practice.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP7c3eRRn/MkNs9sLVIX2xl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rifatulhimel/Basic-ML-algo/blob/master/practice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "xV_uGfl_cNux",
        "outputId": "63a368a3-4482-4cd6-f7dd-32221036f2ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 6.0, 0.0, 20.0)"
            ]
          },
          "metadata": {},
          "execution_count": 1
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = [1, 2, 2.5, 3, 4]\n",
        "y = [1, 4, 7, 9, 15]\n",
        "plt.plot(x, y, 'ro')\n",
        "plt.axis([0, 6, 0, 20])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x, y, 'ro')\n",
        "plt.axis([0, 6, 0, 20])\n",
        "plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "vSIF0r0BcSM5",
        "outputId": "ebe249ec-7048-431b-9f81-e3185d8ce74d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "from six.moves import urllib\n",
        "\n",
        "import tensorflow.compat.v2.feature_column as fc\n",
        "\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "SXw52nPfcVaC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv') # training data\n",
        "dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv') # testing data\n",
        "y_train = dftrain.pop('survived')\n",
        "y_eval = dfeval.pop('survived')"
      ],
      "metadata": {
        "id": "9nyfmbkvcdHN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-NppL_X3chHs",
        "outputId": "ff06e195-a8cd-4ec1-bb0a-6ff8d97345c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xwzwBZNpcjLJ",
        "outputId": "8da2ac01-edd6-46e4-e978-7399f50a4c86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
              "0    male  22.0                   1      0   7.2500  Third  unknown   \n",
              "1  female  38.0                   1      0  71.2833  First        C   \n",
              "2  female  26.0                   0      0   7.9250  Third  unknown   \n",
              "3  female  35.0                   1      0  53.1000  First        C   \n",
              "4    male  28.0                   0      0   8.4583  Third  unknown   \n",
              "\n",
              "   embark_town alone  \n",
              "0  Southampton     n  \n",
              "1    Cherbourg     n  \n",
              "2  Southampton     y  \n",
              "3  Southampton     n  \n",
              "4   Queenstown     y  "
            ],
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              "      <td>28.0</td>\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-2151c4b1-a7e8-48fe-bdf3-ac6615380c57 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-2151c4b1-a7e8-48fe-bdf3-ac6615380c57');\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",
              "\n",
              "\n",
              "<div id=\"df-95acbf4d-9ebe-46f5-8fd7-ea85716101ce\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95acbf4d-9ebe-46f5-8fd7-ea85716101ce')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-95acbf4d-9ebe-46f5-8fd7-ea85716101ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dftrain",
              "summary": "{\n  \"name\": \"dftrain\",\n  \"rows\": 627,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.511817629565819,\n        \"min\": 0.75,\n        \"max\": 80.0,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          28.0,\n          59.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n_siblings_spouses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.597730499456304,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 216,\n        \"samples\": [\n          25.9292,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"C\",\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Cherbourg\",\n          \"unknown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"y\",\n          \"n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "Owj8n3iOcqlq",
        "outputId": "1a33bc29-48a2-44b6-e1d5-42281c70a80f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    0\n",
              "Name: survived, dtype: int64"
            ],
            "text/html": [
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.loc[0], y_train.loc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQ2DjXU4d18C",
        "outputId": "3b35f0c1-329c-4b3e-b375-48426028c147"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(sex                          male\n",
              " age                          22.0\n",
              " n_siblings_spouses              1\n",
              " parch                           0\n",
              " fare                         7.25\n",
              " class                       Third\n",
              " deck                      unknown\n",
              " embark_town           Southampton\n",
              " alone                           n\n",
              " Name: 0, dtype: object,\n",
              " 0)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "J8WyWJ9kd6uZ",
        "outputId": "ab46ca24-fedd-4feb-a6d2-0d7e6cca27c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age  n_siblings_spouses       parch        fare\n",
              "count  627.000000          627.000000  627.000000  627.000000\n",
              "mean    29.631308            0.545455    0.379585   34.385399\n",
              "std     12.511818            1.151090    0.792999   54.597730\n",
              "min      0.750000            0.000000    0.000000    0.000000\n",
              "25%     23.000000            0.000000    0.000000    7.895800\n",
              "50%     28.000000            0.000000    0.000000   15.045800\n",
              "75%     35.000000            1.000000    0.000000   31.387500\n",
              "max     80.000000            8.000000    5.000000  512.329200"
            ],
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              "      <th>age</th>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>627.000000</td>\n",
              "      <td>627.000000</td>\n",
              "      <td>627.000000</td>\n",
              "      <td>627.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>29.631308</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.379585</td>\n",
              "      <td>34.385399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.511818</td>\n",
              "      <td>1.151090</td>\n",
              "      <td>0.792999</td>\n",
              "      <td>54.597730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.045800</td>\n",
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              "      <th>75%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.387500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80.000000</td>\n",
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              "      <td>5.000000</td>\n",
              "      <td>512.329200</td>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\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",
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              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-924222f4-42a4-45b0-b613-8dc47b1ed559');\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",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b737535e-9539-4a32-933a-c9d23c8baac5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dftrain\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 212.388612096364,\n        \"min\": 0.75,\n        \"max\": 627.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.631307814992027,\n          28.0,\n          627.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n_siblings_spouses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 221.15395808171547,\n        \"min\": 0.0,\n        \"max\": 627.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          627.0,\n          0.5454545454545454,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 221.37277310526898,\n        \"min\": 0.0,\n        \"max\": 627.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.379585326953748,\n          5.0,\n          0.7929992125432784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 255.06336536251808,\n        \"min\": 0.0,\n        \"max\": 627.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          34.38539856459331,\n          15.0458,\n          627.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSkS4Fj7d6fC",
        "outputId": "c56199f1-2f4f-46f1-f87d-13c17c4830fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(627, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "SJ46-hEaev-F",
        "outputId": "4e3ce271-72a6-4e1c-c330-b1016a9888f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    0\n",
              "Name: survived, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.age.hist(bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "z9epOcieezpB",
        "outputId": "3999b66d-bcab-4db4-a57d-6b2ffec5ef5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.sex.value_counts().plot(kind='barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "ey5woctEfE3d",
        "outputId": "aa48781e-76eb-4438-d4d5-30bfdba20b54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='sex'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain['class'].value_counts().plot(kind='barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "49KDOzlcfYCK",
        "outputId": "e6461290-42ea-473c-b5ae-d02bdf7d3ee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='class'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([dftrain, y_train], axis=1).groupby('sex').survived.mean().plot(kind='barh').set_xlabel('%survived')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "VLM3I37JfqPT",
        "outputId": "e056d138-9224-4f13-a71b-9386eb06cabc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, '%survived')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([dftrain, y_train], axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DR6uNzHsgmbI",
        "outputId": "e7b95aae-963a-4373-a98d-ce1528dc1115"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex   age  n_siblings_spouses  parch     fare   class     deck  \\\n",
              "0      male  22.0                   1      0   7.2500   Third  unknown   \n",
              "1    female  38.0                   1      0  71.2833   First        C   \n",
              "2    female  26.0                   0      0   7.9250   Third  unknown   \n",
              "3    female  35.0                   1      0  53.1000   First        C   \n",
              "4      male  28.0                   0      0   8.4583   Third  unknown   \n",
              "..      ...   ...                 ...    ...      ...     ...      ...   \n",
              "622    male  28.0                   0      0  10.5000  Second  unknown   \n",
              "623    male  25.0                   0      0   7.0500   Third  unknown   \n",
              "624  female  19.0                   0      0  30.0000   First        B   \n",
              "625  female  28.0                   1      2  23.4500   Third  unknown   \n",
              "626    male  32.0                   0      0   7.7500   Third  unknown   \n",
              "\n",
              "     embark_town alone  survived  \n",
              "0    Southampton     n         0  \n",
              "1      Cherbourg     n         1  \n",
              "2    Southampton     y         1  \n",
              "3    Southampton     n         1  \n",
              "4     Queenstown     y         0  \n",
              "..           ...   ...       ...  \n",
              "622  Southampton     y         0  \n",
              "623  Southampton     y         0  \n",
              "624  Southampton     y         1  \n",
              "625  Southampton     n         0  \n",
              "626   Queenstown     y         0  \n",
              "\n",
              "[627 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ad0cda14-3c43-46f7-ba50-4f082e04cee7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>n_siblings_spouses</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>class</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alone</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>Third</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>n</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>First</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>Third</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>y</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>First</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>Third</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>y</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>622</th>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10.5000</td>\n",
              "      <td>Second</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>y</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>623</th>\n",
              "      <td>male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0500</td>\n",
              "      <td>Third</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>y</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>624</th>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>B</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>y</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>625</th>\n",
              "      <td>female</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>Third</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>n</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>626</th>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Third</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>y</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>627 rows × 10 columns</p>\n",
              "</div>\n",
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              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-5823602e-9828-49ac-ae66-25de8059691f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 627,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.511817629565819,\n        \"min\": 0.75,\n        \"max\": 80.0,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          28.0,\n          59.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n_siblings_spouses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.597730499456304,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 216,\n        \"samples\": [\n          25.9292,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"C\",\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Cherbourg\",\n          \"unknown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"y\",\n          \"n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([dftrain, y_train], axis=1).groupby('sex').survived.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "iHQY49VZg4I1",
        "outputId": "53644e16-f11e-4891-b196-9aa8734e7f41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "female    0.778802\n",
              "male      0.180488\n",
              "Name: survived, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.778802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.180488</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qB1v9elbhAF-",
        "outputId": "38b2a58a-4b4f-4d4a-b703-f4398030b8a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(627, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfeval.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zl6j1_c5lA1h",
        "outputId": "d9e4e3ff-82a0-47ad-e9ca-9a41e02cbb46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(264, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',\n",
        "                       'embark_town', 'alone']\n",
        "NUMERIC_COLUMNS = ['age', 'fare']\n",
        "\n",
        "feature_columns = []\n",
        "for feature_name in CATEGORICAL_COLUMNS:\n",
        "  vocabulary = dftrain[feature_name].unique()  # gets a list of all unique values from given feature column\n",
        "  feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))\n",
        "\n",
        "for feature_name in NUMERIC_COLUMNS:\n",
        "  feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))\n",
        "\n",
        "print(feature_columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBwou45AlD-p",
        "outputId": "25ca1d9f-6712-4f1e-e611-7bceb1f58118"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From <ipython-input-20-792be602bf17>:8: categorical_column_with_vocabulary_list (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.\n",
            "WARNING:tensorflow:From <ipython-input-20-792be602bf17>:11: numeric_column (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[VocabularyListCategoricalColumn(key='sex', vocabulary_list=('male', 'female'), dtype=tf.string, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='n_siblings_spouses', vocabulary_list=(1, 0, 3, 4, 2, 5, 8), dtype=tf.int64, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='parch', vocabulary_list=(0, 1, 2, 5, 3, 4), dtype=tf.int64, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='class', vocabulary_list=('Third', 'First', 'Second'), dtype=tf.string, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='deck', vocabulary_list=('unknown', 'C', 'G', 'A', 'B', 'D', 'F', 'E'), dtype=tf.string, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='embark_town', vocabulary_list=('Southampton', 'Cherbourg', 'Queenstown', 'unknown'), dtype=tf.string, default_value=-1, num_oov_buckets=0), VocabularyListCategoricalColumn(key='alone', vocabulary_list=('n', 'y'), dtype=tf.string, default_value=-1, num_oov_buckets=0), NumericColumn(key='age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='fare', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain[feature_name].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sL0hYGIM2n5-",
        "outputId": "12a7a9a7-3a00-41e5-d745-48ff1f675f6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  7.25  ,  71.2833,   7.925 ,  53.1   ,   8.4583,  21.075 ,\n",
              "        11.1333,  30.0708,  16.7   ,   8.05  ,  31.275 ,   7.8542,\n",
              "        29.125 ,  13.    ,  18.    ,   7.225 ,  26.    ,  35.5   ,\n",
              "        31.3875, 263.    ,   7.8792,   7.8958,  27.7208, 146.5208,\n",
              "         7.75  ,  10.5   ,  82.1708,  52.    ,   7.2292,  11.2417,\n",
              "         9.475 ,  21.    ,  41.5792,  15.5   ,  21.6792,  17.8   ,\n",
              "        39.6875,  76.7292,  61.9792,  46.9   ,  80.    ,  83.475 ,\n",
              "        27.9   ,  15.2458,   8.1583,   8.6625,  73.5   ,  56.4958,\n",
              "         7.65  ,  29.    ,  12.475 ,   9.    ,   9.5   ,   7.7875,\n",
              "        47.1   ,  34.375 ,  61.175 ,  34.6542,  63.3583,  23.    ,\n",
              "        77.2875,   8.6542,   7.775 ,  24.15  ,  14.4542,  14.4583,\n",
              "       247.5208,   7.1417,   6.975 ,   7.05  ,  14.5   ,  15.0458,\n",
              "        26.2833,   9.2167,  79.2   ,   6.75  ,  11.5   ,  12.525 ,\n",
              "         7.3125,  61.3792,   7.7333,  69.55  ,  16.1   ,  55.    ,\n",
              "        25.4667,  28.7125,   0.    ,  15.05  ,  22.025 ,  26.55  ,\n",
              "         6.4958,  10.4625,  15.85  ,  18.7875,  31.    , 113.275 ,\n",
              "        27.    ,   9.35  ,  13.5   ,   7.55  ,  12.275 ,   7.125 ,\n",
              "        90.    ,  20.2125, 512.3292, 153.4625, 135.6333,  19.5   ,\n",
              "        29.7   ,  77.9583,  20.25  ,  78.85  ,  12.875 ,  30.5   ,\n",
              "        12.35  , 110.8833, 108.9   ,  24.    ,  56.9292,  83.1583,\n",
              "       262.375 ,  26.25  , 164.8667, 134.5   ,   6.2375,  20.525 ,\n",
              "        23.25  , 133.65  ,  66.6   ,  75.25  ,  69.3   , 211.5   ,\n",
              "       227.525 ,   7.7292,  12.    , 120.    ,   7.7958,  18.75  ,\n",
              "         6.8583,  32.5   ,  55.9   ,   8.1125,  19.2583,  27.75  ,\n",
              "        89.1042,  51.8625,  38.5   ,   7.725 ,  13.7917,   7.0458,\n",
              "        12.2875,   9.5875,  91.0792,  15.9   ,  19.9667,  49.5042,\n",
              "       151.55  ,  59.4   ,   7.4958,  34.0208,  93.5   ,  57.9792,\n",
              "       221.7792,  22.3583, 106.425 ,  49.5   ,  71.    ,  13.8625,\n",
              "         7.8292,  39.6   ,  79.65  ,  17.4   ,  51.4792,  26.3875,\n",
              "        30.    ,  40.125 ,  15.    ,  78.2667,  33.    ,  15.55  ,\n",
              "        65.    ,  14.4   ,  15.7417,  32.3208,   7.0542,   8.4333,\n",
              "        25.5875,  39.    ,   9.8417,   9.225 ,  10.1708, 211.3375,\n",
              "        57.    ,  13.4167,  26.2875,   7.7417,   9.4833,   8.3625,\n",
              "        23.45  ,  20.575 ,  30.6958,  25.9292,   8.6833,   7.8875,\n",
              "        37.0042,   6.95  ,   8.3   ,   6.4375,  39.4   ,  14.1083,\n",
              "        13.8583,  50.4958,  52.5542,   5.    ,   9.8458,  10.5167])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain['sex'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngCe_HMm3eJy",
        "outputId": "2f25c289-ab65-45be-98a6-3489b800bf5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['male', 'female'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n",
        "  def input_function():\n",
        "    # Convert pandas DataFrame to dictionary of NumPy arrays\n",
        "    data = {key: np.array(value) for key, value in data_df.items()}\n",
        "    # Create dataset from the dictionary of NumPy arrays and labels\n",
        "    ds = tf.data.Dataset.from_tensor_slices((data, label_df.values))\n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(1000)\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
        "    return ds\n",
        "  return input_function"
      ],
      "metadata": {
        "id": "jZr9PAGu3kl-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_input_fn= make_input_fn(dftrain, y_train)\n",
        "eval_input_fn= make_input_fn(dfeval, y_eval)"
      ],
      "metadata": {
        "id": "e6XLXC5hE1BH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_input_fn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "vp8lvJugFRJN",
        "outputId": "e760694d-a128-4cec-c1d1-32c0a6941762"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.make_input_fn.<locals>.input_function()>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>make_input_fn.&lt;locals&gt;.input_function</b><br/>def input_function()</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/content/&lt;ipython-input-23-c88de1d02016&gt;</a>&lt;no docstring&gt;</pre></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uylYUQ4FnHg",
        "outputId": "5a0eee8e-3e6f-438e-9341-2721f8d2b6ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(627, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfeval.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L01Q7uQYFr-o",
        "outputId": "384e6bf3-8879-4005-96a4-bee50d7aa0db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(264, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "BODnS6GGJz97",
        "outputId": "447326ef-6182-4838-c9d9-352d47d72c96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.15.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow==2.15"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_seuNL1J3Dl",
        "outputId": "7262f6ac-5030-4fe1-dab5-b100c46313e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow==2.15 in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
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            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (0.2.0)\n",
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            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (0.2.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (1.26.4)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (24.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (4.25.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (75.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (1.17.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (2.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (4.12.2)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (0.37.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (1.68.1)\n",
            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (2.15.2)\n",
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            "Requirement already satisfied: keras<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15) (2.15.0)\n",
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            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15) (1.2.1)\n",
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            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15) (0.7.2)\n",
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            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow==2.15) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15) (2024.8.30)\n",
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            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15) (0.6.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow==2.15) (3.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWUzh5mnFul_",
        "outputId": "71b7f9fd-02bf-4913-ac5d-a6d9270126d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From <ipython-input-30-577bee643cf4>:2: LinearClassifierV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.keras instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow_estimator/python/estimator/head/head_utils.py:54: BinaryClassHead.__init__ (from tensorflow_estimator.python.estimator.head.binary_class_head) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.keras instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow_estimator/python/estimator/canned/linear.py:944: Estimator.__init__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.keras instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow_estimator/python/estimator/estimator.py:1844: RunConfig.__init__ (from tensorflow_estimator.python.estimator.run_config) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.keras instead.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpuh50wbsa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "linear_est.train(train_input_fn)\n",
        "result=linear_est.evaluate(eval_input_fn)\n",
        "clear_output()\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vIripHMGMZk",
        "outputId": "c500faff-ab83-4d40-fd48-1f8eaa2b4542"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy': 0.75, 'accuracy_baseline': 0.625, 'auc': 0.8305173, 'auc_precision_recall': 0.7898139, 'average_loss': 0.5062568, 'label/mean': 0.375, 'loss': 0.5015317, 'precision': 0.6386555, 'prediction/mean': 0.4643931, 'recall': 0.7676768, 'global_step': 200}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result=list(linear_est.predict(eval_input_fn))\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8niZ1vXLh8S",
        "outputId": "e0c41b9e-af18-4348-fa0f-be031ebbe531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow_estimator/python/estimator/head/base_head.py:786: ClassificationOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.keras instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py:561: RegressionOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.keras instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py:563: PredictOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.keras instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'logits': array([-1.8930913], dtype=float32), 'logistic': array([0.1308924], dtype=float32), 'probabilities': array([0.86910754, 0.1308924 ], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}, {'logits': array([-0.90506303], dtype=float32), 'logistic': array([0.28801116], dtype=float32), 'probabilities': array([0.71198875, 0.28801113], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}, {'logits': array([-1.697793], dtype=float32), 'logistic': array([0.15475374], dtype=float32), 'probabilities': array([0.8452462 , 0.15475371], dtype=float32), 'class_ids': array([0]), 'classes': array([b'0'], dtype=object), 'all_class_ids': array([0, 1], dtype=int32), 'all_classes': array([b'0', b'1'], dtype=object)}, {'logits': 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b'1'], dtype=object)}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dfeval.loc[3])\n",
        "print(y_eval.loc[3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FAQncPANsn9",
        "outputId": "38463b0a-5238-4d44-9a65-f745dc49f707"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sex                        female\n",
            "age                          55.0\n",
            "n_siblings_spouses              0\n",
            "parch                           0\n",
            "fare                         16.0\n",
            "class                      Second\n",
            "deck                      unknown\n",
            "embark_town           Southampton\n",
            "alone                           y\n",
            "Name: 3, dtype: object\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result[3]['probabilities'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0pFMtHOO4De",
        "outputId": "3712dcc4-ba68-4300-deaf-0a3630f2e8c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8553593"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "QQ-8IeMFO7No"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']\n",
        "SPECIES = ['Setosa', 'Versicolor', 'Virginica']"
      ],
      "metadata": {
        "id": "UjHr02Udqbyq"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CSV_COLUMN_NAMES"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAOERiSTqdPN",
        "outputId": "aa74c215-0d9f-4e7a-bbc0-449d44756ee0"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_path= tf.keras.utils.get_file(\"iris_training.csv\", \"https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv\")\n",
        "test_path=tf.keras.utils.get_file(\"iris_test.csv\", \"https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HffuFSYIqdJe",
        "outputId": "6807a681-93a4-4bd0-d715-6315df6b0dea"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv\n",
            "2194/2194 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv\n",
            "573/573 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_data=pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)\n",
        "testing_data=pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)"
      ],
      "metadata": {
        "id": "HkxPCq-WqdAW"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Uf8xF2VHqc4d",
        "outputId": "84763688-6597-4ddb-f79f-73a6f6586930"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLength  SepalWidth  PetalLength  PetalWidth  Species\n",
              "0          6.4         2.8          5.6         2.2        2\n",
              "1          5.0         2.3          3.3         1.0        1\n",
              "2          4.9         2.5          4.5         1.7        2\n",
              "3          4.9         3.1          1.5         0.1        0\n",
              "4          5.7         3.8          1.7         0.3        0"
            ],
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8096bd61-9f8a-498b-ac81-84ee962f86a7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "training_data",
              "summary": "{\n  \"name\": \"training_data\",\n  \"rows\": 120,\n  \"fields\": [\n    {\n      \"column\": \"SepalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8685784774150068,\n        \"min\": 4.4,\n        \"max\": 7.9,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          4.5,\n          6.1,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4271559257155984,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          2.8,\n          3.6,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.822100359444549,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          6.4,\n          5.9,\n          3.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7820392791757137,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          2.2,\n          2.4,\n          1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testing_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7-6lmt_fsRq4",
        "outputId": "39e6384b-28fa-4340-c512-3ef97053d21f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLength  SepalWidth  PetalLength  PetalWidth  Species\n",
              "0          5.9         3.0          4.2         1.5        1\n",
              "1          6.9         3.1          5.4         2.1        2\n",
              "2          5.1         3.3          1.7         0.5        0\n",
              "3          6.0         3.4          4.5         1.6        1\n",
              "4          5.5         2.5          4.0         1.3        1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84455bc3-0721-4bc2-bd09-fe80a5c3581a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLength</th>\n",
              "      <th>SepalWidth</th>\n",
              "      <th>PetalLength</th>\n",
              "      <th>PetalWidth</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84455bc3-0721-4bc2-bd09-fe80a5c3581a')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-84455bc3-0721-4bc2-bd09-fe80a5c3581a 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-84455bc3-0721-4bc2-bd09-fe80a5c3581a');\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",
              "\n",
              "\n",
              "<div id=\"df-41522d98-9615-4dbf-9f3b-eb4f091ded70\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41522d98-9615-4dbf-9f3b-eb4f091ded70')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-41522d98-9615-4dbf-9f3b-eb4f091ded70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "testing_data",
              "summary": "{\n  \"name\": \"testing_data\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"SepalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6536282468326099,\n        \"min\": 4.3,\n        \"max\": 7.1,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          5.9,\n          6.9,\n          6.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4633833595750223,\n        \"min\": 2.2,\n        \"max\": 4.2,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          2.2,\n          2.6,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.537459465870695,\n        \"min\": 1.1,\n        \"max\": 5.9,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          4.2,\n          3.5,\n          4.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6947752468807168,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1.4,\n          1.0,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_y=training_data.pop('Species')\n",
        "test_y=testing_data.pop('Species')"
      ],
      "metadata": {
        "id": "HtJ2jvyFt3pk"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def input_fn(features, labels, training=True, batch_size=256):\n",
        "  dataset=tf.data.Dataset.from_tensor_slices((dict(features), labels))\n",
        "  if training:\n",
        "      dataset=dataset.shuffle(1000).repeat()\n",
        "  return dataset.batch(batch_size)"
      ],
      "metadata": {
        "id": "pou79uS2uOCh"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_feature_columns=[]\n",
        "for key in training_data.keys():\n",
        "  my_feature_columns.append(tf.feature_column.numeric_column(key=key))\n",
        "print(my_feature_columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2RKi9fjwDIf",
        "outputId": "ce0680b5-c4e6-4d39-f6e1-e1505e99a4df"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NumericColumn(key='SepalLength', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='SepalWidth', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='PetalLength', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='PetalWidth', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier=tf.estimator.DNNClassifier(feature_columns=my_feature_columns, hidden_units=[30, 20, 10], n_classes=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWxC3KiQyJwZ",
        "outputId": "c993f58c-97f3-4d75-9b07-289d9a2f6db5"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From <ipython-input-53-0db9147a87f3>:1: DNNClassifierV2.__init__ (from tensorflow_estimator.python.estimator.canned.dnn) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.keras instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow_estimator/python/estimator/head/head_utils.py:59: MultiClassHead.__init__ (from tensorflow_estimator.python.estimator.head.multi_class_head) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.keras instead.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpxm72dtcy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.train(input_fn=lambda: input_fn(training_data, train_y, training=True), steps= 5000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HyYIxA00zIug",
        "outputId": "03746533-9219-410f-9fd1-5fed532cd8e2"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/training/saver.py:1176: get_checkpoint_mtimes (from tensorflow.python.checkpoint.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file utilities to get mtimes.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow_estimator.python.estimator.canned.dnn.DNNClassifierV2 at 0x79e69ccd7760>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.evaluate(input_fn=lambda: input_fn(testing_data, test_y, training=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPaPnVG1zjdg",
        "outputId": "2b292aea-c659-4d2c-e93c-881ad14ba711"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.96666664,\n",
              " 'average_loss': 0.24707682,\n",
              " 'loss': 0.24707682,\n",
              " 'global_step': 10000}"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def input_fn(features, batch_size=256):\n",
        "  return tf.data.Dataset.from_tensor_slices(dict(features)).batch(batch_size)\n",
        "\n",
        "features = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth']\n",
        "predict = {}\n",
        "\n",
        "print('Please type numeric values as prompted.')\n",
        "for feature in features:\n",
        "  valid=True\n",
        "  while valid:\n",
        "    val=input(feature + \":\")\n",
        "    if not val.isdigit():\n",
        "      valid=False\n",
        "  predict[feature]=[float(val)]\n",
        "\n",
        "\n",
        "predictions= classifier.predict(input_fn=lambda: input_fn(predict))\n",
        "for pred_dict in predictions:\n",
        "  class_id=pred_dict['class_ids'][0]\n",
        "  probability=pred_dict['probabilities'][class_id]\n",
        "  print('Prediction is \"{}\" ({:.1f}%)'.format(SPECIES[class_id], 100*probability))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKFIAavx4AKb",
        "outputId": "6b672c8c-b38d-4d75-e102-d041911e09b2"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please type numeric values as prompted.\n",
            "SepalLength:1.2\n",
            "SepalWidth:2.3\n",
            "PetalLength:2.4\n",
            "PetalWidth:1.2\n",
            "Prediction is \"Virginica\" (47.3%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_probability==0.8.0rc0 --user --upgrade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "1ZzMXndgmQ3n",
        "outputId": "af99c070-e5f2-435d-f578-ae69110372b8"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow_probability==0.8.0rc0\n",
            "  Downloading tensorflow_probability-0.8.0rc0-py2.py3-none-any.whl.metadata (14 kB)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_probability==0.8.0rc0) (1.17.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow_probability==0.8.0rc0) (1.26.4)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from tensorflow_probability==0.8.0rc0) (4.4.2)\n",
            "Collecting cloudpickle==1.1.1 (from tensorflow_probability==0.8.0rc0)\n",
            "  Downloading cloudpickle-1.1.1-py2.py3-none-any.whl.metadata (4.0 kB)\n",
            "Downloading tensorflow_probability-0.8.0rc0-py2.py3-none-any.whl (2.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading cloudpickle-1.1.1-py2.py3-none-any.whl (17 kB)\n",
            "Installing collected packages: cloudpickle, tensorflow_probability\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "bigframes 1.27.0 requires cloudpickle>=2.0.0, but you have cloudpickle 1.1.1 which is incompatible.\n",
            "dask 2024.10.0 requires cloudpickle>=3.0.0, but you have cloudpickle 1.1.1 which is incompatible.\n",
            "dopamine-rl 4.0.9 requires tensorflow-probability>=0.13.0, but you have tensorflow-probability 0.8.0rc0 which is incompatible.\n",
            "gym 0.25.2 requires cloudpickle>=1.2.0, but you have cloudpickle 1.1.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed cloudpickle-1.1.1 tensorflow_probability-0.8.0rc0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "cloudpickle"
                ]
              },
              "id": "b83a4a72166341a39918ce00cfb15b40"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "SGk9YUvqA6V_"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade tensorflow\n",
        "!pip install --upgrade tensorflow-probability"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eT1Zc9Iomw5B",
        "outputId": "1cd16b4b-8cd1-4f6a-d5d3-42d1dd5efd98"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Collecting tensorflow\n",
            "  Downloading tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.25.5)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (75.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.17.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.68.1)\n",
            "Collecting tensorboard<2.19,>=2.18 (from tensorflow)\n",
            "  Downloading tensorboard-2.18.0-py3-none-any.whl.metadata (1.6 kB)\n",
            "Collecting keras>=3.5.0 (from tensorflow)\n",
            "  Downloading keras-3.7.0-py3-none-any.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: numpy<2.1.0,>=1.26.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.26.4)\n",
            "Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.12.1)\n",
            "Collecting ml-dtypes<0.5.0,>=0.4.0 (from tensorflow)\n",
            "  Downloading ml_dtypes-0.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.5.0->tensorflow) (13.9.4)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.5.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.5.0->tensorflow) (0.13.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.8.30)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.7)\n",
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            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.1.3)\n",
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            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow) (0.1.2)\n",
            "Downloading tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (615.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m615.3/615.3 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading keras-3.7.0-py3-none-any.whl (1.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ml_dtypes-0.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m21.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tensorboard-2.18.0-py3-none-any.whl (5.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ml-dtypes, tensorboard, keras, tensorflow\n",
            "  Attempting uninstall: ml-dtypes\n",
            "    Found existing installation: ml-dtypes 0.2.0\n",
            "    Uninstalling ml-dtypes-0.2.0:\n",
            "      Successfully uninstalled ml-dtypes-0.2.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.15.2\n",
            "    Uninstalling tensorboard-2.15.2:\n",
            "      Successfully uninstalled tensorboard-2.15.2\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.15.0\n",
            "    Uninstalling keras-2.15.0:\n",
            "      Successfully uninstalled keras-2.15.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.15.0\n",
            "    Uninstalling tensorflow-2.15.0:\n",
            "      Successfully uninstalled tensorflow-2.15.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "dopamine-rl 4.0.9 requires tensorflow-probability>=0.13.0, but you have tensorflow-probability 0.8.0rc0 which is incompatible.\n",
            "tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.18.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed keras-3.7.0 ml-dtypes-0.4.1 tensorboard-2.18.0 tensorflow-2.18.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "keras",
                  "ml_dtypes",
                  "tensorboard",
                  "tensorflow"
                ]
              },
              "id": "922e8ccaa2f749dfa1c48352f4416490"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow-probability in /root/.local/lib/python3.10/site-packages (0.8.0rc0)\n",
            "Collecting tensorflow-probability\n",
            "  Downloading tensorflow_probability-0.25.0-py2.py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (1.4.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (1.17.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (1.26.4)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (4.4.2)\n",
            "Collecting cloudpickle>=1.3 (from tensorflow-probability)\n",
            "  Downloading cloudpickle-3.1.0-py3-none-any.whl.metadata (7.0 kB)\n",
            "Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (0.6.0)\n",
            "Requirement already satisfied: dm-tree in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (0.1.8)\n",
            "Downloading tensorflow_probability-0.25.0-py2.py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m64.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading cloudpickle-3.1.0-py3-none-any.whl (22 kB)\n",
            "Installing collected packages: cloudpickle, tensorflow-probability\n",
            "  Attempting uninstall: cloudpickle\n",
            "    Found existing installation: cloudpickle 1.1.1\n",
            "    Uninstalling cloudpickle-1.1.1:\n",
            "      Successfully uninstalled cloudpickle-1.1.1\n",
            "  Attempting uninstall: tensorflow-probability\n",
            "    Found existing installation: tensorflow-probability 0.8.0rc0\n",
            "    Uninstalling tensorflow-probability-0.8.0rc0:\n",
            "      Successfully uninstalled tensorflow-probability-0.8.0rc0\n",
            "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_probability as tfp"
      ],
      "metadata": {
        "id": "AIHRIsBCA6MV"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "dgvt4qkspmE4"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfd=tfp.distributions"
      ],
      "metadata": {
        "id": "hFlZ-CiXA6BM"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7s2jHk_A5vr",
        "outputId": "117c823e-f8c7-4774-fd4d-0c711f604a0e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'tensorflow_probability.python.distributions' from '/usr/local/lib/python3.10/dist-packages/tensorflow_probability/python/distributions/__init__.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "initial_distribution=tfd.Categorical(probs=[0.8, 0.2])\n",
        "transition_distribution=tfd.Categorical(probs=[[0.7, 0.3],\n",
        "                                              [0.2, 0.8]])\n",
        "observation_distribution=tfd.Normal(loc=[0., 15.], scale=[5., 10.])\n"
      ],
      "metadata": {
        "id": "VTTUfrGYoHSg"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=tfd.HiddenMarkovModel(initial_distribution=initial_distribution,\n",
        "                            transition_distribution=transition_distribution,\n",
        "                            observation_distribution=observation_distribution,\n",
        "                            num_steps=30)"
      ],
      "metadata": {
        "id": "CNpJVG1Ko7bQ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean=model.mean()\n",
        "with tf.compat.v1.Session() as sess:\n",
        "  print(mean.numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LapdcIiIpIk4",
        "outputId": "cc5f477e-5cde-4fa8-c675-5c74c9c7b082"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3.        5.9999995 7.4999995 8.25      8.625     8.812501  8.90625\n",
            " 8.953126  8.9765625 8.988281  8.994141  8.99707   8.998536  8.999269\n",
            " 8.999633  8.999816  8.9999075 8.999953  8.999976  8.999987  8.999991\n",
            " 8.999994  8.999996  8.999998  8.999998  8.999998  8.999998  8.999998\n",
            " 8.999998  8.999998 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EfuBKlytpicT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}