feature_sets.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "feature_sets.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "JndnmDMp66FL",
        "IGINhMIJ5Wyt",
        "pZa8miwu6_tQ"
      ],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/diegoreico/machine-learning-crash-course/blob/master/feature_sets.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "JndnmDMp66FL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
      "metadata": {
        "id": "hMqWDc_m6rUC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "zbIgBK-oXHO7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Feature Sets"
      ]
    },
    {
      "metadata": {
        "id": "bL04rAQwH3pH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Learning Objective:** Create a minimal set of features that performs just as well as a more complex feature set"
      ]
    },
    {
      "metadata": {
        "id": "F8Hci6tAH3pH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "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."
      ]
    },
    {
      "metadata": {
        "id": "F5ZjVwK_qOyR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Setup\n",
        "\n",
        "As before, let's load and prepare the California housing data."
      ]
    },
    {
      "metadata": {
        "id": "SrOYRILAH3pJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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",
        "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://dl.google.com/mlcc/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": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dGnXo7flH3pM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "jLXC8y4AqsIy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1229
        },
        "outputId": "61061002-6af9-4823-e1a8-512a17d619ee"
      },
      "cell_type": "code",
      "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": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training examples summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
              "mean       35.6     -119.5                28.6       2650.6           540.7   \n",
              "std         2.1        2.0                12.6       2153.0           418.6   \n",
              "min        32.5     -124.3                 2.0          2.0             2.0   \n",
              "25%        33.9     -121.8                18.0       1467.0           298.0   \n",
              "50%        34.2     -118.5                29.0       2146.0           437.0   \n",
              "75%        37.7     -118.0                37.0       3166.0           651.0   \n",
              "max        42.0     -114.3                52.0      37937.0          6445.0   \n",
              "\n",
              "       population  households  median_income  rooms_per_person  \n",
              "count     12000.0     12000.0        12000.0           12000.0  \n",
              "mean       1429.0       502.0            3.9               2.0  \n",
              "std        1115.9       380.9            1.9               1.3  \n",
              "min           6.0         2.0            0.5               0.0  \n",
              "25%         792.0       282.0            2.6               1.5  \n",
              "50%        1170.0       410.0            3.6               1.9  \n",
              "75%        1721.0       607.0            4.7               2.3  \n",
              "max       28566.0      6082.0           15.0              55.2  "
            ],
            "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",
              "    </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>-119.5</td>\n",
              "      <td>28.6</td>\n",
              "      <td>2650.6</td>\n",
              "      <td>540.7</td>\n",
              "      <td>1429.0</td>\n",
              "      <td>502.0</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>2153.0</td>\n",
              "      <td>418.6</td>\n",
              "      <td>1115.9</td>\n",
              "      <td>380.9</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>2.0</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>0.0</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>1467.0</td>\n",
              "      <td>298.0</td>\n",
              "      <td>792.0</td>\n",
              "      <td>282.0</td>\n",
              "      <td>2.6</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>2146.0</td>\n",
              "      <td>437.0</td>\n",
              "      <td>1170.0</td>\n",
              "      <td>410.0</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>3166.0</td>\n",
              "      <td>651.0</td>\n",
              "      <td>1721.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>4.7</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>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>55.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Validation examples summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
              "mean       35.7     -119.6                28.5       2627.1           536.4   \n",
              "std         2.1        2.0                12.7       2243.5           428.4   \n",
              "min        32.5     -124.3                 1.0          8.0             1.0   \n",
              "25%        33.9     -121.8                18.0       1451.0           293.0   \n",
              "50%        34.3     -118.5                28.0       2080.0           427.0   \n",
              "75%        37.7     -118.0                37.0       3114.0           644.0   \n",
              "max        42.0     -114.5                52.0      32054.0          5290.0   \n",
              "\n",
              "       population  households  median_income  rooms_per_person  \n",
              "count      5000.0      5000.0         5000.0            5000.0  \n",
              "mean       1430.9       499.4            3.9               1.9  \n",
              "std        1221.3       393.1            1.9               0.8  \n",
              "min           3.0         1.0            0.5               0.1  \n",
              "25%         785.0       278.0            2.5               1.5  \n",
              "50%        1158.0       407.0            3.5               1.9  \n",
              "75%        1720.2       599.0            4.8               2.3  \n",
              "max       35682.0      5050.0           15.0              16.0  "
            ],
            "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",
              "    </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>35.7</td>\n",
              "      <td>-119.6</td>\n",
              "      <td>28.5</td>\n",
              "      <td>2627.1</td>\n",
              "      <td>536.4</td>\n",
              "      <td>1430.9</td>\n",
              "      <td>499.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.7</td>\n",
              "      <td>2243.5</td>\n",
              "      <td>428.4</td>\n",
              "      <td>1221.3</td>\n",
              "      <td>393.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.8</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>0.1</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>1451.0</td>\n",
              "      <td>293.0</td>\n",
              "      <td>785.0</td>\n",
              "      <td>278.0</td>\n",
              "      <td>2.5</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>28.0</td>\n",
              "      <td>2080.0</td>\n",
              "      <td>427.0</td>\n",
              "      <td>1158.0</td>\n",
              "      <td>407.0</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>3114.0</td>\n",
              "      <td>644.0</td>\n",
              "      <td>1720.2</td>\n",
              "      <td>599.0</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>-114.5</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>16.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training targets summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       median_house_value\n",
              "count             12000.0\n",
              "mean                208.1\n",
              "std                 116.5\n",
              "min                  15.0\n",
              "25%                 120.6\n",
              "50%                 180.9\n",
              "75%                 265.2\n",
              "max                 500.0"
            ],
            "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>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>208.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>116.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>120.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>180.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>265.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Validation targets summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       median_house_value\n",
              "count              5000.0\n",
              "mean                205.5\n",
              "std                 114.6\n",
              "min                  26.6\n",
              "25%                 117.5\n",
              "50%                 178.8\n",
              "75%                 264.7\n",
              "max                 500.0"
            ],
            "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>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>205.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>114.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>26.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>117.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>178.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>264.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hLvmkugKLany",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "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"
      ]
    },
    {
      "metadata": {
        "id": "UzoZUSdLIolF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "6372b6f8-6905-4a99-802e-16e241e87e39"
      },
      "cell_type": "code",
      "source": [
        "correlation_dataframe = training_examples.copy()\n",
        "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n",
        "\n",
        "correlation_dataframe.corr()"
      ],
      "execution_count": 5,
      "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>0.0</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",
              "    </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>0.0</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>-0.0</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  housing_median_age  total_rooms  \\\n",
              "latitude                 1.0       -0.9                 0.0         -0.0   \n",
              "longitude               -0.9        1.0                -0.1          0.0   \n",
              "housing_median_age       0.0       -0.1                 1.0         -0.4   \n",
              "total_rooms             -0.0        0.0                -0.4          1.0   \n",
              "total_bedrooms          -0.1        0.1                -0.3          0.9   \n",
              "population              -0.1        0.1                -0.3          0.9   \n",
              "households              -0.1        0.1                -0.3          0.9   \n",
              "median_income           -0.1       -0.0                -0.1          0.2   \n",
              "rooms_per_person         0.1       -0.1                -0.1          0.1   \n",
              "target                  -0.1       -0.0                 0.1          0.1   \n",
              "\n",
              "                    total_bedrooms  population  households  median_income  \\\n",
              "latitude                      -0.1        -0.1        -0.1           -0.1   \n",
              "longitude                      0.1         0.1         0.1           -0.0   \n",
              "housing_median_age            -0.3        -0.3        -0.3           -0.1   \n",
              "total_rooms                    0.9         0.9         0.9            0.2   \n",
              "total_bedrooms                 1.0         0.9         1.0           -0.0   \n",
              "population                     0.9         1.0         0.9           -0.0   \n",
              "households                     1.0         0.9         1.0            0.0   \n",
              "median_income                 -0.0        -0.0         0.0            1.0   \n",
              "rooms_per_person               0.0        -0.1        -0.0            0.2   \n",
              "target                         0.0        -0.0         0.1            0.7   \n",
              "\n",
              "                    rooms_per_person  target  \n",
              "latitude                         0.1    -0.1  \n",
              "longitude                       -0.1    -0.0  \n",
              "housing_median_age              -0.1     0.1  \n",
              "total_rooms                      0.1     0.1  \n",
              "total_bedrooms                   0.0     0.0  \n",
              "population                      -0.1    -0.0  \n",
              "households                      -0.0     0.1  \n",
              "median_income                    0.2     0.7  \n",
              "rooms_per_person                 1.0     0.2  \n",
              "target                           0.2     1.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "RQpktkNpia2P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "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."
      ]
    },
    {
      "metadata": {
        "id": "bjR5jWpFr2xs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "jsvKHzRciH9T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "g3kjQV9WH3pb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "varLu7RNH3pf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "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."
      ]
    },
    {
      "metadata": {
        "id": "DSgUxRIlH3pg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "14079a53-aac2-406d-dac4-5dc137f9d357"
      },
      "cell_type": "code",
      "source": [
        "#\n",
        "# Your code here: add your features of choice as a list of quoted strings.\n",
        "#\n",
        "minimal_features = [\n",
        "    \"latitude\",\n",
        "    \"median_income\"\n",
        "]\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=0.01,\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": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 166.58\n",
            "  period 01 : 125.24\n",
            "  period 02 : 117.40\n",
            "  period 03 : 116.78\n",
            "  period 04 : 117.64\n",
            "  period 05 : 116.01\n",
            "  period 06 : 115.34\n",
            "  period 07 : 115.85\n",
            "  period 08 : 114.61\n",
            "  period 09 : 114.02\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.estimator.canned.linear.LinearRegressor at 0x7fbfb136cdd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMERERGQ6DDBERERkOv8B9M5WPH681e4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfac05d410>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "IGINhMIJ5Wyt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Solution\n",
        "\n",
        "Click below for a solution."
      ]
    },
    {
      "metadata": {
        "id": "BAGoXFPZ5ZE3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "92804df2-e731-4f58-89c3-c8c00f756c80"
      },
      "cell_type": "code",
      "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": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 166.57\n",
            "  period 01 : 121.64\n",
            "  period 02 : 118.01\n",
            "  period 03 : 117.07\n",
            "  period 04 : 116.72\n",
            "  period 05 : 116.71\n",
            "  period 06 : 115.81\n",
            "  period 07 : 115.16\n",
            "  period 08 : 114.63\n",
            "  period 09 : 114.13\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+X/bnmwCGS6WIwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfabd67a10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "RidI9YhKOiY2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "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."
      ]
    },
    {
      "metadata": {
        "id": "hfGUKj2IR_F1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "3402d05e-21ca-4d08-8761-8bf87bae3628"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbfaf729d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFKCAYAAADMuCxnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfae614610>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6N0p91k2iFCP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "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?"
      ]
    },
    {
      "metadata": {
        "id": "wduJ2B28yMFl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "8a4c4b18-d591-4e45-f87d-21b7a052af7a"
      },
      "cell_type": "code",
      "source": [
        "LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
        "\n",
        "def select_and_transform_features(source_df):\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",
        "\n",
        "_ = 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": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 228.20\n",
            "  period 01 : 218.02\n",
            "  period 02 : 207.90\n",
            "  period 03 : 197.92\n",
            "  period 04 : 188.03\n",
            "  period 05 : 178.27\n",
            "  period 06 : 168.69\n",
            "  period 07 : 159.29\n",
            "  period 08 : 150.35\n",
            "  period 09 : 141.75\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHSlghBBCCOF1/j/F2xX0iin1kgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfaf76dd50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pZa8miwu6_tQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Solution\n",
        "\n",
        "Click below for a solution."
      ]
    },
    {
      "metadata": {
        "id": "PzABdyjq7IZU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "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`."
      ]
    },
    {
      "metadata": {
        "id": "xdVF8siZ7Lup",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
        "\n",
        "def select_and_transform_features(source_df):\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": []
    },
    {
      "metadata": {
        "id": "U4iAdY6t7Pkh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "adb05717-9f8d-437d-c1d0-e7c189cdbd9b"
      },
      "cell_type": "code",
      "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": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 228.19\n",
            "  period 01 : 217.99\n",
            "  period 02 : 207.92\n",
            "  period 03 : 197.90\n",
            "  period 04 : 188.01\n",
            "  period 05 : 178.25\n",
            "  period 06 : 168.66\n",
            "  period 07 : 159.24\n",
            "  period 08 : 150.04\n",
            "  period 09 : 141.11\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGACAYAAACz01iHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4A5+rUywAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfabd58c10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}