1.1_AutoencoderSimple_Anomaly_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "01_AutoencoderSimple_Anomaly_Detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOhCiCxMIg3aW8bkpW17lPS"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHJPUGvecGFs"
      },
      "source": [
        "Help is available on : https://www.youtube.com/watch?v=u1vLJBwOFC8"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1EUj0NehX0Ci",
        "outputId": "27fc46fe-681e-483b-a90f-b6c7834e2d63"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('anomaly.csv')\n",
        "\n",
        "print(df.columns) \n",
        "print(df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Date', 'Power', 'Detector', 'Quality'], dtype='object')\n",
            "       Date  Power  Detector Quality\n",
            "0  1-Jan-16     96         8    Good\n",
            "1  2-Jan-16     96        10    Good\n",
            "2  3-Jan-16     91         8    Good\n",
            "3  4-Jan-16     97         9    Good\n",
            "4  5-Jan-16     91        11    Good\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "tWb8SVaTY3vi",
        "outputId": "466b9403-e883-4b6c-d048-7d18ef81d570"
      },
      "source": [
        "df['Power'].plot(kind='hist', title='Power Reading', bins=30, figsize=(12,10)) \n",
        "#Most values between 90 and 100 with some outliers / anomalies"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2fbf336d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "lFs8XsdlY8Gu",
        "outputId": "f355d422-de0f-4be1-884b-4484824bfb07"
      },
      "source": [
        "df['Detector'].plot(kind='hist', title='Detector Reading', bins=30, figsize=(12,10)) \n",
        "#Most values between 8 and 12 with some outliers / anomalies"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2f6281e50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AGCSmAQBgkJgGAIBBYhoAAAaJaQAAGCSmAQBgkJgGAIBBYhoAAAaJaQAAGCSmAQBgkJgGAIBBYhoAAAaJaQAAGCSmAQBgkJgGAIBBYhoAAAaJaQAAGCSmAQBgkJgGAIBBYhoAAAaJaQAAGCSmAQBgkJgGAIBBYhoAAAaJaQAAGDRXTFfVDy56EAAA2GjmPTL94qo6t6p+papus9CJAABgg5grprv7/kl+LskRST5YVa+tqgcvdDIAAFjn5l4z3d2fTvJbSZ6W5EeTvKiqPllVP7Wo4QAAYD2bd830D1XVC5JcmOQ/JHlEd//AdPkFC5wPAADWrS1z3u8Pkrw8yTO6+5u7N3b3pVX1WwuZDAAA1rl5Y/q4JN/s7muSpKpulOTm3f2N7v6ThU0HAADr2Lxrpt+V5BYrrh8wbQMAgE1r3pi+eXf/4+4r0+UDFjMSAABsDPPG9Ner6tjdV6rq3yT55nXcHwAAbvDmXTP9G0n+vKouTVJJvjfJzy5sKgAA2ADmiunu/kBV3S3JUdOmT3X3Py9uLAAAWP/mPTKdJPdKsm16zLFVle5+9UKmAgCADWCumK6qP0lylyTnJ7lm2txJxDQAAJvWvEemtyc5urt73h1X1SuTPDzJFd19j2nbqUl+Mcmu6W7P6O53TLc9PckTMov1X+vuv573uQAAYBnmPZvHxzN70+H+eFWSh+xh+wu6+5jpa3dIH53kxCR3nx7z4qq68X4+HwAArKl5j0wfnOQTVXVukm/t3tjdj9zbA7r7PVW1bc79H5/k9d39rSSfqaqLktw7yd/N+XgAAFhz88b0qav4nE+uqsclOS/JU7r7y0kOS/K+FffZOW37LlV1cpKTk+SOd7zjKo4FAAD7Z65lHt397iQ7ktxkuvyBJB8aeL6XZPZGxmOSXJbk9/d3B919endv7+7tW7duHRgBAABWx1wxXVW/mOQNSf5o2nRYkrfs75N19+XdfU13fzvJyzJbypEklyQ5YsVdD5+2AQDAujXvGxCflOR+Sa5Mku7+dJLb7++TVdWhK67+ZGZvbEySs5KcWFU3q6o7JTkyybn7u38AAFhL866Z/lZ3X1VVSZKq2pLZeab3qqpel+QBSQ6uqp1Jnp3kAVV1zPTYHUl+KUm6+4KqOjPJJ5JcneRJ3X3NnvYLAADrxbwx/e6qekaSW1TVg5P8SpK3XdcDuvvRe9j8iuu4/3OSPGfOeQAAYOnmXeZxSmYftPKxzI4mvyPJby1qKAAA2AjmOjK94g2DL1vsOAAAsHHMFdNV9ZnsYY10d9951ScCAIANYt4109tXXL55kp9JctDqjwMAABvHvB/a8sUVX5d09/9MctyCZwMAgHVt3mUex664eqPMjlTPe1QbAABukOYN4pUf+311ZueIPmHVpwEAgA1k3rN5PHDRgwAAwEYz7zKP/3Jdt3f381dnHAAA2Dj252we90py1nT9EUnOTfLpRQwFAAAbwbwxfXiSY7v7a0lSVacmeXt3P2ZRgwEAwHo378eJH5LkqhXXr5q2AQDApjXvkelXJzm3qt48XX9UkjMWMxIAAGwM857N4zlV9ZdJ7j9tenx3f3hxYwEAwPo37zKPJDkgyZXd/cIkO6vqTguaCQAANoS5Yrqqnp3kaUmePm26SZI/XdRQAACwEcx7ZPonkzwyydeTpLsvTXLrRQ0FAAAbwbwxfVV3d5JOkqq65eJGAgCAjWHemD6zqv4oyYFV9YtJ3pXkZYsbCwAA1r99ns2jqirJnyW5W5IrkxyV5FndffaCZwMAgHVtnzHd3V1V7+juH0wioAEAYDLvMo8PVdW9FjoJAABsMPN+AuJ9kjymqnZkdkaPyuyg9Q8tajAAAFjvrjOmq+qO3f25JD+xRvMAAMCGsa8j029Jcmx3f7aq3tjd/3EthgIAgI1gX2uma8XlOy9yEAAA2Gj2FdO9l8sAALDp7WuZxz2r6srMjlDfYrqcfOcNiN+z0OkAAGAdu86Y7u4br9UgAACw0cx7nmkAAOBaxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMEhMAwDAIDENAACDxDQAAAwS0wAAMGhhMV1Vr6yqK6rq4yu2HVRVZ1fVp6fvt522V1W9qKouqqqPVtWxi5oLAABWyyKPTL8qyUOute2UJOd095FJzpmuJ8lDkxw5fZ2c5CULnAsAAFbFwmK6u9+T5EvX2nx8kjOmy2ckedSK7a/umfclObCqDl3UbAAAsBrWes30Id192XT580kOmS4fluTiFffbOW37LlV1clWdV1Xn7dq1a3GTAgDAPiztDYjd3Ul64HGnd/f27t6+devWBUwGAADzWeuYvnz38o3p+xXT9kuSHLHifodP2wAAYN1a65g+K8lJ0+WTkrx1xfbHTWf1uG+Sr65YDgIAAOvSlkXtuKpel+QBSQ6uqp1Jnp3ktCRnVtUTknw2yQnT3d+R5GFJLkryjSSPX9RcAACwWhYW09396L3c9KA93LeTPGlRswAAwCL4BEQAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQWIaAAAGiWkAABgkpgEAYJCYBgCAQVuW8aRVtSPJ15Jck+Tq7t5eVQcl+bMk25LsSHJCd395GfMBAMA8lnlk+oHdfUx3b5+un5LknO4+Msk503UAAFi31tMyj+OTnDFdPiPJo5Y4CwAA7NOyYrqTvLOqPlhVJ0/bDunuy6bLn09yyJ4eWFUnV9V5VXXerl271mJWAADYo6WsmU7yI919SVXdPsnZVfXJlTd2d1dV7+mB3X16ktOTZPv27Xu8DwAArIWlHJnu7kum71ckeXOSeye5vKoOTZLp+xXLmA0AAOa15jFdVbesqlvvvpzkx5N8PMlZSU6a7nZSkreu9WwAALA/lrHM45Akb66q3c//2u7+q6r6QJIzq+oJST6b5IQlzAYAAHNb85ju7n9Ics89bP9ikget9TwAADBqPZ0aDwAANhQxDQAAg8Q0AAAMEtMAADBoWR/awoJtO+Xtq77PHacdt+r7XE2r/TOv958XAFg+R6YBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFOjTfIadgAAHBkGgAABolpAAAYJKYBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFiGgAABolpAAAYJKYBAGCQmAYAgEFblj0AAMB6tO2Ut6/q/nacdtyq7m+150tWf8bNwJFpAAAYJKYBAGCQZR4AsGR+XQ8blyPTAAAwSEwDAMAgMQ0AAIOsmQYA9mm9nyYOlsWRaQAAGCSmAQBgkJgGAIBBYhoAAAaJaQAAGORsHgAALMRmOAuMI9MAADBITAMAwCAxDQAAg8Q0AAAMEtMAADBITAMAwCAxDQAAg8Q0AAAMEtMAADBITAMAwCAxDQAAg8Q0AAAMEtMAADBITAMAwCAxDQAAg8Q0AAAMEtMAADBITAMAwKB1F9NV9ZCq+lRVXVRVpyx7HgAA2Jt1FdNVdeMkf5jkoUmOTvLoqjp6uVMBAMCerauYTnLvJBd19z9091VJXp/k+CXPBAAAe1TdvewZ/kVV/XSSh3T3L0zXH5vkPt395BX3OTnJydPVo5J8as0HvWE5OMkXlj0E38Xrsv54TdYnr8v64zVZn7wu19/3dffWa2/csoxJro/uPj3J6cue44aiqs7r7u3LnoN/zeuy/nhN1ievy/rjNVmfvC6Ls96WeVyS5IgV1w+ftgEAwLqz3mL6A0mOrKo7VdVNk5yY5KwlzwQAAHu0rpZ5dPfVVfXkJH+d5MZJXtndFyx5rBs6S2bWJ6/L+uM1WZ+8LuuP12R98rosyLp6AyIAAGwk622ZBwAAbBhiGgAABonpTayqDqyqN1TVJ6vqwqr6t8ueabOrqt+sqguq6uNV9bqquvmyZ9qMquqVVXVFVX18xbaDqursqvr09P22y5xxs9nLa/I/pv9+fbSq3lxVBy5zxs1oT6/LitueUlVdVQcvY7bNam+vSVX96vT35YKq+r1lzXdDJKY3txcm+avuvluSeya5cMnzbGpVdViSX0uyvbvvkdmbcE9c7lSb1quSPORa205Jck53H5nknOk6a+dV+e7X5Owk9+juH0ry/5I8fa2HYo+vS6rqiCQ/nuRzaz0Q3/2aVNUDM/tE6Xt2992TPG8Jc91gielNqqpuk+TfJ3lFknT3Vd39leVORWZn2LlFVW1JckCSS5c8z6bU3e9J8qVrbT4+yRnT5TOSPGpNh9rk9vSadPc7u/vq6er7MvtsAtbQXv6uJMkLkjw1ibMcrLG9vCZPTHJad39rus8Vaz7YDZiY3rzulGRXkj+uqg9X1cur6pbLHmoz6+5LMjta8LkklyX5ane/c7lTscIh3X3ZdPnzSQ5Z5jB8l/+c5C+XPQRJVR2f5JLu/siyZ+Ff3DXJ/avq/VX17qq617IHuiER05vXliTHJnlJd/9wkq/Hr62XalqDe3xm/9C5Q5JbVtVjljsVe9Kzc4o64rZOVNUzk1yd5DXLnmWzq6oDkjwjybOWPQv/ypYkByW5b5L/muTMqqrljnTDIaY3r51Jdnb3+6frb8gsrlmeH0vyme7e1d3/nORNSf7dkmfiOy6vqkOTZPru16TrQFX9fJKHJ/m59sEJ68FdMjsg8JGq2pHZ0psPVdX3LnUqdiZ5U8+cm+TbSbwxdJWI6U2quz+f5OKqOmra9KAkn1jiSMyWd9y3qg6Yjhg8KN4Uup6cleSk6fJJSd66xFlIUlUPyWxd7iO7+xvLnoekuz/W3bfv7m3dvS2ziDt2+n8Oy/OWJA9Mkqq6a5KbJvnCUie6ARHTm9uvJnlNVX00yTFJnrvkeTa16bcEb0jyoSQfy+zvp49/XYKqel2Sv0tyVFXtrKonJDktyYOr6tOZ/RbhtGXOuNns5TX5X0luneTsqjq/ql661CE3ob28LizRXl6TVya583S6vNcnOclvclaPjxMHAIBBjkwDAMAgMQ0AAIPENAAADBLTAAAwSEwDAMAgMQ0AAIPENAAADPr/9RVqEtTw8h8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yU4TUV__YwDC",
        "outputId": "31618adc-6546-4573-e925-591984388a2f"
      },
      "source": [
        "#To see how the data is spread betwen Good and Bad\n",
        "print(df.groupby('Quality')['Quality'].count())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Quality\n",
            "Bad      407\n",
            "Good    1054\n",
            "Name: Quality, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31Z52BDqYt-m"
      },
      "source": [
        "df.drop(['Date'], axis=1, inplace=True)\n",
        "#print(df.head())\n",
        "\n",
        "#If there are missing entries, drop them.\n",
        "df.dropna(inplace=True,axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULqHdYx2Yr1K",
        "outputId": "295aa6dc-b585-4ec3-f3f9-5bbf1b7c7777"
      },
      "source": [
        "#COnvert non-numeric to numeric\n",
        "df.Quality[df.Quality == 'Good'] = 1\n",
        "df.Quality[df.Quality == 'Bad'] = 2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qfxkh8i4YoBz",
        "outputId": "31d80848-cd52-48b1-a18e-8b18039b7b3b"
      },
      "source": [
        "good_mask = df['Quality']== 1 #All good to be True for good data points\n",
        "bad_mask = df['Quality']== 2 #All values False for good data points\n",
        "print(good_mask.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    True\n",
            "1    True\n",
            "2    True\n",
            "3    True\n",
            "4    True\n",
            "Name: Quality, dtype: bool\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkW73c0eYlHp",
        "outputId": "41b0034a-7ddf-4d1a-9bdf-8e0033714142"
      },
      "source": [
        "df.drop('Quality',axis=1,inplace=True)\n",
        "\n",
        "df_good = df[good_mask]\n",
        "df_bad = df[bad_mask]\n",
        "\n",
        "print(f\"Good count: {len(df_good)}\")\n",
        "print(f\"Bad count: {len(df_bad)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Good count: 1054\n",
            "Bad count: 407\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptKmCzvSYd-F",
        "outputId": "69592728-3084-4aa5-bb88-e072cc2f0398"
      },
      "source": [
        "# This is the feature vector that goes to the neural net\n",
        "x_good = df_good.values\n",
        "x_bad = df_bad.values\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_good_train, x_good_test = train_test_split(\n",
        "        x_good, test_size=0.25, random_state=42)\n",
        "\n",
        "print(f\"Good train count: {len(x_good_train)}\")\n",
        "print(f\"Good test count: {len(x_good_test)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Good train count: 790\n",
            "Good test count: 264\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4kq68VLYZy-",
        "outputId": "52ff9dcc-1c9f-438a-def3-ca178c6c6313"
      },
      "source": [
        "######### \n",
        "#Define the autoencoder model\n",
        "#Since we're dealing with numeric values we can use only Dense layers.\n",
        "\n",
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(10, input_dim=x_good.shape[1], activation='relu'))\n",
        "model.add(Dense(3, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(x_good.shape[1])) \n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                30        \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 33        \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                40        \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 2)                 22        \n",
            "=================================================================\n",
            "Total params: 125\n",
            "Trainable params: 125\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNaiqSe9YWnj",
        "outputId": "ece4bf1f-5f96-4aa5-cbb3-508e7659ef5f"
      },
      "source": [
        "model.fit(x_good_train,x_good_train,verbose=1,epochs=100)\n",
        "\n",
        "pred = model.predict(x_good_test)\n",
        "score1 = np.sqrt(metrics.mean_squared_error(pred,x_good_test))\n",
        "\n",
        "pred = model.predict(x_good)\n",
        "score2 = np.sqrt(metrics.mean_squared_error(pred,x_good))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "25/25 [==============================] - 1s 2ms/step - loss: 4675.7880\n",
            "Epoch 2/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 4267.7518\n",
            "Epoch 3/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 3852.4171\n",
            "Epoch 4/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 3284.6298\n",
            "Epoch 5/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 2549.4371\n",
            "Epoch 6/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 1802.6026\n",
            "Epoch 7/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 1083.9780\n",
            "Epoch 8/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 515.1662\n",
            "Epoch 9/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 176.6719\n",
            "Epoch 10/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 40.0554\n",
            "Epoch 11/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 5.9178\n",
            "Epoch 12/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 1.1028\n",
            "Epoch 13/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7510\n",
            "Epoch 14/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6803\n",
            "Epoch 15/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.6362\n",
            "Epoch 16/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.6093\n",
            "Epoch 17/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5790\n",
            "Epoch 18/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5883\n",
            "Epoch 19/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.5495\n",
            "Epoch 20/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5649\n",
            "Epoch 21/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.4936\n",
            "Epoch 22/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.5199\n",
            "Epoch 23/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.4787\n",
            "Epoch 24/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.4529\n",
            "Epoch 25/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.4547\n",
            "Epoch 26/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.4290\n",
            "Epoch 27/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.4112\n",
            "Epoch 28/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3868\n",
            "Epoch 29/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.3888\n",
            "Epoch 30/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.3792\n",
            "Epoch 31/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3591\n",
            "Epoch 32/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.3498\n",
            "Epoch 33/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3350\n",
            "Epoch 34/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3212\n",
            "Epoch 35/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3355\n",
            "Epoch 36/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2999\n",
            "Epoch 37/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2916\n",
            "Epoch 38/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2975\n",
            "Epoch 39/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2828\n",
            "Epoch 40/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2630\n",
            "Epoch 41/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2431\n",
            "Epoch 42/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2544\n",
            "Epoch 43/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2362\n",
            "Epoch 44/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2292\n",
            "Epoch 45/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2081\n",
            "Epoch 46/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2227\n",
            "Epoch 47/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2128\n",
            "Epoch 48/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.1873\n",
            "Epoch 49/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1776\n",
            "Epoch 50/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1897\n",
            "Epoch 51/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.1611\n",
            "Epoch 52/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1577\n",
            "Epoch 53/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1446\n",
            "Epoch 54/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1529\n",
            "Epoch 55/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.1443\n",
            "Epoch 56/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.1353\n",
            "Epoch 57/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.1303\n",
            "Epoch 58/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1173\n",
            "Epoch 59/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1162\n",
            "Epoch 60/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.1185\n",
            "Epoch 61/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.1025\n",
            "Epoch 62/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.1051\n",
            "Epoch 63/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.1036\n",
            "Epoch 64/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0913\n",
            "Epoch 65/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0909\n",
            "Epoch 66/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0988\n",
            "Epoch 67/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0788\n",
            "Epoch 68/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0729\n",
            "Epoch 69/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0728\n",
            "Epoch 70/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0673\n",
            "Epoch 71/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0628\n",
            "Epoch 72/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0751\n",
            "Epoch 73/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0593\n",
            "Epoch 74/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0569\n",
            "Epoch 75/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0558\n",
            "Epoch 76/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0489\n",
            "Epoch 77/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0494\n",
            "Epoch 78/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0452\n",
            "Epoch 79/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0418\n",
            "Epoch 80/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0393\n",
            "Epoch 81/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0394\n",
            "Epoch 82/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0387\n",
            "Epoch 83/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0347\n",
            "Epoch 84/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0349\n",
            "Epoch 85/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0315\n",
            "Epoch 86/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0315\n",
            "Epoch 87/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0288\n",
            "Epoch 88/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0282\n",
            "Epoch 89/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0269\n",
            "Epoch 90/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0249\n",
            "Epoch 91/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0230\n",
            "Epoch 92/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0234\n",
            "Epoch 93/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0220\n",
            "Epoch 94/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0209\n",
            "Epoch 95/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0196\n",
            "Epoch 96/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0185\n",
            "Epoch 97/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0181\n",
            "Epoch 98/100\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.0175\n",
            "Epoch 99/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0168\n",
            "Epoch 100/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0161\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wfb6nl3vYTRr",
        "outputId": "b1c0cfad-f5f7-4c7b-9df3-d2c1e91e2a0a"
      },
      "source": [
        "pred = model.predict(x_bad)\n",
        "score3 = np.sqrt(metrics.mean_squared_error(pred,x_bad))\n",
        "\n",
        "print(f\"Insample Good Score (RMSE): {score1}\".format(score1))\n",
        "print(f\"Out of Sample Good Score (RMSE): {score2}\")\n",
        "print(f\"Bad sample Score (RMSE): {score3}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Insample Good Score (RMSE): 0.1249398855967821\n",
            "Out of Sample Good Score (RMSE): 0.12439006223185341\n",
            "Bad sample Score (RMSE): 0.9900362247753991\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ph8CPjF9b-8E"
      },
      "source": [
        "----------------------------"
      ]
    }
  ]
}