Sonar_Using_Keras_Neural_Network.ipynb

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  "metadata": {
    "colab": {
      "name": " Sonar Using Keras Neural Network.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPv3SQFXFHBg3judqRM6l2v",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AhmedHefnawy/Neural-Network-binary-classification-Sonar/blob/master/Sonar_Using_Keras_Neural_Network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UrLDWa4wy1UE",
        "colab_type": "text"
      },
      "source": [
        "## (Sonar, Mines vs. Rocks) Data Set\n",
        "Model--> `Input (60) - Hidden (60) - Output (1)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92M4Bij4zIas",
        "colab_type": "text"
      },
      "source": [
        "#### import libraries, tools"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYVrRD1UyxyA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from google.colab import files\n",
        "import io\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fS2i5Xfea8Q",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "#### reproducibility Handling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhWavqj0dpL_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# fix random seed\n",
        "SEED = 42\n",
        "from tensorflow.random import set_seed\n",
        "from numpy.random import seed\n",
        "seed(SEED)\n",
        "set_seed(SEED)"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bd2fU1nhUgl",
        "colab_type": "text"
      },
      "source": [
        "### Load DataSet from localdrive "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6C_yIqxDeXnr",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "c191b4d9-ead0-4ef6-f15e-36e1b17ad962"
      },
      "source": [
        "uploaded = files.upload()\n"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-01265179-c304-4861-a21a-44e7ddd1f18f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-01265179-c304-4861-a21a-44e7ddd1f18f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving sonar.all-data.csv to sonar.all-data (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZE4hgJ6UhZWc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Dataset = pd.read_csv(io.BytesIO(uploaded['sonar.all-data.csv']),header=None)\n",
        "# Dataset is now stored in a Pandas Dataset"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLGTRf7n6Aa_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "92e62560-9aec-4cdb-d88a-4676ee422bad"
      },
      "source": [
        "Dataset.head()"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.095</td>\n",
              "      <td>0.099</td>\n",
              "      <td>0.154</td>\n",
              "      <td>0.160</td>\n",
              "      <td>0.311</td>\n",
              "      <td>0.211</td>\n",
              "      <td>0.161</td>\n",
              "      <td>0.158</td>\n",
              "      <td>0.224</td>\n",
              "      <td>0.065</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.227</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.300</td>\n",
              "      <td>0.508</td>\n",
              "      <td>0.480</td>\n",
              "      <td>0.578</td>\n",
              "      <td>0.507</td>\n",
              "      <td>0.433</td>\n",
              "      <td>0.555</td>\n",
              "      <td>0.671</td>\n",
              "      <td>0.641</td>\n",
              "      <td>0.710</td>\n",
              "      <td>0.808</td>\n",
              "      <td>0.679</td>\n",
              "      <td>0.386</td>\n",
              "      <td>0.131</td>\n",
              "      <td>0.260</td>\n",
              "      <td>0.512</td>\n",
              "      <td>0.755</td>\n",
              "      <td>0.854</td>\n",
              "      <td>0.851</td>\n",
              "      <td>0.669</td>\n",
              "      <td>0.610</td>\n",
              "      <td>0.494</td>\n",
              "      <td>0.274</td>\n",
              "      <td>0.051</td>\n",
              "      <td>0.283</td>\n",
              "      <td>0.282</td>\n",
              "      <td>0.426</td>\n",
              "      <td>0.264</td>\n",
              "      <td>0.139</td>\n",
              "      <td>0.105</td>\n",
              "      <td>0.134</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.023</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.003</td>\n",
              "      <td>R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.045</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.084</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.118</td>\n",
              "      <td>0.258</td>\n",
              "      <td>0.216</td>\n",
              "      <td>0.348</td>\n",
              "      <td>0.334</td>\n",
              "      <td>0.287</td>\n",
              "      <td>0.492</td>\n",
              "      <td>0.655</td>\n",
              "      <td>0.692</td>\n",
              "      <td>0.780</td>\n",
              "      <td>0.746</td>\n",
              "      <td>0.944</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.887</td>\n",
              "      <td>0.802</td>\n",
              "      <td>0.782</td>\n",
              "      <td>0.521</td>\n",
              "      <td>0.405</td>\n",
              "      <td>0.396</td>\n",
              "      <td>0.391</td>\n",
              "      <td>0.325</td>\n",
              "      <td>0.320</td>\n",
              "      <td>0.327</td>\n",
              "      <td>0.277</td>\n",
              "      <td>0.442</td>\n",
              "      <td>0.203</td>\n",
              "      <td>0.379</td>\n",
              "      <td>0.295</td>\n",
              "      <td>0.198</td>\n",
              "      <td>0.234</td>\n",
              "      <td>0.131</td>\n",
              "      <td>0.418</td>\n",
              "      <td>0.384</td>\n",
              "      <td>0.106</td>\n",
              "      <td>0.184</td>\n",
              "      <td>0.197</td>\n",
              "      <td>0.167</td>\n",
              "      <td>0.058</td>\n",
              "      <td>0.140</td>\n",
              "      <td>0.163</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.074</td>\n",
              "      <td>0.041</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.004</td>\n",
              "      <td>R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.026</td>\n",
              "      <td>0.058</td>\n",
              "      <td>0.110</td>\n",
              "      <td>0.108</td>\n",
              "      <td>0.097</td>\n",
              "      <td>0.228</td>\n",
              "      <td>0.243</td>\n",
              "      <td>0.377</td>\n",
              "      <td>0.560</td>\n",
              "      <td>0.619</td>\n",
              "      <td>0.633</td>\n",
              "      <td>0.706</td>\n",
              "      <td>0.554</td>\n",
              "      <td>0.532</td>\n",
              "      <td>0.648</td>\n",
              "      <td>0.693</td>\n",
              "      <td>0.676</td>\n",
              "      <td>0.755</td>\n",
              "      <td>0.893</td>\n",
              "      <td>0.862</td>\n",
              "      <td>0.797</td>\n",
              "      <td>0.674</td>\n",
              "      <td>0.429</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.533</td>\n",
              "      <td>0.241</td>\n",
              "      <td>0.507</td>\n",
              "      <td>0.853</td>\n",
              "      <td>0.604</td>\n",
              "      <td>0.851</td>\n",
              "      <td>0.851</td>\n",
              "      <td>0.504</td>\n",
              "      <td>0.186</td>\n",
              "      <td>0.271</td>\n",
              "      <td>0.423</td>\n",
              "      <td>0.304</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.676</td>\n",
              "      <td>0.537</td>\n",
              "      <td>0.472</td>\n",
              "      <td>0.465</td>\n",
              "      <td>0.259</td>\n",
              "      <td>0.213</td>\n",
              "      <td>0.222</td>\n",
              "      <td>0.211</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.074</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.011</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.023</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.024</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.008</td>\n",
              "      <td>R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.010</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.110</td>\n",
              "      <td>0.128</td>\n",
              "      <td>0.060</td>\n",
              "      <td>0.126</td>\n",
              "      <td>0.088</td>\n",
              "      <td>0.199</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.226</td>\n",
              "      <td>0.173</td>\n",
              "      <td>0.213</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.228</td>\n",
              "      <td>0.406</td>\n",
              "      <td>0.397</td>\n",
              "      <td>0.274</td>\n",
              "      <td>0.369</td>\n",
              "      <td>0.556</td>\n",
              "      <td>0.485</td>\n",
              "      <td>0.314</td>\n",
              "      <td>0.533</td>\n",
              "      <td>0.526</td>\n",
              "      <td>0.252</td>\n",
              "      <td>0.209</td>\n",
              "      <td>0.356</td>\n",
              "      <td>0.626</td>\n",
              "      <td>0.734</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.395</td>\n",
              "      <td>0.301</td>\n",
              "      <td>0.541</td>\n",
              "      <td>0.881</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.917</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.501</td>\n",
              "      <td>0.321</td>\n",
              "      <td>0.320</td>\n",
              "      <td>0.429</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.266</td>\n",
              "      <td>0.158</td>\n",
              "      <td>0.068</td>\n",
              "      <td>0.029</td>\n",
              "      <td>0.024</td>\n",
              "      <td>0.012</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.012</td>\n",
              "      <td>R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.076</td>\n",
              "      <td>0.067</td>\n",
              "      <td>0.048</td>\n",
              "      <td>0.039</td>\n",
              "      <td>0.059</td>\n",
              "      <td>0.065</td>\n",
              "      <td>0.121</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.356</td>\n",
              "      <td>0.446</td>\n",
              "      <td>0.415</td>\n",
              "      <td>0.395</td>\n",
              "      <td>0.426</td>\n",
              "      <td>0.413</td>\n",
              "      <td>0.453</td>\n",
              "      <td>0.533</td>\n",
              "      <td>0.731</td>\n",
              "      <td>0.619</td>\n",
              "      <td>0.203</td>\n",
              "      <td>0.464</td>\n",
              "      <td>0.415</td>\n",
              "      <td>0.429</td>\n",
              "      <td>0.573</td>\n",
              "      <td>0.540</td>\n",
              "      <td>0.316</td>\n",
              "      <td>0.229</td>\n",
              "      <td>0.700</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.726</td>\n",
              "      <td>0.472</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.546</td>\n",
              "      <td>0.288</td>\n",
              "      <td>0.098</td>\n",
              "      <td>0.195</td>\n",
              "      <td>0.418</td>\n",
              "      <td>0.460</td>\n",
              "      <td>0.322</td>\n",
              "      <td>0.283</td>\n",
              "      <td>0.243</td>\n",
              "      <td>0.198</td>\n",
              "      <td>0.244</td>\n",
              "      <td>0.185</td>\n",
              "      <td>0.084</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.036</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.023</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.011</td>\n",
              "      <td>0.011</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.011</td>\n",
              "      <td>0.009</td>\n",
              "      <td>R</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      0      1      2      3      4   ...     56     57     58     59  60\n",
              "0  0.020  0.037  0.043  0.021  0.095  ...  0.018  0.008  0.009  0.003   R\n",
              "1  0.045  0.052  0.084  0.069  0.118  ...  0.014  0.005  0.005  0.004   R\n",
              "2  0.026  0.058  0.110  0.108  0.097  ...  0.032  0.016  0.009  0.008   R\n",
              "3  0.010  0.017  0.062  0.021  0.021  ...  0.005  0.004  0.004  0.012   R\n",
              "4  0.076  0.067  0.048  0.039  0.059  ...  0.007  0.005  0.011  0.009   R\n",
              "\n",
              "[5 rows x 61 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mStVR4lw6QQm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e129a690-e8f0-4a67-f81a-0d33b2ddac5c"
      },
      "source": [
        "Dataset.shape"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(208, 61)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1I-d6Ts7cil",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b3e314b3-8411-4cf7-fb2a-1077e7f4fb98"
      },
      "source": [
        "pd.set_option('display.max_rows', 500)\n",
        "print(Dataset.dtypes)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0     float64\n",
            "1     float64\n",
            "2     float64\n",
            "3     float64\n",
            "4     float64\n",
            "5     float64\n",
            "6     float64\n",
            "7     float64\n",
            "8     float64\n",
            "9     float64\n",
            "10    float64\n",
            "11    float64\n",
            "12    float64\n",
            "13    float64\n",
            "14    float64\n",
            "15    float64\n",
            "16    float64\n",
            "17    float64\n",
            "18    float64\n",
            "19    float64\n",
            "20    float64\n",
            "21    float64\n",
            "22    float64\n",
            "23    float64\n",
            "24    float64\n",
            "25    float64\n",
            "26    float64\n",
            "27    float64\n",
            "28    float64\n",
            "29    float64\n",
            "30    float64\n",
            "31    float64\n",
            "32    float64\n",
            "33    float64\n",
            "34    float64\n",
            "35    float64\n",
            "36    float64\n",
            "37    float64\n",
            "38    float64\n",
            "39    float64\n",
            "40    float64\n",
            "41    float64\n",
            "42    float64\n",
            "43    float64\n",
            "44    float64\n",
            "45    float64\n",
            "46    float64\n",
            "47    float64\n",
            "48    float64\n",
            "49    float64\n",
            "50    float64\n",
            "51    float64\n",
            "52    float64\n",
            "53    float64\n",
            "54    float64\n",
            "55    float64\n",
            "56    float64\n",
            "57    float64\n",
            "58    float64\n",
            "59    float64\n",
            "60     object\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BruRVOl86AL",
        "colab_type": "text"
      },
      "source": [
        "#### Statistical Analysis_________"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzECoIrl9N7z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dYu4mWD89Ohq",
        "colab_type": "text"
      },
      "source": [
        "#### describe data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68T8tJ_d9ASO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "b82558bb-31b8-4a38-f24e-0f235e3a6167"
      },
      "source": [
        "\n",
        "pd.set_option('precision', 3)\n",
        "Dataset.describe()"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>208.000</td>\n",
              "      <td>2.080e+02</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>208.000</td>\n",
              "      <td>2.080e+02</td>\n",
              "      <td>2.080e+02</td>\n",
              "      <td>208.000</td>\n",
              "      <td>2.080e+02</td>\n",
              "      <td>2.080e+02</td>\n",
              "      <td>2.080e+02</td>\n",
              "      <td>2.080e+02</td>\n",
              "      <td>2.080e+02</td>\n",
              "      <td>2.080e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.029</td>\n",
              "      <td>3.844e-02</td>\n",
              "      <td>0.044</td>\n",
              "      <td>0.054</td>\n",
              "      <td>0.075</td>\n",
              "      <td>0.105</td>\n",
              "      <td>0.122</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.178</td>\n",
              "      <td>0.208</td>\n",
              "      <td>0.236</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.273</td>\n",
              "      <td>0.297</td>\n",
              "      <td>0.320</td>\n",
              "      <td>0.378</td>\n",
              "      <td>0.416</td>\n",
              "      <td>0.452</td>\n",
              "      <td>0.505</td>\n",
              "      <td>0.563</td>\n",
              "      <td>0.609</td>\n",
              "      <td>0.624</td>\n",
              "      <td>0.647</td>\n",
              "      <td>0.673</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.702</td>\n",
              "      <td>0.694</td>\n",
              "      <td>0.642</td>\n",
              "      <td>0.581</td>\n",
              "      <td>0.504</td>\n",
              "      <td>0.439</td>\n",
              "      <td>0.417</td>\n",
              "      <td>0.403</td>\n",
              "      <td>0.393</td>\n",
              "      <td>0.385</td>\n",
              "      <td>0.364</td>\n",
              "      <td>0.340</td>\n",
              "      <td>0.326</td>\n",
              "      <td>0.311</td>\n",
              "      <td>0.289</td>\n",
              "      <td>0.278</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.214</td>\n",
              "      <td>0.197</td>\n",
              "      <td>0.161</td>\n",
              "      <td>0.122</td>\n",
              "      <td>0.091</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.016</td>\n",
              "      <td>1.342e-02</td>\n",
              "      <td>1.071e-02</td>\n",
              "      <td>0.011</td>\n",
              "      <td>9.290e-03</td>\n",
              "      <td>8.222e-03</td>\n",
              "      <td>7.820e-03</td>\n",
              "      <td>7.949e-03</td>\n",
              "      <td>7.941e-03</td>\n",
              "      <td>6.507e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.023</td>\n",
              "      <td>3.296e-02</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.047</td>\n",
              "      <td>0.056</td>\n",
              "      <td>0.059</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.085</td>\n",
              "      <td>0.118</td>\n",
              "      <td>0.134</td>\n",
              "      <td>0.133</td>\n",
              "      <td>0.140</td>\n",
              "      <td>0.141</td>\n",
              "      <td>0.164</td>\n",
              "      <td>0.205</td>\n",
              "      <td>0.233</td>\n",
              "      <td>0.264</td>\n",
              "      <td>0.262</td>\n",
              "      <td>0.258</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.258</td>\n",
              "      <td>0.256</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.239</td>\n",
              "      <td>0.245</td>\n",
              "      <td>0.237</td>\n",
              "      <td>0.246</td>\n",
              "      <td>0.237</td>\n",
              "      <td>0.240</td>\n",
              "      <td>0.221</td>\n",
              "      <td>0.214</td>\n",
              "      <td>0.213</td>\n",
              "      <td>0.207</td>\n",
              "      <td>0.231</td>\n",
              "      <td>0.259</td>\n",
              "      <td>0.264</td>\n",
              "      <td>0.240</td>\n",
              "      <td>0.213</td>\n",
              "      <td>0.199</td>\n",
              "      <td>0.179</td>\n",
              "      <td>0.171</td>\n",
              "      <td>0.169</td>\n",
              "      <td>0.139</td>\n",
              "      <td>0.133</td>\n",
              "      <td>0.152</td>\n",
              "      <td>0.134</td>\n",
              "      <td>0.087</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.036</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.012</td>\n",
              "      <td>9.634e-03</td>\n",
              "      <td>7.060e-03</td>\n",
              "      <td>0.007</td>\n",
              "      <td>7.088e-03</td>\n",
              "      <td>5.736e-03</td>\n",
              "      <td>5.785e-03</td>\n",
              "      <td>6.470e-03</td>\n",
              "      <td>6.181e-03</td>\n",
              "      <td>5.031e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.002</td>\n",
              "      <td>6.000e-04</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.011</td>\n",
              "      <td>0.029</td>\n",
              "      <td>0.024</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.027</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.049</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.051</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.056</td>\n",
              "      <td>0.024</td>\n",
              "      <td>0.024</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.048</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.061</td>\n",
              "      <td>0.048</td>\n",
              "      <td>0.040</td>\n",
              "      <td>0.048</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.012</td>\n",
              "      <td>0.036</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>8.000e-04</td>\n",
              "      <td>5.000e-04</td>\n",
              "      <td>0.001</td>\n",
              "      <td>6.000e-04</td>\n",
              "      <td>4.000e-04</td>\n",
              "      <td>3.000e-04</td>\n",
              "      <td>3.000e-04</td>\n",
              "      <td>1.000e-04</td>\n",
              "      <td>6.000e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.013</td>\n",
              "      <td>1.645e-02</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.024</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.067</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.097</td>\n",
              "      <td>0.111</td>\n",
              "      <td>0.129</td>\n",
              "      <td>0.133</td>\n",
              "      <td>0.166</td>\n",
              "      <td>0.175</td>\n",
              "      <td>0.165</td>\n",
              "      <td>0.196</td>\n",
              "      <td>0.206</td>\n",
              "      <td>0.242</td>\n",
              "      <td>0.299</td>\n",
              "      <td>0.351</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.407</td>\n",
              "      <td>0.450</td>\n",
              "      <td>0.541</td>\n",
              "      <td>0.526</td>\n",
              "      <td>0.544</td>\n",
              "      <td>0.532</td>\n",
              "      <td>0.535</td>\n",
              "      <td>0.464</td>\n",
              "      <td>0.411</td>\n",
              "      <td>0.346</td>\n",
              "      <td>0.281</td>\n",
              "      <td>0.258</td>\n",
              "      <td>0.218</td>\n",
              "      <td>0.179</td>\n",
              "      <td>0.154</td>\n",
              "      <td>0.160</td>\n",
              "      <td>0.174</td>\n",
              "      <td>0.174</td>\n",
              "      <td>0.186</td>\n",
              "      <td>0.163</td>\n",
              "      <td>0.159</td>\n",
              "      <td>0.155</td>\n",
              "      <td>0.127</td>\n",
              "      <td>0.094</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.064</td>\n",
              "      <td>0.045</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.012</td>\n",
              "      <td>0.008</td>\n",
              "      <td>7.275e-03</td>\n",
              "      <td>5.075e-03</td>\n",
              "      <td>0.005</td>\n",
              "      <td>4.150e-03</td>\n",
              "      <td>4.400e-03</td>\n",
              "      <td>3.700e-03</td>\n",
              "      <td>3.600e-03</td>\n",
              "      <td>3.675e-03</td>\n",
              "      <td>3.100e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.023</td>\n",
              "      <td>3.080e-02</td>\n",
              "      <td>0.034</td>\n",
              "      <td>0.044</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.107</td>\n",
              "      <td>0.112</td>\n",
              "      <td>0.152</td>\n",
              "      <td>0.182</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.249</td>\n",
              "      <td>0.264</td>\n",
              "      <td>0.281</td>\n",
              "      <td>0.282</td>\n",
              "      <td>0.305</td>\n",
              "      <td>0.308</td>\n",
              "      <td>0.368</td>\n",
              "      <td>0.435</td>\n",
              "      <td>0.542</td>\n",
              "      <td>0.618</td>\n",
              "      <td>0.665</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.699</td>\n",
              "      <td>0.721</td>\n",
              "      <td>0.754</td>\n",
              "      <td>0.746</td>\n",
              "      <td>0.732</td>\n",
              "      <td>0.681</td>\n",
              "      <td>0.607</td>\n",
              "      <td>0.490</td>\n",
              "      <td>0.430</td>\n",
              "      <td>0.391</td>\n",
              "      <td>0.351</td>\n",
              "      <td>0.313</td>\n",
              "      <td>0.321</td>\n",
              "      <td>0.306</td>\n",
              "      <td>0.313</td>\n",
              "      <td>0.283</td>\n",
              "      <td>0.278</td>\n",
              "      <td>0.260</td>\n",
              "      <td>0.245</td>\n",
              "      <td>0.223</td>\n",
              "      <td>0.178</td>\n",
              "      <td>0.148</td>\n",
              "      <td>0.121</td>\n",
              "      <td>0.102</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0.045</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.014</td>\n",
              "      <td>1.140e-02</td>\n",
              "      <td>9.550e-03</td>\n",
              "      <td>0.009</td>\n",
              "      <td>7.500e-03</td>\n",
              "      <td>6.850e-03</td>\n",
              "      <td>5.950e-03</td>\n",
              "      <td>5.800e-03</td>\n",
              "      <td>6.400e-03</td>\n",
              "      <td>5.300e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.036</td>\n",
              "      <td>4.795e-02</td>\n",
              "      <td>0.058</td>\n",
              "      <td>0.065</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0.134</td>\n",
              "      <td>0.154</td>\n",
              "      <td>0.170</td>\n",
              "      <td>0.233</td>\n",
              "      <td>0.269</td>\n",
              "      <td>0.302</td>\n",
              "      <td>0.331</td>\n",
              "      <td>0.351</td>\n",
              "      <td>0.386</td>\n",
              "      <td>0.453</td>\n",
              "      <td>0.536</td>\n",
              "      <td>0.659</td>\n",
              "      <td>0.679</td>\n",
              "      <td>0.731</td>\n",
              "      <td>0.809</td>\n",
              "      <td>0.817</td>\n",
              "      <td>0.832</td>\n",
              "      <td>0.849</td>\n",
              "      <td>0.872</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.894</td>\n",
              "      <td>0.917</td>\n",
              "      <td>0.900</td>\n",
              "      <td>0.852</td>\n",
              "      <td>0.735</td>\n",
              "      <td>0.642</td>\n",
              "      <td>0.580</td>\n",
              "      <td>0.556</td>\n",
              "      <td>0.596</td>\n",
              "      <td>0.593</td>\n",
              "      <td>0.557</td>\n",
              "      <td>0.519</td>\n",
              "      <td>0.441</td>\n",
              "      <td>0.435</td>\n",
              "      <td>0.424</td>\n",
              "      <td>0.388</td>\n",
              "      <td>0.384</td>\n",
              "      <td>0.325</td>\n",
              "      <td>0.272</td>\n",
              "      <td>0.232</td>\n",
              "      <td>0.200</td>\n",
              "      <td>0.154</td>\n",
              "      <td>0.120</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.021</td>\n",
              "      <td>1.673e-02</td>\n",
              "      <td>1.490e-02</td>\n",
              "      <td>0.015</td>\n",
              "      <td>1.210e-02</td>\n",
              "      <td>1.058e-02</td>\n",
              "      <td>1.043e-02</td>\n",
              "      <td>1.035e-02</td>\n",
              "      <td>1.033e-02</td>\n",
              "      <td>8.525e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.137</td>\n",
              "      <td>2.339e-01</td>\n",
              "      <td>0.306</td>\n",
              "      <td>0.426</td>\n",
              "      <td>0.401</td>\n",
              "      <td>0.382</td>\n",
              "      <td>0.373</td>\n",
              "      <td>0.459</td>\n",
              "      <td>0.683</td>\n",
              "      <td>0.711</td>\n",
              "      <td>0.734</td>\n",
              "      <td>0.706</td>\n",
              "      <td>0.713</td>\n",
              "      <td>0.997</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.999</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.966</td>\n",
              "      <td>0.931</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.965</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.950</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.930</td>\n",
              "      <td>0.899</td>\n",
              "      <td>0.825</td>\n",
              "      <td>0.773</td>\n",
              "      <td>0.776</td>\n",
              "      <td>0.703</td>\n",
              "      <td>0.729</td>\n",
              "      <td>0.552</td>\n",
              "      <td>0.334</td>\n",
              "      <td>0.198</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.100</td>\n",
              "      <td>7.090e-02</td>\n",
              "      <td>3.900e-02</td>\n",
              "      <td>0.035</td>\n",
              "      <td>4.470e-02</td>\n",
              "      <td>3.940e-02</td>\n",
              "      <td>3.550e-02</td>\n",
              "      <td>4.400e-02</td>\n",
              "      <td>3.640e-02</td>\n",
              "      <td>4.390e-02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            0          1        2   ...         57         58         59\n",
              "count  208.000  2.080e+02  208.000  ...  2.080e+02  2.080e+02  2.080e+02\n",
              "mean     0.029  3.844e-02    0.044  ...  7.949e-03  7.941e-03  6.507e-03\n",
              "std      0.023  3.296e-02    0.038  ...  6.470e-03  6.181e-03  5.031e-03\n",
              "min      0.002  6.000e-04    0.002  ...  3.000e-04  1.000e-04  6.000e-04\n",
              "25%      0.013  1.645e-02    0.019  ...  3.600e-03  3.675e-03  3.100e-03\n",
              "50%      0.023  3.080e-02    0.034  ...  5.800e-03  6.400e-03  5.300e-03\n",
              "75%      0.036  4.795e-02    0.058  ...  1.035e-02  1.033e-02  8.525e-03\n",
              "max      0.137  2.339e-01    0.306  ...  4.400e-02  3.640e-02  4.390e-02\n",
              "\n",
              "[8 rows x 60 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YosWyClX9coo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "5aec8d20-0b5e-4220-c009-5302afccf974"
      },
      "source": [
        "Dataset.groupby(60).size()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60\n",
              "M    111\n",
              "R     97\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcIAfnko9okF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtDNdIaL9tAY",
        "colab_type": "text"
      },
      "source": [
        "#### Visualizatio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QY03egB9wFa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "outputId": "4743a472-4dea-4ca1-844f-e234d17d55c9"
      },
      "source": [
        "Dataset.hist(sharex=False, sharey=False, xlabelsize=1, ylabelsize=1, figsize=(12,12))\n",
        "plt.show()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 64 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nieK7ZJr-BHk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtmQufsW-Bcy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "outputId": "93b5b8a8-c303-4f1f-93d8-77f901cf5bd0"
      },
      "source": [
        "Dataset.plot(kind='density', subplots=True, layout=(8,8), sharex=False, legend=False, fontsize=1, figsize=(12,12))\n",
        "plt.show()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 64 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDXKq2P4-Lvd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBj0sylU-mac",
        "colab_type": "text"
      },
      "source": [
        "correlation study"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1Gut9Q1-MFm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "outputId": "5d54ec3f-6f32-496b-ab93-bdc982fd7e6f"
      },
      "source": [
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "cax = ax.matshow(Dataset.corr(), vmin=-1, vmax=1, interpolation='none')\n",
        "fig.colorbar(cax)\n",
        "fig.set_size_inches(10,10)\n",
        "plt.show()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvfHTyha-Ls_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbWr3PwA6XgX",
        "colab_type": "text"
      },
      "source": [
        "#### Split dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2CPucwY6S4O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# input (X) >< output (Y) \n",
        "array = Dataset.values\n",
        "X = array[:,0:-1].astype(float)\n",
        "Y = array[:,-1]"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGX5Jv7sEIV4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "815a775c-49be-4e35-d517-6313ae68415a"
      },
      "source": [
        "ss = StandardScaler()\n",
        "X = ss.fit_transform(X)\n",
        "X.shape"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(208, 60)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oct8r6L8_fBq",
        "colab_type": "text"
      },
      "source": [
        "#### encode output classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shxewulD6dqM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder   = LabelEncoder()\n",
        "encoder.fit(Y) # model the encoding required\n",
        "encoded_Y = encoder.transform(Y) # apply the encoding to create a new output variable "
      ],
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csOrXBTlDeNn",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Create neural network Base_Line_model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Krvw3QLVsKC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "EPOCHS     = 50\n",
        "BATCH_SIZE = 8\n",
        "VERBOSE    = 0\n",
        "FOLDS      = 10"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5upJeL0_1ND",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# creat baseline model\n",
        "def baseline_model():\n",
        "    model = Sequential()\n",
        "    model.add(Dense(60, input_dim=(60), activation = 'relu'))\n",
        "    model.add(Dense(1, activation = 'sigmoid'))\n",
        "    model.compile(loss = 'binary_crossentropy', metrics = ['accuracy'], optimizer = 'adam')\n",
        "    return model"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKSSHrBrW_D5",
        "colab_type": "text"
      },
      "source": [
        "### Building a single model to catch the performance time-curve for single One-time run "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSr3w-nZW_D6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "e5eb744d-e417-498c-d57c-d042ce8da5d0"
      },
      "source": [
        "single_model = baseline_model()\n",
        "%time history = single_model.fit(X, encoded_Y, epochs = 200, batch_size = 8, verbose = 0, validation_split = 0.1)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597d49e730> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "CPU times: user 10 s, sys: 684 ms, total: 10.7 s\n",
            "Wall time: 8.43 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t3tAyUnNW_EE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "00005da4-1ff6-4473-9636-5166c8e41b1d"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.show()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wya04-iVW_EH",
        "colab_type": "text"
      },
      "source": [
        "### Evaluating Model using K-Fold Crossvalidation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rwPrvQ6D0_M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "2c5685d6-a8e4-472d-e44e-3d9a7816445d"
      },
      "source": [
        "kfold      = StratifiedKFold(n_splits = FOLDS, shuffle = True, random_state = SEED)\n",
        "estimators = make_pipeline(StandardScaler(), KerasClassifier(build_fn = baseline_model, epochs = EPOCHS, batch_size = BATCH_SIZE, verbose = VERBOSE))\n",
        "results    = cross_val_score(estimators, X, encoded_Y, cv = kfold)\n",
        "P          = print(\"\\n\\nMean Accuracy : {}% \\nSTD Accuracy : {}%\".format(round(results.mean()*100,2),round(results.std()*100,2)))\n",
        "P"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 610 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597dbdf950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597d3278c8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597d1c66a8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597d0f0488> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597cf3c730> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597cdef510> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f59704431e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "\n",
            "\n",
            "Mean Accuracy : 87.05% \n",
            "STD Accuracy : 7.35%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7SXHii-BEnYp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7VAhXAaLr4O",
        "colab_type": "text"
      },
      "source": [
        "## Tuning Layers and Neurons in Model ____________________\n",
        "### by making it smaller and making it larger."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLqCm72VL5Lk",
        "colab_type": "text"
      },
      "source": [
        "##### _______Evaluate a Smaller Network\n",
        "\n",
        "to put pressure on the network during training to pick out the\n",
        "most important structure in the input data to model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UgGfLbxDW481",
        "colab_type": "text"
      },
      "source": [
        "`Model--> Input (60) - Hidden (30) - Output (1)`\n",
        "\n",
        "there is a lot of redundancy in the input variables for this problem. The data\n",
        "describes the same signal from di↵erent angles. Perhaps some of those angles are more relevant\n",
        "than others. We can force a type of feature extraction by the network by restricting the\n",
        "representational space in the first hidden layer.\n",
        "In this experiment we take our baseline model with 60 neurons in the hidden layer and\n",
        "reduce it by half to 30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwIsQ61xXetY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b9980c78-e9d8-4f6f-e576-f36b6c75aac3"
      },
      "source": [
        "%%time \n",
        "def small_model():\n",
        "    model = Sequential()\n",
        "    model.add(Dense(30, input_dim=(60), activation = 'relu'))\n",
        "    model.add(Dense(1, activation = 'sigmoid'))\n",
        "    model.compile(loss = 'binary_crossentropy', metrics = ['accuracy'], optimizer = 'adam')\n",
        "    return model"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 18 µs, sys: 1 µs, total: 19 µs\n",
            "Wall time: 23.1 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvM_wVjHEFFP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "11aa6188-2be9-4fcc-959c-7e79851004e9"
      },
      "source": [
        "%%time \n",
        "kfold           = StratifiedKFold(n_splits = FOLDS, shuffle = True, random_state = SEED)\n",
        "estimators      = make_pipeline(StandardScaler(), KerasClassifier(build_fn = small_model, epochs = EPOCHS, batch_size = BATCH_SIZE, verbose = VERBOSE))\n",
        "Smaller_results = cross_val_score(estimators, X, encoded_Y, cv = kfold)\n",
        "P_Smaller       = print(\"\\n\\nMean Accuracy : {}% \\nSTD Accuracy : {}%\\n________\\n\".format(round(Smaller_results.mean()*100,2),round(Smaller_results.std()*100,2)))\n",
        "P_Smaller"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598c7c7c80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597d49e1e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598c63d6a8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598b1de598> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597dd06510> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598da88488> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f596dc11268> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598b2488c8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598c5a1730> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598c5a17b8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "\n",
            "\n",
            "Mean Accuracy : 84.14% \n",
            "STD Accuracy : 5.63%\n",
            "________\n",
            "\n",
            "CPU times: user 20.5 s, sys: 1.3 s, total: 21.8 s\n",
            "Wall time: 17 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy24lmw-XmOd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJVB-1cibG17",
        "colab_type": "text"
      },
      "source": [
        "##### ________Evaluate a Larger Network\n",
        "\n",
        "with more layers ofers more opportunity for the network to extract\n",
        "key features and recombine them in useful nonlinear ways"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xuBtweeubrKU",
        "colab_type": "text"
      },
      "source": [
        "` Model--> Input (60) - Hidden (60) - Hidden (60) - Output (1)`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWlFH6mucFV3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "4e9dc269-e4f3-4281-97cb-3f5b222db7d0"
      },
      "source": [
        "%%time \n",
        "def large_model():\n",
        "    model = Sequential()\n",
        "    model.add(Dense(60, input_dim=(60), activation = 'relu'))\n",
        "    model.add(Dense(60, activation = 'relu'))\n",
        "    model.add(Dense(1, activation = 'sigmoid'))\n",
        "    model.compile(loss = 'binary_crossentropy', metrics = ['accuracy'], optimizer = 'adam')\n",
        "    return model"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 10 µs, sys: 0 ns, total: 10 µs\n",
            "Wall time: 15 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bz3bCe_KbI_2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "c1f44f69-cc20-4387-d1b9-d76b65d57ffd"
      },
      "source": [
        "%%time \n",
        "kfold         = StratifiedKFold(n_splits = FOLDS, shuffle = True, random_state = SEED)\n",
        "estimators    = make_pipeline(StandardScaler(), KerasClassifier(build_fn = large_model, epochs = EPOCHS, batch_size = BATCH_SIZE, verbose = VERBOSE))\n",
        "Large_results = cross_val_score(estimators, X, encoded_Y, cv = kfold)\n",
        "P_larger      = print(\"\\n\\nMean Accuracy : {}% \\nSTD Accuracy : {}%\\n________\\n\".format(round(Large_results.mean()*100,2),round(Large_results.std()*100,2)))\n",
        "P_larger"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598c5a16a8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597d60a488> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597cea8840> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597d8d22f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f597d8d2598> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598c5bc048> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f598b251598> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f596da9f2f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f596da9fae8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f596da9f9d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "\n",
            "\n",
            "Mean Accuracy : 86.95% \n",
            "STD Accuracy : 7.97%\n",
            "________\n",
            "\n",
            "CPU times: user 22.5 s, sys: 1.44 s, total: 24 s\n",
            "Wall time: 18.2 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCfdRdCvcW7E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "85c2141c-9ee2-4aa1-d2ae-f85ae3fadbc4"
      },
      "source": [
        "\n",
        "print(\"Accuracy comparasion in 3 model topologies\\n=============\\n\")\n",
        "\n",
        "print(\"Model--> Input (60) - Hidden (60) - Output (1) ===>\")\n",
        "print(\"\\n\\t\\t\\tMean Accuracy : {}% \\n\\t\\t\\tSTD Accuracy : {}%\\n*************\".format(round(results.mean()*100,2),round(results.std()*100,2)))\n",
        "\n",
        "print(\"\\nSmall Model--> Input (60) - Hidden (30) - Output (1) ===>\")\n",
        "print(\"\\n\\t\\t\\tMean Accuracy : {}% \\n\\t\\t\\tSTD Accuracy : {}%\\n*********\".format(round(Smaller_results.mean()*100,2),round(Smaller_results.std()*100,2)))\n",
        "\n",
        "print(\"\\nLarg Model--> Input (60) - Hidden (60) - Hidden (60) - Output (1) ===>\")\n",
        "print(\"\\n\\t\\t\\tMean Accuracy : {}% \\n\\t\\t\\tSTD Accuracy : {}%\\n*******\".format(round(Large_results.mean()*100,2),round(Large_results.std()*100,2)))\n"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy comparasion in 3 model topologies\n",
            "=============\n",
            "\n",
            "Model--> Input (60) - Hidden (60) - Output (1) ===>\n",
            "\n",
            "\t\t\tMean Accuracy : 87.05% \n",
            "\t\t\tSTD Accuracy : 7.35%\n",
            "*************\n",
            "\n",
            "Small Model--> Input (60) - Hidden (30) - Output (1) ===>\n",
            "\n",
            "\t\t\tMean Accuracy : 84.14% \n",
            "\t\t\tSTD Accuracy : 5.63%\n",
            "*********\n",
            "\n",
            "Larg Model--> Input (60) - Hidden (60) - Hidden (60) - Output (1) ===>\n",
            "\n",
            "\t\t\tMean Accuracy : 86.95% \n",
            "\t\t\tSTD Accuracy : 7.97%\n",
            "*******\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HePsGpfhdiau",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# "
      ],
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ea6LUn-if-no",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}