Tensor_Flow_basics_and_Regression.ipynb

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    "colab": {
      "name": "Tensor Flow basics and Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPlXimfARr8N0LCEHIL8idq",
      "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/madhavgupta6803/Soc-Project/blob/main/Tensor_Flow_basics_and_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "-HurY0PTSfKG"
      },
      "outputs": [],
      "source": [
        "%tensorflow_version 2.x\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(tf.version)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZeVupMZogQs0",
        "outputId": "4f8e1e4f-b643-4d4c-84d2-934c9706c526"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<module 'tensorflow._api.v2.version' from '/usr/local/lib/python3.7/dist-packages/tensorflow/_api/v2/version/__init__.py'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "string = tf.Variable(\"This is string\", tf.string)"
      ],
      "metadata": {
        "id": "aE9R7pxlgZwY"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "number = tf.Variable(324, tf.int16)\n",
        "floating = tf.Variable(3.567, tf.float64)"
      ],
      "metadata": {
        "id": "IpmgUfmuPc-G"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rank1_tensor = tf.Variable([\"Test\", \"OK\", \"Tim\"], tf.string)\n",
        "rank2_tensor = tf.Variable([[\"Test\", \"OK\"],[\"test\", \"Tim\"]], tf.string)\n"
      ],
      "metadata": {
        "id": "M-OTEGW3RFyZ"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.rank(floating)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3w9SegtSSLk",
        "outputId": "712bacc3-595c-4fac-f067-7294a39e510d"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int32, numpy=0>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rank1_tensor.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YA58omNZUiCF",
        "outputId": "35e680d5-ed9e-4c29-9eb6-3c83b615c6da"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([3])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t=tf.zeros([5,5,5,5])\n",
        "print(t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SolzpJD_WDid",
        "outputId": "79617472-8e8e-45cc-83d8-056747db082e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t= tf.reshape(t,[25,5,5])"
      ],
      "metadata": {
        "id": "sziENnR_F2OC"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6GeUrf3GApL",
        "outputId": "94969cb7-0dc0-45f9-b21c-2baea29f51fa"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
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            "\n",
            " [[0. 0. 0. 0. 0.]\n",
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            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]]], shape=(25, 5, 5), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "L= reversed(list(range(1,11)))"
      ],
      "metadata": {
        "id": "weFNG9_HGDXx"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(L)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJVp2_JmMy2-",
        "outputId": "0d06aae4-c638-42d8-93bf-22b8ae8d818a"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<list_reverseiterator object at 0x7f3fbe1307d0>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q sklearn"
      ],
      "metadata": {
        "id": "FDw96PXwM1x3"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "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\n"
      ],
      "metadata": {
        "id": "dXZMtbX3EKmh"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset.\n",
        "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",
        "print(dftrain.head())\n",
        "y_train = dftrain.pop('survived')\n",
        "y_eval = dfeval.pop('survived')\n",
        "print(dftrain.loc[1],y_train.loc[4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MyFkw-kE7sB",
        "outputId": "76cf9090-e88a-4b06-d920-5e1e7274c8c1"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   survived     sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
            "0         0    male  22.0                   1      0   7.2500  Third  unknown   \n",
            "1         1  female  38.0                   1      0  71.2833  First        C   \n",
            "2         1  female  26.0                   0      0   7.9250  Third  unknown   \n",
            "3         1  female  35.0                   1      0  53.1000  First        C   \n",
            "4         0    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  \n",
            "sex                      female\n",
            "age                        38.0\n",
            "n_siblings_spouses            1\n",
            "parch                         0\n",
            "fare                    71.2833\n",
            "class                     First\n",
            "deck                          C\n",
            "embark_town           Cherbourg\n",
            "alone                         n\n",
            "Name: 1, dtype: object 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.describe()"
      ],
      "metadata": {
        "id": "5rJQNa6UxfAs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "21abdef4-3e0c-46a8-8a28-55e2810c2abb"
      },
      "execution_count": 73,
      "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e21fa25-068a-47a3-b17c-123489a25350\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>n_siblings_spouses</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <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",
              "      <td>0.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.045800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e21fa25-068a-47a3-b17c-123489a25350')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2e21fa25-068a-47a3-b17c-123489a25350 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-2e21fa25-068a-47a3-b17c-123489a25350');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYEC8Duj5GFX",
        "outputId": "0ee4213d-8ab2-43e1-f4b0-555d86fa05bb"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(627, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain[\"class\"].value_counts().plot(kind=\"barh\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "WFjURwE35cRS",
        "outputId": "85d8bdb1-475d-4964-d4c5-429a4276aa8b"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3fbe166710>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([dftrain, y_train], axis=1).groupby('sex').survived.mean().plot(kind='barh').set_xlabel('% survive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "JTV1kdFG78CL",
        "outputId": "9e48504a-ab89-4542-ce64-48544759462d"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, '% survive')"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "bQ0EfcXH8Vcm",
        "outputId": "a257c2c4-fb26-4e99-c030-f5b3b7d15edf"
      },
      "execution_count": 77,
      "outputs": [
        {
          "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": [
        ""
      ],
      "metadata": {
        "id": "fR73k_5KQAPa"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3qcbvOYbIwa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ef90028-7f76-4601-a4e6-9ccfc33d745e"
      },
      "source": [
        "def make_input_fn(data_df, label_df, num_epochs=11, shuffle=True, batch_size=32):\n",
        "  def input_function():  # inner function, this will be returned\n",
        "    ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))  # create tf.data.Dataset object with data and its label\n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(1000)  # randomize order of data\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)  # split dataset into batches of 32 and repeat process for number of epochs\n",
        "    return ds  # return a batch of the dataset\n",
        "  return input_function  # return a function object for use\n",
        "\n",
        "train_input_fn = make_input_fn(dftrain, y_train)  # here we will call the input_function that was returned to us to get a dataset object we can feed to the model\n",
        "eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)\n",
        "\n",
        "linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)\n",
        "# We create a linear estimtor by passing the feature columns we created earlier\n"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpbw8l7nqj\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpbw8l7nqj', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "linear_est.train(train_input_fn)  # train\n",
        "result = linear_est.evaluate(eval_input_fn)  # get model metrics/stats by testing on tetsing data\n",
        "\n",
        "clear_output()  # clears consoke output\n",
        "print(result['accuracy'])  # the result variable is simply a dict of stats about our model\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vzXcRTh8JNhL",
        "outputId": "c9dd4b55-d220-4b8a-8b8d-8001ac2fb729"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7689394\n",
            "{'accuracy': 0.7689394, 'accuracy_baseline': 0.625, 'auc': 0.8321396, 'auc_precision_recall': 0.7785185, 'average_loss': 0.49428034, 'label/mean': 0.375, 'loss': 0.4840579, 'precision': 0.73170733, 'prediction/mean': 0.30901793, 'recall': 0.6060606, 'global_step': 220}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = list(linear_est.predict(eval_input_fn))\n",
        "print(dfeval.loc[4])\n",
        "print(y_eval.loc[4])\n",
        "print(result[4][\"probabilities\"][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_uq9z9rQ0cE",
        "outputId": "14af433f-bae4-437b-b871-22a08a0cef02"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow_estimator/python/estimator/canned/linear.py:1478: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
            "  getter=tf.compat.v1.get_variable)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpbw8l7nqj/model.ckpt-220\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "sex                          male\n",
            "age                          34.0\n",
            "n_siblings_spouses              0\n",
            "parch                           0\n",
            "fare                         13.0\n",
            "class                      Second\n",
            "deck                            D\n",
            "embark_town           Southampton\n",
            "alone                           y\n",
            "Name: 4, dtype: object\n",
            "1\n",
            "0.19797835\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "T-oRXN2tMDNW"
      },
      "execution_count": 80,
      "outputs": []
    }
  ]
}