EEE_485_Demo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!rm -r ML"
      ],
      "metadata": {
        "id": "jgOzmp5zRgCX"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgib7aqKFc-m",
        "outputId": "0cd88701-8487-49c5-d7a4-ecd7c2eba157"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ML'...\n",
            "remote: Enumerating objects: 268, done.\u001b[K\n",
            "remote: Counting objects:   0% (1/105)\u001b[K\rremote: Counting objects:   1% (2/105)\u001b[K\rremote: Counting objects:   2% (3/105)\u001b[K\rremote: Counting objects:   3% (4/105)\u001b[K\rremote: Counting objects:   4% (5/105)\u001b[K\rremote: Counting objects:   5% (6/105)\u001b[K\rremote: Counting objects:   6% (7/105)\u001b[K\rremote: Counting objects:   7% (8/105)\u001b[K\rremote: Counting objects:   8% (9/105)\u001b[K\rremote: Counting objects:   9% (10/105)\u001b[K\rremote: Counting objects:  10% (11/105)\u001b[K\rremote: Counting objects:  11% (12/105)\u001b[K\rremote: Counting objects:  12% (13/105)\u001b[K\rremote: Counting objects:  13% (14/105)\u001b[K\rremote: Counting objects:  14% (15/105)\u001b[K\rremote: Counting objects:  15% (16/105)\u001b[K\rremote: Counting objects:  16% (17/105)\u001b[K\rremote: Counting objects:  17% (18/105)\u001b[K\rremote: Counting objects:  18% (19/105)\u001b[K\rremote: Counting objects:  19% (20/105)\u001b[K\rremote: Counting 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(105/105)\u001b[K\rremote: Counting objects: 100% (105/105), done.\u001b[K\n",
            "remote: Compressing objects: 100% (105/105), done.\u001b[K\n",
            "remote: Total 268 (delta 49), reused 0 (delta 0), pack-reused 163\u001b[K\n",
            "Receiving objects: 100% (268/268), 6.09 MiB | 26.74 MiB/s, done.\n",
            "Resolving deltas: 100% (116/116), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/fuat-arslan/ML.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip"
      ],
      "metadata": {
        "id": "XmAJn5nMGMiU"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_profiling \n",
        "from pandas_profiling import ProfileReport"
      ],
      "metadata": {
        "id": "sjc3WSyvFsJ5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/ML/raw_data/archive/sdss-IV-dr16-70k.csv\")"
      ],
      "metadata": {
        "id": "Ejs3t8cqGH6_"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# profile = ProfileReport(data)\n",
        "# profile"
      ],
      "metadata": {
        "id": "SC47wqWqGf5V"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PREPROCESSING"
      ],
      "metadata": {
        "id": "5gNF9fuFH6Ws"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ML"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Exx3WFwhJbcn",
        "outputId": "8e257625-df00-4db3-e0c5-df22bcfe971f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ML\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from Preprocess.Preprocess import pd_np_Converter, OutlierRemoval, MinMax, Encoder\n",
        "from Preprocess.Feauture_Engineering import PCA"
      ],
      "metadata": {
        "id": "EQjnxcRAJLLS"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# rerun is constant, objid is not a feature\n",
        "data = data.drop(columns=[\"rerun\", \"objid\"])\n",
        "# move class to end of data\n",
        "label = data.pop(\"class\")\n",
        "data[\"class\"] = label\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "efiH9f4NGj-7",
        "outputId": "ed380f99-9f72-45c9-d624-f6ba192b6c11"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ra       dec  psfMag_u  psfMag_g  psfMag_r  psfMag_i  psfMag_z  \\\n",
              "0  339.63895 -0.025366  24.16277  22.12683  20.49114  19.84393  19.39186   \n",
              "1  339.14540 -1.176621  24.14103  25.11438  21.40677  19.78998  22.82654   \n",
              "2  339.09143 -1.226396  22.52415  21.39598  20.74594  20.26970  19.80169   \n",
              "3  339.18984 -1.083550  18.44084  17.32474  16.90879  16.75017  16.68101   \n",
              "4  339.21747 -1.248835  19.55987  19.37768  19.28220  19.27481  19.10290   \n",
              "\n",
              "    run  camcol  field  redshift  plate    mjd  fiberid   class  \n",
              "0  4207       3    235  0.330663    674  52201      119  GALAXY  \n",
              "1  3388       1    180 -0.000051    674  52201      121    STAR  \n",
              "2  3388       1    180  0.185197    674  52201      122  GALAXY  \n",
              "3  3388       1    180 -0.000061    674  52201      123    STAR  \n",
              "4  3388       1    181  0.863151    674  52201      124     QSO  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dcd1185b-20fd-4e17-8f63-2ac272e0f80f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ra</th>\n",
              "      <th>dec</th>\n",
              "      <th>psfMag_u</th>\n",
              "      <th>psfMag_g</th>\n",
              "      <th>psfMag_r</th>\n",
              "      <th>psfMag_i</th>\n",
              "      <th>psfMag_z</th>\n",
              "      <th>run</th>\n",
              "      <th>camcol</th>\n",
              "      <th>field</th>\n",
              "      <th>redshift</th>\n",
              "      <th>plate</th>\n",
              "      <th>mjd</th>\n",
              "      <th>fiberid</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>339.63895</td>\n",
              "      <td>-0.025366</td>\n",
              "      <td>24.16277</td>\n",
              "      <td>22.12683</td>\n",
              "      <td>20.49114</td>\n",
              "      <td>19.84393</td>\n",
              "      <td>19.39186</td>\n",
              "      <td>4207</td>\n",
              "      <td>3</td>\n",
              "      <td>235</td>\n",
              "      <td>0.330663</td>\n",
              "      <td>674</td>\n",
              "      <td>52201</td>\n",
              "      <td>119</td>\n",
              "      <td>GALAXY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>339.14540</td>\n",
              "      <td>-1.176621</td>\n",
              "      <td>24.14103</td>\n",
              "      <td>25.11438</td>\n",
              "      <td>21.40677</td>\n",
              "      <td>19.78998</td>\n",
              "      <td>22.82654</td>\n",
              "      <td>3388</td>\n",
              "      <td>1</td>\n",
              "      <td>180</td>\n",
              "      <td>-0.000051</td>\n",
              "      <td>674</td>\n",
              "      <td>52201</td>\n",
              "      <td>121</td>\n",
              "      <td>STAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>339.09143</td>\n",
              "      <td>-1.226396</td>\n",
              "      <td>22.52415</td>\n",
              "      <td>21.39598</td>\n",
              "      <td>20.74594</td>\n",
              "      <td>20.26970</td>\n",
              "      <td>19.80169</td>\n",
              "      <td>3388</td>\n",
              "      <td>1</td>\n",
              "      <td>180</td>\n",
              "      <td>0.185197</td>\n",
              "      <td>674</td>\n",
              "      <td>52201</td>\n",
              "      <td>122</td>\n",
              "      <td>GALAXY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>339.18984</td>\n",
              "      <td>-1.083550</td>\n",
              "      <td>18.44084</td>\n",
              "      <td>17.32474</td>\n",
              "      <td>16.90879</td>\n",
              "      <td>16.75017</td>\n",
              "      <td>16.68101</td>\n",
              "      <td>3388</td>\n",
              "      <td>1</td>\n",
              "      <td>180</td>\n",
              "      <td>-0.000061</td>\n",
              "      <td>674</td>\n",
              "      <td>52201</td>\n",
              "      <td>123</td>\n",
              "      <td>STAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>339.21747</td>\n",
              "      <td>-1.248835</td>\n",
              "      <td>19.55987</td>\n",
              "      <td>19.37768</td>\n",
              "      <td>19.28220</td>\n",
              "      <td>19.27481</td>\n",
              "      <td>19.10290</td>\n",
              "      <td>3388</td>\n",
              "      <td>1</td>\n",
              "      <td>181</td>\n",
              "      <td>0.863151</td>\n",
              "      <td>674</td>\n",
              "      <td>52201</td>\n",
              "      <td>124</td>\n",
              "      <td>QSO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dcd1185b-20fd-4e17-8f63-2ac272e0f80f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dcd1185b-20fd-4e17-8f63-2ac272e0f80f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dcd1185b-20fd-4e17-8f63-2ac272e0f80f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert data to numpy \n",
        "converter = pd_np_Converter(data)\n",
        "data_np = converter.to_nup()\n",
        "X = data_np[:, :-1].astype(float)\n",
        "Y = data_np[:, -1].reshape(-1,1)\n",
        "X, Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_sUcNzFIajL",
        "outputId": "119522c4-4467-4c74-8185-1e5d9de6629b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 3.3963895e+02, -2.5366490e-02,  2.4162770e+01, ...,\n",
              "          6.7400000e+02,  5.2201000e+04,  1.1900000e+02],\n",
              "        [ 3.3914540e+02, -1.1766212e+00,  2.4141030e+01, ...,\n",
              "          6.7400000e+02,  5.2201000e+04,  1.2100000e+02],\n",
              "        [ 3.3909143e+02, -1.2263956e+00,  2.2524150e+01, ...,\n",
              "          6.7400000e+02,  5.2201000e+04,  1.2200000e+02],\n",
              "        ...,\n",
              "        [ 2.3794580e+02, -1.0211284e+00,  2.3042780e+01, ...,\n",
              "          3.4300000e+02,  5.1692000e+04,  2.8600000e+02],\n",
              "        [ 2.3795178e+02, -1.0445313e+00,  2.1985720e+01, ...,\n",
              "          3.4300000e+02,  5.1692000e+04,  2.8700000e+02],\n",
              "        [ 2.3802744e+02, -8.9427257e-01,  1.9447550e+01, ...,\n",
              "          3.4300000e+02,  5.1692000e+04,  2.8800000e+02]]), array([['GALAXY'],\n",
              "        ['STAR'],\n",
              "        ['GALAXY'],\n",
              "        ...,\n",
              "        ['GALAXY'],\n",
              "        ['GALAXY'],\n",
              "        ['QSO']], dtype=object))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# then apply outlier removal and standard normalized\n",
        "OutRemove = OutlierRemoval(X, Y, 3) # 3 denotes threshold value\n",
        "X_clean, Y_clean = OutRemove.fast()\n",
        "X_clean.shape, Y_clean.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCMaUH9xJy8r",
        "outputId": "9a4cb3de-141d-40f5-9309-6867fb474fc1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((66329, 14), (66329, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# since psf_Mag's are highly linearly correlated, they are decorrelated using PCA\n",
        "# we are not decreasing the dimension!!\n",
        "pca = PCA()\n",
        "decorrelated_psf = pca.fast(X_clean[:, 2:7], 5)\n",
        "decorrelated_psf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rMiCGqL0KtTm",
        "outputId": "77a8a7bf-7f8d-4ce0-a533-0e13b3a55bc0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.01095209, -0.00501874, -0.00076389, -0.00051687, -0.00017521],\n",
              "       [-0.02288711, -0.00089556,  0.00095841, -0.01047953,  0.00600617],\n",
              "       [-0.00901848,  0.00166024, -0.00029926,  0.00044585, -0.00022209],\n",
              "       ...,\n",
              "       [-0.00487142, -0.00426251,  0.00098315, -0.00143684, -0.00023805],\n",
              "       [ 0.00376348, -0.00290885, -0.00032283,  0.00010291, -0.00011273],\n",
              "       [ 0.0110536 ,  0.00388839,  0.00174748, -0.00012445, -0.00072617]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# map decorrleted_psf to -3,3\n",
        "mapper = MinMax(low = -3, high = 3)\n",
        "psf = mapper.fast(decorrelated_psf)\n",
        "psf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOcol7WXNZe3",
        "outputId": "6737a63e-c078-46f7-cc07-158657b6b4e1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.00659912, -0.44134628, -0.67973026,  0.03733094, -0.39212334],\n",
              "       [-1.74732434,  0.02172388, -0.43924667, -1.29560489,  0.30016497],\n",
              "       [-0.8865931 ,  0.30876363, -0.6148543 ,  0.16613592, -0.39737398],\n",
              "       ...,\n",
              "       [-0.6292132 , -0.35641448, -0.43579097, -0.08575483, -0.39916092],\n",
              "       [-0.09330395, -0.20438616, -0.61814465,  0.12025261, -0.38512614],\n",
              "       [ 0.35914438,  0.55900553, -0.32906811,  0.08983296, -0.4538284 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# change old values by new decorrelated values\n",
        "X_clean[:, 2:7] = psf"
      ],
      "metadata": {
        "id": "lXv3xzYJTHaz"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# label encoder\n",
        "encode = Encoder()\n",
        "Y = encode.fast(Y_clean)\n",
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3L_lle-HTYVk",
        "outputId": "00171a60-78fa-4005-9c57-403a3454a3f6"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0],\n",
              "       [0, 0, 1],\n",
              "       [1, 0, 0],\n",
              "       ...,\n",
              "       [1, 0, 0],\n",
              "       [1, 0, 0],\n",
              "       [0, 1, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Neural Network Model"
      ],
      "metadata": {
        "id": "ZFWklVjzUmC6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from Models.NeuralNetwork.Layers import Dense\n",
        "from Models.NeuralNetwork.Optimizer import Optimizer\n",
        "from Models.NeuralNetwork.Trainer import NeuralNetwork\n",
        "from Metrics.Metrics import Evaluator, kFold"
      ],
      "metadata": {
        "id": "vukQ8BXpUwNz"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# L Layer Neural Network Build\n",
        "layers = [Dense(14, 32), Dense(32, 8), Dense(8, 3, activation = \"softmax\")]\n",
        "model_NN = NeuralNetwork(layers, \"CrossEntopy\")\n",
        "adam = Optimizer(\"adam\", 0.01, None, 0.9, 0.99)\n",
        "\n",
        "model_NN.learn(X_clean, Y, optimizer = adam, max_epoch = 60, batch_size = 64)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "Y3Mn5TJrURpV",
        "outputId": "63e6b2cb-cb94-4592-ba11-441cbb40c472"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost after epoch 0: 0.170402\n",
            "Cost after epoch 10: 0.084535\n",
            "Cost after epoch 20: 0.077402\n",
            "Cost after epoch 30: 0.074975\n",
            "Cost after epoch 40: 0.073297\n",
            "Cost after epoch 50: 0.070538\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict, acc, confusion_matrix calculate\n",
        "soft_out = model_NN.predict(X_clean)\n",
        "pred = np.argmax(soft_out, axis = 0)\n",
        "true_label = np.argmax(Y, axis = 1)\n",
        "label_dict = {\"Galaxy\": 0, \"QSO\": 1, \"Stars\": 2}\n",
        "score = Evaluator(pred, true_label, label_dict)\n",
        "score.scores()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "mRlj-ZGjU7Pr",
        "outputId": "04fcd9f4-40ec-4502-9785-2518b8d597ad"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.98\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9769180901264907, array([[0.99187042, 0.97712942, 0.98444474],\n",
              "        [0.85382685, 0.95219216, 0.90033076],\n",
              "        [0.96627826, 0.98443396, 0.97527162]]))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv = kFold()\n",
        "\n",
        "structure = [Dense(14, 32), Dense(32, 8), Dense(8, 3, activation = \"softmax\")]\n",
        "cv_NN = NeuralNetwork(structure, \"CrossEntopy\")\n",
        "adam = Optimizer(\"adam\", 0.01, None, 0.9, 0.99)\n",
        "\n",
        "cv_eval = cv.eval(cv_NN, X_clean, Y, \"CrossEntropy\", 3, adam, 50, 64)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "WQ4IsKm7Zfke",
        "outputId": "b494220c-63cd-4df6-d777-733b02cf5ea3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost after epoch 0: 0.184766\n",
            "Cost after epoch 10: 0.091209\n",
            "Cost after epoch 20: 0.080477\n",
            "Cost after epoch 30: 0.076698\n",
            "Cost after epoch 40: 0.073706\n",
            "Accuracy of fold 1 : % 97.7566\n",
            "\n",
            "\n",
            "Cost after epoch 0: 0.187106\n",
            "Cost after epoch 10: 0.089957\n",
            "Cost after epoch 20: 0.081723\n",
            "Cost after epoch 30: 0.077039\n",
            "Cost after epoch 40: 0.074876\n",
            "Accuracy of fold 2 : % 97.6932\n",
            "\n",
            "\n",
            "Cost after epoch 0: 0.176011\n",
            "Cost after epoch 10: 0.084098\n",
            "Cost after epoch 20: 0.076863\n",
            "Cost after epoch 30: 0.073361\n",
            "Cost after epoch 40: 0.071744\n",
            "Accuracy of fold 3 : % 97.2817\n",
            "\n",
            "\n",
            "Average loss: 0.0872\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from Preprocess.Feauture_Engineering import BackwardElimination"
      ],
      "metadata": {
        "id": "kSoq94KUlZ_G"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "build = [Dense(14, 32), Dense(32, 8), Dense(8, 3, activation = \"softmax\")]\n",
        "model_nn = NeuralNetwork(build, \"CrossEntopy\")\n",
        "adam = Optimizer(\"adam\", 0.01, None, 0.9, 0.99)"
      ],
      "metadata": {
        "id": "D4oD3ExeljJs"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Backward = BackwardElimination(3, 0, model_nn, converter.columns)\n",
        "eliminated_data = Backward.fast(X_clean, Y, adam, 30, 64)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XoGU39JMlVWv",
        "outputId": "57bf2cb5-8114-447e-8a05-c62a1696971c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost after epoch 0: 0.194737\n",
            "Cost after epoch 10: 0.086541\n",
            "Cost after epoch 20: 0.079501\n",
            "\n",
            " Accuracy  : % 97.8592\n",
            "\n",
            " The feature of ra is removed\n",
            "Cost after epoch 0: 0.670193\n",
            "Cost after epoch 10: 0.437970\n",
            "Cost after epoch 20: 0.428230\n",
            "\n",
            " Accuracy  : % 97.829\n",
            "\n",
            "\n",
            "\n",
            " The feature of dec is removed\n",
            "Cost after epoch 0: 0.523256\n",
            "Cost after epoch 10: 0.352124\n",
            "Cost after epoch 20: 0.369337\n",
            "\n",
            " Accuracy  : % 97.9149\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_u is removed\n",
            "Cost after epoch 0: 0.668199\n",
            "Cost after epoch 10: 0.585461\n",
            "Cost after epoch 20: 0.527105\n",
            "\n",
            " Accuracy  : % 97.1415\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_g is removed\n",
            "Cost after epoch 0: 1.153405\n",
            "Cost after epoch 10: 0.525836\n",
            "Cost after epoch 20: 0.512217\n",
            "\n",
            " Accuracy  : % 97.532\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_r is removed\n",
            "Cost after epoch 0: 7.227977\n",
            "Cost after epoch 10: 7.216693\n",
            "Cost after epoch 20: 7.180912\n",
            "\n",
            " Accuracy  : % 78.9398\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_i is removed\n",
            "Cost after epoch 0: 0.634722\n",
            "Cost after epoch 10: 0.407741\n",
            "Cost after epoch 20: 0.385286\n",
            "\n",
            " Accuracy  : % 97.8652\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_z is removed\n",
            "Cost after epoch 0: 0.719228\n",
            "Cost after epoch 10: 0.518859\n",
            "Cost after epoch 20: 0.489469\n",
            "\n",
            " Accuracy  : % 97.9526\n",
            "\n",
            "\n",
            "\n",
            " The feature of run is removed\n",
            "Cost after epoch 0: 0.999544\n",
            "Cost after epoch 10: 0.510998\n",
            "Cost after epoch 20: 0.500659\n",
            "\n",
            " Accuracy  : % 97.8169\n",
            "\n",
            "\n",
            "\n",
            " The feature of camcol is removed\n",
            "Cost after epoch 0: 27.887319\n",
            "Cost after epoch 10: 27.842181\n",
            "Cost after epoch 20: 27.820109\n",
            "\n",
            " Accuracy  : % 91.9643\n",
            "\n",
            "\n",
            "\n",
            " The feature of field is removed\n",
            "Cost after epoch 0: 27.954630\n",
            "Cost after epoch 10: 27.864377\n",
            "Cost after epoch 20: 27.818944\n",
            "\n",
            " Accuracy  : % 91.4261\n",
            "\n",
            "\n",
            "\n",
            " The feature of redshift is removed\n",
            "Cost after epoch 0: 2.149473\n",
            "Cost after epoch 10: 1.160863\n",
            "Cost after epoch 20: 1.055331\n",
            "\n",
            " Accuracy  : % 94.0825\n",
            "\n",
            "\n",
            "\n",
            " The feature of plate is removed\n",
            "Cost after epoch 0: 0.751276\n",
            "Cost after epoch 10: 0.504201\n",
            "Cost after epoch 20: 0.525697\n",
            "\n",
            " Accuracy  : % 97.6782\n",
            "\n",
            "\n",
            "\n",
            " The feature of mjd is removed\n",
            "Cost after epoch 0: 2.843842\n",
            "Cost after epoch 10: 2.732917\n",
            "Cost after epoch 20: 2.715016\n",
            "\n",
            " Accuracy  : % 91.61\n",
            "\n",
            "\n",
            "\n",
            " The feature of fiberid is removed\n",
            "Cost after epoch 0: 0.664620\n",
            "Cost after epoch 10: 0.461979\n",
            "Cost after epoch 20: 0.432152\n",
            "\n",
            " Accuracy  : % 97.8923\n",
            "\n",
            "\n",
            "The column psfMag_z is dropped since accuray increased from 97.8592 to [97.9526]\n",
            "Elapsed time for droping psfMag_z column is 2.0 mins 29.174608945846558 secs\n",
            "Cost after epoch 0: 6.143995\n",
            "Cost after epoch 10: 2.731260\n",
            "Cost after epoch 20: 2.725819\n",
            "\n",
            " Accuracy  : % 92.1739\n",
            "\n",
            " The feature of ra is removed\n",
            "Cost after epoch 0: 0.698355\n",
            "Cost after epoch 10: 0.477191\n",
            "Cost after epoch 20: 0.420429\n",
            "\n",
            " Accuracy  : % 97.8833\n",
            "\n",
            "\n",
            "\n",
            " The feature of dec is removed\n",
            "Cost after epoch 0: 3.080719\n",
            "Cost after epoch 10: 2.595795\n",
            "Cost after epoch 20: 0.279133\n",
            "\n",
            " Accuracy  : % 97.7521\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_u is removed\n",
            "Cost after epoch 0: 0.806533\n",
            "Cost after epoch 10: 0.478350\n",
            "Cost after epoch 20: 0.456750\n",
            "\n",
            " Accuracy  : % 97.6044\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_g is removed\n",
            "Cost after epoch 0: 0.721701\n",
            "Cost after epoch 10: 0.535511\n",
            "Cost after epoch 20: 0.423026\n",
            "\n",
            " Accuracy  : % 97.6828\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_r is removed\n",
            "Cost after epoch 0: 0.746507\n",
            "Cost after epoch 10: 0.455661\n",
            "Cost after epoch 20: 0.433064\n",
            "\n",
            " Accuracy  : % 97.6737\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_i is removed\n",
            "Cost after epoch 0: 1.300305\n",
            "Cost after epoch 10: 0.513267\n",
            "Cost after epoch 20: 0.472200\n",
            "\n",
            " Accuracy  : % 97.8637\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_z is removed\n",
            "Cost after epoch 0: 0.697400\n",
            "Cost after epoch 10: 0.379483\n",
            "Cost after epoch 20: 0.379454\n",
            "\n",
            " Accuracy  : % 97.7838\n",
            "\n",
            "\n",
            "\n",
            " The feature of run is removed\n",
            "Cost after epoch 0: 0.938879\n",
            "Cost after epoch 10: 0.522103\n",
            "Cost after epoch 20: 0.436212\n",
            "\n",
            " Accuracy  : % 97.8727\n",
            "\n",
            "\n",
            "\n",
            " The feature of camcol is removed\n",
            "Cost after epoch 0: 1.806118\n",
            "Cost after epoch 10: 0.526469\n",
            "Cost after epoch 20: 0.471506\n",
            "\n",
            " Accuracy  : % 97.8863\n",
            "\n",
            "\n",
            "\n",
            " The feature of field is removed\n",
            "Cost after epoch 0: 3.374355\n",
            "Cost after epoch 10: 3.347961\n",
            "Cost after epoch 20: 3.317664\n",
            "\n",
            " Accuracy  : % 89.5657\n",
            "\n",
            "\n",
            "\n",
            " The feature of redshift is removed\n",
            "Cost after epoch 0: 0.777380\n",
            "Cost after epoch 10: 0.491199\n",
            "Cost after epoch 20: 0.490735\n",
            "\n",
            " Accuracy  : % 98.0069\n",
            "\n",
            "\n",
            "\n",
            " The feature of plate is removed\n",
            "Cost after epoch 0: 0.620012\n",
            "Cost after epoch 10: 0.475245\n",
            "Cost after epoch 20: 0.439633\n",
            "\n",
            " Accuracy  : % 97.7039\n",
            "\n",
            "\n",
            "\n",
            " The feature of mjd is removed\n",
            "Cost after epoch 0: 0.856292\n",
            "Cost after epoch 10: 0.502720\n",
            "Cost after epoch 20: 0.485946\n",
            "\n",
            " Accuracy  : % 97.8923\n",
            "\n",
            "\n",
            "The column redshift is dropped since accuray increased from 92.1739 to [98.0069]\n",
            "Elapsed time for droping redshift column is 2.0 mins 17.923540830612183 secs\n",
            "Cost after epoch 0: 0.472535\n",
            "Cost after epoch 10: 0.437086\n",
            "Cost after epoch 20: 0.458259\n",
            "\n",
            " Accuracy  : % 97.7491\n",
            "\n",
            " The feature of ra is removed\n",
            "Cost after epoch 0: 2.791289\n",
            "Cost after epoch 10: 2.738264\n",
            "Cost after epoch 20: 2.732197\n",
            "\n",
            " Accuracy  : % 91.9417\n",
            "\n",
            "\n",
            "\n",
            " The feature of dec is removed\n",
            "Cost after epoch 0: 0.622422\n",
            "Cost after epoch 10: 0.510863\n",
            "Cost after epoch 20: 0.451274\n",
            "\n",
            " Accuracy  : % 97.7024\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_u is removed\n",
            "Cost after epoch 0: 0.676946\n",
            "Cost after epoch 10: 0.608384\n",
            "Cost after epoch 20: 0.584608\n",
            "\n",
            " Accuracy  : % 97.4868\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_g is removed\n",
            "Cost after epoch 0: 0.692520\n",
            "Cost after epoch 10: 0.380946\n",
            "Cost after epoch 20: 0.460723\n",
            "\n",
            " Accuracy  : % 97.4611\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_r is removed\n",
            "Cost after epoch 0: 0.607717\n",
            "Cost after epoch 10: 0.421772\n",
            "Cost after epoch 20: 0.415953\n",
            "\n",
            " Accuracy  : % 96.843\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_i is removed\n",
            "Cost after epoch 0: 0.653806\n",
            "Cost after epoch 10: 0.546901\n",
            "Cost after epoch 20: 0.477380\n",
            "\n",
            " Accuracy  : % 97.7943\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_z is removed\n",
            "Cost after epoch 0: 7.275917\n",
            "Cost after epoch 10: 0.432126\n",
            "Cost after epoch 20: 0.413761\n",
            "\n",
            " Accuracy  : % 97.8426\n",
            "\n",
            "\n",
            "\n",
            " The feature of run is removed\n",
            "Cost after epoch 0: 0.572958\n",
            "Cost after epoch 10: 0.414131\n",
            "Cost after epoch 20: 0.400498\n",
            "\n",
            " Accuracy  : % 97.8501\n",
            "\n",
            "\n",
            "\n",
            " The feature of camcol is removed\n",
            "Cost after epoch 0: 2.867611\n",
            "Cost after epoch 10: 2.698715\n",
            "Cost after epoch 20: 2.704608\n",
            "\n",
            " Accuracy  : % 91.4924\n",
            "\n",
            "\n",
            "\n",
            " The feature of field is removed\n",
            "Cost after epoch 0: 1.356595\n",
            "Cost after epoch 10: 0.713199\n",
            "Cost after epoch 20: 0.643384\n",
            "\n",
            " Accuracy  : % 94.2318\n",
            "\n",
            "\n",
            "\n",
            " The feature of redshift is removed\n",
            "Cost after epoch 0: 0.665742\n",
            "Cost after epoch 10: 0.489861\n",
            "Cost after epoch 20: 0.476231\n",
            "\n",
            " Accuracy  : % 97.8848\n",
            "\n",
            "\n",
            "\n",
            " The feature of plate is removed\n",
            "Cost after epoch 0: 0.595388\n",
            "Cost after epoch 10: 0.368832\n",
            "Cost after epoch 20: 0.397970\n",
            "\n",
            " Accuracy  : % 97.9466\n",
            "\n",
            "\n",
            "The column plate is dropped since accuray increased from 97.7491 to [97.9466]\n",
            "Elapsed time for droping plate column is 2.0 mins 6.374308347702026 secs\n",
            "Cost after epoch 0: 0.392365\n",
            "Cost after epoch 10: 0.405546\n",
            "Cost after epoch 20: 0.407642\n",
            "\n",
            " Accuracy  : % 97.9873\n",
            "\n",
            " The feature of ra is removed\n",
            "Cost after epoch 0: 0.492584\n",
            "Cost after epoch 10: 0.404127\n",
            "Cost after epoch 20: 0.381505\n",
            "\n",
            " Accuracy  : % 97.8682\n",
            "\n",
            "\n",
            "\n",
            " The feature of dec is removed\n",
            "Cost after epoch 0: 0.710959\n",
            "Cost after epoch 10: 0.421338\n",
            "Cost after epoch 20: 0.404576\n",
            "\n",
            " Accuracy  : % 98.0084\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_u is removed\n",
            "Cost after epoch 0: 0.734802\n",
            "Cost after epoch 10: 0.547965\n",
            "Cost after epoch 20: 0.553864\n",
            "\n",
            " Accuracy  : % 97.4838\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_g is removed\n",
            "Cost after epoch 0: 2.872960\n",
            "Cost after epoch 10: 2.757823\n",
            "Cost after epoch 20: 2.755207\n",
            "\n",
            " Accuracy  : % 92.0095\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_r is removed\n",
            "Cost after epoch 0: 2.821758\n",
            "Cost after epoch 10: 2.731244\n",
            "Cost after epoch 20: 2.738647\n",
            "\n",
            " Accuracy  : % 92.103\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_i is removed\n",
            "Cost after epoch 0: 7.303923\n",
            "Cost after epoch 10: 7.186104\n",
            "Cost after epoch 20: 7.151540\n",
            "\n",
            " Accuracy  : % 79.0619\n",
            "\n",
            "\n",
            "\n",
            " The feature of psfMag_z is removed\n",
            "Cost after epoch 0: 2.786539\n",
            "Cost after epoch 10: 2.699005\n",
            "Cost after epoch 20: 2.680240\n",
            "\n",
            " Accuracy  : % 92.0427\n",
            "\n",
            "\n",
            "\n",
            " The feature of run is removed\n",
            "Cost after epoch 0: 2.831010\n",
            "Cost after epoch 10: 2.719360\n",
            "Cost after epoch 20: 2.704490\n",
            "\n",
            " Accuracy  : % 92.1633\n",
            "\n",
            "\n",
            "\n",
            " The feature of camcol is removed\n",
            "Cost after epoch 0: 0.522682\n",
            "Cost after epoch 10: 0.478145\n",
            "Cost after epoch 20: 0.433264\n",
            "\n",
            " Accuracy  : % 98.0084\n",
            "\n",
            "\n",
            "\n",
            " The feature of field is removed\n",
            "Cost after epoch 0: 1.571935\n",
            "Cost after epoch 10: 1.292271\n",
            "Cost after epoch 20: 1.326993\n",
            "\n",
            " Accuracy  : % 94.1624\n",
            "\n",
            "\n",
            "\n",
            " The feature of redshift is removed\n",
            "Cost after epoch 0: 0.588640\n",
            "Cost after epoch 10: 0.450246\n",
            "Cost after epoch 20: 0.448805\n",
            "\n",
            " Accuracy  : % 97.5395\n",
            "\n",
            "\n",
            "The column dec is dropped since accuray increased from 97.9873 to [98.0084]\n",
            "Elapsed time for droping dec column is 1.0 mins 57.87867450714111 secs\n",
            "Cost after epoch 0: 2.141907\n",
            "Cost after epoch 10: 0.268956\n",
            "Cost after epoch 20: 0.241622\n",
            "\n",
            " Accuracy  : % 97.8637\n",
            "\n",
            " The feature of ra is removed\n",
            "Cost after epoch 0: 0.726734\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-24dae48dd8b3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mBackward\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBackwardElimination\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_nn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\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[0meliminated_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBackward\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_clean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/content/ML/Preprocess/Feauture_Engineering.py\u001b[0m in \u001b[0;36mfast\u001b[0;34m(self, X, Y, *argv)\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margv\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--> 130\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/content/ML/Models/NeuralNetwork/Trainer.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, X, Y, optimizer, max_epoch, batch_size)\u001b[0m\n\u001b[1;32m     44\u001b[0m                     \u001b[0mdA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m                 \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\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     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/ML/Models/NeuralNetwork/Optimizer.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, layer_list)\u001b[0m\n\u001b[1;32m     65\u001b[0m                 \u001b[0ms_corrected\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"b\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\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     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m                 \u001b[0mnew_W\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv_corrected\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'W'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ms_corrected\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"W\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m                 \u001b[0mnew_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbais\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv_corrected\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ms_corrected\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"b\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepsilon\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     69\u001b[0m                 \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_W\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnew_b\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eliminated_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UF5MBWpI1YbX",
        "outputId": "8844a4f7-7cea-4e18-d4d0-b2fd33a6267d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(66329, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "build2 = [Dense(14, 32), Dense(32, 8), Dense(8, 3, activation = \"softmax\")]\n",
        "NN = NeuralNetwork(build2, \"CrossEntopy\")\n",
        "adam = Optimizer(\"adam\", 0.01, None, 0.9, 0.99)\n",
        "\n",
        "NN.learn(eliminated_data, Y, optimizer = adam, max_epoch = 60, batch_size = 64)"
      ],
      "metadata": {
        "id": "rTKSkQa01cgY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}