feature_sets.ipynb

{
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  "metadata": {
    "colab": {
      "name": "feature_sets.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "JndnmDMp66FL",
        "IGINhMIJ5Wyt",
        "pZa8miwu6_tQ"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/apachon/google-machine-learning-crash-course/blob/master/feature_sets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JndnmDMp66FL",
        "colab_type": "text"
      },
      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMqWDc_m6rUC",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zbIgBK-oXHO7",
        "colab_type": "text"
      },
      "source": [
        "# Feature Sets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bL04rAQwH3pH",
        "colab_type": "text"
      },
      "source": [
        "**Learning Objective:** Create a minimal set of features that performs just as well as a more complex feature set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F8Hci6tAH3pH",
        "colab_type": "text"
      },
      "source": [
        "So far, we've thrown all of our features into the model. Models with fewer features use fewer resources and are easier to maintain. Let's see if we can build a model on a minimal set of housing features that will perform equally as well as one that uses all the features in the data set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5ZjVwK_qOyR",
        "colab_type": "text"
      },
      "source": [
        "## Setup\n",
        "\n",
        "As before, let's load and prepare the California housing data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrOYRILAH3pJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5d29ddca-fb68-4264-c687-530a62bc063b"
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import math\n",
        "\n",
        "from IPython import display\n",
        "from matplotlib import cm\n",
        "from matplotlib import gridspec\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import metrics\n",
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.data import Dataset\n",
        "\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format\n",
        "\n",
        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
        "\n",
        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
        "    np.random.permutation(california_housing_dataframe.index))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dGnXo7flH3pM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocess_features(california_housing_dataframe):\n",
        "  \"\"\"Prepares input features from California housing data set.\n",
        "\n",
        "  Args:\n",
        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
        "      from the California housing data set.\n",
        "  Returns:\n",
        "    A DataFrame that contains the features to be used for the model, including\n",
        "    synthetic features.\n",
        "  \"\"\"\n",
        "  selected_features = california_housing_dataframe[\n",
        "    [\"latitude\",\n",
        "     \"longitude\",\n",
        "     \"housing_median_age\",\n",
        "     \"total_rooms\",\n",
        "     \"total_bedrooms\",\n",
        "     \"population\",\n",
        "     \"households\",\n",
        "     \"median_income\"]]\n",
        "  processed_features = selected_features.copy()\n",
        "  # Create a synthetic feature.\n",
        "  processed_features[\"rooms_per_person\"] = (\n",
        "    california_housing_dataframe[\"total_rooms\"] /\n",
        "    california_housing_dataframe[\"population\"])\n",
        "  return processed_features\n",
        "\n",
        "def preprocess_targets(california_housing_dataframe):\n",
        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
        "\n",
        "  Args:\n",
        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
        "      from the California housing data set.\n",
        "  Returns:\n",
        "    A DataFrame that contains the target feature.\n",
        "  \"\"\"\n",
        "  output_targets = pd.DataFrame()\n",
        "  # Scale the target to be in units of thousands of dollars.\n",
        "  output_targets[\"median_house_value\"] = (\n",
        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
        "  return output_targets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLXC8y4AqsIy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1bbcf174-b6a0-4e75-8153-ee7e3d3529bb"
      },
      "source": [
        "# Choose the first 12000 (out of 17000) examples for training.\n",
        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
        "\n",
        "# Choose the last 5000 (out of 17000) examples for validation.\n",
        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
        "\n",
        "# Double-check that we've done the right thing.\n",
        "print(\"Training examples summary:\")\n",
        "display.display(training_examples.describe())\n",
        "print(\"Validation examples summary:\")\n",
        "display.display(validation_examples.describe())\n",
        "\n",
        "print(\"Training targets summary:\")\n",
        "display.display(training_targets.describe())\n",
        "print(\"Validation targets summary:\")\n",
        "display.display(validation_targets.describe())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training examples summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>rooms_per_person</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12000.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>12000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.6</td>\n",
              "      <td>-119.6</td>\n",
              "      <td>28.6</td>\n",
              "      <td>2633.8</td>\n",
              "      <td>537.4</td>\n",
              "      <td>1427.4</td>\n",
              "      <td>499.9</td>\n",
              "      <td>3.9</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>2178.9</td>\n",
              "      <td>422.7</td>\n",
              "      <td>1152.0</td>\n",
              "      <td>385.8</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>32.5</td>\n",
              "      <td>-124.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.9</td>\n",
              "      <td>-121.8</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1457.0</td>\n",
              "      <td>295.0</td>\n",
              "      <td>785.0</td>\n",
              "      <td>281.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>34.3</td>\n",
              "      <td>-118.5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2116.0</td>\n",
              "      <td>430.0</td>\n",
              "      <td>1161.0</td>\n",
              "      <td>407.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>37.7</td>\n",
              "      <td>-118.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>3142.0</td>\n",
              "      <td>648.0</td>\n",
              "      <td>1713.2</td>\n",
              "      <td>603.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>42.0</td>\n",
              "      <td>-114.3</td>\n",
              "      <td>52.0</td>\n",
              "      <td>32054.0</td>\n",
              "      <td>5290.0</td>\n",
              "      <td>35682.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>55.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       latitude  longitude  ...  median_income  rooms_per_person\n",
              "count   12000.0    12000.0  ...        12000.0           12000.0\n",
              "mean       35.6     -119.6  ...            3.9               2.0\n",
              "std         2.1        2.0  ...            1.9               1.1\n",
              "min        32.5     -124.3  ...            0.5               0.0\n",
              "25%        33.9     -121.8  ...            2.6               1.5\n",
              "50%        34.3     -118.5  ...            3.6               1.9\n",
              "75%        37.7     -118.0  ...            4.8               2.3\n",
              "max        42.0     -114.3  ...           15.0              55.2\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
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        {
          "output_type": "stream",
          "text": [
            "Validation examples summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>rooms_per_person</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.6</td>\n",
              "      <td>-119.5</td>\n",
              "      <td>28.5</td>\n",
              "      <td>2667.3</td>\n",
              "      <td>544.3</td>\n",
              "      <td>1434.8</td>\n",
              "      <td>504.3</td>\n",
              "      <td>3.9</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.5</td>\n",
              "      <td>2182.6</td>\n",
              "      <td>418.5</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>381.5</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>32.5</td>\n",
              "      <td>-124.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.9</td>\n",
              "      <td>-121.8</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1473.0</td>\n",
              "      <td>302.0</td>\n",
              "      <td>801.0</td>\n",
              "      <td>282.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>34.2</td>\n",
              "      <td>-118.5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2152.0</td>\n",
              "      <td>443.0</td>\n",
              "      <td>1183.0</td>\n",
              "      <td>415.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>37.7</td>\n",
              "      <td>-118.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>3173.0</td>\n",
              "      <td>652.0</td>\n",
              "      <td>1750.2</td>\n",
              "      <td>609.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>41.8</td>\n",
              "      <td>-114.5</td>\n",
              "      <td>52.0</td>\n",
              "      <td>37937.0</td>\n",
              "      <td>6445.0</td>\n",
              "      <td>28566.0</td>\n",
              "      <td>6082.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       latitude  longitude  ...  median_income  rooms_per_person\n",
              "count    5000.0     5000.0  ...         5000.0            5000.0\n",
              "mean       35.6     -119.5  ...            3.9               2.0\n",
              "std         2.2        2.0  ...            1.9               1.3\n",
              "min        32.5     -124.3  ...            0.5               0.1\n",
              "25%        33.9     -121.8  ...            2.5               1.5\n",
              "50%        34.2     -118.5  ...            3.5               1.9\n",
              "75%        37.7     -118.0  ...            4.8               2.3\n",
              "max        41.8     -114.5  ...           15.0              52.0\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training targets summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>208.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>116.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>121.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>180.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>267.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "       median_house_value\n",
              "count             12000.0\n",
              "mean                208.2\n",
              "std                 116.2\n",
              "min                  15.0\n",
              "25%                 121.4\n",
              "50%                 180.6\n",
              "75%                 267.0\n",
              "max                 500.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Validation targets summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\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>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>205.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>115.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>116.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>179.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>260.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       median_house_value\n",
              "count              5000.0\n",
              "mean                205.2\n",
              "std                 115.4\n",
              "min                  15.0\n",
              "25%                 116.1\n",
              "50%                 179.5\n",
              "75%                 260.7\n",
              "max                 500.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLvmkugKLany",
        "colab_type": "text"
      },
      "source": [
        "## Task 1: Develop a Good Feature Set\n",
        "\n",
        "**What's the best performance you can get with just 2 or 3 features?**\n",
        "\n",
        "A **correlation matrix** shows pairwise correlations, both for each feature compared to the target and for each feature compared to other features.\n",
        "\n",
        "Here, correlation is defined as the [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient).  You don't have to understand the mathematical details for this exercise.\n",
        "\n",
        "Correlation values have the following meanings:\n",
        "\n",
        "  * `-1.0`: perfect negative correlation\n",
        "  * `0.0`: no correlation\n",
        "  * `1.0`: perfect positive correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzoZUSdLIolF",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "2e9bec6e-45fa-4a61-fefc-1e1adcab736a"
      },
      "source": [
        "correlation_dataframe = training_examples.copy()\n",
        "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n",
        "\n",
        "correlation_dataframe.corr()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>rooms_per_person</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>-0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_median_age</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rooms</th>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bedrooms</th>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population</th>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>households</th>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_income</th>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rooms_per_person</th>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    latitude  longitude  ...  rooms_per_person  target\n",
              "latitude                 1.0       -0.9  ...               0.1    -0.1\n",
              "longitude               -0.9        1.0  ...              -0.1    -0.1\n",
              "housing_median_age       0.0       -0.1  ...              -0.1     0.1\n",
              "total_rooms             -0.0        0.1  ...               0.1     0.1\n",
              "total_bedrooms          -0.1        0.1  ...               0.0     0.0\n",
              "population              -0.1        0.1  ...              -0.1    -0.0\n",
              "households              -0.1        0.1  ...              -0.0     0.1\n",
              "median_income           -0.1       -0.0  ...               0.2     0.7\n",
              "rooms_per_person         0.1       -0.1  ...               1.0     0.2\n",
              "target                  -0.1       -0.1  ...               0.2     1.0\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQpktkNpia2P",
        "colab_type": "text"
      },
      "source": [
        "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n",
        "\n",
        "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n",
        "\n",
        "Use this information to try removing features.  You can also try developing additional synthetic features, such as ratios of two raw features.\n",
        "\n",
        "For convenience, we've included the training code from the previous exercise."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjR5jWpFr2xs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def construct_feature_columns(input_features):\n",
        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
        "\n",
        "  Args:\n",
        "    input_features: The names of the numerical input features to use.\n",
        "  Returns:\n",
        "    A set of feature columns\n",
        "  \"\"\" \n",
        "  return set([tf.feature_column.numeric_column(my_feature)\n",
        "              for my_feature in input_features])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsvKHzRciH9T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
        "    \"\"\"Trains a linear regression model.\n",
        "  \n",
        "    Args:\n",
        "      features: pandas DataFrame of features\n",
        "      targets: pandas DataFrame of targets\n",
        "      batch_size: Size of batches to be passed to the model\n",
        "      shuffle: True or False. Whether to shuffle the data.\n",
        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
        "    Returns:\n",
        "      Tuple of (features, labels) for next data batch\n",
        "    \"\"\"\n",
        "    \n",
        "    # Convert pandas data into a dict of np arrays.\n",
        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
        "    \n",
        "    # Construct a dataset, and configure batching/repeating.\n",
        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
        "\n",
        "    # Shuffle the data, if specified.\n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(10000)\n",
        "    \n",
        "    # Return the next batch of data.\n",
        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
        "    return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3kjQV9WH3pb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(\n",
        "    learning_rate,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets):\n",
        "  \"\"\"Trains a linear regression model.\n",
        "  \n",
        "  In addition to training, this function also prints training progress information,\n",
        "  as well as a plot of the training and validation loss over time.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: A `float`, the learning rate.\n",
        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
        "      consists of a forward and backward pass using a single batch.\n",
        "    batch_size: A non-zero `int`, the batch size.\n",
        "    training_examples: A `DataFrame` containing one or more columns from\n",
        "      `california_housing_dataframe` to use as input features for training.\n",
        "    training_targets: A `DataFrame` containing exactly one column from\n",
        "      `california_housing_dataframe` to use as target for training.\n",
        "    validation_examples: A `DataFrame` containing one or more columns from\n",
        "      `california_housing_dataframe` to use as input features for validation.\n",
        "    validation_targets: A `DataFrame` containing exactly one column from\n",
        "      `california_housing_dataframe` to use as target for validation.\n",
        "      \n",
        "  Returns:\n",
        "    A `LinearRegressor` object trained on the training data.\n",
        "  \"\"\"\n",
        "\n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "\n",
        "  # Create a linear regressor object.\n",
        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  linear_regressor = tf.estimator.LinearRegressor(\n",
        "      feature_columns=construct_feature_columns(training_examples),\n",
        "      optimizer=my_optimizer\n",
        "  )\n",
        "    \n",
        "  # Create input functions.\n",
        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
        "                                          training_targets[\"median_house_value\"], \n",
        "                                          batch_size=batch_size)\n",
        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
        "                                                  training_targets[\"median_house_value\"], \n",
        "                                                  num_epochs=1, \n",
        "                                                  shuffle=False)\n",
        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
        "                                                    validation_targets[\"median_house_value\"], \n",
        "                                                    num_epochs=1, \n",
        "                                                    shuffle=False)\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training model...\")\n",
        "  print(\"RMSE (on training data):\")\n",
        "  training_rmse = []\n",
        "  validation_rmse = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    linear_regressor.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period,\n",
        "    )\n",
        "    # Take a break and compute predictions.\n",
        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
        "    \n",
        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
        "    \n",
        "    # Compute training and validation loss.\n",
        "    training_root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
        "    validation_root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_rmse.append(training_root_mean_squared_error)\n",
        "    validation_rmse.append(validation_root_mean_squared_error)\n",
        "  print(\"Model training finished.\")\n",
        "\n",
        "  \n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.ylabel(\"RMSE\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(training_rmse, label=\"training\")\n",
        "  plt.plot(validation_rmse, label=\"validation\")\n",
        "  plt.legend()\n",
        "\n",
        "  return linear_regressor"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "varLu7RNH3pf",
        "colab_type": "text"
      },
      "source": [
        "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DSgUxRIlH3pg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "36591433-d5fa-48ae-9dde-4d4cc33377d2"
      },
      "source": [
        "#\n",
        "# Your code here: add your features of choice as a list of quoted strings.\n",
        "#\n",
        "minimal_features = [\"latitude\", \n",
        "#                   \"longitude\", \n",
        "#                   \"households\", \n",
        "#                   \"total_rooms\", \n",
        "#                   \"total_bedrooms\"\n",
        "                    \"median_income\"]\n",
        "\n",
        "assert minimal_features, \"You must select at least one feature!\"\n",
        "\n",
        "minimal_training_examples = training_examples[minimal_features]\n",
        "minimal_validation_examples = validation_examples[minimal_features]\n",
        "\n",
        "#\n",
        "# Don't forget to adjust these parameters.\n",
        "#\n",
        "train_model(\n",
        "    learning_rate=0.001,\n",
        "    steps=1000,\n",
        "    batch_size=5,\n",
        "    training_examples=minimal_training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=minimal_validation_examples,\n",
        "    validation_targets=validation_targets)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 222.99\n",
            "  period 01 : 207.96\n",
            "  period 02 : 193.43\n",
            "  period 03 : 179.52\n",
            "  period 04 : 166.38\n",
            "  period 05 : 154.44\n",
            "  period 06 : 144.28\n",
            "  period 07 : 136.26\n",
            "  period 08 : 130.11\n",
            "  period 09 : 126.26\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow_estimator.python.estimator.canned.linear.LinearRegressor at 0x7f7c0a1dee10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGINhMIJ5Wyt",
        "colab_type": "text"
      },
      "source": [
        "### Solution\n",
        "\n",
        "Click below for a solution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAGoXFPZ5ZE3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "minimal_features = [\n",
        "  \"median_income\",\n",
        "  \"latitude\",\n",
        "]\n",
        "\n",
        "minimal_training_examples = training_examples[minimal_features]\n",
        "minimal_validation_examples = validation_examples[minimal_features]\n",
        "\n",
        "_ = train_model(\n",
        "    learning_rate=0.01,\n",
        "    steps=500,\n",
        "    batch_size=5,\n",
        "    training_examples=minimal_training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=minimal_validation_examples,\n",
        "    validation_targets=validation_targets)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RidI9YhKOiY2",
        "colab_type": "text"
      },
      "source": [
        "## Task 2: Make Better Use of Latitude\n",
        "\n",
        "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n",
        "\n",
        "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfGUKj2IR_F1",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "31621f8b-be38-4ee5-c9fb-b7fa566102da"
      },
      "source": [
        "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7c0a4a3ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6N0p91k2iFCP",
        "colab_type": "text"
      },
      "source": [
        "**Try creating some synthetic features that do a better job with latitude.**\n",
        "\n",
        "For example, you could have a feature that maps `latitude` to a value of `|latitude - 38|`, and call this `distance_from_san_francisco`.\n",
        "\n",
        "Or you could break the space into 10 different buckets.  `latitude_32_to_33`, `latitude_33_to_34`, etc., each showing a value of `1.0` if `latitude` is within that bucket range and a value of `0.0` otherwise.\n",
        "\n",
        "Use the correlation matrix to help guide development, and then add them to your model if you find something that looks good.\n",
        "\n",
        "What's the best validation performance you can get?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wduJ2B28yMFl",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#\n",
        "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n",
        "#\n",
        "\n",
        "def select_and_transform_features(source_df):\n",
        "  LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
        "  selected_examples = pd.DataFrame()\n",
        "  selected_examples[\"median_income\"] = source_df[\"median_income\"]\n",
        "  for r in LATITUDE_RANGES:\n",
        "    selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n",
        "      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n",
        "  return selected_examples\n",
        "\n",
        "selected_training_examples = select_and_transform_features(training_examples)\n",
        "selected_validation_examples = select_and_transform_features(validation_examples)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZa8miwu6_tQ",
        "colab_type": "text"
      },
      "source": [
        "### Solution\n",
        "\n",
        "Click below for a solution."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzABdyjq7IZU",
        "colab_type": "text"
      },
      "source": [
        "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n",
        "\n",
        "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdVF8siZ7Lup",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def select_and_transform_features(source_df):\n",
        "  LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
        "  selected_examples = pd.DataFrame()\n",
        "  selected_examples[\"median_income\"] = source_df[\"median_income\"]\n",
        "  for r in LATITUDE_RANGES:\n",
        "    selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n",
        "      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n",
        "  return selected_examples\n",
        "\n",
        "selected_training_examples = select_and_transform_features(training_examples)\n",
        "selected_validation_examples = select_and_transform_features(validation_examples)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4iAdY6t7Pkh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "ea24d4b7-c781-45b3-9812-bb1df8593f7f"
      },
      "source": [
        "_ = train_model(\n",
        "    learning_rate=0.01,\n",
        "    steps=500,\n",
        "    batch_size=5,\n",
        "    training_examples=selected_training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=selected_validation_examples,\n",
        "    validation_targets=validation_targets)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 228.15\n",
            "  period 01 : 217.95\n",
            "  period 02 : 207.84\n",
            "  period 03 : 197.82\n",
            "  period 04 : 187.94\n",
            "  period 05 : 178.17\n",
            "  period 06 : 168.57\n",
            "  period 07 : 159.16\n",
            "  period 08 : 149.98\n",
            "  period 09 : 141.07\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}