029</td>\n",
              "      <td>-7.519587</td>\n",
              "      <td>8.272319</td>\n",
              "      <td>-0.226929</td>\n",
              "      <td>9.598988</td>\n",
              "      <td>-6.206041</td>\n",
              "      <td>8.517298</td>\n",
              "      <td>-5.666873</td>\n",
              "      <td>-2.116232</td>\n",
              "      <td>0.541791</td>\n",
              "      <td>1.816243</td>\n",
              "      <td>-42.779983</td>\n",
              "      <td>-20.937885</td>\n",
              "      <td>-9.705260</td>\n",
              "      <td>14.816165</td>\n",
              "      <td>1.262866</td>\n",
              "      <td>-0.303951</td>\n",
              "      <td>1.809701</td>\n",
              "      <td>4.557476</td>\n",
              "      <td>-12.985569</td>\n",
              "      <td>-7.697543</td>\n",
              "      <td>4.204487</td>\n",
              "      <td>...</td>\n",
              "      <td>18.491528</td>\n",
              "      <td>-1.918535</td>\n",
              "      <td>-5.838430</td>\n",
              "      <td>-10.570840</td>\n",
              "      <td>-1.093453</td>\n",
              "      <td>2.965080</td>\n",
              "      <td>-3.849068</td>\n",
              "      <td>1.245051</td>\n",
              "      <td>2.715543</td>\n",
              "      <td>4.425853</td>\n",
              "      <td>-1.971128</td>\n",
              "      <td>-7.329719</td>\n",
              "      <td>-148.345039</td>\n",
              "      <td>40.898106</td>\n",
              "      <td>20.497793</td>\n",
              "      <td>-4.561070</td>\n",
              "      <td>-10.484152</td>\n",
              "      <td>-1.747373</td>\n",
              "      <td>-0.064685</td>\n",
              "      <td>-3.032060</td>\n",
              "      <td>8.036460</td>\n",
              "      <td>10.336002</td>\n",
              "      <td>4.066221</td>\n",
              "      <td>1.933434</td>\n",
              "      <td>-6.421969</td>\n",
              "      <td>-11.824464</td>\n",
              "      <td>-92.234198</td>\n",
              "      <td>19.299180</td>\n",
              "      <td>6.553223</td>\n",
              "      <td>4.075681</td>\n",
              "      <td>1.276298</td>\n",
              "      <td>-1.446441</td>\n",
              "      <td>-4.643255</td>\n",
              "      <td>-6.686234</td>\n",
              "      <td>-6.573541</td>\n",
              "      <td>-4.156974</td>\n",
              "      <td>0.073804</td>\n",
              "      <td>3.759688</td>\n",
              "      <td>5.247951</td>\n",
              "      <td>3.721544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051942.csv</td>\n",
              "      <td>-13.942517</td>\n",
              "      <td>16.986055</td>\n",
              "      <td>0.160635</td>\n",
              "      <td>-6.116024</td>\n",
              "      <td>1.341991</td>\n",
              "      <td>1.884656</td>\n",
              "      <td>-0.114189</td>\n",
              "      <td>-1.163381</td>\n",
              "      <td>0.111710</td>\n",
              "      <td>1.100787</td>\n",
              "      <td>-1.560973</td>\n",
              "      <td>0.951617</td>\n",
              "      <td>1.781463</td>\n",
              "      <td>-2.915724</td>\n",
              "      <td>-33.588887</td>\n",
              "      <td>-27.811733</td>\n",
              "      <td>-14.386305</td>\n",
              "      <td>-0.593122</td>\n",
              "      <td>3.187961</td>\n",
              "      <td>1.907903</td>\n",
              "      <td>2.073609</td>\n",
              "      <td>5.361425</td>\n",
              "      <td>0.799453</td>\n",
              "      <td>3.672207</td>\n",
              "      <td>-1.970351</td>\n",
              "      <td>-6.966366</td>\n",
              "      <td>3.538575</td>\n",
              "      <td>2.385499</td>\n",
              "      <td>-40.617778</td>\n",
              "      <td>-18.075555</td>\n",
              "      <td>-13.978297</td>\n",
              "      <td>13.907001</td>\n",
              "      <td>4.564689</td>\n",
              "      <td>-1.967002</td>\n",
              "      <td>1.758176</td>\n",
              "      <td>1.140235</td>\n",
              "      <td>-6.871987</td>\n",
              "      <td>-8.772784</td>\n",
              "      <td>4.187676</td>\n",
              "      <td>...</td>\n",
              "      <td>15.724005</td>\n",
              "      <td>3.369163</td>\n",
              "      <td>-16.063316</td>\n",
              "      <td>1.385592</td>\n",
              "      <td>-4.986005</td>\n",
              "      <td>-2.107837</td>\n",
              "      <td>-2.179201</td>\n",
              "      <td>5.679459</td>\n",
              "      <td>1.752660</td>\n",
              "      <td>2.170490</td>\n",
              "      <td>-3.319463</td>\n",
              "      <td>-3.920442</td>\n",
              "      <td>-143.816558</td>\n",
              "      <td>44.995828</td>\n",
              "      <td>10.293235</td>\n",
              "      <td>3.244500</td>\n",
              "      <td>-17.243322</td>\n",
              "      <td>1.975545</td>\n",
              "      <td>-1.052552</td>\n",
              "      <td>2.319438</td>\n",
              "      <td>0.448552</td>\n",
              "      <td>14.676697</td>\n",
              "      <td>3.004140</td>\n",
              "      <td>-1.240601</td>\n",
              "      <td>-6.783255</td>\n",
              "      <td>-4.748608</td>\n",
              "      <td>-90.813006</td>\n",
              "      <td>19.302017</td>\n",
              "      <td>6.584077</td>\n",
              "      <td>4.017872</td>\n",
              "      <td>1.374164</td>\n",
              "      <td>-1.735286</td>\n",
              "      <td>-4.439455</td>\n",
              "      <td>-6.556509</td>\n",
              "      <td>-6.764448</td>\n",
              "      <td>-3.956773</td>\n",
              "      <td>0.172861</td>\n",
              "      <td>3.617642</td>\n",
              "      <td>5.101208</td>\n",
              "      <td>3.848679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051951.csv</td>\n",
              "      <td>-16.615458</td>\n",
              "      <td>11.517269</td>\n",
              "      <td>-3.334848</td>\n",
              "      <td>2.820421</td>\n",
              "      <td>-3.090004</td>\n",
              "      <td>-1.182308</td>\n",
              "      <td>3.788207</td>\n",
              "      <td>-0.450359</td>\n",
              "      <td>-0.896186</td>\n",
              "      <td>-1.032655</td>\n",
              "      <td>0.074169</td>\n",
              "      <td>2.502380</td>\n",
              "      <td>1.170511</td>\n",
              "      <td>-4.495555</td>\n",
              "      <td>-34.008285</td>\n",
              "      <td>-28.040871</td>\n",
              "      <td>-15.176749</td>\n",
              "      <td>-0.342281</td>\n",
              "      <td>2.888443</td>\n",
              "      <td>3.572747</td>\n",
              "      <td>2.225189</td>\n",
              "      <td>1.286794</td>\n",
              "      <td>5.865099</td>\n",
              "      <td>0.649260</td>\n",
              "      <td>1.130613</td>\n",
              "      <td>-10.439737</td>\n",
              "      <td>2.283344</td>\n",
              "      <td>7.670113</td>\n",
              "      <td>-41.610817</td>\n",
              "      <td>-18.468035</td>\n",
              "      <td>-14.754615</td>\n",
              "      <td>11.022530</td>\n",
              "      <td>10.652982</td>\n",
              "      <td>-4.901523</td>\n",
              "      <td>2.481983</td>\n",
              "      <td>-0.609270</td>\n",
              "      <td>-5.135995</td>\n",
              "      <td>-9.283733</td>\n",
              "      <td>2.817285</td>\n",
              "      <td>...</td>\n",
              "      <td>16.333669</td>\n",
              "      <td>-5.372078</td>\n",
              "      <td>2.069832</td>\n",
              "      <td>-13.108830</td>\n",
              "      <td>-3.461962</td>\n",
              "      <td>3.491321</td>\n",
              "      <td>-7.369757</td>\n",
              "      <td>9.007126</td>\n",
              "      <td>2.111371</td>\n",
              "      <td>-3.256127</td>\n",
              "      <td>4.242150</td>\n",
              "      <td>-8.983606</td>\n",
              "      <td>-155.528001</td>\n",
              "      <td>46.461434</td>\n",
              "      <td>18.229451</td>\n",
              "      <td>-6.124871</td>\n",
              "      <td>-5.925072</td>\n",
              "      <td>-9.289061</td>\n",
              "      <td>8.709727</td>\n",
              "      <td>-5.925247</td>\n",
              "      <td>1.486077</td>\n",
              "      <td>15.781685</td>\n",
              "      <td>8.743140</td>\n",
              "      <td>-4.309536</td>\n",
              "      <td>-12.819176</td>\n",
              "      <td>0.670538</td>\n",
              "      <td>-96.516070</td>\n",
              "      <td>20.165895</td>\n",
              "      <td>6.530921</td>\n",
              "      <td>4.366928</td>\n",
              "      <td>1.874537</td>\n",
              "      <td>-1.264240</td>\n",
              "      <td>-4.601370</td>\n",
              "      <td>-7.380104</td>\n",
              "      <td>-6.765051</td>\n",
              "      <td>-4.795895</td>\n",
              "      <td>-0.142217</td>\n",
              "      <td>4.513976</td>\n",
              "      <td>5.589680</td>\n",
              "      <td>4.133777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052001.csv</td>\n",
              "      <td>-14.901009</td>\n",
              "      <td>11.829469</td>\n",
              "      <td>-2.387713</td>\n",
              "      <td>-2.285974</td>\n",
              "      <td>1.407089</td>\n",
              "      <td>-1.045960</td>\n",
              "      <td>0.842273</td>\n",
              "      <td>0.620345</td>\n",
              "      <td>-1.633252</td>\n",
              "      <td>-0.748041</td>\n",
              "      <td>2.787932</td>\n",
              "      <td>-0.966331</td>\n",
              "      <td>-2.020034</td>\n",
              "      <td>2.368544</td>\n",
              "      <td>-38.166810</td>\n",
              "      <td>-29.320360</td>\n",
              "      <td>-17.281130</td>\n",
              "      <td>-2.530257</td>\n",
              "      <td>7.680359</td>\n",
              "      <td>0.890663</td>\n",
              "      <td>1.793597</td>\n",
              "      <td>-2.569239</td>\n",
              "      <td>9.219761</td>\n",
              "      <td>3.404513</td>\n",
              "      <td>-11.212679</td>\n",
              "      <td>4.059596</td>\n",
              "      <td>-1.644348</td>\n",
              "      <td>1.728998</td>\n",
              "      <td>-50.768401</td>\n",
              "      <td>-27.146654</td>\n",
              "      <td>-6.819432</td>\n",
              "      <td>9.814301</td>\n",
              "      <td>12.170227</td>\n",
              "      <td>-1.273131</td>\n",
              "      <td>-0.966727</td>\n",
              "      <td>2.760106</td>\n",
              "      <td>-11.195634</td>\n",
              "      <td>-11.650892</td>\n",
              "      <td>2.209793</td>\n",
              "      <td>...</td>\n",
              "      <td>17.681337</td>\n",
              "      <td>-0.986858</td>\n",
              "      <td>-5.401551</td>\n",
              "      <td>-5.202345</td>\n",
              "      <td>-9.189033</td>\n",
              "      <td>0.365711</td>\n",
              "      <td>5.176042</td>\n",
              "      <td>1.020652</td>\n",
              "      <td>-0.191831</td>\n",
              "      <td>0.200366</td>\n",
              "      <td>-1.543488</td>\n",
              "      <td>0.470902</td>\n",
              "      <td>-145.532675</td>\n",
              "      <td>34.959908</td>\n",
              "      <td>15.845293</td>\n",
              "      <td>-4.941190</td>\n",
              "      <td>-5.908722</td>\n",
              "      <td>-0.115163</td>\n",
              "      <td>-6.648395</td>\n",
              "      <td>1.326402</td>\n",
              "      <td>7.293542</td>\n",
              "      <td>11.096184</td>\n",
              "      <td>4.358365</td>\n",
              "      <td>-2.062385</td>\n",
              "      <td>-5.042809</td>\n",
              "      <td>-8.033187</td>\n",
              "      <td>-104.450262</td>\n",
              "      <td>20.071514</td>\n",
              "      <td>5.817534</td>\n",
              "      <td>4.178104</td>\n",
              "      <td>0.773622</td>\n",
              "      <td>-1.695457</td>\n",
              "      <td>-4.425433</td>\n",
              "      <td>-6.174227</td>\n",
              "      <td>-6.267368</td>\n",
              "      <td>-4.195333</td>\n",
              "      <td>0.875528</td>\n",
              "      <td>2.831728</td>\n",
              "      <td>5.129934</td>\n",
              "      <td>3.443231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052012.csv</td>\n",
              "      <td>-10.957738</td>\n",
              "      <td>11.224407</td>\n",
              "      <td>-3.723723</td>\n",
              "      <td>0.598672</td>\n",
              "      <td>-0.611849</td>\n",
              "      <td>-1.948979</td>\n",
              "      <td>4.007658</td>\n",
              "      <td>-1.467236</td>\n",
              "      <td>-3.409793</td>\n",
              "      <td>4.410814</td>\n",
              "      <td>-1.756222</td>\n",
              "      <td>-1.280287</td>\n",
              "      <td>3.259351</td>\n",
              "      <td>-2.932321</td>\n",
              "      <td>-36.804014</td>\n",
              "      <td>-31.497293</td>\n",
              "      <td>-17.264134</td>\n",
              "      <td>2.306598</td>\n",
              "      <td>4.763889</td>\n",
              "      <td>-0.112567</td>\n",
              "      <td>-0.010983</td>\n",
              "      <td>4.548816</td>\n",
              "      <td>8.764420</td>\n",
              "      <td>-5.350343</td>\n",
              "      <td>0.300175</td>\n",
              "      <td>-4.715967</td>\n",
              "      <td>2.908829</td>\n",
              "      <td>0.087515</td>\n",
              "      <td>-54.366777</td>\n",
              "      <td>-23.969394</td>\n",
              "      <td>-2.384697</td>\n",
              "      <td>16.866013</td>\n",
              "      <td>-3.366085</td>\n",
              "      <td>-9.997336</td>\n",
              "      <td>15.088265</td>\n",
              "      <td>3.657500</td>\n",
              "      <td>-14.052971</td>\n",
              "      <td>-14.076881</td>\n",
              "      <td>7.793119</td>\n",
              "      <td>...</td>\n",
              "      <td>14.470587</td>\n",
              "      <td>-3.339071</td>\n",
              "      <td>2.723825</td>\n",
              "      <td>-13.168508</td>\n",
              "      <td>-4.750325</td>\n",
              "      <td>3.393357</td>\n",
              "      <td>-6.391881</td>\n",
              "      <td>9.143458</td>\n",
              "      <td>1.499128</td>\n",
              "      <td>-2.535933</td>\n",
              "      <td>2.693992</td>\n",
              "      <td>-7.388402</td>\n",
              "      <td>-147.891530</td>\n",
              "      <td>34.005596</td>\n",
              "      <td>21.496532</td>\n",
              "      <td>2.080427</td>\n",
              "      <td>-12.277327</td>\n",
              "      <td>-11.521218</td>\n",
              "      <td>5.920881</td>\n",
              "      <td>5.779895</td>\n",
              "      <td>-0.334133</td>\n",
              "      <td>4.989202</td>\n",
              "      <td>10.168495</td>\n",
              "      <td>5.702478</td>\n",
              "      <td>-11.493905</td>\n",
              "      <td>-12.183361</td>\n",
              "      <td>-99.799474</td>\n",
              "      <td>19.501556</td>\n",
              "      <td>6.082232</td>\n",
              "      <td>4.638709</td>\n",
              "      <td>0.718792</td>\n",
              "      <td>-1.198328</td>\n",
              "      <td>-4.514852</td>\n",
              "      <td>-6.879041</td>\n",
              "      <td>-6.294109</td>\n",
              "      <td>-4.465999</td>\n",
              "      <td>0.447696</td>\n",
              "      <td>3.458606</td>\n",
              "      <td>5.150128</td>\n",
              "      <td>4.078408</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 113 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       label        0,1  ...      7,13      7,14\n",
              "0  Fan_Cyclone3000_r4_MK2_20151102051929.csv -17.307276  ...  5.247951  3.721544\n",
              "1  Fan_Cyclone3000_r4_MK2_20151102051942.csv -13.942517  ...  5.101208  3.848679\n",
              "2  Fan_Cyclone3000_r4_MK2_20151102051951.csv -16.615458  ...  5.589680  4.133777\n",
              "3  Fan_Cyclone3000_r4_MK2_20151102052001.csv -14.901009  ...  5.129934  3.443231\n",
              "4  Fan_Cyclone3000_r4_MK2_20151102052012.csv -10.957738  ...  5.150128  4.078408\n",
              "\n",
              "[5 rows x 113 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ea_9WZP9G8n9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "b79bdffb-e084-41e3-92a1-ac473c82c419"
      },
      "source": [
        "df5 = pd.read_csv(a[5])\n",
        "df5.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>filename</th>\n",
              "      <th>power_factor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...</td>\n",
              "      <td>0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161033.csv</td>\n",
              "      <td>0.636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161054.csv</td>\n",
              "      <td>0.637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161121.csv</td>\n",
              "      <td>0.634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161142.csv</td>\n",
              "      <td>0.634</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename  power_factor\n",
              "0  WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...         0.070\n",
              "1     Laptop_Schenker-W503_r1_MK1_20160126161033.csv         0.636\n",
              "2     Laptop_Schenker-W503_r1_MK1_20160126161054.csv         0.637\n",
              "3     Laptop_Schenker-W503_r1_MK1_20160126161121.csv         0.634\n",
              "4     Laptop_Schenker-W503_r1_MK1_20160126161142.csv         0.634"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzqwgGmPRTX5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "8382aad7-4da3-45a6-807b-9a1d60d27623"
      },
      "source": [
        "df6 = pd.read_csv(a[6])\n",
        "df6.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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>filename</th>\n",
              "      <th>min_power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...</td>\n",
              "      <td>1.275098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161033.csv</td>\n",
              "      <td>13.332800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161054.csv</td>\n",
              "      <td>7.257608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161121.csv</td>\n",
              "      <td>7.405846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161142.csv</td>\n",
              "      <td>7.664086</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename  min_power\n",
              "0  WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...   1.275098\n",
              "1     Laptop_Schenker-W503_r1_MK1_20160126161033.csv  13.332800\n",
              "2     Laptop_Schenker-W503_r1_MK1_20160126161054.csv   7.257608\n",
              "3     Laptop_Schenker-W503_r1_MK1_20160126161121.csv   7.405846\n",
              "4     Laptop_Schenker-W503_r1_MK1_20160126161142.csv   7.664086"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0h19pT4F5U9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.concat( [df1['label']   ,   df4['0,2']                #, df0['1,1'] , df0['1,2'] , df0['2,1']\n",
        "                                     # ,df0['0,1'],  df0['2,2'] \n",
        "                 ,df1['3_har'] , df1['5_har'] , \n",
        "                 df1['7_har'] ,df1['9_har'] , df1['11_har'], df1['13_har'],  \n",
        "                 df0['v_rms'] , df2['i_rms']  ] , axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wD-9lOUVHxLZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "01fc02aa-de50-431f-df91-ce28bf5a0292"
      },
      "source": [
        "df.head(2)"
      ],
      "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>label</th>\n",
              "      <th>0,2</th>\n",
              "      <th>3_har</th>\n",
              "      <th>5_har</th>\n",
              "      <th>7_har</th>\n",
              "      <th>9_har</th>\n",
              "      <th>11_har</th>\n",
              "      <th>13_har</th>\n",
              "      <th>v_rms</th>\n",
              "      <th>i_rms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan</td>\n",
              "      <td>12.425824</td>\n",
              "      <td>0.023331</td>\n",
              "      <td>0.017029</td>\n",
              "      <td>0.001326</td>\n",
              "      <td>0.002706</td>\n",
              "      <td>0.001070</td>\n",
              "      <td>0.000483</td>\n",
              "      <td>204.791927</td>\n",
              "      <td>1.871610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan</td>\n",
              "      <td>16.986055</td>\n",
              "      <td>0.017799</td>\n",
              "      <td>0.008058</td>\n",
              "      <td>0.001346</td>\n",
              "      <td>0.000844</td>\n",
              "      <td>0.001015</td>\n",
              "      <td>0.000425</td>\n",
              "      <td>204.820971</td>\n",
              "      <td>1.637165</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label        0,2     3_har  ...    13_har       v_rms     i_rms\n",
              "0   Fan  12.425824  0.023331  ...  0.000483  204.791927  1.871610\n",
              "1   Fan  16.986055  0.017799  ...  0.000425  204.820971  1.637165\n",
              "\n",
              "[2 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuXSkgF-Hx91",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "48614c7d-788b-4def-f815-d5837c3c94b5"
      },
      "source": [
        " x = df\n",
        " y = x[\"label\"]\n",
        " X = x.drop(labels = [\"label\"], axis = 1)\n",
        " print( np.shape(X) , np.shape(y) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1339, 9) (1339,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oa-Do9AhO94F",
        "colab_type": "text"
      },
      "source": [
        "## **Algorithm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqc_yQjaH8eu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8c0c50a4-caff-40a1-ef70-1bbb28e06a91"
      },
      "source": [
        "# importing necessary libraries \n",
        "from sklearn import datasets \n",
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.model_selection import train_test_split \n",
        "X = X \n",
        "y = y \n",
        "# dividing X, y into train and test data \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0) \n",
        "\n",
        "# training a KNN classifier \n",
        "from sklearn.neighbors import KNeighborsClassifier \n",
        "knn = KNeighborsClassifier(n_neighbors = 1 , ).fit(X_train, y_train) \n",
        "\n",
        "# accuracy on X_test \n",
        "accuracy = knn.score(X_test, y_test) \n",
        "print (accuracy) \n",
        "\n",
        "# creating a confusion matrix \n",
        "knn_predictions = knn.predict(X_test) \n",
        "cm = confusion_matrix(y_test, knn_predictions) \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8686567164179104\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NsLpGERH_G6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "224dc0cb-9991-4cc7-edc4-e0df2604c084"
      },
      "source": [
        "# importing necessary libraries \n",
        "from sklearn import datasets \n",
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.model_selection import train_test_split \n",
        "X = X\n",
        "y = y \n",
        "\n",
        "# dividing X, y into train and test data \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0) \n",
        "\n",
        "# training a DescisionTreeClassifier \n",
        "from sklearn.tree import DecisionTreeClassifier \n",
        "dtree_model = DecisionTreeClassifier(max_depth = 18,   ).fit(X_train, y_train) \n",
        "dtree_predictions = dtree_model.predict(X_test) \n",
        "\n",
        "accuracy = dtree_model.score(X_test, y_test) \n",
        "print(accuracy)\n",
        "\n",
        "# creating a confusion matrix \n",
        "cm = confusion_matrix(y_test, dtree_predictions) \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8985074626865671\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKLFQXdaIB9H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bafc66df-00aa-4214-f923-00baa1b2a0f6"
      },
      "source": [
        "from sklearn.datasets import make_classification\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "clf = GradientBoostingClassifier( n_estimators= 1000 )\n",
        "clf.fit(X_train, y_train)\n",
        "accuracy = clf.score(X_test, y_test) \n",
        "print(accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8537313432835821\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nl25DERfINTn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a2572dc9-db73-43ec-e869-53b5cc3af67a"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.ensemble import ExtraTreesClassifier \n",
        "# Building the model \n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y) \n",
        "extra_tree_forest = ExtraTreesClassifier(n_estimators = 4000, \n",
        "\t\t\t\t\t\t\t\t\t\tcriterion ='entropy', max_features = 1  ).fit(X_train, y_train) \n",
        "          \n",
        "extra_tree_forest_predictions = extra_tree_forest.predict(X_test) \n",
        "\n",
        "accuracy = extra_tree_forest.score(X_test, y_test) \n",
        "print(accuracy)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9731343283582089\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVg_CldqIZoZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "49858c21-c71c-4edd-e96c-d71215f9b104"
      },
      "source": [
        "import seaborn as sns\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve\n",
        "\n",
        "\n",
        "kfold = StratifiedKFold(n_splits=2)\n",
        "\n",
        "# Modeling step Test differents algorithms \n",
        "import seaborn as sns\n",
        "random_state = 2\n",
        "classifiers = []\n",
        "classifiers.append(SVC(random_state=random_state))\n",
        "classifiers.append(DecisionTreeClassifier(random_state=random_state))\n",
        "classifiers.append(AdaBoostClassifier(DecisionTreeClassifier(random_state=random_state),random_state=random_state,learning_rate=0.1))\n",
        "classifiers.append(RandomForestClassifier(random_state=random_state))\n",
        "classifiers.append(ExtraTreesClassifier(random_state=random_state))\n",
        "classifiers.append(GradientBoostingClassifier(random_state=random_state))\n",
        "classifiers.append(MLPClassifier(random_state=random_state))\n",
        "classifiers.append(KNeighborsClassifier())\n",
        "classifiers.append(LogisticRegression(random_state = random_state))\n",
        "classifiers.append(LinearDiscriminantAnalysis())\n",
        "\n",
        "cv_results = []\n",
        "for classifier in classifiers :\n",
        "    cv_results.append(cross_val_score(classifier, X_train, y = y_train, scoring = \"accuracy\", cv = kfold, n_jobs=4))\n",
        "\n",
        "cv_means = []\n",
        "cv_std = []\n",
        "for cv_result in cv_results:\n",
        "    cv_means.append(cv_result.mean())\n",
        "    cv_std.append(cv_result.std())\n",
        "\n",
        "cv_res = pd.DataFrame({\"CrossValMeans\":cv_means,\"CrossValerrors\": cv_std,\"Algorithm\":[\"SVC\",\"DecisionTree\",\"AdaBoost\",\n",
        "\"RandomForest\",\"ExtraTrees\",\"GradientBoosting\",\"MultipleLayerPerceptron\",\"KNeighboors\",\"LogisticRegression\",\"LinearDiscriminantAnalysis\"]})\n",
        "\n",
        "g = sns.barplot(\"CrossValMeans\",\"Algorithm\",data = cv_res, palette=\"Set3\",orient = \"h\",**{'xerr':cv_std})\n",
        "g.set_xlabel(\"Mean Accuracy\")\n",
        "g = g.set_title(\"Cross validation scores\") \n",
        "print(cv_res)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   CrossValMeans  CrossValerrors                   Algorithm\n",
            "0       0.205179        0.003984                         SVC\n",
            "1       0.761952        0.002988                DecisionTree\n",
            "2       0.771912        0.000996                    AdaBoost\n",
            "3       0.907371        0.002988                RandomForest\n",
            "4       0.932271        0.005976                  ExtraTrees\n",
            "5       0.720120        0.008964            GradientBoosting\n",
            "6       0.390438        0.009960     MultipleLayerPerceptron\n",
            "7       0.418327        0.025896                 KNeighboors\n",
            "8       0.316733        0.025896          LogisticRegression\n",
            "9       0.491036        0.028884  LinearDiscriminantAnalysis\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5Ii5U7iJEpn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqA6AnHmPCo4",
        "colab_type": "text"
      },
      "source": [
        "## **Merging**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zNsl32mPFO5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "26352d2d-4ce8-406a-eed3-40d35d0e5c76"
      },
      "source": [
        "a = os.listdir()\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['v_rms.csv',\n",
              " 'harmonic_features.csv',\n",
              " 'i_rms.csv',\n",
              " 'max_power.csv',\n",
              " 'all_coif2_7_labelled.csv',\n",
              " 'power_factor.csv',\n",
              " 'min_power.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4sdKuM9Pa8I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "483e2ce7-8c71-4971-c78d-6e815cdce127"
      },
      "source": [
        "d1 = pd.read_csv('v_rms.csv')\n",
        "d1.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",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>filename</th>\n",
              "      <th>v_rms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051929.csv</td>\n",
              "      <td>204.791927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051942.csv</td>\n",
              "      <td>204.820971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051951.csv</td>\n",
              "      <td>205.330804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052001.csv</td>\n",
              "      <td>204.802931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052012.csv</td>\n",
              "      <td>204.551234</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    filename       v_rms\n",
              "0  Fan_Cyclone3000_r4_MK2_20151102051929.csv  204.791927\n",
              "1  Fan_Cyclone3000_r4_MK2_20151102051942.csv  204.820971\n",
              "2  Fan_Cyclone3000_r4_MK2_20151102051951.csv  205.330804\n",
              "3  Fan_Cyclone3000_r4_MK2_20151102052001.csv  204.802931\n",
              "4  Fan_Cyclone3000_r4_MK2_20151102052012.csv  204.551234"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZGVkRpxIoSa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "316e4d59-255f-42cb-c0d2-262b915fe08a"
      },
      "source": [
        "d2 = pd.read_csv('max_power.csv')\n",
        "d2.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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>filename</th>\n",
              "      <th>max_power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...</td>\n",
              "      <td>1935.744269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161033.csv</td>\n",
              "      <td>83.850171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161054.csv</td>\n",
              "      <td>82.943542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161121.csv</td>\n",
              "      <td>83.127137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Laptop_Schenker-W503_r1_MK1_20160126161142.csv</td>\n",
              "      <td>83.028246</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename    max_power\n",
              "0  WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...  1935.744269\n",
              "1     Laptop_Schenker-W503_r1_MK1_20160126161033.csv    83.850171\n",
              "2     Laptop_Schenker-W503_r1_MK1_20160126161054.csv    82.943542\n",
              "3     Laptop_Schenker-W503_r1_MK1_20160126161121.csv    83.127137\n",
              "4     Laptop_Schenker-W503_r1_MK1_20160126161142.csv    83.028246"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZfA40RiJNRn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "c65a430e-4fb0-47ce-c7bb-088153516681"
      },
      "source": [
        "f = [d1, d2]\n",
        "pd.concat(f,axis = 1).reindex( d1['filename']).head(1)"
      ],
      "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>filename</th>\n",
              "      <th>v_rms</th>\n",
              "      <th>filename</th>\n",
              "      <th>max_power</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>filename</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fan_Cyclone3000_r4_MK2_20151102051929.csv</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                          filename  v_rms filename  max_power\n",
              "filename                                                                     \n",
              "Fan_Cyclone3000_r4_MK2_20151102051929.csv      NaN    NaN      NaN        NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52Z39e5PJg8v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "7271cba9-bf12-4eec-a9cf-ba524441187f"
      },
      "source": [
        "f = [d1, d2]\n",
        "pd.concat(f, ignore_index=False)#.head(2)"
      ],
      "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",
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              "    }\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>filename</th>\n",
              "      <th>v_rms</th>\n",
              "      <th>max_power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051929.csv</td>\n",
              "      <td>204.791927</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051942.csv</td>\n",
              "      <td>204.820971</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051951.csv</td>\n",
              "      <td>205.330804</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052001.csv</td>\n",
              "      <td>204.802931</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052012.csv</td>\n",
              "      <td>204.551234</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1921.780226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1932.972122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1923.609448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1951.974631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1338</th>\n",
              "      <td>WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1934.394769</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2678 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               filename  ...    max_power\n",
              "0             Fan_Cyclone3000_r4_MK2_20151102051929.csv  ...          NaN\n",
              "1             Fan_Cyclone3000_r4_MK2_20151102051942.csv  ...          NaN\n",
              "2             Fan_Cyclone3000_r4_MK2_20151102051951.csv  ...          NaN\n",
              "3             Fan_Cyclone3000_r4_MK2_20151102052001.csv  ...          NaN\n",
              "4             Fan_Cyclone3000_r4_MK2_20151102052012.csv  ...          NaN\n",
              "...                                                 ...  ...          ...\n",
              "1334  WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...  ...  1921.780226\n",
              "1335  WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...  ...  1932.972122\n",
              "1336  WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...  ...  1923.609448\n",
              "1337  WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...  ...  1951.974631\n",
              "1338  WaterHeater_StiebelEltronSN10S_r1_MK1_20160126...  ...  1934.394769\n",
              "\n",
              "[2678 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hu0y7fI1KOdC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "eec8cded-0e8d-4a44-a9d2-4fd4dc0f707e"
      },
      "source": [
        "pd.merge( d1 , d2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>v_rms</th>\n",
              "      <th>max_power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051929.csv</td>\n",
              "      <td>204.791927</td>\n",
              "      <td>655.882518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051942.csv</td>\n",
              "      <td>204.820971</td>\n",
              "      <td>631.207010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051951.csv</td>\n",
              "      <td>205.330804</td>\n",
              "      <td>645.807135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052001.csv</td>\n",
              "      <td>204.802931</td>\n",
              "      <td>627.207569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052012.csv</td>\n",
              "      <td>204.551234</td>\n",
              "      <td>636.328168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>LEDLight_FLRGB_r4_MK2_20151102050542.csv</td>\n",
              "      <td>190.469965</td>\n",
              "      <td>147.062863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>LEDLight_FLRGB_r4_MK2_20151102050559.csv</td>\n",
              "      <td>189.909938</td>\n",
              "      <td>186.587251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>LEDLight_FLRGB_r4_MK2_20151102050610.csv</td>\n",
              "      <td>188.616935</td>\n",
              "      <td>106.472982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>LEDLight_FLRGB_r4_MK2_20151102050621.csv</td>\n",
              "      <td>189.852598</td>\n",
              "      <td>175.338325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1338</th>\n",
              "      <td>LEDLight_FLRGB_r4_MK2_20151102050631.csv</td>\n",
              "      <td>187.077704</td>\n",
              "      <td>183.218888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1339 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       filename       v_rms   max_power\n",
              "0     Fan_Cyclone3000_r4_MK2_20151102051929.csv  204.791927  655.882518\n",
              "1     Fan_Cyclone3000_r4_MK2_20151102051942.csv  204.820971  631.207010\n",
              "2     Fan_Cyclone3000_r4_MK2_20151102051951.csv  205.330804  645.807135\n",
              "3     Fan_Cyclone3000_r4_MK2_20151102052001.csv  204.802931  627.207569\n",
              "4     Fan_Cyclone3000_r4_MK2_20151102052012.csv  204.551234  636.328168\n",
              "...                                         ...         ...         ...\n",
              "1334   LEDLight_FLRGB_r4_MK2_20151102050542.csv  190.469965  147.062863\n",
              "1335   LEDLight_FLRGB_r4_MK2_20151102050559.csv  189.909938  186.587251\n",
              "1336   LEDLight_FLRGB_r4_MK2_20151102050610.csv  188.616935  106.472982\n",
              "1337   LEDLight_FLRGB_r4_MK2_20151102050621.csv  189.852598  175.338325\n",
              "1338   LEDLight_FLRGB_r4_MK2_20151102050631.csv  187.077704  183.218888\n",
              "\n",
              "[1339 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcqsanZRQNP9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAJ_09zdQ7Or",
        "colab_type": "text"
      },
      "source": [
        "## **done**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COQohFKqQ9Q9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "6f637ba4-c83d-4a9a-ac32-5840a420ece8"
      },
      "source": [
        "a = os.listdir()\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['v_rms.csv',\n",
              " 'harmonic_features.csv',\n",
              " 'i_rms.csv',\n",
              " 'max_power.csv',\n",
              " 'all_coif2_7_labelled.csv',\n",
              " 'power_factor.csv',\n",
              " 'min_power.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Ku9HC2ERBRf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_m = pd.merge(df0, df3,)# df5,df6 )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhcvZeBcRwEB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_m2 = pd.merge(df_m, df5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWoz3lCBSEc-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_m3 = pd.merge(df_m2, df6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dX1ySsRVSLNI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_m3 = pd.merge(df_m3, df2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SNLqUtPSovZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "6afbede6-fa64-4f3d-a6e0-a6c666b2c144"
      },
      "source": [
        "df_m3.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>filename</th>\n",
              "      <th>v_rms</th>\n",
              "      <th>max_power</th>\n",
              "      <th>power_factor</th>\n",
              "      <th>min_power</th>\n",
              "      <th>i_rms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051929.csv</td>\n",
              "      <td>204.791927</td>\n",
              "      <td>655.882518</td>\n",
              "      <td>0.210</td>\n",
              "      <td>-0.116771</td>\n",
              "      <td>1.871610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051942.csv</td>\n",
              "      <td>204.820971</td>\n",
              "      <td>631.207010</td>\n",
              "      <td>0.197</td>\n",
              "      <td>0.013548</td>\n",
              "      <td>1.637165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102051951.csv</td>\n",
              "      <td>205.330804</td>\n",
              "      <td>645.807135</td>\n",
              "      <td>0.193</td>\n",
              "      <td>0.008170</td>\n",
              "      <td>1.648813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052001.csv</td>\n",
              "      <td>204.802931</td>\n",
              "      <td>627.207569</td>\n",
              "      <td>0.191</td>\n",
              "      <td>0.004317</td>\n",
              "      <td>1.650404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fan_Cyclone3000_r4_MK2_20151102052012.csv</td>\n",
              "      <td>204.551234</td>\n",
              "      <td>636.328168</td>\n",
              "      <td>0.204</td>\n",
              "      <td>-0.051664</td>\n",
              "      <td>1.692684</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    filename       v_rms  ...  min_power     i_rms\n",
              "0  Fan_Cyclone3000_r4_MK2_20151102051929.csv  204.791927  ...  -0.116771  1.871610\n",
              "1  Fan_Cyclone3000_r4_MK2_20151102051942.csv  204.820971  ...   0.013548  1.637165\n",
              "2  Fan_Cyclone3000_r4_MK2_20151102051951.csv  205.330804  ...   0.008170  1.648813\n",
              "3  Fan_Cyclone3000_r4_MK2_20151102052001.csv  204.802931  ...   0.004317  1.650404\n",
              "4  Fan_Cyclone3000_r4_MK2_20151102052012.csv  204.551234  ...  -0.051664  1.692684\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3lwp65lSVTd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.concat( [df1  ,  df_m3.drop( ['filename'] , axis = 1)  , df4.drop(['label'], axis=1) ] , axis = 1)\n",
        "                 \n",
        "                #  df4['0,2']                #, df0['1,1'] , df0['1,2'] , df0['2,1']\n",
        "                #                      # ,df0['0,1'],  df0['2,2'] \n",
        "                #  ,df1['3_har'] , df1['5_har'] , \n",
        "                #  df1['7_har'] ,df1['9_har'] , df1['11_har'], df1['13_har'],  \n",
        "                #  df0['v_rms'] , df2['i_rms']  ] , axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkRTFk0ATgA9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "0497202d-c244-49fe-f197-5243841af85a"
      },
      "source": [
        "df"
      ],
      "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>label</th>\n",
              "      <th>3_har</th>\n",
              "      <th>5_har</th>\n",
              "      <th>7_har</th>\n",
              "      <th>9_har</th>\n",
              "      <th>11_har</th>\n",
              "      <th>13_har</th>\n",
              "      <th>15_har</th>\n",
              "      <th>17_har</th>\n",
              "      <th>19_har</th>\n",
              "      <th>21_har</th>\n",
              "      <th>23_har</th>\n",
              "      <th>25_har</th>\n",
              "      <th>27_har</th>\n",
              "      <th>29_har</th>\n",
              "      <th>31_har</th>\n",
              "      <th>33_har</th>\n",
              "      <th>35_har</th>\n",
              "      <th>37_har</th>\n",
              "      <th>39_har</th>\n",
              "      <th>41_har</th>\n",
              "      <th>43_har</th>\n",
              "      <th>45_har</th>\n",
              "      <th>47_har</th>\n",
              "      <th>49_har</th>\n",
              "      <th>v_rms</th>\n",
              "      <th>max_power</th>\n",
              "      <th>power_factor</th>\n",
              "      <th>min_power</th>\n",
              "      <th>i_rms</th>\n",
              "      <th>0,1</th>\n",
              "      <th>0,2</th>\n",
              "      <th>0,3</th>\n",
              "      <th>0,4</th>\n",
              "      <th>0,5</th>\n",
              "      <th>0,6</th>\n",
              "      <th>0,7</th>\n",
              "      <th>0,8</th>\n",
              "      <th>0,9</th>\n",
              "      <th>0,10</th>\n",
              "      <th>...</th>\n",
              "      <th>5,3</th>\n",
              "      <th>5,4</th>\n",
              "      <th>5,5</th>\n",
              "      <th>5,6</th>\n",
              "      <th>5,7</th>\n",
              "      <th>5,8</th>\n",
              "      <th>5,9</th>\n",
              "      <th>5,10</th>\n",
              "      <th>5,11</th>\n",
              "      <th>5,12</th>\n",
              "      <th>5,13</th>\n",
              "      <th>5,14</th>\n",
              "      <th>6,1</th>\n",
              "      <th>6,2</th>\n",
              "      <th>6,3</th>\n",
              "      <th>6,4</th>\n",
              "      <th>6,5</th>\n",
              "      <th>6,6</th>\n",
              "      <th>6,7</th>\n",
              "      <th>6,8</th>\n",
              "      <th>6,9</th>\n",
              "      <th>6,10</th>\n",
              "      <th>6,11</th>\n",
              "      <th>6,12</th>\n",
              "      <th>6,13</th>\n",
              "      <th>6,14</th>\n",
              "      <th>7,1</th>\n",
              "      <th>7,2</th>\n",
              "      <th>7,3</th>\n",
              "      <th>7,4</th>\n",
              "      <th>7,5</th>\n",
              "      <th>7,6</th>\n",
              "      <th>7,7</th>\n",
              "      <th>7,8</th>\n",
              "      <th>7,9</th>\n",
              "      <th>7,10</th>\n",
              "      <th>7,11</th>\n",
              "      <th>7,12</th>\n",
              "      <th>7,13</th>\n",
              "      <th>7,14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.023331</td>\n",
              "      <td>0.017029</td>\n",
              "      <td>0.001326</td>\n",
              "      <td>0.002706</td>\n",
              "      <td>0.001070</td>\n",
              "      <td>0.000483</td>\n",
              "      <td>0.000382</td>\n",
              "      <td>0.000286</td>\n",
              "      <td>0.000227</td>\n",
              "      <td>0.000171</td>\n",
              "      <td>0.000145</td>\n",
              "      <td>0.000132</td>\n",
              "      <td>0.000123</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.000084</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>0.000074</td>\n",
              "      <td>0.000083</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.000065</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>0.000052</td>\n",
              "      <td>204.791927</td>\n",
              "      <td>655.882518</td>\n",
              "      <td>0.210</td>\n",
              "      <td>-0.116771</td>\n",
              "      <td>1.871610</td>\n",
              "      <td>-17.307276</td>\n",
              "      <td>12.425824</td>\n",
              "      <td>-2.066512</td>\n",
              "      <td>-1.173022</td>\n",
              "      <td>-0.572505</td>\n",
              "      <td>-0.163204</td>\n",
              "      <td>0.774277</td>\n",
              "      <td>1.383390</td>\n",
              "      <td>-2.216174</td>\n",
              "      <td>-0.758174</td>\n",
              "      <td>...</td>\n",
              "      <td>18.491528</td>\n",
              "      <td>-1.918535</td>\n",
              "      <td>-5.838430</td>\n",
              "      <td>-10.570840</td>\n",
              "      <td>-1.093453</td>\n",
              "      <td>2.965080</td>\n",
              "      <td>-3.849068</td>\n",
              "      <td>1.245051</td>\n",
              "      <td>2.715543</td>\n",
              "      <td>4.425853</td>\n",
              "      <td>-1.971128</td>\n",
              "      <td>-7.329719</td>\n",
              "      <td>-148.345039</td>\n",
              "      <td>40.898106</td>\n",
              "      <td>20.497793</td>\n",
              "      <td>-4.561070</td>\n",
              "      <td>-10.484152</td>\n",
              "      <td>-1.747373</td>\n",
              "      <td>-0.064685</td>\n",
              "      <td>-3.032060</td>\n",
              "      <td>8.036460</td>\n",
              "      <td>10.336002</td>\n",
              "      <td>4.066221</td>\n",
              "      <td>1.933434</td>\n",
              "      <td>-6.421969</td>\n",
              "      <td>-11.824464</td>\n",
              "      <td>-92.234198</td>\n",
              "      <td>19.299180</td>\n",
              "      <td>6.553223</td>\n",
              "      <td>4.075681</td>\n",
              "      <td>1.276298</td>\n",
              "      <td>-1.446441</td>\n",
              "      <td>-4.643255</td>\n",
              "      <td>-6.686234</td>\n",
              "      <td>-6.573541</td>\n",
              "      <td>-4.156974</td>\n",
              "      <td>0.073804</td>\n",
              "      <td>3.759688</td>\n",
              "      <td>5.247951</td>\n",
              "      <td>3.721544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.017799</td>\n",
              "      <td>0.008058</td>\n",
              "      <td>0.001346</td>\n",
              "      <td>0.000844</td>\n",
              "      <td>0.001015</td>\n",
              "      <td>0.000425</td>\n",
              "      <td>0.000588</td>\n",
              "      <td>0.000238</td>\n",
              "      <td>0.000295</td>\n",
              "      <td>0.000168</td>\n",
              "      <td>0.000217</td>\n",
              "      <td>0.000166</td>\n",
              "      <td>0.000130</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.000094</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0.000084</td>\n",
              "      <td>0.000078</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>0.000063</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.000067</td>\n",
              "      <td>204.820971</td>\n",
              "      <td>631.207010</td>\n",
              "      <td>0.197</td>\n",
              "      <td>0.013548</td>\n",
              "      <td>1.637165</td>\n",
              "      <td>-13.942517</td>\n",
              "      <td>16.986055</td>\n",
              "      <td>0.160635</td>\n",
              "      <td>-6.116024</td>\n",
              "      <td>1.341991</td>\n",
              "      <td>1.884656</td>\n",
              "      <td>-0.114189</td>\n",
              "      <td>-1.163381</td>\n",
              "      <td>0.111710</td>\n",
              "      <td>1.100787</td>\n",
              "      <td>...</td>\n",
              "      <td>15.724005</td>\n",
              "      <td>3.369163</td>\n",
              "      <td>-16.063316</td>\n",
              "      <td>1.385592</td>\n",
              "      <td>-4.986005</td>\n",
              "      <td>-2.107837</td>\n",
              "      <td>-2.179201</td>\n",
              "      <td>5.679459</td>\n",
              "      <td>1.752660</td>\n",
              "      <td>2.170490</td>\n",
              "      <td>-3.319463</td>\n",
              "      <td>-3.920442</td>\n",
              "      <td>-143.816558</td>\n",
              "      <td>44.995828</td>\n",
              "      <td>10.293235</td>\n",
              "      <td>3.244500</td>\n",
              "      <td>-17.243322</td>\n",
              "      <td>1.975545</td>\n",
              "      <td>-1.052552</td>\n",
              "      <td>2.319438</td>\n",
              "      <td>0.448552</td>\n",
              "      <td>14.676697</td>\n",
              "      <td>3.004140</td>\n",
              "      <td>-1.240601</td>\n",
              "      <td>-6.783255</td>\n",
              "      <td>-4.748608</td>\n",
              "      <td>-90.813006</td>\n",
              "      <td>19.302017</td>\n",
              "      <td>6.584077</td>\n",
              "      <td>4.017872</td>\n",
              "      <td>1.374164</td>\n",
              "      <td>-1.735286</td>\n",
              "      <td>-4.439455</td>\n",
              "      <td>-6.556509</td>\n",
              "      <td>-6.764448</td>\n",
              "      <td>-3.956773</td>\n",
              "      <td>0.172861</td>\n",
              "      <td>3.617642</td>\n",
              "      <td>5.101208</td>\n",
              "      <td>3.848679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.006896</td>\n",
              "      <td>0.005013</td>\n",
              "      <td>0.000347</td>\n",
              "      <td>0.001234</td>\n",
              "      <td>0.000414</td>\n",
              "      <td>0.000353</td>\n",
              "      <td>0.000367</td>\n",
              "      <td>0.000265</td>\n",
              "      <td>0.000174</td>\n",
              "      <td>0.000159</td>\n",
              "      <td>0.000123</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>0.000083</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.000073</td>\n",
              "      <td>0.000067</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.000045</td>\n",
              "      <td>205.330804</td>\n",
              "      <td>645.807135</td>\n",
              "      <td>0.193</td>\n",
              "      <td>0.008170</td>\n",
              "      <td>1.648813</td>\n",
              "      <td>-16.615458</td>\n",
              "      <td>11.517269</td>\n",
              "      <td>-3.334848</td>\n",
              "      <td>2.820421</td>\n",
              "      <td>-3.090004</td>\n",
              "      <td>-1.182308</td>\n",
              "      <td>3.788207</td>\n",
              "      <td>-0.450359</td>\n",
              "      <td>-0.896186</td>\n",
              "      <td>-1.032655</td>\n",
              "      <td>...</td>\n",
              "      <td>16.333669</td>\n",
              "      <td>-5.372078</td>\n",
              "      <td>2.069832</td>\n",
              "      <td>-13.108830</td>\n",
              "      <td>-3.461962</td>\n",
              "      <td>3.491321</td>\n",
              "      <td>-7.369757</td>\n",
              "      <td>9.007126</td>\n",
              "      <td>2.111371</td>\n",
              "      <td>-3.256127</td>\n",
              "      <td>4.242150</td>\n",
              "      <td>-8.983606</td>\n",
              "      <td>-155.528001</td>\n",
              "      <td>46.461434</td>\n",
              "      <td>18.229451</td>\n",
              "      <td>-6.124871</td>\n",
              "      <td>-5.925072</td>\n",
              "      <td>-9.289061</td>\n",
              "      <td>8.709727</td>\n",
              "      <td>-5.925247</td>\n",
              "      <td>1.486077</td>\n",
              "      <td>15.781685</td>\n",
              "      <td>8.743140</td>\n",
              "      <td>-4.309536</td>\n",
              "      <td>-12.819176</td>\n",
              "      <td>0.670538</td>\n",
              "      <td>-96.516070</td>\n",
              "      <td>20.165895</td>\n",
              "      <td>6.530921</td>\n",
              "      <td>4.366928</td>\n",
              "      <td>1.874537</td>\n",
              "      <td>-1.264240</td>\n",
              "      <td>-4.601370</td>\n",
              "      <td>-7.380104</td>\n",
              "      <td>-6.765051</td>\n",
              "      <td>-4.795895</td>\n",
              "      <td>-0.142217</td>\n",
              "      <td>4.513976</td>\n",
              "      <td>5.589680</td>\n",
              "      <td>4.133777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.008346</td>\n",
              "      <td>0.002116</td>\n",
              "      <td>0.000966</td>\n",
              "      <td>0.001707</td>\n",
              "      <td>0.001421</td>\n",
              "      <td>0.000272</td>\n",
              "      <td>0.000306</td>\n",
              "      <td>0.000234</td>\n",
              "      <td>0.000232</td>\n",
              "      <td>0.000155</td>\n",
              "      <td>0.000153</td>\n",
              "      <td>0.000154</td>\n",
              "      <td>0.000109</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.000078</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>0.000041</td>\n",
              "      <td>0.000045</td>\n",
              "      <td>0.000041</td>\n",
              "      <td>204.802931</td>\n",
              "      <td>627.207569</td>\n",
              "      <td>0.191</td>\n",
              "      <td>0.004317</td>\n",
              "      <td>1.650404</td>\n",
              "      <td>-14.901009</td>\n",
              "      <td>11.829469</td>\n",
              "      <td>-2.387713</td>\n",
              "      <td>-2.285974</td>\n",
              "      <td>1.407089</td>\n",
              "      <td>-1.045960</td>\n",
              "      <td>0.842273</td>\n",
              "      <td>0.620345</td>\n",
              "      <td>-1.633252</td>\n",
              "      <td>-0.748041</td>\n",
              "      <td>...</td>\n",
              "      <td>17.681337</td>\n",
              "      <td>-0.986858</td>\n",
              "      <td>-5.401551</td>\n",
              "      <td>-5.202345</td>\n",
              "      <td>-9.189033</td>\n",
              "      <td>0.365711</td>\n",
              "      <td>5.176042</td>\n",
              "      <td>1.020652</td>\n",
              "      <td>-0.191831</td>\n",
              "      <td>0.200366</td>\n",
              "      <td>-1.543488</td>\n",
              "      <td>0.470902</td>\n",
              "      <td>-145.532675</td>\n",
              "      <td>34.959908</td>\n",
              "      <td>15.845293</td>\n",
              "      <td>-4.941190</td>\n",
              "      <td>-5.908722</td>\n",
              "      <td>-0.115163</td>\n",
              "      <td>-6.648395</td>\n",
              "      <td>1.326402</td>\n",
              "      <td>7.293542</td>\n",
              "      <td>11.096184</td>\n",
              "      <td>4.358365</td>\n",
              "      <td>-2.062385</td>\n",
              "      <td>-5.042809</td>\n",
              "      <td>-8.033187</td>\n",
              "      <td>-104.450262</td>\n",
              "      <td>20.071514</td>\n",
              "      <td>5.817534</td>\n",
              "      <td>4.178104</td>\n",
              "      <td>0.773622</td>\n",
              "      <td>-1.695457</td>\n",
              "      <td>-4.425433</td>\n",
              "      <td>-6.174227</td>\n",
              "      <td>-6.267368</td>\n",
              "      <td>-4.195333</td>\n",
              "      <td>0.875528</td>\n",
              "      <td>2.831728</td>\n",
              "      <td>5.129934</td>\n",
              "      <td>3.443231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fan</td>\n",
              "      <td>0.011693</td>\n",
              "      <td>0.003311</td>\n",
              "      <td>0.000870</td>\n",
              "      <td>0.001272</td>\n",
              "      <td>0.001291</td>\n",
              "      <td>0.000419</td>\n",
              "      <td>0.000343</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0.000184</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.000096</td>\n",
              "      <td>0.000121</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>0.000064</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>0.000075</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>0.000058</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>204.551234</td>\n",
              "      <td>636.328168</td>\n",
              "      <td>0.204</td>\n",
              "      <td>-0.051664</td>\n",
              "      <td>1.692684</td>\n",
              "      <td>-10.957738</td>\n",
              "      <td>11.224407</td>\n",
              "      <td>-3.723723</td>\n",
              "      <td>0.598672</td>\n",
              "      <td>-0.611849</td>\n",
              "      <td>-1.948979</td>\n",
              "      <td>4.007658</td>\n",
              "      <td>-1.467236</td>\n",
              "      <td>-3.409793</td>\n",
              "      <td>4.410814</td>\n",
              "      <td>...</td>\n",
              "      <td>14.470587</td>\n",
              "      <td>-3.339071</td>\n",
              "      <td>2.723825</td>\n",
              "      <td>-13.168508</td>\n",
              "      <td>-4.750325</td>\n",
              "      <td>3.393357</td>\n",
              "      <td>-6.391881</td>\n",
              "      <td>9.143458</td>\n",
              "      <td>1.499128</td>\n",
              "      <td>-2.535933</td>\n",
              "      <td>2.693992</td>\n",
              "      <td>-7.388402</td>\n",
              "      <td>-147.891530</td>\n",
              "      <td>34.005596</td>\n",
              "      <td>21.496532</td>\n",
              "      <td>2.080427</td>\n",
              "      <td>-12.277327</td>\n",
              "      <td>-11.521218</td>\n",
              "      <td>5.920881</td>\n",
              "      <td>5.779895</td>\n",
              "      <td>-0.334133</td>\n",
              "      <td>4.989202</td>\n",
              "      <td>10.168495</td>\n",
              "      <td>5.702478</td>\n",
              "      <td>-11.493905</td>\n",
              "      <td>-12.183361</td>\n",
              "      <td>-99.799474</td>\n",
              "      <td>19.501556</td>\n",
              "      <td>6.082232</td>\n",
              "      <td>4.638709</td>\n",
              "      <td>0.718792</td>\n",
              "      <td>-1.198328</td>\n",
              "      <td>-4.514852</td>\n",
              "      <td>-6.879041</td>\n",
              "      <td>-6.294109</td>\n",
              "      <td>-4.465999</td>\n",
              "      <td>0.447696</td>\n",
              "      <td>3.458606</td>\n",
              "      <td>5.150128</td>\n",
              "      <td>4.078408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>LEDLight</td>\n",
              "      <td>0.356813</td>\n",
              "      <td>0.119018</td>\n",
              "      <td>0.101729</td>\n",
              "      <td>0.032103</td>\n",
              "      <td>0.025244</td>\n",
              "      <td>0.014373</td>\n",
              "      <td>0.015942</td>\n",
              "      <td>0.009384</td>\n",
              "      <td>0.005080</td>\n",
              "      <td>0.005661</td>\n",
              "      <td>0.004422</td>\n",
              "      <td>0.004713</td>\n",
              "      <td>0.004378</td>\n",
              "      <td>0.003854</td>\n",
              "      <td>0.003678</td>\n",
              "      <td>0.003733</td>\n",
              "      <td>0.003990</td>\n",
              "      <td>0.003885</td>\n",
              "      <td>0.003087</td>\n",
              "      <td>0.004065</td>\n",
              "      <td>0.004314</td>\n",
              "      <td>0.002900</td>\n",
              "      <td>0.002470</td>\n",
              "      <td>0.003061</td>\n",
              "      <td>190.469965</td>\n",
              "      <td>147.062863</td>\n",
              "      <td>1.565</td>\n",
              "      <td>-3.117734</td>\n",
              "      <td>0.354378</td>\n",
              "      <td>-71.997934</td>\n",
              "      <td>-15.054708</td>\n",
              "      <td>-7.240501</td>\n",
              "      <td>12.989703</td>\n",
              "      <td>11.941097</td>\n",
              "      <td>-8.206393</td>\n",
              "      <td>-0.838616</td>\n",
              "      <td>8.811147</td>\n",
              "      <td>2.164149</td>\n",
              "      <td>1.480793</td>\n",
              "      <td>...</td>\n",
              "      <td>9.613435</td>\n",
              "      <td>2.325417</td>\n",
              "      <td>-1.135969</td>\n",
              "      <td>-13.790836</td>\n",
              "      <td>-4.272798</td>\n",
              "      <td>5.299877</td>\n",
              "      <td>0.037066</td>\n",
              "      <td>1.460569</td>\n",
              "      <td>-3.011883</td>\n",
              "      <td>-2.063686</td>\n",
              "      <td>3.478081</td>\n",
              "      <td>0.657810</td>\n",
              "      <td>-198.259302</td>\n",
              "      <td>4.496607</td>\n",
              "      <td>5.433192</td>\n",
              "      <td>-1.892418</td>\n",
              "      <td>1.633665</td>\n",
              "      <td>-11.218237</td>\n",
              "      <td>0.580917</td>\n",
              "      <td>10.147438</td>\n",
              "      <td>12.028573</td>\n",
              "      <td>10.871960</td>\n",
              "      <td>8.921421</td>\n",
              "      <td>-1.695237</td>\n",
              "      <td>1.599370</td>\n",
              "      <td>-5.155088</td>\n",
              "      <td>-135.073021</td>\n",
              "      <td>2.630148</td>\n",
              "      <td>1.124619</td>\n",
              "      <td>0.077705</td>\n",
              "      <td>-0.363021</td>\n",
              "      <td>-0.651596</td>\n",
              "      <td>-1.924641</td>\n",
              "      <td>-1.598217</td>\n",
              "      <td>-1.144529</td>\n",
              "      <td>-0.715375</td>\n",
              "      <td>0.968146</td>\n",
              "      <td>0.901481</td>\n",
              "      <td>1.262521</td>\n",
              "      <td>1.478240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>LEDLight</td>\n",
              "      <td>0.313426</td>\n",
              "      <td>0.126891</td>\n",
              "      <td>0.082383</td>\n",
              "      <td>0.044711</td>\n",
              "      <td>0.027781</td>\n",
              "      <td>0.019323</td>\n",
              "      <td>0.028278</td>\n",
              "      <td>0.013895</td>\n",
              "      <td>0.011920</td>\n",
              "      <td>0.010885</td>\n",
              "      <td>0.010636</td>\n",
              "      <td>0.009320</td>\n",
              "      <td>0.007603</td>\n",
              "      <td>0.007341</td>\n",
              "      <td>0.007527</td>\n",
              "      <td>0.006775</td>\n",
              "      <td>0.005717</td>\n",
              "      <td>0.007115</td>\n",
              "      <td>0.006512</td>\n",
              "      <td>0.004976</td>\n",
              "      <td>0.005606</td>\n",
              "      <td>0.005927</td>\n",
              "      <td>0.004696</td>\n",
              "      <td>0.003984</td>\n",
              "      <td>189.909938</td>\n",
              "      <td>186.587251</td>\n",
              "      <td>1.772</td>\n",
              "      <td>-0.302318</td>\n",
              "      <td>0.397982</td>\n",
              "      <td>-42.469638</td>\n",
              "      <td>-0.965051</td>\n",
              "      <td>-8.411518</td>\n",
              "      <td>4.246129</td>\n",
              "      <td>1.281788</td>\n",
              "      <td>-3.890493</td>\n",
              "      <td>-0.451836</td>\n",
              "      <td>-0.950706</td>\n",
              "      <td>1.172445</td>\n",
              "      <td>0.019213</td>\n",
              "      <td>...</td>\n",
              "      <td>4.525365</td>\n",
              "      <td>1.702070</td>\n",
              "      <td>5.702965</td>\n",
              "      <td>-4.316177</td>\n",
              "      <td>-3.330419</td>\n",
              "      <td>3.427978</td>\n",
              "      <td>-1.110349</td>\n",
              "      <td>-1.654682</td>\n",
              "      <td>-4.583914</td>\n",
              "      <td>4.728680</td>\n",
              "      <td>7.346489</td>\n",
              "      <td>3.367240</td>\n",
              "      <td>-183.112871</td>\n",
              "      <td>14.749036</td>\n",
              "      <td>2.190766</td>\n",
              "      <td>0.270336</td>\n",
              "      <td>-4.547578</td>\n",
              "      <td>4.013359</td>\n",
              "      <td>-8.759840</td>\n",
              "      <td>4.699188</td>\n",
              "      <td>5.601084</td>\n",
              "      <td>2.590795</td>\n",
              "      <td>2.031462</td>\n",
              "      <td>1.719714</td>\n",
              "      <td>-8.256885</td>\n",
              "      <td>-1.663870</td>\n",
              "      <td>-131.909391</td>\n",
              "      <td>-5.046602</td>\n",
              "      <td>-1.131567</td>\n",
              "      <td>-0.982289</td>\n",
              "      <td>-0.030533</td>\n",
              "      <td>-0.038744</td>\n",
              "      <td>0.071392</td>\n",
              "      <td>-0.299320</td>\n",
              "      <td>0.320164</td>\n",
              "      <td>-0.233614</td>\n",
              "      <td>-0.094811</td>\n",
              "      <td>-0.630931</td>\n",
              "      <td>-0.629493</td>\n",
              "      <td>-0.303729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>LEDLight</td>\n",
              "      <td>0.391114</td>\n",
              "      <td>0.094905</td>\n",
              "      <td>0.103598</td>\n",
              "      <td>0.031663</td>\n",
              "      <td>0.030213</td>\n",
              "      <td>0.017206</td>\n",
              "      <td>0.016939</td>\n",
              "      <td>0.008324</td>\n",
              "      <td>0.007214</td>\n",
              "      <td>0.005411</td>\n",
              "      <td>0.006467</td>\n",
              "      <td>0.004947</td>\n",
              "      <td>0.004536</td>\n",
              "      <td>0.004284</td>\n",
              "      <td>0.003936</td>\n",
              "      <td>0.004234</td>\n",
              "      <td>0.003402</td>\n",
              "      <td>0.003474</td>\n",
              "      <td>0.003117</td>\n",
              "      <td>0.003371</td>\n",
              "      <td>0.002715</td>\n",
              "      <td>0.002559</td>\n",
              "      <td>0.002527</td>\n",
              "      <td>0.002209</td>\n",
              "      <td>188.616935</td>\n",
              "      <td>106.472982</td>\n",
              "      <td>1.583</td>\n",
              "      <td>-0.338430</td>\n",
              "      <td>0.354514</td>\n",
              "      <td>-41.112243</td>\n",
              "      <td>7.562016</td>\n",
              "      <td>-1.177282</td>\n",
              "      <td>6.333763</td>\n",
              "      <td>-4.901236</td>\n",
              "      <td>-5.935616</td>\n",
              "      <td>7.522660</td>\n",
              "      <td>3.360279</td>\n",
              "      <td>-3.404446</td>\n",
              "      <td>-4.260912</td>\n",
              "      <td>...</td>\n",
              "      <td>9.923058</td>\n",
              "      <td>4.650013</td>\n",
              "      <td>-12.490210</td>\n",
              "      <td>0.961700</td>\n",
              "      <td>-4.844721</td>\n",
              "      <td>-8.125392</td>\n",
              "      <td>6.313472</td>\n",
              "      <td>-0.053915</td>\n",
              "      <td>7.150834</td>\n",
              "      <td>-5.631543</td>\n",
              "      <td>2.650379</td>\n",
              "      <td>-5.266985</td>\n",
              "      <td>-159.401576</td>\n",
              "      <td>33.597653</td>\n",
              "      <td>14.361713</td>\n",
              "      <td>-1.131775</td>\n",
              "      <td>-3.969851</td>\n",
              "      <td>-8.840954</td>\n",
              "      <td>-3.203832</td>\n",
              "      <td>9.061309</td>\n",
              "      <td>1.325977</td>\n",
              "      <td>7.669931</td>\n",
              "      <td>6.536807</td>\n",
              "      <td>3.542126</td>\n",
              "      <td>-8.360659</td>\n",
              "      <td>-11.123940</td>\n",
              "      <td>-110.293395</td>\n",
              "      <td>19.728324</td>\n",
              "      <td>4.603525</td>\n",
              "      <td>2.983157</td>\n",
              "      <td>-0.909128</td>\n",
              "      <td>-1.681056</td>\n",
              "      <td>-4.135605</td>\n",
              "      <td>-4.844913</td>\n",
              "      <td>-4.038407</td>\n",
              "      <td>-2.245428</td>\n",
              "      <td>-0.162092</td>\n",
              "      <td>2.493132</td>\n",
              "      <td>3.151385</td>\n",
              "      <td>2.502930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>LEDLight</td>\n",
              "      <td>0.214931</td>\n",
              "      <td>0.199550</td>\n",
              "      <td>0.069968</td>\n",
              "      <td>0.050326</td>\n",
              "      <td>0.021760</td>\n",
              "      <td>0.016758</td>\n",
              "      <td>0.012651</td>\n",
              "      <td>0.010804</td>\n",
              "      <td>0.009351</td>\n",
              "      <td>0.008770</td>\n",
              "      <td>0.009481</td>\n",
              "      <td>0.009889</td>\n",
              "      <td>0.009411</td>\n",
              "      <td>0.007467</td>\n",
              "      <td>0.007814</td>\n",
              "      <td>0.007628</td>\n",
              "      <td>0.006722</td>\n",
              "      <td>0.006500</td>\n",
              "      <td>0.006902</td>\n",
              "      <td>0.007081</td>\n",
              "      <td>0.006412</td>\n",
              "      <td>0.005114</td>\n",
              "      <td>0.004769</td>\n",
              "      <td>0.003697</td>\n",
              "      <td>189.852598</td>\n",
              "      <td>175.338325</td>\n",
              "      <td>1.706</td>\n",
              "      <td>-4.161994</td>\n",
              "      <td>0.383864</td>\n",
              "      <td>-45.503188</td>\n",
              "      <td>-0.614706</td>\n",
              "      <td>-12.193679</td>\n",
              "      <td>6.354054</td>\n",
              "      <td>2.398623</td>\n",
              "      <td>-6.606266</td>\n",
              "      <td>-0.592757</td>\n",
              "      <td>1.231234</td>\n",
              "      <td>-0.768102</td>\n",
              "      <td>-1.040161</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.684539</td>\n",
              "      <td>-7.376767</td>\n",
              "      <td>-4.426213</td>\n",
              "      <td>-11.361073</td>\n",
              "      <td>-7.266410</td>\n",
              "      <td>-1.920547</td>\n",
              "      <td>-5.151930</td>\n",
              "      <td>-0.290739</td>\n",
              "      <td>-3.604179</td>\n",
              "      <td>-3.697015</td>\n",
              "      <td>0.594725</td>\n",
              "      <td>-2.023418</td>\n",
              "      <td>-208.532615</td>\n",
              "      <td>-7.049016</td>\n",
              "      <td>1.051266</td>\n",
              "      <td>1.809629</td>\n",
              "      <td>9.421616</td>\n",
              "      <td>6.900103</td>\n",
              "      <td>11.940495</td>\n",
              "      <td>8.505710</td>\n",
              "      <td>-0.562398</td>\n",
              "      <td>-7.807338</td>\n",
              "      <td>-4.840974</td>\n",
              "      <td>-4.088859</td>\n",
              "      <td>-8.198596</td>\n",
              "      <td>-8.535181</td>\n",
              "      <td>-122.658377</td>\n",
              "      <td>-4.339888</td>\n",
              "      <td>-1.687324</td>\n",
              "      <td>-0.972658</td>\n",
              "      <td>-0.413022</td>\n",
              "      <td>0.721265</td>\n",
              "      <td>1.256877</td>\n",
              "      <td>1.802895</td>\n",
              "      <td>2.395726</td>\n",
              "      <td>2.054111</td>\n",
              "      <td>1.586551</td>\n",
              "      <td>1.352533</td>\n",
              "      <td>1.091660</td>\n",
              "      <td>0.919217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1338</th>\n",
              "      <td>LEDLight</td>\n",
              "      <td>0.198991</td>\n",
              "      <td>0.159780</td>\n",
              "      <td>0.063119</td>\n",
              "      <td>0.065849</td>\n",
              "      <td>0.029477</td>\n",
              "      <td>0.024318</td>\n",
              "      <td>0.021363</td>\n",
              "      <td>0.018092</td>\n",
              "      <td>0.014131</td>\n",
              "      <td>0.014228</td>\n",
              "      <td>0.012916</td>\n",
              "      <td>0.009288</td>\n",
              "      <td>0.008853</td>\n",
              "      <td>0.009527</td>\n",
              "      <td>0.008269</td>\n",
              "      <td>0.011429</td>\n",
              "      <td>0.008841</td>\n",
              "      <td>0.006948</td>\n",
              "      <td>0.009132</td>\n",
              "      <td>0.008257</td>\n",
              "      <td>0.005702</td>\n",
              "      <td>0.007232</td>\n",
              "      <td>0.006551</td>\n",
              "      <td>0.005005</td>\n",
              "      <td>187.077704</td>\n",
              "      <td>183.218888</td>\n",
              "      <td>1.849</td>\n",
              "      <td>-0.342073</td>\n",
              "      <td>0.418905</td>\n",
              "      <td>-69.197554</td>\n",
              "      <td>-3.954941</td>\n",
              "      <td>-5.906611</td>\n",
              "      <td>7.301360</td>\n",
              "      <td>6.370855</td>\n",
              "      <td>-5.453914</td>\n",
              "      <td>1.038503</td>\n",
              "      <td>3.552267</td>\n",
              "      <td>2.020737</td>\n",
              "      <td>1.050757</td>\n",
              "      <td>...</td>\n",
              "      <td>6.943065</td>\n",
              "      <td>5.234970</td>\n",
              "      <td>-2.813334</td>\n",
              "      <td>-11.952251</td>\n",
              "      <td>5.059655</td>\n",
              "      <td>-0.528555</td>\n",
              "      <td>0.251150</td>\n",
              "      <td>-1.512228</td>\n",
              "      <td>-4.621919</td>\n",
              "      <td>9.247069</td>\n",
              "      <td>-1.950321</td>\n",
              "      <td>2.086209</td>\n",
              "      <td>-196.079190</td>\n",
              "      <td>25.173554</td>\n",
              "      <td>1.109999</td>\n",
              "      <td>6.905088</td>\n",
              "      <td>-12.545147</td>\n",
              "      <td>-0.703260</td>\n",
              "      <td>-8.872757</td>\n",
              "      <td>7.282073</td>\n",
              "      <td>-0.718459</td>\n",
              "      <td>3.299594</td>\n",
              "      <td>3.106294</td>\n",
              "      <td>6.043757</td>\n",
              "      <td>-7.873022</td>\n",
              "      <td>-9.822421</td>\n",
              "      <td>-113.538047</td>\n",
              "      <td>-7.351495</td>\n",
              "      <td>-4.591877</td>\n",
              "      <td>-1.367256</td>\n",
              "      <td>1.694285</td>\n",
              "      <td>4.126318</td>\n",
              "      <td>5.318662</td>\n",
              "      <td>4.674317</td>\n",
              "      <td>3.412493</td>\n",
              "      <td>1.874939</td>\n",
              "      <td>-0.045260</td>\n",
              "      <td>-1.635452</td>\n",
              "      <td>-2.000264</td>\n",
              "      <td>-1.005509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1339 rows × 142 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         label     3_har     5_har  ...      7,12      7,13      7,14\n",
              "0          Fan  0.023331  0.017029  ...  3.759688  5.247951  3.721544\n",
              "1          Fan  0.017799  0.008058  ...  3.617642  5.101208  3.848679\n",
              "2          Fan  0.006896  0.005013  ...  4.513976  5.589680  4.133777\n",
              "3          Fan  0.008346  0.002116  ...  2.831728  5.129934  3.443231\n",
              "4          Fan  0.011693  0.003311  ...  3.458606  5.150128  4.078408\n",
              "...        ...       ...       ...  ...       ...       ...       ...\n",
              "1334  LEDLight  0.356813  0.119018  ...  0.901481  1.262521  1.478240\n",
              "1335  LEDLight  0.313426  0.126891  ... -0.630931 -0.629493 -0.303729\n",
              "1336  LEDLight  0.391114  0.094905  ...  2.493132  3.151385  2.502930\n",
              "1337  LEDLight  0.214931  0.199550  ...  1.352533  1.091660  0.919217\n",
              "1338  LEDLight  0.198991  0.159780  ... -1.635452 -2.000264 -1.005509\n",
              "\n",
              "[1339 rows x 142 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MJD7Zk3Tg8F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv('all_merged.csv' , index = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9TeklGuUFQO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9974e325-b228-435d-d092-b4db17a4f476"
      },
      "source": [
        " x = df[['label' , '3_har' , '5_har' , '7_har' ,'9_har', 'v_rms' , \n",
        "         'max_power' , 'min_power' , 'power_factor', 'i_rms' , '0,1', ]]\n",
        "         #'0,2' ,'1,1',  '1,2' , '2,1' ,'3,1']]\n",
        " y = x[\"label\"]\n",
        " X = x.drop(labels = [\"label\"], axis = 1)\n",
        " print( np.shape(X) , np.shape(y) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1339, 10) (1339,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppq7lUmvVduP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1b77cff2-6560-47de-a3f3-68f14b5ff001"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.ensemble import ExtraTreesClassifier \n",
        "# Building the model \n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y) \n",
        "extra_tree_forest = ExtraTreesClassifier(n_estimators = 4000, \n",
        "\t\t\t\t\t\t\t\t\t\tcriterion ='entropy', max_features = 1  ).fit(X_train, y_train) \n",
        "          \n",
        "extra_tree_forest_predictions = extra_tree_forest.predict(X_test) \n",
        "\n",
        "accuracy = extra_tree_forest.score(X_test, y_test) \n",
        "print(accuracy * 100)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99.1044776119403\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPMK-ErBVTa7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "b5a7477f-b86c-46cf-89b3-7b475a88ad8c"
      },
      "source": [
        "import seaborn as sns\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve\n",
        "\n",
        "\n",
        "kfold = StratifiedKFold(n_splits=2)\n",
        "\n",
        "# Modeling step Test differents algorithms \n",
        "import seaborn as sns\n",
        "random_state = 2\n",
        "classifiers = []\n",
        "classifiers.append(SVC(random_state=random_state))\n",
        "classifiers.append(DecisionTreeClassifier(random_state=random_state))\n",
        "classifiers.append(AdaBoostClassifier(DecisionTreeClassifier(random_state=random_state),random_state=random_state,learning_rate=0.1))\n",
        "classifiers.append(RandomForestClassifier(random_state=random_state))\n",
        "classifiers.append(ExtraTreesClassifier(random_state=random_state))\n",
        "classifiers.append(GradientBoostingClassifier(random_state=random_state))\n",
        "classifiers.append(MLPClassifier(random_state=random_state))\n",
        "classifiers.append(KNeighborsClassifier())\n",
        "classifiers.append(LogisticRegression(random_state = random_state))\n",
        "classifiers.append(LinearDiscriminantAnalysis())\n",
        "\n",
        "cv_results = []\n",
        "for classifier in classifiers :\n",
        "    cv_results.append(cross_val_score(classifier, X_train, y = y_train, scoring = \"accuracy\", cv = kfold, n_jobs=4))\n",
        "\n",
        "cv_means = []\n",
        "cv_std = []\n",
        "for cv_result in cv_results:\n",
        "    cv_means.append(cv_result.mean())\n",
        "    cv_std.append(cv_result.std())\n",
        "\n",
        "cv_res = pd.DataFrame({\"CrossValMeans\":cv_means,\"CrossValerrors\": cv_std,\"Algorithm\":[\"SVC\",\"DecisionTree\",\"AdaBoost\",\n",
        "\"RandomForest\",\"ExtraTrees\",\"GradientBoosting\",\"MultipleLayerPerceptron\",\"KNeighboors\",\"LogisticRegression\",\"LinearDiscriminantAnalysis\"]})\n",
        "\n",
        "g = sns.barplot(\"CrossValMeans\",\"Algorithm\",data = cv_res, palette=\"Set3\",orient = \"h\",**{'xerr':cv_std})\n",
        "g.set_xlabel(\"Mean Accuracy\")\n",
        "g = g.set_title(\"Cross validation scores\") \n",
        "print(cv_res)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   CrossValMeans  CrossValerrors                   Algorithm\n",
            "0       0.206175        0.006972                         SVC\n",
            "1       0.833665        0.008964                DecisionTree\n",
            "2       0.830677        0.003984                    AdaBoost\n",
            "3       0.955179        0.018924                RandomForest\n",
            "4       0.977092        0.012948                  ExtraTrees\n",
            "5       0.801793        0.012948            GradientBoosting\n",
            "6       0.301793        0.006972     MultipleLayerPerceptron\n",
            "7       0.530876        0.000996                 KNeighboors\n",
            "8       0.189243        0.011952          LogisticRegression\n",
            "9       0.452191        0.007968  LinearDiscriminantAnalysis\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
        "id": "RqlhDGuoVYb_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}