Model_flow_prediction.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sappyb/Codes-Doc/blob/master/Model_flow_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1vNWiSnT1Z3W"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from collections import Counter\n",
        "from sklearn.model_selection import train_test_split\n",
        "from google.colab import drive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQhkRddL8sFR",
        "outputId": "ed3ae8f4-1036-49e2-9a91-5efc261b8704"
      },
      "source": [
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvW2FvsL9Ai_"
      },
      "source": [
        "images_dir = '/content/gdrive/My Drive/rate_distribution'\n",
        "model_dir = '/content/gdrive/My Drive/rate_distribution/mymodel_4'\n",
        "work_dir = '/content/gdrive/My Drive/rate_distribution'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQQRQd507gxh"
      },
      "source": [
        "applications = ['2d', '3d', '4d', 'nekbone', 'lammps', 'milc']\n",
        "ranks = ['1024']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZmR8t9nyxc1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01f47d0f-1cf8-4de7-a6cb-b91a9b6e73e8"
      },
      "source": [
        "prepivot_train = pd.DataFrame()\n",
        "prepivot_test = pd.DataFrame()\n",
        "number_of_flows_per_application = []\n",
        "for i in applications:\n",
        "  for j in ranks:\n",
        "    f_name = '/content/gdrive/My Drive/csvs/full-{}-{}.csv'.format(i, j)\n",
        "    print(f_name)\n",
        "    data_a = pd.read_csv(f_name, on_bad_lines='skip')\n",
        "    data_a = data_a.sample(frac=1).reset_index(drop=True)\n",
        "    print(i)\n",
        "    print(j)\n",
        "    print(data_a.shape)\n",
        "\n",
        "    if j == '32':\n",
        "      if i == 'amg':\n",
        "        data_a = data_a.head(int(100 * 3.5))\n",
        "      elif i == 'kripke':\n",
        "        data_a = data_a.head(int(100 * 4))\n",
        "        data_a = pd.DataFrame(pd.np.repeat(data_a.values,2,axis=0),columns=data_a.columns)\n",
        "      elif i == 'laghos':\n",
        "        data_a = data_a.head(int(100 * 6))\n",
        "      else:\n",
        "        data_a = data_a.head(int(100))\n",
        "    if j == '64':\n",
        "      if i == 'amg':\n",
        "        data_a = data_a.head(int(220 * 3.5))\n",
        "      elif i == 'kripke':\n",
        "        data_a = data_a.head(int(220 * 4))\n",
        "        data_a = pd.DataFrame(pd.np.repeat(data_a.values,2,axis=0),columns=data_a.columns)\n",
        "      elif i == 'laghos':\n",
        "        data_a = data_a.head(int(220 * 6))\n",
        "      else:\n",
        "        data_a = data_a.head(int(220))\n",
        "    if j == '128':\n",
        "      if i == 'amg':\n",
        "        data_a = data_a.head(int(460 * 3.5))\n",
        "      if i == 'kripke':\n",
        "        data_a = data_a.head(int(460 * 4))\n",
        "        data_a = pd.DataFrame(pd.np.repeat(data_a.values,2,axis=0),columns=data_a.columns)\n",
        "      if i == 'laghos':\n",
        "        data_a = data_a.head(int(460 * 6))\n",
        "      else:\n",
        "        data_a = data_a.head(int(460))\n",
        "    if j == '256':\n",
        "      if i == 'amg':\n",
        "        data_a = data_a.head(int(950 * 3.5))\n",
        "      elif i == 'kripke':\n",
        "        data_a = data_a.head(int(950 * 4))\n",
        "        data_a = pd.DataFrame(pd.np.repeat(data_a.values,2,axis=0),columns=data_a.columns)\n",
        "      elif i == 'laghos':\n",
        "        data_a = data_a.head(int(950 * 6))\n",
        "      else:\n",
        "        data_a = data_a.head(int(950))\n",
        "    if j == '512':\n",
        "      if i == 'amg':\n",
        "        data_a = data_a.head(int(1950 * 3.5))\n",
        "      elif i == 'kripke':\n",
        "        data_a = data_a.head(int(1950 * 4))\n",
        "        data_a = pd.DataFrame(pd.np.repeat(data_a.values,2,axis=0),columns=data_a.columns)\n",
        "      elif i == 'laghos':\n",
        "        data_a = data_a.head(int(1950 * 6))\n",
        "      else:\n",
        "        data_a = data_a.head(int(1950))\n",
        "    if j == '1024':\n",
        "      data_a = data_a.head(int(3900))\n",
        "      print(data_a.shape)\n",
        "\n",
        "    data_list = []\n",
        "    for k in range(1, 246):\n",
        "      data_list.append('Data_{}'.format(k))\n",
        "    data_a['result'] = data_a[data_list].sum(axis=1)\n",
        "    print(data_a['result'])\n",
        "    elephant = []\n",
        "    for z in data_a['result'].to_list():\n",
        "        if z > (1 * 10**7):\n",
        "          elephant.append(1)\n",
        "        else:\n",
        "          elephant.append(0)\n",
        "    data_a['Is_Elephant'] = elephant\n",
        "    data_a['Application'] = i\n",
        "    data_a['Rank'] = j\n",
        "    prepivot_train = pd.concat([prepivot_train, data_a])\n",
        "    number_of_flows_per_application.append(data_a.shape[0])\n",
        "    elephant.clear()\n",
        "print(prepivot_train)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/My Drive/csvs/full-2d-1024.csv\n",
            "2d\n",
            "1024\n",
            "(4096, 301)\n",
            "(3900, 301)\n",
            "0       77930752\n",
            "1       77963456\n",
            "2       77996352\n",
            "3       77932736\n",
            "4       77922368\n",
            "          ...   \n",
            "3895    77930432\n",
            "3896    77851584\n",
            "3897    77963392\n",
            "3898    78029312\n",
            "3899    77930560\n",
            "Name: result, Length: 3900, dtype: int64\n",
            "/content/gdrive/My Drive/csvs/full-3d-1024.csv\n",
            "3d\n",
            "1024\n",
            "(6144, 301)\n",
            "(3900, 301)\n",
            "0       57495040\n",
            "1       57502272\n",
            "2       57590720\n",
            "3       57568000\n",
            "4       57502272\n",
            "          ...   \n",
            "3895    57582336\n",
            "3896    57534976\n",
            "3897    57535168\n",
            "3898    57568000\n",
            "3899    57568000\n",
            "Name: result, Length: 3900, dtype: int64\n",
            "/content/gdrive/My Drive/csvs/full-4d-1024.csv\n",
            "4d\n",
            "1024\n",
            "(8192, 301)\n",
            "(3900, 301)\n",
            "0       50988928\n",
            "1       51021824\n",
            "2       51021632\n",
            "3       50988800\n",
            "4       51053568\n",
            "          ...   \n",
            "3895    51036096\n",
            "3896    51007424\n",
            "3897    51021824\n",
            "3898    51021568\n",
            "3899    51054592\n",
            "Name: result, Length: 3900, dtype: int64\n",
            "/content/gdrive/My Drive/csvs/full-nekbone-1024.csv\n",
            "nekbone\n",
            "1024\n",
            "(5709, 246)\n",
            "(3900, 246)\n",
            "0              0\n",
            "1              0\n",
            "2           3080\n",
            "3              0\n",
            "4              0\n",
            "          ...   \n",
            "3895    48686240\n",
            "3896           0\n",
            "3897           0\n",
            "3898           0\n",
            "3899           0\n",
            "Name: result, Length: 3900, dtype: int64\n",
            "/content/gdrive/My Drive/csvs/full-lammps-1024.csv\n",
            "lammps\n",
            "1024\n",
            "(92968, 297)\n",
            "(3900, 297)\n",
            "0        2157504\n",
            "1         966144\n",
            "2         240448\n",
            "3         966144\n",
            "4        2158592\n",
            "          ...   \n",
            "3895      240448\n",
            "3896        1088\n",
            "3897      240448\n",
            "3898        1088\n",
            "3899    15631453\n",
            "Name: result, Length: 3900, dtype: int64\n",
            "/content/gdrive/My Drive/csvs/full-milc-1024.csv\n",
            "milc\n",
            "1024\n",
            "(11608, 298)\n",
            "(3900, 298)\n",
            "0       46793200\n",
            "1       46811904\n",
            "2            752\n",
            "3            752\n",
            "4            752\n",
            "          ...   \n",
            "3895    46793200\n",
            "3896    46792448\n",
            "3897    46841600\n",
            "3898    46836208\n",
            "3899         752\n",
            "Name: result, Length: 3900, dtype: int64\n",
            "      FlowID  Data_1  Data_2  Data_3  Data_4  Data_5  Data_6  Data_7  Data_8  \\\n",
            "0        972  328896  263296  328960  328896  328896  296192  328960  328960   \n",
            "1       3687  296128  296064  328960  296128  328960  361728  296192  328832   \n",
            "2       1350  296000  296128  328960  328896  296128  328896  342336  315584   \n",
            "3       3506  298240  296064  328896  314560  310528  328960  328896  329024   \n",
            "4       2731  361792  263168  328960  296128  328896  328960  328960  296128   \n",
            "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
            "3895       0  219136  278656  328704  169104       0       0  497792  230400   \n",
            "3896       0  139264  417920  306176  132224       0   87040  410752  306176   \n",
            "3897       0  330752  167040  328704  169088       0  260096  237696  236544   \n",
            "3898  319488  178176  178304  194560  125064  266248  201728  162944  192512   \n",
            "3899       8       0       0       0       8       8       0       0       0   \n",
            "\n",
            "      Data_9  ...  Data_295  Data_296  Data_297  Data_298  Data_299  Data_300  \\\n",
            "0     328960  ...  303360.0  296064.0  328960.0  296000.0  328896.0  329088.0   \n",
            "1     296128  ...  328896.0  296128.0  328896.0  329024.0  361728.0  329088.0   \n",
            "2     328960  ...  328960.0  299136.0  325888.0  361728.0  328960.0  343424.0   \n",
            "3     296064  ...  328960.0  296000.0  296128.0  328960.0  328960.0  328960.0   \n",
            "4     361792  ...  312448.0  312576.0  328896.0  296064.0  296128.0  328896.0   \n",
            "...      ...  ...       ...       ...       ...       ...       ...       ...   \n",
            "3895  267408  ...  356496.0       0.0       0.0       NaN       NaN       NaN   \n",
            "3896  191616  ...  247936.0       0.0       0.0       NaN       NaN       NaN   \n",
            "3897  261248  ...  342144.0       0.0  121856.0       NaN       NaN       NaN   \n",
            "3898  172168  ...  210056.0  137224.0  271360.0       NaN       NaN       NaN   \n",
            "3899       8  ...       8.0       8.0       8.0       NaN       NaN       NaN   \n",
            "\n",
            "        result  Is_Elephant  Application  Rank  \n",
            "0     77930752            1           2d  1024  \n",
            "1     77963456            1           2d  1024  \n",
            "2     77996352            1           2d  1024  \n",
            "3     77932736            1           2d  1024  \n",
            "4     77922368            1           2d  1024  \n",
            "...        ...          ...          ...   ...  \n",
            "3895  46793200            1         milc  1024  \n",
            "3896  46792448            1         milc  1024  \n",
            "3897  46841600            1         milc  1024  \n",
            "3898  46836208            1         milc  1024  \n",
            "3899       752            0         milc  1024  \n",
            "\n",
            "[23400 rows x 305 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2YYNKFYJVxB"
      },
      "source": [
        "prepivot_train = prepivot_train.fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDWnAuYy5gkD"
      },
      "source": [
        "num_of_samples_train = prepivot_train.shape[0]\n",
        "num_of_time_steps = 301\n",
        "num_of_features = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4Gl65OQKAK0"
      },
      "source": [
        "labels_train = prepivot_train['Is_Elephant'].to_list()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7QdBstktKCye",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "c2503125-0066-48eb-8b06-adba2642cb5f"
      },
      "source": [
        "for i in range(301):\n",
        "  prepivot_train = prepivot_train.drop('Time_{}'.format(i), axis='columns')\n",
        "  prepivot_train = prepivot_train.drop('Rate_{}'.format(i), axis='columns')\n",
        "  prepivot_train = prepivot_train.drop('Packet_Rate_{}'.format(i), axis='columns')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "\"['Time_0'] not found in axis\"",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-23a66b85a092>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m301\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mprepivot_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepivot_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Time_{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'columns'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mprepivot_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepivot_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Rate_{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'columns'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mprepivot_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepivot_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Packet_Rate_{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'columns'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5342\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5343\u001b[0m         \"\"\"\n\u001b[0;32m-> 5344\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5345\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5346\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4709\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4710\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4711\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4713\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4751\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4752\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4753\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4754\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6998\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6999\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7000\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask].tolist()} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7001\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7002\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['Time_0'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepivot_train.iloc[:, 1:-4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "o6gz5KDXHkdg",
        "outputId": "bbb3dba4-c402-498c-d858-a23d0f744450"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Data_1  Data_2  Data_3  Data_4  Data_5  Data_6  Data_7  Data_8  Data_9  \\\n",
              "0     328896  263296  328960  328896  328896  296192  328960  328960  328960   \n",
              "1     296128  296064  328960  296128  328960  361728  296192  328832  296128   \n",
              "2     296000  296128  328960  328896  296128  328896  342336  315584  328960   \n",
              "3     298240  296064  328896  314560  310528  328960  328896  329024  296064   \n",
              "4     361792  263168  328960  296128  328896  328960  328960  296128  361792   \n",
              "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
              "3895  219136  278656  328704  169104       0       0  497792  230400  267408   \n",
              "3896  139264  417920  306176  132224       0   87040  410752  306176  191616   \n",
              "3897  330752  167040  328704  169088       0  260096  237696  236544  261248   \n",
              "3898  178176  178304  194560  125064  266248  201728  162944  192512  172168   \n",
              "3899       0       0       0       8       8       0       0       0       8   \n",
              "\n",
              "      Data_10  ...  Data_291  Data_292  Data_293  Data_294  Data_295  \\\n",
              "0      295936  ...  328896.0  329024.0  328960.0  321664.0  303360.0   \n",
              "1      329024  ...  328960.0  328896.0  328896.0  296192.0  328896.0   \n",
              "2      296128  ...  296192.0  328960.0  328960.0  296064.0  328960.0   \n",
              "3      328960  ...  329024.0  328896.0  328960.0  296128.0  328960.0   \n",
              "4      296064  ...  321728.0  309504.0  322816.0  328960.0  312448.0   \n",
              "...       ...  ...       ...       ...       ...       ...       ...   \n",
              "3895        0  ...       0.0       0.0  497728.0  141376.0  356496.0   \n",
              "3896        0  ...       0.0       0.0  497792.0  249856.0  247936.0   \n",
              "3897        0  ...       0.0  200704.0  297024.0  155712.0  342144.0   \n",
              "3898   250888  ...  216072.0  223232.0  123008.0  223232.0  210056.0   \n",
              "3899        8  ...       8.0       0.0       0.0       0.0       8.0   \n",
              "\n",
              "      Data_296  Data_297  Data_298  Data_299  Data_300  \n",
              "0     296064.0  328960.0  296000.0  328896.0  329088.0  \n",
              "1     296128.0  328896.0  329024.0  361728.0  329088.0  \n",
              "2     299136.0  325888.0  361728.0  328960.0  343424.0  \n",
              "3     296000.0  296128.0  328960.0  328960.0  328960.0  \n",
              "4     312576.0  328896.0  296064.0  296128.0  328896.0  \n",
              "...        ...       ...       ...       ...       ...  \n",
              "3895       0.0       0.0       0.0       0.0       0.0  \n",
              "3896       0.0       0.0       0.0       0.0       0.0  \n",
              "3897       0.0  121856.0       0.0       0.0       0.0  \n",
              "3898  137224.0  271360.0       0.0       0.0       0.0  \n",
              "3899       8.0       8.0       0.0       0.0       0.0  \n",
              "\n",
              "[23400 rows x 300 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c4aca53-3c6c-40d8-ba9c-f8ea53564e7f\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data_1</th>\n",
              "      <th>Data_2</th>\n",
              "      <th>Data_3</th>\n",
              "      <th>Data_4</th>\n",
              "      <th>Data_5</th>\n",
              "      <th>Data_6</th>\n",
              "      <th>Data_7</th>\n",
              "      <th>Data_8</th>\n",
              "      <th>Data_9</th>\n",
              "      <th>Data_10</th>\n",
              "      <th>...</th>\n",
              "      <th>Data_291</th>\n",
              "      <th>Data_292</th>\n",
              "      <th>Data_293</th>\n",
              "      <th>Data_294</th>\n",
              "      <th>Data_295</th>\n",
              "      <th>Data_296</th>\n",
              "      <th>Data_297</th>\n",
              "      <th>Data_298</th>\n",
              "      <th>Data_299</th>\n",
              "      <th>Data_300</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>328896</td>\n",
              "      <td>263296</td>\n",
              "      <td>328960</td>\n",
              "      <td>328896</td>\n",
              "      <td>328896</td>\n",
              "      <td>296192</td>\n",
              "      <td>328960</td>\n",
              "      <td>328960</td>\n",
              "      <td>328960</td>\n",
              "      <td>295936</td>\n",
              "      <td>...</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>329024.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>321664.0</td>\n",
              "      <td>303360.0</td>\n",
              "      <td>296064.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>296000.0</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>329088.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>296128</td>\n",
              "      <td>296064</td>\n",
              "      <td>328960</td>\n",
              "      <td>296128</td>\n",
              "      <td>328960</td>\n",
              "      <td>361728</td>\n",
              "      <td>296192</td>\n",
              "      <td>328832</td>\n",
              "      <td>296128</td>\n",
              "      <td>329024</td>\n",
              "      <td>...</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>296192.0</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>296128.0</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>329024.0</td>\n",
              "      <td>361728.0</td>\n",
              "      <td>329088.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>296000</td>\n",
              "      <td>296128</td>\n",
              "      <td>328960</td>\n",
              "      <td>328896</td>\n",
              "      <td>296128</td>\n",
              "      <td>328896</td>\n",
              "      <td>342336</td>\n",
              "      <td>315584</td>\n",
              "      <td>328960</td>\n",
              "      <td>296128</td>\n",
              "      <td>...</td>\n",
              "      <td>296192.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>296064.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>299136.0</td>\n",
              "      <td>325888.0</td>\n",
              "      <td>361728.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>343424.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>298240</td>\n",
              "      <td>296064</td>\n",
              "      <td>328896</td>\n",
              "      <td>314560</td>\n",
              "      <td>310528</td>\n",
              "      <td>328960</td>\n",
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              "      <td>321728.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepivot_train.iloc[:, :]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "1D_N2i1_LIdh",
        "outputId": "2cff70bd-00fb-452d-c31e-2d13f429910a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      FlowID  Data_1  Data_2  Data_3  Data_4  Data_5  Data_6  Data_7  Data_8  \\\n",
              "0        972  328896  263296  328960  328896  328896  296192  328960  328960   \n",
              "1       3687  296128  296064  328960  296128  328960  361728  296192  328832   \n",
              "2       1350  296000  296128  328960  328896  296128  328896  342336  315584   \n",
              "3       3506  298240  296064  328896  314560  310528  328960  328896  329024   \n",
              "4       2731  361792  263168  328960  296128  328896  328960  328960  296128   \n",
              "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
              "3895       0  219136  278656  328704  169104       0       0  497792  230400   \n",
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              "3899       8       0       0       0       8       8       0       0       0   \n",
              "\n",
              "      Data_9  ...  Data_295  Data_296  Data_297  Data_298  Data_299  Data_300  \\\n",
              "0     328960  ...  303360.0  296064.0  328960.0  296000.0  328896.0  329088.0   \n",
              "1     296128  ...  328896.0  296128.0  328896.0  329024.0  361728.0  329088.0   \n",
              "2     328960  ...  328960.0  299136.0  325888.0  361728.0  328960.0  343424.0   \n",
              "3     296064  ...  328960.0  296000.0  296128.0  328960.0  328960.0  328960.0   \n",
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              "...      ...  ...       ...       ...       ...       ...       ...       ...   \n",
              "3895  267408  ...  356496.0       0.0       0.0       0.0       0.0       0.0   \n",
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              "3897  261248  ...  342144.0       0.0  121856.0       0.0       0.0       0.0   \n",
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              "3899       8  ...       8.0       8.0       8.0       0.0       0.0       0.0   \n",
              "\n",
              "        result  Is_Elephant  Application  Rank  \n",
              "0     77930752            1           2d  1024  \n",
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              "...        ...          ...          ...   ...  \n",
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              "3897  46841600            1         milc  1024  \n",
              "3898  46836208            1         milc  1024  \n",
              "3899       752            0         milc  1024  \n",
              "\n",
              "[23400 rows x 305 columns]"
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              "      <th>Data_6</th>\n",
              "      <th>Data_7</th>\n",
              "      <th>Data_8</th>\n",
              "      <th>Data_9</th>\n",
              "      <th>...</th>\n",
              "      <th>Data_295</th>\n",
              "      <th>Data_296</th>\n",
              "      <th>Data_297</th>\n",
              "      <th>Data_298</th>\n",
              "      <th>Data_299</th>\n",
              "      <th>Data_300</th>\n",
              "      <th>result</th>\n",
              "      <th>Is_Elephant</th>\n",
              "      <th>Application</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>972</td>\n",
              "      <td>328896</td>\n",
              "      <td>263296</td>\n",
              "      <td>328960</td>\n",
              "      <td>328896</td>\n",
              "      <td>328896</td>\n",
              "      <td>296192</td>\n",
              "      <td>328960</td>\n",
              "      <td>328960</td>\n",
              "      <td>328960</td>\n",
              "      <td>...</td>\n",
              "      <td>303360.0</td>\n",
              "      <td>296064.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>296000.0</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>329088.0</td>\n",
              "      <td>77930752</td>\n",
              "      <td>1</td>\n",
              "      <td>2d</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3687</td>\n",
              "      <td>296128</td>\n",
              "      <td>296064</td>\n",
              "      <td>328960</td>\n",
              "      <td>296128</td>\n",
              "      <td>328960</td>\n",
              "      <td>361728</td>\n",
              "      <td>296192</td>\n",
              "      <td>328832</td>\n",
              "      <td>296128</td>\n",
              "      <td>...</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>296128.0</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>329024.0</td>\n",
              "      <td>361728.0</td>\n",
              "      <td>329088.0</td>\n",
              "      <td>77963456</td>\n",
              "      <td>1</td>\n",
              "      <td>2d</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1350</td>\n",
              "      <td>296000</td>\n",
              "      <td>296128</td>\n",
              "      <td>328960</td>\n",
              "      <td>328896</td>\n",
              "      <td>296128</td>\n",
              "      <td>328896</td>\n",
              "      <td>342336</td>\n",
              "      <td>315584</td>\n",
              "      <td>328960</td>\n",
              "      <td>...</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>299136.0</td>\n",
              "      <td>325888.0</td>\n",
              "      <td>361728.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>343424.0</td>\n",
              "      <td>77996352</td>\n",
              "      <td>1</td>\n",
              "      <td>2d</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3506</td>\n",
              "      <td>298240</td>\n",
              "      <td>296064</td>\n",
              "      <td>328896</td>\n",
              "      <td>314560</td>\n",
              "      <td>310528</td>\n",
              "      <td>328960</td>\n",
              "      <td>328896</td>\n",
              "      <td>329024</td>\n",
              "      <td>296064</td>\n",
              "      <td>...</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>296000.0</td>\n",
              "      <td>296128.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>328960.0</td>\n",
              "      <td>77932736</td>\n",
              "      <td>1</td>\n",
              "      <td>2d</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2731</td>\n",
              "      <td>361792</td>\n",
              "      <td>263168</td>\n",
              "      <td>328960</td>\n",
              "      <td>296128</td>\n",
              "      <td>328896</td>\n",
              "      <td>328960</td>\n",
              "      <td>328960</td>\n",
              "      <td>296128</td>\n",
              "      <td>361792</td>\n",
              "      <td>...</td>\n",
              "      <td>312448.0</td>\n",
              "      <td>312576.0</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>296064.0</td>\n",
              "      <td>296128.0</td>\n",
              "      <td>328896.0</td>\n",
              "      <td>77922368</td>\n",
              "      <td>1</td>\n",
              "      <td>2d</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3895</th>\n",
              "      <td>0</td>\n",
              "      <td>219136</td>\n",
              "      <td>278656</td>\n",
              "      <td>328704</td>\n",
              "      <td>169104</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>497792</td>\n",
              "      <td>230400</td>\n",
              "      <td>267408</td>\n",
              "      <td>...</td>\n",
              "      <td>356496.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46793200</td>\n",
              "      <td>1</td>\n",
              "      <td>milc</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3896</th>\n",
              "      <td>0</td>\n",
              "      <td>139264</td>\n",
              "      <td>417920</td>\n",
              "      <td>306176</td>\n",
              "      <td>132224</td>\n",
              "      <td>0</td>\n",
              "      <td>87040</td>\n",
              "      <td>410752</td>\n",
              "      <td>306176</td>\n",
              "      <td>191616</td>\n",
              "      <td>...</td>\n",
              "      <td>247936.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46792448</td>\n",
              "      <td>1</td>\n",
              "      <td>milc</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3897</th>\n",
              "      <td>0</td>\n",
              "      <td>330752</td>\n",
              "      <td>167040</td>\n",
              "      <td>328704</td>\n",
              "      <td>169088</td>\n",
              "      <td>0</td>\n",
              "      <td>260096</td>\n",
              "      <td>237696</td>\n",
              "      <td>236544</td>\n",
              "      <td>261248</td>\n",
              "      <td>...</td>\n",
              "      <td>342144.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>121856.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46841600</td>\n",
              "      <td>1</td>\n",
              "      <td>milc</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3898</th>\n",
              "      <td>319488</td>\n",
              "      <td>178176</td>\n",
              "      <td>178304</td>\n",
              "      <td>194560</td>\n",
              "      <td>125064</td>\n",
              "      <td>266248</td>\n",
              "      <td>201728</td>\n",
              "      <td>162944</td>\n",
              "      <td>192512</td>\n",
              "      <td>172168</td>\n",
              "      <td>...</td>\n",
              "      <td>210056.0</td>\n",
              "      <td>137224.0</td>\n",
              "      <td>271360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46836208</td>\n",
              "      <td>1</td>\n",
              "      <td>milc</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3899</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>752</td>\n",
              "      <td>0</td>\n",
              "      <td>milc</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23400 rows × 305 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      display:flex;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
              "\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cdcc7620-5b9e-4883-87f0-f1ab656b4a28 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_test_probe = np.array(prepivot_train.iloc[:, 1:-4])"
      ],
      "metadata": {
        "id": "P1tnGzh4Ilg6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "row_with_max_value = np.amax(data_test_probe, axis=0)\n",
        "row_with_max_value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8knnCQGSJQ47",
        "outputId": "427b9071-e0bc-40d8-9b85-573dfac7656f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([497792., 673920., 361792., 361856., 475136., 497728., 676992.,\n",
              "       435328., 372096., 361856., 497792., 655488., 754816., 470095.,\n",
              "       389120., 497792., 579712., 729216., 446464., 361856., 497728.,\n",
              "       505984., 710784., 408576., 394624., 497664., 497792., 695424.,\n",
              "       361856., 374144., 497664., 497728., 637056., 756864., 361856.,\n",
              "       381952., 497792., 612480., 750720., 406656., 361856., 497728.,\n",
              "       562304., 720000., 446464., 361856., 497728., 500864., 704640.,\n",
              "       398336., 361856., 497664., 497792., 695424., 361792., 361856.,\n",
              "       435200., 497792., 649344., 741504., 361856., 365568., 497792.,\n",
              "       614528., 734336., 375944., 371712., 497792., 544896., 710784.,\n",
              "       446464., 361856., 497664., 497792., 690304., 376832., 368000.,\n",
              "       497664., 497792., 685184., 361856., 390592., 368000., 497792.,\n",
              "       647296., 763008., 460596., 379904., 497792., 611456., 748672.,\n",
              "       407688., 370048., 497728., 562304., 721024., 446464., 361856.,\n",
              "       488448., 497792., 689280., 374784., 361856., 480256., 497792.,\n",
              "       693376., 361856., 361856., 361856., 497792., 663680., 728192.,\n",
              "       372096., 396288., 497792., 606336., 745600., 415872., 361856.,\n",
              "       497728., 550016., 714880., 446464., 361920., 497664., 497792.,\n",
              "       699520., 394624., 361856., 408576., 497792., 664704., 361856.,\n",
              "       394624., 497664., 497728., 656512., 667776., 373120., 383360.,\n",
              "       497792., 606336., 745600., 415872., 374144., 497728., 521344.,\n",
              "       693376., 431104., 392576., 497664., 497792., 690304., 361792.,\n",
              "       361856., 635496., 635480., 667776., 394624., 372096., 361856.,\n",
              "       497792., 663680., 583808., 361856., 361792., 497792., 649344.,\n",
              "       749696., 361856., 385024., 497792., 566400., 723072., 446464.,\n",
              "       394624., 497664., 504960., 712832., 408576., 374144., 497728.,\n",
              "       497792., 689280., 379264., 361856., 432128., 497728., 662656.,\n",
              "       763008., 361856., 391168., 497792., 563328., 722048., 446464.,\n",
              "       361856., 497664., 504960., 708736., 405504., 396776., 497664.,\n",
              "       497792., 706688., 361792., 361856., 492544., 497792., 655488.,\n",
              "       755840., 361856., 390144., 497792., 571520., 729216., 444480.,\n",
              "       361856., 497664., 550016., 697472., 440320., 361856., 492544.,\n",
              "       497792., 700544., 391168., 368000., 496640., 497792., 705664.,\n",
              "       361856., 390592., 443392., 497728., 669824., 704640., 361856.,\n",
              "       394240., 497792., 601216., 738432., 405632., 370048., 497728.,\n",
              "       517248., 718976., 417792., 361856., 452608., 497792., 691328.,\n",
              "       377856., 361856., 432128., 497792., 704640., 361856., 361856.,\n",
              "       402432., 497792., 674944., 466048., 372096., 390144., 497792.,\n",
              "       636032., 737408., 369800., 361856., 497792., 596096., 739456.,\n",
              "       427136., 361920., 497792., 515200., 703616., 416768., 361856.,\n",
              "       446464., 497792., 686208., 371712., 394624., 497664., 497728.,\n",
              "       689280., 361856., 373120., 404480., 497728., 617600., 742528.,\n",
              "       361856., 374144., 497792., 394624., 394624., 394688.])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum(row_with_max_value) // 300"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVg1oYOjJ02i",
        "outputId": "7a11b373-76ad-41e9-e7da-f62c658f1cba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "502293.0"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.amax(data_test_probe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFg4iK-UMCsu",
        "outputId": "36346357-4a85-43c6-ebcb-581ab5bf3028"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "763008.0"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.amax(np.array(prepivot_train['result']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x70yizmPLOsC",
        "outputId": "87de0e5d-c079-465d-be7a-77f1716f5794"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "78095040"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))"
      ],
      "metadata": {
        "id": "NRtZK-fVY1Ix"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JS84ze18zPLx"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "xp_train = scaler.fit_transform(prepivot_train.iloc[:, 2:-4])\n",
        "#xp_train = prepivot_train.iloc[:, 2:-4]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVAbjeIyDLES"
      },
      "source": [
        "Arranging the data from 2D to 3D,\n",
        "\n",
        "X axis = Sample\n",
        "Y axis = Time_step\n",
        "Z axis = Features\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxO1OtAFDaE7"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LShPQ1elLtH9"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, Dropout\n",
        "from keras.preprocessing import sequence\n",
        "from keras.regularizers import l2\n",
        "from keras import optimizers\n",
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NIKST-KM5Ao"
      },
      "source": [
        "epochs = 50\n",
        "batch_size = 1024\n",
        "time_steps = 301"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOroEgaiNTlO"
      },
      "source": [
        "labels_1_train = [[i] for i in labels_train]\n",
        "labels_1_train = np.array(labels_1_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq60vI0yUvb8"
      },
      "source": [
        "train_X = xp_train\n",
        "train_Y = labels_1_train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "h4IxqHuhVb2k",
        "outputId": "8e8d4c20-1299-48fd-f454-a522406cf10c"
      },
      "source": [
        "model = Sequential()\n",
        "#model.add(Dense(1, activation='sigmoid'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "optimizer = tf.keras.optimizers.Nadam(learning_rate=0.01)\n",
        "model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "hist = model.fit(train_X, train_Y, validation_split=0.2, epochs=epochs, batch_size=batch_size)\n",
        "print(model.summary())\n",
        "model.save('model.keras')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 43ms/step - accuracy: 0.6573 - loss: 0.4745 - val_accuracy: 0.9391 - val_loss: 0.3990\n",
            "Epoch 2/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.9655 - loss: 0.2300 - val_accuracy: 0.9985 - val_loss: 0.3476\n",
            "Epoch 3/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9991 - loss: 0.2062 - val_accuracy: 1.0000 - val_loss: 0.3027\n",
            "Epoch 4/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 1.0000 - loss: 0.1772 - val_accuracy: 1.0000 - val_loss: 0.2649\n",
            "Epoch 5/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 1.0000 - loss: 0.1564 - val_accuracy: 1.0000 - val_loss: 0.2329\n",
            "Epoch 6/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 1.0000 - loss: 0.1374 - val_accuracy: 1.0000 - val_loss: 0.2059\n",
            "Epoch 7/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.1223 - val_accuracy: 1.0000 - val_loss: 0.1829\n",
            "Epoch 8/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 1.0000 - loss: 0.1086 - val_accuracy: 1.0000 - val_loss: 0.1633\n",
            "Epoch 9/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 0.0971 - val_accuracy: 1.0000 - val_loss: 0.1466\n",
            "Epoch 10/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 1.0000 - loss: 0.0861 - val_accuracy: 1.0000 - val_loss: 0.1321\n",
            "Epoch 11/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0771 - val_accuracy: 1.0000 - val_loss: 0.1198\n",
            "Epoch 12/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - accuracy: 1.0000 - loss: 0.0712 - val_accuracy: 1.0000 - val_loss: 0.1089\n",
            "Epoch 13/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 1.0000 - loss: 0.0642 - val_accuracy: 1.0000 - val_loss: 0.0995\n",
            "Epoch 14/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 1.0000 - loss: 0.0590 - val_accuracy: 1.0000 - val_loss: 0.0911\n",
            "Epoch 15/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - accuracy: 1.0000 - loss: 0.0545 - val_accuracy: 1.0000 - val_loss: 0.0838\n",
            "Epoch 16/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 1.0000 - loss: 0.0496 - val_accuracy: 1.0000 - val_loss: 0.0774\n",
            "Epoch 17/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 1.0000 - loss: 0.0468 - val_accuracy: 1.0000 - val_loss: 0.0716\n",
            "Epoch 18/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 1.0000 - loss: 0.0424 - val_accuracy: 1.0000 - val_loss: 0.0666\n",
            "Epoch 19/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - accuracy: 1.0000 - loss: 0.0397 - val_accuracy: 1.0000 - val_loss: 0.0620\n",
            "Epoch 20/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - accuracy: 1.0000 - loss: 0.0372 - val_accuracy: 1.0000 - val_loss: 0.0579\n",
            "Epoch 21/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 1.0000 - loss: 0.0347 - val_accuracy: 1.0000 - val_loss: 0.0541\n",
            "Epoch 22/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 1.0000 - loss: 0.0322 - val_accuracy: 1.0000 - val_loss: 0.0508\n",
            "Epoch 23/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0305 - val_accuracy: 1.0000 - val_loss: 0.0477\n",
            "Epoch 24/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 1.0000 - loss: 0.0287 - val_accuracy: 1.0000 - val_loss: 0.0450\n",
            "Epoch 25/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0271 - val_accuracy: 1.0000 - val_loss: 0.0425\n",
            "Epoch 26/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 0.0253 - val_accuracy: 1.0000 - val_loss: 0.0401\n",
            "Epoch 27/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 0.0239 - val_accuracy: 1.0000 - val_loss: 0.0380\n",
            "Epoch 28/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 1.0000 - loss: 0.0229 - val_accuracy: 1.0000 - val_loss: 0.0360\n",
            "Epoch 29/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 0.0212 - val_accuracy: 1.0000 - val_loss: 0.0343\n",
            "Epoch 30/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0204 - val_accuracy: 1.0000 - val_loss: 0.0326\n",
            "Epoch 31/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 1.0000 - loss: 0.0195 - val_accuracy: 1.0000 - val_loss: 0.0310\n",
            "Epoch 32/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 1.0000 - loss: 0.0187 - val_accuracy: 1.0000 - val_loss: 0.0296\n",
            "Epoch 33/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0175 - val_accuracy: 1.0000 - val_loss: 0.0283\n",
            "Epoch 34/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 0.0171 - val_accuracy: 1.0000 - val_loss: 0.0270\n",
            "Epoch 35/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0160 - val_accuracy: 1.0000 - val_loss: 0.0259\n",
            "Epoch 36/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0155 - val_accuracy: 1.0000 - val_loss: 0.0248\n",
            "Epoch 37/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 0.0147 - val_accuracy: 1.0000 - val_loss: 0.0238\n",
            "Epoch 38/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 0.0142 - val_accuracy: 1.0000 - val_loss: 0.0228\n",
            "Epoch 39/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0136 - val_accuracy: 1.0000 - val_loss: 0.0219\n",
            "Epoch 40/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 1.0000 - loss: 0.0131 - val_accuracy: 1.0000 - val_loss: 0.0211\n",
            "Epoch 41/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 1.0000 - loss: 0.0125 - val_accuracy: 1.0000 - val_loss: 0.0203\n",
            "Epoch 42/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 1.0000 - loss: 0.0122 - val_accuracy: 1.0000 - val_loss: 0.0195\n",
            "Epoch 43/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0115 - val_accuracy: 1.0000 - val_loss: 0.0188\n",
            "Epoch 44/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 1.0000 - loss: 0.0112 - val_accuracy: 1.0000 - val_loss: 0.0181\n",
            "Epoch 45/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 1.0000 - loss: 0.0108 - val_accuracy: 1.0000 - val_loss: 0.0175\n",
            "Epoch 46/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 1.0000 - loss: 0.0104 - val_accuracy: 1.0000 - val_loss: 0.0169\n",
            "Epoch 47/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 1.0000 - loss: 0.0101 - val_accuracy: 1.0000 - val_loss: 0.0163\n",
            "Epoch 48/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 1.0000 - loss: 0.0097 - val_accuracy: 1.0000 - val_loss: 0.0158\n",
            "Epoch 49/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - accuracy: 1.0000 - loss: 0.0094 - val_accuracy: 1.0000 - val_loss: 0.0153\n",
            "Epoch 50/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 1.0000 - loss: 0.0091 - val_accuracy: 1.0000 - val_loss: 0.0148\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m300\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m903\u001b[0m (3.53 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">903</span> (3.53 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m300\u001b[0m (1.17 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">300</span> (1.17 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m603\u001b[0m (2.36 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">603</span> (2.36 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KNDNj0vDdfq"
      },
      "source": [
        "Testing with other data, incomplete\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YevV3VIiYKX-"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_C-YWyUfTbb",
        "outputId": "efdb2309-0c03-4628-e0fc-b8e5fdc0f338"
      },
      "source": [
        "#model = tf.keras.models.load_model(model_dir)\n",
        "#applications = ['subcomm3d']\n",
        "#ranks = ['32', '64', '128', '256', '512']\n",
        "app = []\n",
        "accuracy = []\n",
        "count = 0\n",
        "current = 0\n",
        "print(number_of_flows_per_application)\n",
        "for i in applications:\n",
        "  for j in ranks:\n",
        "    label_test = prepivot_train.iloc[current:current+number_of_flows_per_application[count], :]['Is_Elephant'].to_list()\n",
        "    dataq_test = xp_train\n",
        "    labels_1_test = [[i] for i in label_test]\n",
        "    labels_1_test = np.array(labels_1_test)\n",
        "    test_X_1 = dataq_test[current:current+number_of_flows_per_application[count], :]\n",
        "    test_Y_1 = labels_1_train[current:current+number_of_flows_per_application[count], :]\n",
        "    scores = model.evaluate(test_X_1, test_Y_1, verbose=0)\n",
        "    predict= model.predict(test_X_1, verbose=1)\n",
        "    print(predict.shape)\n",
        "    app.append('{}-{}'.format(i, j))\n",
        "    accuracy.append(scores[1]*100)\n",
        "    print(\"Accuracy of {} {}: {}\".format(i, j, scores[1]*100))\n",
        "    current += number_of_flows_per_application[count]\n",
        "    count += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3900, 3900, 3900, 3900, 3900, 3900]\n",
            "\u001b[1m122/122\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
            "(3900, 1)\n",
            "Accuracy of 2d 1024: 100.0\n",
            "\u001b[1m122/122\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
            "(3900, 1)\n",
            "Accuracy of 3d 1024: 100.0\n",
            "\u001b[1m122/122\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
            "(3900, 1)\n",
            "Accuracy of 4d 1024: 100.0\n",
            "\u001b[1m122/122\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
            "(3900, 1)\n",
            "Accuracy of nekbone 1024: 100.0\n",
            "\u001b[1m122/122\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
            "(3900, 1)\n",
            "Accuracy of lammps 1024: 100.0\n",
            "\u001b[1m122/122\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
            "(3900, 1)\n",
            "Accuracy of milc 1024: 100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFR4F6H-fveS"
      },
      "source": [
        "import tensorflow as tf\n",
        "model = tf.keras.models.load_model(model_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdgMRcMSHeUD"
      },
      "source": [
        "applications = ['laghos']\n",
        "ranks = ['32', '64', '128', '256', '512']\n",
        "#applications = ['swfft']\n",
        "ranks = ['32','64', '128', '256', '512']\n",
        "#applications = ['amg', 'kripke', 'laghos', 'stencil4d', 'subcomm3d', 'sw4lite', 'minife']\n",
        "#ranks = ['32', '64', '128', '256', '512']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJqMSIr1uFtb",
        "outputId": "99d2f0e9-bebd-4a17-9402-1fe11740b94e"
      },
      "source": [
        "for a in applications:\n",
        "  for r in ranks:\n",
        "    #f_name = '/content/gdrive/My Drive/csvstest/full-{}-{}.csv'.format(a, r)\n",
        "    f_name = '/content/gdrive/My Drive/csvs/full-{}-{}.csv'.format(a, r)\n",
        "    print(f_name)\n",
        "    data_a = pd.read_csv(f_name)\n",
        "    data_a = data_a.sample(frac=1).reset_index(drop=True)\n",
        "    data_list = []\n",
        "    range_of_columns = 301\n",
        "    for k in range(300):\n",
        "      data_list.append('Data_{}'.format(k))\n",
        "    data_a['result'] = data_a[data_list].sum(axis=1)\n",
        "    elephant = []\n",
        "    for z in data_a['result'].to_list():\n",
        "        if z > (1 * 10**8):\n",
        "          elephant.append(1)\n",
        "        else:\n",
        "          elephant.append(0)\n",
        "    data_a['Is_Elephant'] = elephant\n",
        "    data_a = data_a.fillna(0)\n",
        "    labels_train = data_a['Is_Elephant'].to_list()\n",
        "    #print(data_a)\n",
        "    #print(labels_train)\n",
        "    for k in range(301):\n",
        "      if 'Time_{}'.format(k) in data_a.columns:\n",
        "        data_a = data_a.drop('Time_{}'.format(k), axis='columns')\n",
        "      if 'Rate_{}'.format(k) in data_a.columns:\n",
        "        data_a = data_a.drop('Rate_{}'.format(k), axis='columns')\n",
        "      if 'Packet_Rate_{}'.format(k) in data_a.columns:\n",
        "        data_a = data_a.drop('Packet_Rate_{}'.format(k), axis='columns')\n",
        "    result = pd.DataFrame()\n",
        "    shape_of_df = data_a.iloc[:, 2:8].shape[1] # Current shape of dataframe\n",
        "    #print(shape_of_df)\n",
        "    number_of_times = range_of_columns // shape_of_df\n",
        "    number_of_add = range_of_columns % shape_of_df\n",
        "    for ii in range(number_of_times):\n",
        "      result = pd.concat([result, data_a.iloc[:, 2:(shape_of_df+2)]], axis=1)\n",
        "    result = pd.concat([result, data_a.iloc[:, 2:(number_of_add+2)]], axis=1)\n",
        "    #print(shape_of_df, number_of_times)\n",
        "    #print(result.shape)\n",
        "    #print(result)\n",
        "    scaler = MinMaxScaler(feature_range=(0,1))\n",
        "    xp_train = scaler.fit_transform(result)\n",
        "\n",
        "    labels_1_train = [[i] for i in labels_train]\n",
        "    labels_1_train = np.array(labels_1_train)\n",
        "    train_X = xp_train\n",
        "    train_Y = labels_1_train\n",
        "    scores = model.evaluate(train_X, train_Y, verbose=0)\n",
        "    predict= model.predict(train_X, verbose=1)\n",
        "    #print(predict)\n",
        "    print(\"Accuracy of {} {}: {}\".format(a, r , scores[1]*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/My Drive/csvs/full-laghos-32.csv\n",
            "9/9 [==============================] - 0s 2ms/step\n",
            "Accuracy of laghos 32: 24.372759461402893\n",
            "/content/gdrive/My Drive/csvs/full-laghos-64.csv\n",
            "21/21 [==============================] - 0s 2ms/step\n",
            "Accuracy of laghos 64: 56.711918115615845\n",
            "/content/gdrive/My Drive/csvs/full-laghos-128.csv\n",
            "46/46 [==============================] - 0s 1ms/step\n",
            "Accuracy of laghos 128: 34.83068346977234\n",
            "/content/gdrive/My Drive/csvs/full-laghos-256.csv\n",
            "98/98 [==============================] - 0s 1ms/step\n",
            "Accuracy of laghos 256: 25.07195472717285\n",
            "/content/gdrive/My Drive/csvs/full-laghos-512.csv\n",
            "211/211 [==============================] - 0s 1ms/step\n",
            "Accuracy of laghos 512: 60.05648970603943\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QaqXwS5BaFk8"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cKMoS-XGVEz",
        "outputId": "489d7ccf-8fe2-4f46-c530-299a739715f9"
      },
      "source": [
        "print(train_X.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(51221, 301)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yh_JtFMPU5tQ",
        "outputId": "e0429371-ef7c-49a2-aed2-1296b2e9ecbd"
      },
      "source": [
        "\n",
        "!saved_model_cli show --dir model --tag_set serve --signature_def serving_default"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The given SavedModel SignatureDef contains the following input(s):\n",
            "  inputs['dense_1_input'] tensor_info:\n",
            "      dtype: DT_FLOAT\n",
            "      shape: (-1, 301)\n",
            "      name: serving_default_dense_1_input:0\n",
            "The given SavedModel SignatureDef contains the following output(s):\n",
            "  outputs['dense_1'] tensor_info:\n",
            "      dtype: DT_FLOAT\n",
            "      shape: (-1, 1)\n",
            "      name: StatefulPartitionedCall:0\n",
            "Method name is: tensorflow/serving/predict\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "Fzb1UpthEOEI",
        "outputId": "7e23d30f-7681-4199-eb6d-93f1f7c7fa99"
      },
      "source": [
        "from google.colab import files\n",
        "!zip -r model.zip model\n",
        "files.download(\"model.zip\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: model/ (stored 0%)\n",
            "  adding: model/variables/ (stored 0%)\n",
            "  adding: model/variables/variables.index (deflated 55%)\n",
            "  adding: model/variables/variables.data-00000-of-00001 (deflated 32%)\n",
            "  adding: model/saved_model.pb (deflated 85%)\n",
            "  adding: model/keras_metadata.pb (deflated 78%)\n",
            "  adding: model/assets/ (stored 0%)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_d4ed9002-6853-457e-8300-7f14d5054744\", \"model.zip\", 13855)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "VfkMhbNwFUhS",
        "outputId": "b27bc545-15d3-42d6-efe5-6e183cd4412c"
      },
      "source": [
        "files.download(\"test_num.txt\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_c97e8c78-450d-4362-b7f5-4de1b00a97bd\", \"test_num.txt\", 138757689)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exC3mK4pIndp"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3h_IYCHH4eO",
        "outputId": "072b54a2-a12e-4d0b-83d4-d5cf488b18d2"
      },
      "source": [
        "print(train_X)\n",
        "np.savetxt('test_num.txt', train_X, fmt='%f')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3.67508196e-02 4.63011706e-02 3.80958194e-02 ... 3.26176514e-02\n",
            "  3.87473501e-02 1.10198917e-01]\n",
            " [0.00000000e+00 3.17889014e-05 0.00000000e+00 ... 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00]\n",
            " [7.39453110e-05 2.59333857e-03 4.44408841e-04 ... 3.99917377e-04\n",
            "  4.90472786e-04 1.28737053e-03]\n",
            " ...\n",
            " [1.50238092e-04 1.62759175e-04 8.13795875e-05 ... 7.34425026e-05\n",
            "  1.60563981e-04 4.70809793e-04]\n",
            " [9.61758537e-03 1.04191303e-02 5.20956515e-03 ... 4.70146770e-03\n",
            "  1.02786036e-02 3.01391831e-02]\n",
            " [9.61758537e-03 1.04191303e-02 5.20956515e-03 ... 4.70146770e-03\n",
            "  1.02786036e-02 3.01391831e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PKZrQoWaG5L",
        "outputId": "04ee3df5-a453-4253-ca45-ae69a1ae30b5"
      },
      "source": [
        "predict= model.predict(train_X, verbose=1)\n",
        "print(predict)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1601/1601 [==============================] - 2s 1ms/step\n",
            "[[0.9857718 ]\n",
            " [0.00612363]\n",
            " [0.0072327 ]\n",
            " ...\n",
            " [0.00630432]\n",
            " [0.04248509]\n",
            " [0.04241738]]\n"
          ]
        }
      ]
    }
  ]
}